From 17cd48d3e6ddeeed7f8e8b7858aa511eddd0256c Mon Sep 17 00:00:00 2001 From: Uma Annamalai Date: Mon, 25 Sep 2023 13:23:45 -0700 Subject: [PATCH] Add sklearn instrumentation and ML model feature support (#921) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add tree model function traces (#691) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Follow two digit convention * Make if-else a one-liner * Abstract to re-usable instrumentation function * Use wrap_method_trace & change to Function group Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai * Fixup: use if-else one-liner * Use hasattr instead of model name check * Change component_sklearn to mlmodel_sklearn * Fixup: replace in model names with hasattr Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add config setting for sklearn inference event capture. (#706) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Follow two digit convention * Make if-else a one-liner * Abstract to re-usable instrumentation function * Add ML inference event capture config setting. * [Mega-Linter] Apply linters fixes * Fixup: remove component_sklearn files * Add high security mode testing for ML events setting. * [Mega-Linter] Apply linters fixes Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai * Capture scorer results (#694) * Add score results attributes to metric scorers * Test un-subclassable types * [Mega-Linter] Apply linters fixes * [Mega-Linter] Apply linters fixes * Trigger tests * Remove custom subclassing code. * [Mega-Linter] Apply linters fixes * Remove unused function * Add test for iterable score results * Change name of object proxy * Fixup: rename proxy in tests too Co-authored-by: hmstepanek Co-authored-by: Tim Pansino Co-authored-by: TimPansino * Add ensemble model function traces (#697) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Remove breakpoints * Remove commited config files * Group tests into more readable format * Pin startlette latest < 0.23.1 * Convert PY3 checks to one-liners * Use tuple checks for sklearn version Use tuple checks for sklearn version, string checks can result in unexpected out of order comparisons. Also use direct comparisons for easier readability. * Fix VotingRegressor test Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Include training step in metric scorer name (#712) * Include training step in scorer name * Add fit_predict data proxying * Remove name comments * Fix predict being called before fit * Re-use existing fixture * Add cluster model function traces (#700) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Add cluster model instrumentaton * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Fix some cluster model tests * Fix tests after ensemble PR merge * Add transform to tests * Remove accidental commits * Modify cluster tests to be more readable * Break up instrumentation models * Remove duplicate ensemble module defs * Modify VotingRegressor test Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add calibration model function traces (#709) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Remove breakpoints * Create tests/instrumentation for calibration models * Fix calibration tests * Remove commented out code * Remove yaml file in commit * Remove duplicate ensemble module defs Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add svm model function traces (#733) * Add svm models * Remove extra conditionals from testing * Add semi supervised models (#732) * Add pipeline model function traces (#730) * Add pipeline models * Remove commented code * Add neural network model function traces (#729) * Add neural network models * Fixup: merge conflict Co-authored-by: Hannah Stepanek * Add neighbors models (#728) * Add mixture models (#725) * Add model selection model function traces (#726) * Add outline for model selection tests * Add some testing to model selection * Add hooks * Add estimator * Finish testing for model selection * Add naive bayes models (#724) * Add multioutput models (#723) * Add multiclass models (#722) * Add kernel ridge model function traces (#721) * Add kernel ridge models * Modify VotingRegressor test * Add custom feature events for sklearn (#727) * Add function traces around model methods * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Instrument predict function. * Add data trains fixture. * Add testing and cleanup for custom feature events. * Update test_tree_models. * Add back training step logic to predict proxy. * Remove unused files. * Add tree model function traces (#691) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Follow two digit convention * Make if-else a one-liner * Abstract to re-usable instrumentation function * Use wrap_method_trace & change to Function group Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai * Fixup: use if-else one-liner * Use hasattr instead of model name check * Change component_sklearn to mlmodel_sklearn * Fixup: replace in model names with hasattr Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add config setting for sklearn inference event capture. (#706) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Follow two digit convention * Make if-else a one-liner * Abstract to re-usable instrumentation function * Add ML inference event capture config setting. * [Mega-Linter] Apply linters fixes * Fixup: remove component_sklearn files * Add high security mode testing for ML events setting. * [Mega-Linter] Apply linters fixes Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai * Capture scorer results (#694) * Add score results attributes to metric scorers * Test un-subclassable types * [Mega-Linter] Apply linters fixes * [Mega-Linter] Apply linters fixes * Trigger tests * Remove custom subclassing code. * [Mega-Linter] Apply linters fixes * Remove unused function * Add test for iterable score results * Change name of object proxy * Fixup: rename proxy in tests too Co-authored-by: hmstepanek Co-authored-by: Tim Pansino Co-authored-by: TimPansino * Add ensemble model function traces (#697) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Remove breakpoints * Remove commited config files * Group tests into more readable format * Pin startlette latest < 0.23.1 * Convert PY3 checks to one-liners * Use tuple checks for sklearn version Use tuple checks for sklearn version, string checks can result in unexpected out of order comparisons. Also use direct comparisons for easier readability. * Fix VotingRegressor test Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add function traces around model methods * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Instrument predict function. * Add data trains fixture. * Add testing and cleanup for custom feature events. * Update test_tree_models. * Include training step in metric scorer name (#712) * Include training step in scorer name * Add fit_predict data proxying * Remove name comments * Fix predict being called before fit * Re-use existing fixture * Add cluster model function traces (#700) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Add cluster model instrumentaton * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Fix some cluster model tests * Fix tests after ensemble PR merge * Add transform to tests * Remove accidental commits * Modify cluster tests to be more readable * Break up instrumentation models * Remove duplicate ensemble module defs * Modify VotingRegressor test Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add calibration model function traces (#709) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Remove breakpoints * Create tests/instrumentation for calibration models * Fix calibration tests * Remove commented out code * Remove yaml file in commit * Remove duplicate ensemble module defs Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add back training step logic to predict proxy. * Remove unused files. * Address py27 test failures and review comments. * Fix py 3.7 failures. * Remove old component_sklearn.py file * Fix lint errors * [Mega-Linter] Apply linters fixes * Merge redis fix. Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai Co-authored-by: hmstepanek Co-authored-by: Tim Pansino Co-authored-by: lrafeei * Add feature selection model function traces (#719) * Add feature selection models * Modify VotingRegressor test * Add dummy model function traces (#718) * Add gaussian process model function traces (#720) * Add gaussian process models * Modify VotingRegressor test * Modify Gaussian Process models tests * Add discriminant analysis model (#717) * Add cross decomposition models (#716) * Add covariance model (#714) * Add compose models (#713) * Add linear model function traces (#703) * Add sklearn to tox * Add function traces around model methods * Support Python 2.7 & 3.7 sklearn * Add test for multiple calls to model method * Fixup: add comments & organize * Add ensemble models * Add ensemble model tests * Edit tests * Add ensemble library models from sklearn * Start tests with empty commit * Clean up tests * Initial linear model commit * Clean up tests for linear models * Fix tests for various versions of sklearn * Fix ensemble tests with changes from tree PR * [Mega-Linter] Apply linters fixes * Remove breakpoints * Merge changes from ensemble PR * Fix tests for v0.20.0 * Rewrite linear tests to be more readable * Break up instrumentation in config * Remove commented code * Remove yaml file in commit * Remove duplicate ensemble module defs * Remove old no longer used file * Remove commented out code. * Change test name and modify VotingRegressor test * Push empty commit * Modify VotingRegression test * Add estimator to VotingRegressor * Revert VotingRegressor test * Fix ensemble tests * Add different models for VotingRegressor test Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei * Add ml_model function wrapper API (#739) * Add function traces around model methods * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Instrument predict function. * Add data trains fixture. * Add testing and cleanup for custom feature events. * Update test_tree_models. * Add back training step logic to predict proxy. * Remove unused files. * Add ml_model wrapper and tests. * Add column name logic. * Update branch. * Update column name mapping logic. * Fix py2 failures. * Fix pypy37 failure. * Revise feature_names logic. * Add more ml_model wrapper testing. * Fix linter unused import * [Mega-Linter] Apply linters fixes * Bump tests. * Bump tests. Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai * Report feature event w/o value (#754) Report the feature event w/o the raw value if `machine_learning.inference_event_value.enabled` is False. * Prediction metric stats (#715) * Add function traces around model methods * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Instrument predict function. * Add data trains fixture. * Add testing and cleanup for custom feature events. * Update test_tree_models. * Add back training step logic to predict proxy. * Remove unused files. * Update branch. * Add impl and testing for label events. * [Mega-Linter] Apply linters fixes * Add feature_name to expected events for int test. * Fix py37 test. * Add function traces around model methods * Add test for multiple calls to model method * Fixup: add comments & organize * Refactor * Instrument predict function. * Add data trains fixture. * Add testing and cleanup for custom feature events. * Update test_tree_models. * Add back training step logic to predict proxy. * Remove unused files. * Update branch. * Add impl and testing for label events. * Add feature_name to expected events for int test. * [Mega-Linter] Apply linters fixes * Fix py37 test. * Update logic to not report value when setting is disabled. * Fixup flake8 unused import * Touch up label event logic and rename test_feature_events to test_inference_events. * Add test case for multilabel output. * Fix 2D np array test. * Add metrics about prediction data * Fixup * Fix user provided label names * Fixup label related column name tests * [Mega-Linter] Apply linters fixes * Assert label events in multilabel output test * Remove return of stats * Remove spaces from version specification * Fix py2.7 output difference in pandas test --------- Co-authored-by: Uma Annamalai Co-authored-by: umaannamalai Co-authored-by: hmstepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add new ML event type (#802) * Add new machine learning event data type Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * Validate new machine learning event data type Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * Fixup: minor inconsistencies * Fixup * Remove code coverage fixture * Fix lint errors * Increase timeout for python tests --------- Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * Add machine learning and ml_event config options (#811) * Add machine learning config options * machine_learning.enabled * machine_learning.inference_events.enable * machine_learning.inference_events.value.enabled * event_harvest_config.harvest_limits.ml_event_data Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * Replace all TODOs w/ new config settings * [Mega-Linter] Apply linters fixes * Trigger tests * Add insights settings & tests * Remove collect_custom_events & inference_events.enabled * Revert inference_events_value=>.value * Remove TODO * Fixup: format docstring * Remove file * Add tests for machine_learning.enabled --------- Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: hmstepanek * Dimensional Metrics (#815) * Wiring dimensional metrics * Squashed commit of the following: commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 363122a0efe0ad9f4784fc1f67fda046cb9bb7e8 Author: Hannah Stepanek Date: Mon May 1 13:34:35 2023 -0700 Pin virtualenv, fix pip arg deprecation & disable kafka tests (#803) * Pin virtualenv * Fixup: use 20.21.1 instead * Replace install-options with config-settings See https://github.com/pypa/pip/issues/11358. * Temporarily disable kafka tests * Add dimensional stats table to stats engine * Add attribute processing to metric identity * Add testing for dimensional metrics * Cover tags as list not dict * Commit suggestions from code review * Add OTLP protocol class & protos (#821) * Add protos under packages for otlp * Add common otlp proto payload methods * Add new oltp protocol class * Remove ML event from log message * Remove params, add api-key header & expose path The params are not relevant to OTLP so remove these. The api-key header is how we provide the license key to OTLP so add this. The path to upload dimensional metrics and events are different in OTLP so expose the path so it can be overriden inside the coresponding data_collector methods. * Add otlp_port and otlp_host settings * Default to JSON if protobuf not available & warn * Move otlp_utils to core * Call encode in protocol class * Patch issues with data collector * Move resource to utils & add log proto imports --------- Co-authored-by: Tim Pansino * Fix Testing Failures (#828) * Fix tastypie tests * Adjust asgiref pinned version * Make aioredis key PID unique * Pin more asgiref versions * Fix pytest test filtering when running tox (#823) Co-authored-by: Uma Annamalai * OTLP Serialization for Dimensional Metrics (#826) * Add protos under packages for otlp * Add common otlp proto payload methods * Add new oltp protocol class * Remove ML event from log message * Remove params, add api-key header & expose path The params are not relevant to OTLP so remove these. The api-key header is how we provide the license key to OTLP so add this. The path to upload dimensional metrics and events are different in OTLP so expose the path so it can be overriden inside the coresponding data_collector methods. * Add metric protos * Use protos to create payload * Squashed commit of the following: commit 6f15520cea6a1098915c9ca340dbe42de6a5de1d Author: Tim Pansino Date: Mon May 15 14:28:50 2023 -0700 TEMP commit 1a28d36f86dd3f1fa5ca7a8f56357d168aac69db Author: Tim Pansino Date: Thu May 11 17:28:27 2023 -0700 Cover tags as list not dict commit 71261e3d468320569742a72c690f6ff4e9b3e621 Merge: 459e08567 c2d4629df Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Thu May 11 16:59:11 2023 -0700 Merge branch 'main' into feature-dimensional-metrics commit 459e08567102cfadce398b57d245ecf99408400d Author: Tim Pansino Date: Thu May 11 16:57:16 2023 -0700 Add testing for dimensional metrics commit ed33957cd2b20bc1f6e9759a0bad5e4f4a86a38c Author: Tim Pansino Date: Thu May 11 16:56:31 2023 -0700 Add attribute processing to metric identity commit 6caf71ef4386395d950060e0e996f80dbcbfbc32 Author: Tim Pansino Date: Thu May 11 16:56:16 2023 -0700 Add dimensional stats table to stats engine commit 5e1cc9dea6d0d9623130dedd0f787408a8439388 Author: Tim Pansino Date: Wed May 10 16:00:42 2023 -0700 Squashed commit of the following: commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 363122a0efe0ad9f4784fc1f67fda046cb9bb7e8 Author: Hannah Stepanek Date: Mon May 1 13:34:35 2023 -0700 Pin virtualenv, fix pip arg deprecation & disable kafka tests (#803) * Pin virtualenv * Fixup: use 20.21.1 instead * Replace install-options with config-settings See https://github.com/pypa/pip/issues/11358. * Temporarily disable kafka tests commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit dc81a50a9fc5f2a5ce6978aa064fdfab1618328b Author: Tim Pansino Date: Sat May 6 14:16:14 2023 -0700 Wiring dimensional metrics commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Squashed commit of the following: commit 7a384c5935f8d6d24db9b488a7e48a6854efedd6 Author: Tim Pansino Date: Thu Jun 1 12:10:59 2023 -0700 Cleaning out agent protocol commit c87d31d5d3a91eb7c584f32f9831fbebc1ffe378 Author: Tim Pansino Date: Thu Jun 1 12:10:46 2023 -0700 Change content-type header commit 5750e546797b16f96e71161f794cb34a253418a6 Author: Tim Pansino Date: Thu Jun 1 12:05:52 2023 -0700 Add common utilities for OTLP * Remove testing logic * Adding metric serialization helpers * Squashed commit of the following: commit a47e209925a210e85bb6c57f0a2efa9e99630b7f Author: Tim Pansino Date: Tue Jun 6 11:11:30 2023 -0700 Commit suggestions from code review commit 1a28d36f86dd3f1fa5ca7a8f56357d168aac69db Author: Tim Pansino Date: Thu May 11 17:28:27 2023 -0700 Cover tags as list not dict commit 71261e3d468320569742a72c690f6ff4e9b3e621 Merge: 459e08567 c2d4629df Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Thu May 11 16:59:11 2023 -0700 Merge branch 'main' into feature-dimensional-metrics commit 459e08567102cfadce398b57d245ecf99408400d Author: Tim Pansino Date: Thu May 11 16:57:16 2023 -0700 Add testing for dimensional metrics commit ed33957cd2b20bc1f6e9759a0bad5e4f4a86a38c Author: Tim Pansino Date: Thu May 11 16:56:31 2023 -0700 Add attribute processing to metric identity commit 6caf71ef4386395d950060e0e996f80dbcbfbc32 Author: Tim Pansino Date: Thu May 11 16:56:16 2023 -0700 Add dimensional stats table to stats engine commit 5e1cc9dea6d0d9623130dedd0f787408a8439388 Author: Tim Pansino Date: Wed May 10 16:00:42 2023 -0700 Squashed commit of the following: commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 363122a0efe0ad9f4784fc1f67fda046cb9bb7e8 Author: Hannah Stepanek Date: Mon May 1 13:34:35 2023 -0700 Pin virtualenv, fix pip arg deprecation & disable kafka tests (#803) * Pin virtualenv * Fixup: use 20.21.1 instead * Replace install-options with config-settings See https://github.com/pypa/pip/issues/11358. * Temporarily disable kafka tests commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit dc81a50a9fc5f2a5ce6978aa064fdfab1618328b Author: Tim Pansino Date: Sat May 6 14:16:14 2023 -0700 Wiring dimensional metrics commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add protobuf to agent features tests * Proper bucket dimensional metric serialization * Wiring up OTLP protocol for metrics * Correct metrics payloads * Make default content-encoding header configurable * Clean up otlp encoding * Expand OTLP metrics testing * Squashed commit of the following: commit 30f0bf5ce27f239f70b236c639a49715f33ce948 Author: Hannah Stepanek Date: Fri Jun 9 16:12:09 2023 -0700 Add OTLP protocol class & protos (#821) * Add protos under packages for otlp * Add common otlp proto payload methods * Add new oltp protocol class * Remove ML event from log message * Remove params, add api-key header & expose path The params are not relevant to OTLP so remove these. The api-key header is how we provide the license key to OTLP so add this. The path to upload dimensional metrics and events are different in OTLP so expose the path so it can be overriden inside the coresponding data_collector methods. * Add otlp_port and otlp_host settings * Default to JSON if protobuf not available & warn * Move otlp_utils to core * Call encode in protocol class * Patch issues with data collector * Move resource to utils & add log proto imports --------- Co-authored-by: Tim Pansino commit e970884dac0e1f9c703c6fdbff408fb923502f51 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Thu Jun 8 13:17:28 2023 -0700 Dimensional Metrics (#815) * Wiring dimensional metrics * Squashed commit of the following: commit c2d4629dfd7787354b6607160bb952913975d5f7 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed May 10 15:59:13 2023 -0700 Add required option for tox v4 (#795) * Add required option for tox v4 * Update tox in GHA * Remove py27 no-cache-dir commit a9636498ab5c20c266fb044a08359c0c9bbcf826 Author: Hannah Stepanek Date: Tue May 9 10:46:39 2023 -0700 Run coverage around pytest (#813) * Run coverage around pytest * Trigger tests * Fixup * Add redis client_no_touch to ignore list * Temporarily remove kafka from coverage * Remove coverage for old libs commit 3d8284540e0acd867c2cf680f43449bc128c0779 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Wed May 3 14:50:30 2023 -0700 Omit some frameworks from coverage analysis (#810) * Omit some frameworks from coverage analysis * Remove commas * Change format of omit * Add relative_files option to coverage * Add absolute directory * Add envsitepackagedir * Add coveragerc file * Add codecov.yml * [Mega-Linter] Apply linters fixes * Revert coveragerc file settings * Add files in packages and more frameworks * Remove commented line --------- Co-authored-by: lrafeei Co-authored-by: Hannah Stepanek commit fd0fa35466b630e34e8476cc53ad0e163564e2de Author: Uma Annamalai Date: Tue May 2 10:55:36 2023 -0700 Add testing for genshi and mako. (#799) * Add testing for genshi and mako. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit be4fb3dda0e734889acd6bc53cf91f26c18c2118 Author: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Date: Mon May 1 16:01:09 2023 -0700 Add tests for Waitress (#797) * Change import format * Initial commit * Add more tests to adapter_waitress * Remove commented out code * [Mega-Linter] Apply linters fixes * Add assertions to all tests * Add more NR testing to waitress --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 7103506ca5639d339e3e47dfb9e4affb546c839b Author: Hannah Stepanek Date: Mon May 1 14:12:31 2023 -0700 Add tests for pyodbc (#796) * Add tests for pyodbc * Move imports into tests to get import coverage * Fixup: remove time import * Trigger tests --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 363122a0efe0ad9f4784fc1f67fda046cb9bb7e8 Author: Hannah Stepanek Date: Mon May 1 13:34:35 2023 -0700 Pin virtualenv, fix pip arg deprecation & disable kafka tests (#803) * Pin virtualenv * Fixup: use 20.21.1 instead * Replace install-options with config-settings See https://github.com/pypa/pip/issues/11358. * Temporarily disable kafka tests * Add dimensional stats table to stats engine * Add attribute processing to metric identity * Add testing for dimensional metrics * Cover tags as list not dict * Commit suggestions from code review * Fix missing resource error * Add global settings override for otlp_host test * Fix unbound local variable * Remove redundant and miscategorized tests * Migrate and merge otlp utils to core. * Fix virtualenv for newer tox versions and Py27 * Fix validator for Py27 * Fix dimensional metric normalization * Fix lint errors * Fix pypy 27 naming * Add debug override for metric serialization * Fix exit code passthrough in tox script * Make otlp_encode more robust * Add json vs protobuf testing fixture * Remove sklearn py27 testing * Validate resource in OTLP * Revert unrelated changes from code review * Fixup: service.provider assertion --------- Co-authored-by: Hannah Stepanek * Fix attribute name mismatches from mlops sdk (#845) * Convert numerical -> numeric * Adjust attr names to match mlops sdk * Add feature_/label_ prefix to type & name attrs * model_name -> modelName * Set event type to inferenceData * Backport main into develop-scikitlearn (#847) * Containerized CI Pipeline (#836) * Revert "Remove Python 2.7 and pypy2 testing (#835)" This reverts commit abb6405d2bfd629ed83f48e8a17b4a28e3a3c352. * Containerize CI process * Publish new docker container for CI images * Rename github actions job * Copyright tag scripts * Drop debug line * Swap to new CI image * Move pip install to just main python * Remove libcurl special case from tox * Install special case packages into main image * Remove unused packages * Remove all other triggers besides manual * Add make run command * Cleanup small bugs * Fix CI Image Tagging (#838) * Correct templated CI image name * Pin pypy2.7 in image * Fix up scripting * Temporarily Restore Old CI Pipeline (#841) * Restore old pipelines * Remove python 2 from setup-python * Rework CI Pipeline (#839) Change pypy to pypy27 in tox. Fix checkout logic Pin tox requires * Fix Tests on New CI (#843) * Remove non-root user * Test new CI image * Change pypy to pypy27 in tox. * Fix checkout logic * Fetch git tags properly * Pin tox requires * Adjust default db settings for github actions * Rename elasticsearch services * Reset to new pipelines * [Mega-Linter] Apply linters fixes * Fix timezone * Fix docker networking * Pin dev image to new sha * Standardize gearman DB settings * Fix elasticsearch settings bug * Fix gearman bug * Add missing odbc headers * Add more debug messages * Swap out dev ci image * Fix required virtualenv version * Swap out dev ci image * Swap out dev ci image * Remove aioredis v1 for EOL * Add coverage paths for docker container * Unpin ci container --------- Co-authored-by: TimPansino * Fix pypy27 dependency * Add skip for OTLP on py27 --------- Co-authored-by: TimPansino Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Use Dimensional Metrics in SKLearn (#850) * Convert ML custom metrics to dimensional with tags * Rename _class to class_ * Remove typo * Adjust ML metric tests for dimensional metrics * Pin sklearn to <1.11.0 for testing * [Mega-Linter] Apply linters fixes --------- Co-authored-by: TimPansino * Fixup dependency pinning * Hook up ml event to OTLP (#822) * Use protos and otlp protocol class for ml_events * inferenceData -> InferenceData * Add LogsData import * Add utf-8 encoding for json otlp payload * Cast timestamp to int * Use ml_event validator in tests * Fixup payload tests * Change str_value -> string_value * Move event payload gen into otlp_utils * Fixup: put back print * Fixup: cast as str for py27 * Fixup lint errors * Skip py2 protobuf --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix OTLP Count Metric Serialization (#856) * Fix metric filtering in OTLP encoding * Add regression test for duplicate metrics * Make error message more clear * Add pylint ignore C0123 * Add explanation comment * Linting fixups * Merge main (#874) * Exclude command line functionality from test coverage (#855) * FIX: resilient environment settings (#825) if the application uses generalimport to manage optional depedencies, it's possible that generalimport.MissingOptionalDependency is raised. In this case, we should not report the module as it is not actually loaded and is not a runtime dependency of the application. Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Replace drop_transaction logic by using transaction context manager (#832) * Replace drop_transaction call * [Mega-Linter] Apply linters fixes * Empty commit to start tests * Change logic in BG Wrappers --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Upgrade to Pypy38 for TypedDict (#861) * Fix base branch * Revert tox dependencies * Replace all pypy37 with pypy38 * Remove action.yml file * Push Empty Commit * Fix skip_missing_interpreters behavior * Fix skip_missing_interpreters behavior * Pin dev CI image sha * Remove unsupported Tornado tests * Add latest tests to Tornado * Remove pypy38 (for now) --------- Co-authored-by: Tim Pansino * Add profile_trace testing (#858) * Include isort stdlibs for determining stdlib modules * Use isort & sys to eliminate std & builtin modules Previously, the logic would fail to identify third party modules installed within the local user socpe. This fixes that issue by skipping builtin and stdlib modules by name, instead of attempting to identify third party modules based on file paths. * Handle importlib_metadata.version being a callable * Add isort into third party notices * [Mega-Linter] Apply linters fixes * Remove Python 2.7 and pypy2 testing (#835) * Change setup-python to @v2 for py2.7 * Remove py27 and pypy testing * Fix syntax errors * Fix comma related syntax errors * Fix more issues in tox * Remove gearman test * Containerized CI Pipeline (#836) * Revert "Remove Python 2.7 and pypy2 testing (#835)" This reverts commit abb6405d2bfd629ed83f48e8a17b4a28e3a3c352. * Containerize CI process * Publish new docker container for CI images * Rename github actions job * Copyright tag scripts * Drop debug line * Swap to new CI image * Move pip install to just main python * Remove libcurl special case from tox * Install special case packages into main image * Remove unused packages * Remove all other triggers besides manual * Add make run command * Cleanup small bugs * Fix CI Image Tagging (#838) * Correct templated CI image name * Pin pypy2.7 in image * Fix up scripting * Temporarily Restore Old CI Pipeline (#841) * Restore old pipelines * Remove python 2 from setup-python * Rework CI Pipeline (#839) Change pypy to pypy27 in tox. Fix checkout logic Pin tox requires * Fix Tests on New CI (#843) * Remove non-root user * Test new CI image * Change pypy to pypy27 in tox. * Fix checkout logic * Fetch git tags properly * Pin tox requires * Adjust default db settings for github actions * Rename elasticsearch services * Reset to new pipelines * [Mega-Linter] Apply linters fixes * Fix timezone * Fix docker networking * Pin dev image to new sha * Standardize gearman DB settings * Fix elasticsearch settings bug * Fix gearman bug * Add missing odbc headers * Add more debug messages * Swap out dev ci image * Fix required virtualenv version * Swap out dev ci image * Swap out dev ci image * Remove aioredis v1 for EOL * Add coverage paths for docker container * Unpin ci container --------- Co-authored-by: TimPansino * Trigger tests * Add testing for profile trace. * [Mega-Linter] Apply linters fixes * Ignore __call__ from coverage on profile_trace. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: hmstepanek Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Add Transaction API Tests (#857) * Test for suppress_apdex_metric * Add custom_metrics tests * Add distributed_trace_headers testing in existing tests * [Mega-Linter] Apply linters fixes * Remove redundant if-statement * Ignore deprecated transaction function from coverage * [Mega-Linter] Apply linters fixes * Push empty commit * Update newrelic/api/transaction.py --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add tests for jinja2. (#842) * Add tests for jinja2. * [Mega-Linter] Apply linters fixes * Update tox.ini Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix starlette testing matrix for updated behavior. (#869) Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Correct Serverless Distributed Tracing Logic (#870) * Fix serverless logic for distributed tracing * Test stubs * Collapse testing changes * Add negative testing to regular DT test suite * Apply linter fixes * [Mega-Linter] Apply linters fixes --------- Co-authored-by: TimPansino * Fix Kafka CI (#863) * Reenable kafka testing * Add kafka dev lib * Sync install python with devcontainer * Fix kafka local host setting * Drop set -u flag * Pin CI image dev sha * Add parallel flag to kafka * Fix proper exit status * Build librdkafka from source * Updated dev image sha * Remove coverage exclusions * Add new options to better emulate GHA * Reconfigure kafka networking Co-authored-by: Hannah Stepanek * Fix kafka ports on GHA * Run kafka tests serially * Separate kafka consumer groups * Put CI container makefile back * Remove confluent kafka Py27 for latest * Roll back ubuntu version update * Update dev ci sha --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Change image tag to latest (#871) * Change image tag to latest * Use built sha * Fixup * Replace w/ latest * Add full version for pypy3.8 to tox (#872) * Add full version for pypy3.8 * Remove solrpy from tests * Fix merge conflict * Fix tests for scikit-learn >= 1.3.0 In 1.3.0 sklearn renamed fit to _fit in BaseDecisionTree. * Add gfortran to container * Use ci image sha * Add pkg-config * New CI build --------- Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Ahmed Helil Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: lrafeei Co-authored-by: Tim Pansino Co-authored-by: Uma Annamalai Co-authored-by: hmstepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Only send 1 event per prediction & fix tests (#867) * Only send 1 event per prediction This changes from sending 1 event per feature/label value to 1 event per prediction. This change is referred to as a change in "schema". * Replace feature_name & feature_value and label_name & label_value to feature. = and label. = . * Remove feature_type and label_type. * Add new_relic_data_schema_version * Remove pypy3.8-scipy will not compile * Remove scipy compile deps * Set ci build back to latest * Merge redis changes mlops (#914) * Exclude command line functionality from test coverage (#855) * FIX: resilient environment settings (#825) if the application uses generalimport to manage optional depedencies, it's possible that generalimport.MissingOptionalDependency is raised. In this case, we should not report the module as it is not actually loaded and is not a runtime dependency of the application. Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Replace drop_transaction logic by using transaction context manager (#832) * Replace drop_transaction call * [Mega-Linter] Apply linters fixes * Empty commit to start tests * Change logic in BG Wrappers --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Upgrade to Pypy38 for TypedDict (#861) * Fix base branch * Revert tox dependencies * Replace all pypy37 with pypy38 * Remove action.yml file * Push Empty Commit * Fix skip_missing_interpreters behavior * Fix skip_missing_interpreters behavior * Pin dev CI image sha * Remove unsupported Tornado tests * Add latest tests to Tornado * Remove pypy38 (for now) --------- Co-authored-by: Tim Pansino * Add profile_trace testing (#858) * Include isort stdlibs for determining stdlib modules * Use isort & sys to eliminate std & builtin modules Previously, the logic would fail to identify third party modules installed within the local user socpe. This fixes that issue by skipping builtin and stdlib modules by name, instead of attempting to identify third party modules based on file paths. * Handle importlib_metadata.version being a callable * Add isort into third party notices * [Mega-Linter] Apply linters fixes * Remove Python 2.7 and pypy2 testing (#835) * Change setup-python to @v2 for py2.7 * Remove py27 and pypy testing * Fix syntax errors * Fix comma related syntax errors * Fix more issues in tox * Remove gearman test * Containerized CI Pipeline (#836) * Revert "Remove Python 2.7 and pypy2 testing (#835)" This reverts commit abb6405d2bfd629ed83f48e8a17b4a28e3a3c352. * Containerize CI process * Publish new docker container for CI images * Rename github actions job * Copyright tag scripts * Drop debug line * Swap to new CI image * Move pip install to just main python * Remove libcurl special case from tox * Install special case packages into main image * Remove unused packages * Remove all other triggers besides manual * Add make run command * Cleanup small bugs * Fix CI Image Tagging (#838) * Correct templated CI image name * Pin pypy2.7 in image * Fix up scripting * Temporarily Restore Old CI Pipeline (#841) * Restore old pipelines * Remove python 2 from setup-python * Rework CI Pipeline (#839) Change pypy to pypy27 in tox. Fix checkout logic Pin tox requires * Fix Tests on New CI (#843) * Remove non-root user * Test new CI image * Change pypy to pypy27 in tox. * Fix checkout logic * Fetch git tags properly * Pin tox requires * Adjust default db settings for github actions * Rename elasticsearch services * Reset to new pipelines * [Mega-Linter] Apply linters fixes * Fix timezone * Fix docker networking * Pin dev image to new sha * Standardize gearman DB settings * Fix elasticsearch settings bug * Fix gearman bug * Add missing odbc headers * Add more debug messages * Swap out dev ci image * Fix required virtualenv version * Swap out dev ci image * Swap out dev ci image * Remove aioredis v1 for EOL * Add coverage paths for docker container * Unpin ci container --------- Co-authored-by: TimPansino * Trigger tests * Add testing for profile trace. * [Mega-Linter] Apply linters fixes * Ignore __call__ from coverage on profile_trace. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: hmstepanek Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Add Transaction API Tests (#857) * Test for suppress_apdex_metric * Add custom_metrics tests * Add distributed_trace_headers testing in existing tests * [Mega-Linter] Apply linters fixes * Remove redundant if-statement * Ignore deprecated transaction function from coverage * [Mega-Linter] Apply linters fixes * Push empty commit * Update newrelic/api/transaction.py --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add tests for jinja2. (#842) * Add tests for jinja2. * [Mega-Linter] Apply linters fixes * Update tox.ini Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Initial commit of redis changes from main * Fix merging * Spell framework correctly * Fix redis merge issues --------- Co-authored-by: Ahmed Helil Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek Co-authored-by: lrafeei Co-authored-by: Tim Pansino Co-authored-by: Uma Annamalai Co-authored-by: hmstepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Make inferences a separate event (#910) * Change feature value conversion to string * Create separate inference_id for each event * Add option to create metadata * Add label mapping option * Allow feature to be pre-existing type instead of string * Fix prediction tests now that each prediction is event * [Mega-Linter] Apply linters fixes * Trigger tests * Remove label_mapping option * Rearrange metadata to be attribute of mlmodel * Fix sanitize logic to not use type() * Add prediction_id alongside inference_id * [Mega-Linter] Apply linters fixes * Trigger tests * Pin anyio version to below 4.0.0 --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix logic bug in warning (#915) Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Add wrap_mlmodel to newrelic.agent (#920) * Merge main into mlops (#912) * Exclude command line functionality from test coverage (#855) * FIX: resilient environment settings (#825) if the application uses generalimport to manage optional depedencies, it's possible that generalimport.MissingOptionalDependency is raised. In this case, we should not report the module as it is not actually loaded and is not a runtime dependency of the application. Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Replace drop_transaction logic by using transaction context manager (#832) * Replace drop_transaction call * [Mega-Linter] Apply linters fixes * Empty commit to start tests * Change logic in BG Wrappers --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Upgrade to Pypy38 for TypedDict (#861) * Fix base branch * Revert tox dependencies * Replace all pypy37 with pypy38 * Remove action.yml file * Push Empty Commit * Fix skip_missing_interpreters behavior * Fix skip_missing_interpreters behavior * Pin dev CI image sha * Remove unsupported Tornado tests * Add latest tests to Tornado * Remove pypy38 (for now) --------- Co-authored-by: Tim Pansino * Add profile_trace testing (#858) * Include isort stdlibs for determining stdlib modules * Use isort & sys to eliminate std & builtin modules Previously, the logic would fail to identify third party modules installed within the local user socpe. This fixes that issue by skipping builtin and stdlib modules by name, instead of attempting to identify third party modules based on file paths. * Handle importlib_metadata.version being a callable * Add isort into third party notices * [Mega-Linter] Apply linters fixes * Remove Python 2.7 and pypy2 testing (#835) * Change setup-python to @v2 for py2.7 * Remove py27 and pypy testing * Fix syntax errors * Fix comma related syntax errors * Fix more issues in tox * Remove gearman test * Containerized CI Pipeline (#836) * Revert "Remove Python 2.7 and pypy2 testing (#835)" This reverts commit abb6405d2bfd629ed83f48e8a17b4a28e3a3c352. * Containerize CI process * Publish new docker container for CI images * Rename github actions job * Copyright tag scripts * Drop debug line * Swap to new CI image * Move pip install to just main python * Remove libcurl special case from tox * Install special case packages into main image * Remove unused packages * Remove all other triggers besides manual * Add make run command * Cleanup small bugs * Fix CI Image Tagging (#838) * Correct templated CI image name * Pin pypy2.7 in image * Fix up scripting * Temporarily Restore Old CI Pipeline (#841) * Restore old pipelines * Remove python 2 from setup-python * Rework CI Pipeline (#839) Change pypy to pypy27 in tox. Fix checkout logic Pin tox requires * Fix Tests on New CI (#843) * Remove non-root user * Test new CI image * Change pypy to pypy27 in tox. * Fix checkout logic * Fetch git tags properly * Pin tox requires * Adjust default db settings for github actions * Rename elasticsearch services * Reset to new pipelines * [Mega-Linter] Apply linters fixes * Fix timezone * Fix docker networking * Pin dev image to new sha * Standardize gearman DB settings * Fix elasticsearch settings bug * Fix gearman bug * Add missing odbc headers * Add more debug messages * Swap out dev ci image * Fix required virtualenv version * Swap out dev ci image * Swap out dev ci image * Remove aioredis v1 for EOL * Add coverage paths for docker container * Unpin ci container --------- Co-authored-by: TimPansino * Trigger tests * Add testing for profile trace. * [Mega-Linter] Apply linters fixes * Ignore __call__ from coverage on profile_trace. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: hmstepanek Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Add Transaction API Tests (#857) * Test for suppress_apdex_metric * Add custom_metrics tests * Add distributed_trace_headers testing in existing tests * [Mega-Linter] Apply linters fixes * Remove redundant if-statement * Ignore deprecated transaction function from coverage * [Mega-Linter] Apply linters fixes * Push empty commit * Update newrelic/api/transaction.py --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add tests for jinja2. (#842) * Add tests for jinja2. * [Mega-Linter] Apply linters fixes * Update tox.ini Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix starlette testing matrix for updated behavior. (#869) Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Correct Serverless Distributed Tracing Logic (#870) * Fix serverless logic for distributed tracing * Test stubs * Collapse testing changes * Add negative testing to regular DT test suite * Apply linter fixes * [Mega-Linter] Apply linters fixes --------- Co-authored-by: TimPansino * Fix Kafka CI (#863) * Reenable kafka testing * Add kafka dev lib * Sync install python with devcontainer * Fix kafka local host setting * Drop set -u flag * Pin CI image dev sha * Add parallel flag to kafka * Fix proper exit status * Build librdkafka from source * Updated dev image sha * Remove coverage exclusions * Add new options to better emulate GHA * Reconfigure kafka networking Co-authored-by: Hannah Stepanek * Fix kafka ports on GHA * Run kafka tests serially * Separate kafka consumer groups * Put CI container makefile back * Remove confluent kafka Py27 for latest * Roll back ubuntu version update * Update dev ci sha --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Change image tag to latest (#871) * Change image tag to latest * Use built sha * Fixup * Replace w/ latest * Add full version for pypy3.8 to tox (#872) * Add full version for pypy3.8 * Remove solrpy from tests * Instrument RedisCluster (#809) * Add instrumentation for RedisCluster * Add tests for redis cluster * Ignore Django instrumentation from older versions (#859) * Ignore Django instrumentation from older versions * Ignore Django instrumentation from older versions * Fix text concatenation * Update newrelic/hooks/framework_django.py Co-authored-by: Hannah Stepanek * Update newrelic/hooks/framework_django.py Co-authored-by: Hannah Stepanek --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Modify postgresql tests to include WITH query (#885) * Modify postgresql tests to include WITH * [Mega-Linter] Apply linters fixes --------- Co-authored-by: lrafeei * Develop redis addons (#888) * Added separate instrumentation for redis.asyncio.client (#808) * Added separate instrumentation for redis.asyncio.client Merge main branch updates Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Modify redis tests * removed redis.asyncio from aioredis instrumentation removed aioredis instrumentation in redis asyncio client removed redis.asyncio from aioredis instrumentation --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Lalleh Rafeei Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Redis asyncio testing (#881) * Add/modify redis asyncio tests * Change to psubscribe * Tweak redis async tests/instrumentation * [Mega-Linter] Apply linters fixes * Push empty commit * Exclude older instrumentation from coverage * Resolve requested testing changes * Tweak async pubsub test * Fix pubsub test --------- Co-authored-by: lrafeei * Remove aioredis and aredis from tox (#891) * Remove aioredis and aredis from tox * Add aredis and aioredis to coverage ignore * Push empty commit * Fix codecov ignore file --------- Co-authored-by: Ahmed Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: lrafeei * Add google firestore instrumentation (#893) * Add instrumentation for Google Firestore documents and collections (#876) * Initial GCP firestore instrumentation commit. * Add testing for documents and collections + test generators Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Trim whitespace --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Firestore CI (#877) * Add firestore CI runner * Correct hook file name * Setup emulator credentials * Swap dependency to firestore alone * Hacky setup for firestore * Fix firestore hostname * Ensure firestore connection * Fix CI issues * Refactor Firestore Hooks (#879) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Firestore Sync Client Instrumentation (#880) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Client instrumentation * Add query and aggregation query instrumentation * Fix deprecation warning * Simplify collection lookup * Combine query test files * Rename methods for clarity * Instrument Firestore batching * Add transaction instrumentation * Consumer iterators on <=Py38 * Allow better parallelization in firestore tests * Clean out unnecessary code * [Mega-Linter] Apply linters fixes * Better parallelization safeguards * Add collection group instrumentation * [Mega-Linter] Apply linters fixes * Change imports to native APIs * Swap target functions to lambdas * Convert exercise functions to fixtures --------- Co-authored-by: TimPansino * Update datastore_trace wrapper to take instance info (#883) * Update datastore trace wrapper to take instance info. * [Mega-Linter] Apply linters fixes * Make instance info args optional. * [Mega-Linter] Apply linters fixes * Add datastore trace testing. * Add background task decorator. * [Mega-Linter] Apply linters fixes * Fix typo in validator. --------- Co-authored-by: umaannamalai * Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Trace Async Wrapper Argument (#886) * Add async_wrapper to datastore_trace api * Add async wrapper argument to all trace APIs * Add testing for automatic and manual asyncwrappers * Firstore Async Instrumentation (#882) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Client instrumentation * Add query and aggregation query instrumentation * Fix deprecation warning * Simplify collection lookup * Combine query test files * Rename methods for clarity * Instrument Firestore batching * Add transaction instrumentation * Consumer iterators on <=Py38 * Add async generator wrapper * Allow better parallelization in firestore tests * Fix issue in async generator wrapper * Add async client instrumentation * Squashed commit of the following: commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests * Add async collection instrumentation * Add async document instrumentation * Async Query instrumentation * Add async batch instrumentation * Add instrumentation for AsyncTransaction * Squashed commit of the following: commit c836f8f377f9391af86452e81b59f834330b18fb Author: TimPansino Date: Thu Jul 27 19:54:35 2023 +0000 [Mega-Linter] Apply linters fixes commit 02a55a11017fd27b45f06ab719a33917cf185aac Author: Tim Pansino Date: Thu Jul 27 12:46:46 2023 -0700 Add collection group instrumentation commit ab1f4ff5d2e88e6def42fa3c99c619f9673ce918 Author: Tim Pansino Date: Thu Jul 27 12:00:33 2023 -0700 Better parallelization safeguards commit fa5f39a2b037421cf017a062901c0ea1ec2b9723 Author: TimPansino Date: Wed Jul 26 22:59:11 2023 +0000 [Mega-Linter] Apply linters fixes commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests * Remove reset_firestore * Re-merge of test_query * Use public API imports * Add async collection group instrumentation * Refactor exercise functions to fixtures * Squashed commit of the following: commit 09c5e11498b4c200057190e859f8151241c421f3 Author: Tim Pansino Date: Wed Aug 2 14:33:24 2023 -0700 Add testing for automatic and manual asyncwrappers commit fc3ef6bfb8cb2f9cd6c8ffdf7bfd953be41cc974 Author: Tim Pansino Date: Wed Aug 2 14:33:05 2023 -0700 Add async wrapper argument to all trace APIs commit 479f9e236e2212e0f9cdf51627996068027acd82 Merge: faf3cccea edd1f94b0 Author: Tim Pansino Date: Wed Aug 2 13:44:24 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-async-wrapper-argument commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit faf3ccceae127128aff81fc59d95dd3f49699a3c Author: Tim Pansino Date: Mon Jul 31 15:10:56 2023 -0700 Add async_wrapper to datastore_trace api * Remove custom wrapper code from firestore * Undo wrapper edits --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Firestore Instance Info (#887) * Add instance info testing to query * Instance info for query.stream * Squashed commit of the following: commit 1c426c84b2c8ee36c6a40bf6bbfcb862c90db1cf Author: umaannamalai Date: Mon Jul 31 23:01:49 2023 +0000 [Mega-Linter] Apply linters fixes commit 7687c0695783fe40a86e705ec9790c19248f0c1e Author: Uma Annamalai Date: Mon Jul 31 15:47:09 2023 -0700 Make instance info args optional. commit 53f8400ce0d0e8b53bfcaba4b54f898a63e3d68b Author: umaannamalai Date: Mon Jul 31 22:23:20 2023 +0000 [Mega-Linter] Apply linters fixes commit d95d477cdfd54de4490211e3c4dd7de2504057f3 Author: Uma Annamalai Date: Mon Jul 31 15:20:41 2023 -0700 Update datastore trace wrapper to take instance info. * Add instance info testing to all apis * Separate transaction instance info tests * Implement all instance info getters * Squashed commit of the following: commit db3561e54f773730f269455ae323865b6230a613 Merge: 844e556ab edd1f94b0 Author: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Date: Wed Aug 2 22:10:32 2023 +0000 Merge branch 'develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 844e556abfbca63573e51a2647141e07ce9e942f Author: Tim Pansino Date: Wed Aug 2 15:09:49 2023 -0700 Remove custom wrapper code from firestore commit ad2999ff50b6b17b5774f69ca8116ee901f47474 Author: Tim Pansino Date: Wed Aug 2 14:58:38 2023 -0700 Squashed commit of the following: commit 09c5e11498b4c200057190e859f8151241c421f3 Author: Tim Pansino Date: Wed Aug 2 14:33:24 2023 -0700 Add testing for automatic and manual asyncwrappers commit fc3ef6bfb8cb2f9cd6c8ffdf7bfd953be41cc974 Author: Tim Pansino Date: Wed Aug 2 14:33:05 2023 -0700 Add async wrapper argument to all trace APIs commit 479f9e236e2212e0f9cdf51627996068027acd82 Merge: faf3cccea edd1f94b0 Author: Tim Pansino Date: Wed Aug 2 13:44:24 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-async-wrapper-argument commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit faf3ccceae127128aff81fc59d95dd3f49699a3c Author: Tim Pansino Date: Mon Jul 31 15:10:56 2023 -0700 Add async_wrapper to datastore_trace api commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 29579fc2ecd8199b0227922425556d3279f17e57 Merge: 4a8a3fe04 7596fb40d Author: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Date: Wed Aug 2 19:54:09 2023 +0000 Merge branch 'develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 7596fb40dd739572a4224978173d61f5c9da9b3d Author: Uma Annamalai Date: Wed Aug 2 12:53:29 2023 -0700 Update datastore_trace wrapper to take instance info (#883) * Update datastore trace wrapper to take instance info. * [Mega-Linter] Apply linters fixes * Make instance info args optional. * [Mega-Linter] Apply linters fixes * Add datastore trace testing. * Add background task decorator. * [Mega-Linter] Apply linters fixes * Fix typo in validator. --------- Co-authored-by: umaannamalai commit 4a8a3fe0486801ab88f7ddac05e89d96b6ae6fc0 Merge: 7bf6f4978 dcc92a914 Author: Tim Pansino Date: Mon Jul 31 14:51:20 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 7bf6f4978f058206c3cfb2b9c0efed963ca610ef Author: Tim Pansino Date: Mon Jul 31 14:34:26 2023 -0700 Refactor exercise functions to fixtures commit d3e473204bb2d840d6a73ec1b5de897e11e193ee Author: Tim Pansino Date: Thu Jul 27 13:20:37 2023 -0700 Add async collection group instrumentation commit 5902515202f7c7985b787bd00bb66b7f89699e19 Author: Tim Pansino Date: Thu Jul 27 13:09:13 2023 -0700 Use public API imports commit 9266924d8ef965852dec415973d7d9699031f011 Author: Tim Pansino Date: Thu Jul 27 13:04:19 2023 -0700 Re-merge of test_query commit b6bc9a47f28f2da29a8c5bf77dd37273c83a3757 Author: Tim Pansino Date: Thu Jul 27 13:01:27 2023 -0700 Remove reset_firestore commit 87fbe6203def1e583ff8fea58d4d6a0e70bfa606 Author: Tim Pansino Date: Thu Jul 27 13:00:37 2023 -0700 Squashed commit of the following: commit c836f8f377f9391af86452e81b59f834330b18fb Author: TimPansino Date: Thu Jul 27 19:54:35 2023 +0000 [Mega-Linter] Apply linters fixes commit 02a55a11017fd27b45f06ab719a33917cf185aac Author: Tim Pansino Date: Thu Jul 27 12:46:46 2023 -0700 Add collection group instrumentation commit ab1f4ff5d2e88e6def42fa3c99c619f9673ce918 Author: Tim Pansino Date: Thu Jul 27 12:00:33 2023 -0700 Better parallelization safeguards commit fa5f39a2b037421cf017a062901c0ea1ec2b9723 Author: TimPansino Date: Wed Jul 26 22:59:11 2023 +0000 [Mega-Linter] Apply linters fixes commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit e04ec6f7959001558951bb0b716bf7c2f9062380 Author: Tim Pansino Date: Thu Jul 27 11:55:44 2023 -0700 Add instrumentation for AsyncTransaction commit 6b7fc79b2466bc729d07878193643f989f95bf04 Author: Tim Pansino Date: Wed Jul 26 16:56:04 2023 -0700 Add async batch instrumentation commit c392e78fba4cde9334dc7e1b40a7a6531e9b672c Author: Tim Pansino Date: Wed Jul 26 16:36:03 2023 -0700 Async Query instrumentation commit aab244bcb45cc5cb6cb2be870a8182da95128582 Author: Tim Pansino Date: Wed Jul 26 16:20:58 2023 -0700 Add async document instrumentation commit 3fb6a6cd32c3a7fcfa1874aeb68e2cf3c23ea85c Author: Tim Pansino Date: Wed Jul 26 16:11:17 2023 -0700 Add async collection instrumentation commit 7851baf92ece9d7aa85c0286b32aa8249d3b2191 Author: Tim Pansino Date: Wed Jul 26 15:58:12 2023 -0700 Squashed commit of the following: commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit c49a1cf0b079c53f61192de589efa32044712b58 Author: Tim Pansino Date: Wed Jul 26 15:54:13 2023 -0700 Add async client instrumentation commit c857358cc89d064fa7dddb5a6a0f2069496db708 Author: Tim Pansino Date: Wed Jul 26 15:53:21 2023 -0700 Fix issue in async generator wrapper commit 5693dd2f3ca2c23bc170b1e2cd9ea87862d9d80f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit fbe40eaf4eb9da57fd6cb881328087fedc0dc2d9 Author: Tim Pansino Date: Wed Jul 26 14:22:53 2023 -0700 Add async generator wrapper commit b9a91e574a8e183249549f223bb4090226467f80 Author: Tim Pansino Date: Wed Jul 26 12:21:25 2023 -0700 Consumer iterators on <=Py38 commit ef06df5dca7d6e6f0f7e96700544514b99e9c132 Author: Tim Pansino Date: Wed Jul 26 12:01:25 2023 -0700 Add transaction instrumentation commit 2ce45c85ebf0a6951884c675d6cad77486988b7b Author: Tim Pansino Date: Tue Jul 25 15:55:50 2023 -0700 Instrument Firestore batching commit d17b62f720c98216fe5e80df13234ab84ccd9924 Author: Tim Pansino Date: Tue Jul 25 15:31:48 2023 -0700 Rename methods for clarity commit 6214f0bc5926b0b76acdf4bad612cc2710eeb3c7 Author: Tim Pansino Date: Tue Jul 25 15:30:23 2023 -0700 Combine query test files commit b4e87005d6b15c777563dc9ba1885612b384c61e Author: Tim Pansino Date: Tue Jul 25 15:23:03 2023 -0700 Simplify collection lookup commit a0c78a22dbd4ac43d7ef0cb444614683ce76142b Author: Tim Pansino Date: Tue Jul 25 15:18:51 2023 -0700 Fix deprecation warning commit 44598cc271e4a8d5d2962284894a9547372efdbe Author: Tim Pansino Date: Tue Jul 25 15:15:13 2023 -0700 Add query and aggregation query instrumentation commit b9eaa5b35144be48243e6315b8c64ad599d6a4de Author: Tim Pansino Date: Tue Jul 25 13:33:42 2023 -0700 Client instrumentation commit 19f5a48326b6aa51c1deb7e3acc2e5e6ba6ef749 Author: Tim Pansino Date: Mon Jul 24 15:55:52 2023 -0700 Remove unnecessary settings lookups commit ba7850a06a48005612e59b44c1a509d28f99f86d Author: Tim Pansino Date: Mon Jul 24 15:44:54 2023 -0700 Simplify existing instrumentation commit e07ffc3efb351769614c67425f7352dc4217e6be Author: Tim Pansino Date: Mon Jul 24 15:44:10 2023 -0700 Remove unnecessary instrumentation * Add instance info to async client * Simplify lookup logic for instance info * Precompute closures for memory usage * Undo wrapper edits * Fix typo * Change port from int ot str * Fix Generator Wrappers (#890) * Fix async wrapper implementations * Add regression testing --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Base Devcontainer on CI Image (#873) * Base devcontainer on ci image * Build arm image on main * Fix syntax --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add support for redis v5. (#895) * Instrumentat methods added in redis v5 release. * Update metrics in redis tests. * Use importlib.metadata first to avoid deprecation warnings (#878) * Use importlib.metadata first to avoid deprecation warnings * Use get distribution name of module before fetching its version * Add support for Python versions < 3.9 * Fix conditional for packages_distributions * Linter fixes * Remove fixture in favor of test skips --------- Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Fix Normalization Rules (#894) * Fix cross agent tests to run from anywhere * Cover failures in rules engine with testing Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Patch metrics not being properly ignored * Patch normalization rule init default arguments * Clean up to match other fixture setups --------- Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix database instance metric bug (#905) * Remove enable_datastore_instance_feature This was added in 2016 when the database instance feature was first developed. It appears to be a method of gating this feature internally within the agent at the time that it was implemented. However, it is not needed now and database instrumentations that don't call this are actually broken in that the metric that is used to create the service map (namely `Datastore/instance/MySQL//`) does not get created due to not calling this enable feature function. * Rename cross agent test * Add Database/instance metric check * Add check for both path and file (#907) * Update structlog instrumentation. (#865) * Add structlog instrumentation. (#685) * Add initial structlog instrumentation. * Cleanup. * Add processor filtering and attribute testing. * Add more filtering tests. * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei * Remove pylint codes from flake8 config (#701) * Create pytest fixtures and cleanup tests. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Updates to release structlog instrumentation. * Update pypy testing versions. * Update from pypy37 to pypy38 for structlog. --------- Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Merge main into MLops dev branch * GraphQL Async Instrumentation Support (#908) * Add GraphQL Server Sanic Instrumentation * Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Add co-authors Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Comment out Copyright notice message Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Finalize Sanic testing * Fix flask framework details with callable * Parametrized testing for graphql-server * GraphQL Async Resolvers Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * GraphQL Proper Coro and Promise Support (#508) * Fix GraphQL async issues * Fix nonlocal binding issues in python 2 * Fix promises with async graphql * Issues with promises * Fix promises in graphql2 * Fixed all graphql async issues * Fix Py27 quirks * Update tox * Fix importing paths of graphqlserver * Fix broken import path * Unpin pypy37 * Fix weird import issues * Fix graphql impl coros (#522) * Strawberry Async Updates (#521) * Parameterize strawberry tests * Remove duplicate functions * Fix strawberry version testing * Updates * Finalize strawberry updates * Clean out code * Ariadne Async Testing (#523) * Parameterize ariadne tests * Fixing ariadne tests * Fixing ariadne middleware * Set 0 extra spans for graphql core tests * Add spans attr to strawberry tests * Graphene Async Testing (#524) * Graphene Async Testing * Fix missing extra spans numbers * Graphene promise tests * Fix py2 imports * Removed unused __init__ * Update code level metrics validator for py2 * Unify graphql testing imports * Fix ariadne imports * Fix other imports * Fix import issues * Merge main into develop-graphql-async (#892) * Update Versioning Scheme (#651) * Update versioning scheme to 3 semver digits * Fix version indexing Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei * Remove version truncation * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei Co-authored-by: TimPansino * Fix Trace Finalizer Crashes (#652) * Patch crashes in various traces with None settings * Add tests for graphql trace types to unittests * Add test to ensure traces don't crash in finalizer * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: TimPansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Add usage tracking metrics for Kafka clients. (#658) * Add usage tracking metrics for Kafka clients. * Fix double import lint error * [Mega-Linter] Apply linters fixes * Create version util file and add metrics to consumer. * Address linting errors. * Add missing semi-colon. * [Mega-Linter] Apply linters fixes * Bump tests. Co-authored-by: Hannah Stepanek Co-authored-by: hmstepanek Co-authored-by: umaannamalai * Deprecate add_custom_parameter(s) API (#655) * Deprecate add_custom_parameter(s) API * Fix unicode tests and some pylint errors * Fix more pylint errors * Revert "Fix more pylint errors" This reverts commit 807ec1c5c40fe421300ccdcd6fedd81f288dce2c. * Edit deprecation message in add_custom_parameters * Add usage metrics for Daphne and Hypercorn. (#665) * Add usage metrics for Daphne and Hypercorn. * [Mega-Linter] Apply linters fixes Co-authored-by: umaannamalai * Fix Flask view support in Code Level Metrics (#664) * Fix Flask view support in Code Level Metrics Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * [Mega-Linter] Apply linters fixes * Bump tests * Fix CLM tests for flaskrest * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: TimPansino Co-authored-by: Uma Annamalai * Fix aioredis version crash (#661) Co-authored-by: Uma Annamalai * Add double wrapped testing for Hypercorn and Daphne and dispatcher argument to WSGI API. (#667) * Add double wrapped app tests. * Fix linting errors. * [Mega-Linter] Apply linters fixes * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add Python 3.11 Support (#654) * Add py311 tests * Fix typo * Added 3.11 support for aiohttp framework Co-authored-by: Timothy Pansino * Set up environment to run Python 3.11 Co-authored-by: Timothy Pansino * Add Python 3.11 support for agent_features Co-authored-by: Timothy Pansino * Partial Python 3.11 support added for Tornado Co-authored-by: Timothy Pansino * Adjust postgres versions * Fix tornado install path locally * Remove aioredis py311 tests * Update 3.11 to dev in tests * Fix sanic instrumentation and imp/importlib deprecation Co-authored-by: Timothy Pansino * Simplify wheel build options * Update cibuildwheel for 3.11 * Remove falconmaster py311 test Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino * Remove devcontainer submodule (#669) * Uncomment NewRelicContextFormatter from agent.py (#676) * Fix botocore tests for botocore v1.28.1+ (#675) * Fix botocore tests for botocore v1.28.1+ Co-authored-by: Timothy Pansino * Fix boto3 tests for botocore v1.28.1+ Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Fix boto3 tests for python 2.7 Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Feature increased custom event limit (#674) * Update reservoir size for custom events. * [Mega-Linter] Apply linters fixes * Increase custom event limit. (#666) * Remove duplicated CUSTOM_EVENT_RESERVOIR_SIZE Co-authored-by: Tim Pansino Co-authored-by: TimPansino Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add python 3.11 stable release to GHA (#671) * Double kafka test runners (#677) Co-authored-by: Hannah Stepanek * Fix failing flask_rest tests (#683) * Pin flask-restx in flask_rest tests for 2.7 flask-restx dropped support for 2.7 in 1.0.1. * Drop support for flask-restplus flask-restx replaced flask-restplus. flask-restplus's latest version supports 3.6 which we don't even support anymore. * Fix failing botocore tests (#684) * Change queue url for botocore>=1.29.0 botocore >=1.29.0 uses sqs.us-east-1.amazonaws.com url instead of queue.amazonaws.com. * Use tuple version instead of str * Change botocore129->botocore128 * Add record_log_event to public api (#681) * Add patch for sentry SDK to correct ASGI v2/v3 detection. (#680) * Add patch for sentry to correct ASGI v2/v3 detection. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * [Mega-Linter] Apply linters fixes Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Update pip install command (#688) * Validator transfer from fixtures.py to validators directory, Part 1 (#672) * Move validate_transaction_metrics to validators directory * Comment out original validate_transaction_metrics from fixtures.py * Move validate_time_metrics_outside_transaction to validators directory * Move validate_internal_metrics into validators directory and fixed validate_transaction_metrics * Move validate_transaction_errors into validators directory * Move validate_application_errors into validators directory * Move validate_custom_parameters into validators directory * Move validate_synthetics_event into validators directory * Move validate_transaction_event_attributes into validators directory * Move validate_non_transaction_error_event into validators directory * Fix import issues * Fix (more) import issues * Fix validate_transaction_metrics import in aioredis * Remove commented code from fixtures.py * Initialize ExternalNode properties (#687) Co-authored-by: Hannah Stepanek * Fix package_version_utils.py logic (#689) * Fix package_version_utils.py logic Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Move description of func into func itself * typecast lists into tuples * Remove breakpoints * Empty _test_package_version_utils.py * Make changes to the test Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Pin Github Actions Runner to Ubuntu 20 for Py27 (#698) * Pin Github Actions runner to ubuntu 20 for Py27 * Upgrade setup-python * Fix Confluent Kafka Producer Arguments (#699) * Add confluentkafka test for posargs/kwargs * Fix confluent kafka topic argument bug * More sensible producer arguments * Fix tornado master tests & instrument redis 4.3.5 (#695) * Remove 3.7 testing of tornado master tornadomaster dropped support for 3.7 * Instrument new redis 4.3.5 client methods Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Remove pylint codes from flake8 config (#701) * Validator transfer from fixtures.py to validators directory, Part 2 (#690) * Move validate_transaction_metrics to validators directory * Comment out original validate_transaction_metrics from fixtures.py * Move validate_time_metrics_outside_transaction to validators directory * Move validate_internal_metrics into validators directory and fixed validate_transaction_metrics * Move validate_transaction_errors into validators directory * Move validate_application_errors into validators directory * Move validate_custom_parameters into validators directory * Move validate_synthetics_event into validators directory * Move validate_transaction_event_attributes into validators directory * Move validate_non_transaction_error_event into validators directory * Move validate_application_error_trace_count into validators directory * Move validate_application_error_event_count into validators directory * Move validate_synthetics_transaction_trace into validators directory * Move validate_tt_collector_json to validators directory * Move validate_transaction_trace_attributes into validator directory * Move validate_transaction_error_trace_attributes into validator directory * Move validate_error_trace_collector_json into validator directory * Move validate_error_event_collector_json into validator directory * Move validate_transaction_event_collector_json into validator directory * Fix import issues from merge * Fix some pylint errors * Revert 'raise ValueError' to be PY2 compatible * Delete commented lines * Fix bug in celery where works don't report data (#696) This fixes Missing information from Celery workers when using MAX_TASKS_PER_CHILD issue. Previously, if celery was run with the --loglevel=INFO flag, an agent instance would be created for the main celery process and after the first worker shutdown, all following worker's agent instances would point to that agent instance instead of creating a new instance. This was root caused to incorrectly creating an agent instance when application activate was not set. Now no agent instance will be created for the main celery process. * Reverts removal of flask_restful hooks. (#705) * Update instrumented methods in redis. (#707) Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Add TraceCache Guarded Iteration (#704) * Add MutableMapping API to TraceCache * Update trace cache usage to use guarded APIs. * [Mega-Linter] Apply linters fixes * Bump tests * Fix keys iterator * Comments for trace cache methods * Reorganize tests * Fix fixture refs * Fix testing refs * [Mega-Linter] Apply linters fixes * Bump tests * Upper case constant Co-authored-by: TimPansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Fix Type Constructor Classes in Code Level Metrics (#708) * Fix CLM exception catching * Reorganize CLM Tests * Add type constructor tests to CLM * Fix line number * Pin tox version * Fix lambda tests in CLM * Fix lint issues * Turn helper func into pytest fixture Co-authored-by: Hannah Stepanek * Fix sanic and starlette tests (#734) * Fix sanic tests * Tweak test fix for sanic * Remove test for v18.12 in sanic (no longer supported) * Pin starlette latest to v0.23.1 (for now) * Add comment in tox about pinned starlette version * Add methods to instrument (#738) * Add card to instrumented methods in Redis (#740) * Add DevContainer (#711) * Add devcontainer setup * Add newrelic env vars to passenv * Add default extensions * Add devcontainer instructions to contributing docs * Convert smart quotes in contributing docs. * Apply proper RST formatting * [Mega-Linter] Apply linters fixes * Add GHCR to prerequisites * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: TimPansino * Module classmethod fix (#662) * Fix function_wrapper calls to module * Fix wrapper in pika hook * Revert elasticsearch instrumentation * Revert some wrap_function_wrappers to orig * Remove comments/breakpoints * Fix hooks in elasticsearch Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Fix log decorating to be JSON compatible (#736) * Initial addition of JSON capability * Add NR-LINKING metadata JSON combatibility * Remove breakpoint * Hardcode local log decorating tests * Tweak linking metatdata parsing/adding * Revert "Fix log decorating to be JSON compatible" (#746) * Revert "Fix log decorating to be JSON compatible (#736)" This reverts commit 0db5fee1e5d44b0791dc517ac9f5d88d1240a340. * [Mega-Linter] Apply linters fixes * Trigger tests Co-authored-by: hmstepanek * Add apdexPerfZone attribute to Transaction. (#753) Co-authored-by: Enriqueta De Leon Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Enriqueta De Leon Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Fix tests in starlette v0.23.1 (#752) * Fix tests in starlette v0.23.1 * Fix conditional tests * Add comment to bg_task test * Support `redis.asyncio` (#744) * Support `redis.asyncio` * Fix `flake8` issues Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Redis Asyncio Testing (#750) * Add standardized method for package version tuples * Adapt aioredis tests to redis.asyncio * Standardize version tuple * Refresh uninstrumented redis methods * Fix aioredis version checking * Remove aioredis version function * CodeCov Integration (#710) * Add aggregate coverage settings to tox.ini * Refactor coverage fixture for GHA * Send coverage data files * Linter fixes * Configure codecov report * Yield cov handle from fixture * Fix empty coverage fixture * Specify artifact download dir * Find coverage files with find command * Add concurrency cancelling to github actions * uncomment test deps * Fix or symbol * Fix concurrency groups * Linter fixes * Add comment for yield None in fixture * [Mega-Linter] Apply linters fixes * Bump Tests --------- Co-authored-by: TimPansino * Mergify (#761) * Add mergify config file * Remove priority * Clean up mergify rules * Add non-draft requirement for merge * Add merge method * [Mega-Linter] Apply linters fixes * Don't update draft PRs. * Remove merge rules for develop branches * Linting --------- Co-authored-by: TimPansino * Elasticsearch v8 support (#741) * Fix function_wrapper calls to module * Fix wrapper in pika hook * Revert elasticsearch instrumentation * Revert some wrap_function_wrappers to orig * Remove comments/breakpoints * Fix hooks in elasticsearch * Add new client methods from v8 and their hooks * Add elasticsearch v8 to workflow and tox * Fix indices for elasticsearch01 * Disable xpack security in elasticsearch v8.0 * Start to add try/except blocks in tests * Add support for v8 transport * add support for v8 connection * Add tests-WIP * Clean up most tests * Clean up unused instrumentation Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Remove elastic search source code * Elasticsearch v8 testing Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Scope ES fixture * ES v8 only supports Python3.6+ * Refactor transport tests for v8 Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek Co-authored-by: Kate Anderson Co-authored-by: Enriqueta De Leon * Remove extra comments * Added perform_request_kwargs to test_transport * Fix some linter issues * Remove extra newline * Group es v7 v8 process modules together * Add auto signature detection & binding * Use bind_arguments in ES * Add test for wrapped function * Add validator for datastore trace inputs * Use common bind_arguments for PY3 * Fix tests in starlette v0.23.1 (#752) * Fix tests in starlette v0.23.1 * Fix conditional tests * Add comment to bg_task test * Split below es 8 methods from es 8 methods Note the previous tests in this file to check whether a method was instrumented, did not test anything because they were checking whether the list of methods that we instrumented were instrumented instead of whether there were uninstrumented methods on the es client that we missed. Because we decided due to lack of reporting of bugs by our customers, to not support the buggy wrapping on previous es versions (below es8), we only added tests to assert all methods were wrapped from es8+. We also are only testing es8+ wrapping of methods since the previous versions wrapping behavior may not have been correct due to the signature of the methods changing without us detecting it due to lack of tests. Since our customers have not reported any issues, it seems not worth it at this time to go back and fix these bugs. * Remove signature auto detection implementation * Fixup: remove signature autodetection * Fixup: cleanup * Test method calls on all es versions * Fixup: don't run some methods on es7 --------- Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: mary-martinez Co-authored-by: enriqueta Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Update contributors workspace link in CONTRIBUTING.rst. (#760) * Update link in CONTRIBUTING.rst. * Update to RST syntax. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add Retry to Pip Install (#763) * Add retry to pip install * Fix retry backoff constant * Fix script failures --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add aiohttp support for expected status codes (#735) * Add aiohttp support for expected status codes * Adjust naming convention * Fix expected tests for new validator behavior --------- Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Tim Pansino * Fix PyPy Priority Sampling Test (#766) * Fix pypy priority sampling * [Mega-Linter] Apply linters fixes * Bump tests --------- Co-authored-by: TimPansino * Config linter fixes (#768) * Fix default value and lazy logging pylint * Fix default value and lazy logging pylint * Fix unnecessary 'else' in pylint * Fix logging-not-lazy in pylint * Fix redefined built-in error in Pylint * Fix implicit string concatenation in Pylint * Fix dict() to {} in Pylint * Make sure eval is OK to use for Pylint * Fix logging format string for Pylint * Change list comprehension to generator expression * [Mega-Linter] Apply linters fixes * Rerun tests --------- Co-authored-by: lrafeei * Sync tests w/ agents/cross_agent_tests/pull/150 (#770) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Infinite Tracing Batching & Compression (#762) * Infinite Tracing Batching and Compression settings (#756) * Add compression setting * Add batching setting * Infinite Tracing Compression (#758) * Initial commit * Add compression option in StreamingRPC * Add compression default to tests * Add test to confirm compression settings * Remove commented out code * Set compression settings from settings override * Infinite Tracing Batching (#759) * Initial infinite tracing batching implementation * Add RecordSpanBatch method to mock grpc server * Span batching settings and testing. Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Add final 8t batching tests * Rename serialization test * Formatting * Guard unittests from failing due to batching * Linting * Simplify batching algorithm * Properly wire batching parametrization * Fix incorrect validator use * Data loss on reconnect regression testing Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Test stream buffer batch sizes * Fix logic in supportability metrics for spans * Clean up nested conditionals in stream buffer * Compression parametrization in serialization test * Formatting * Update 8t test_no_delay_on_ok * Update protobufs * Remove unnecessary patching from test * Fix waiting in supportability metric tests * Add sleep to waiting in test * Reorder sleep and condition check * Mark no data loss xfail for py2. * Fix conditional check * Fix flake8 linter issues --------- Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Infinite Tracing Supportability Feature Toggle Metrics (#769) * Add 8T feature toggle supportability metrics * Remove supportability metrics when 8t is disabled. * Formatting --------- Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Fix DT settings for txn feature tests (#771) * Fix pyramid testing versions (#764) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix Ariadne Middleware Testing (#776) * Fix ariadne middleware testing Co-authored-by: Uma Annamalai Co-authored-by: Lalleh Rafeei * [Mega-Linter] Apply linters fixes * Bump tests --------- Co-authored-by: Uma Annamalai Co-authored-by: Lalleh Rafeei Co-authored-by: TimPansino * Exclude merged PRs from automatic mergify actions. (#774) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Refactor Code Coverage (#765) * Reorder dependency of code coverage fixture * Fix tests with coverage disabled * Refactor code coverage fixture * Clean out old coverage settings * Fix missing code coverage fixture * Fix pypy priority sampling * Start coverage from pytest-cov for better tracking * Refactor coverage config file * Ripping out coverage fixtures * Move tool config to bottom of tox.ini * Disabling py27 warning * Renaming env var --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add GraphQL Introspection Setting (#783) * Add graphql introspection setting * Sort settings object hierarchy * Add test for introspection queries setting * Expand introspection queries testing * [Mega-Linter] Apply linters fixes * Adjust introspection detection for graphql --------- Co-authored-by: TimPansino * Fix instance info tests for redis. (#784) * Fix instance info tests for redis. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai * Fix Redis Instance Info (#790) * Fix failing redis test for new default behavior * Revert "Fix instance info tests for redis. (#784)" This reverts commit f7108e3c2a54ab02a1104f6c16bd5fd799b9fc7e. * Guard GraphQL Settings Lookup (#787) * Guard graphql settings lookup * [Mega-Linter] Apply linters fixes * Bump tests * Update graphql settings test --------- Co-authored-by: TimPansino Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Errors Inbox Improvements (#791) * Errors inbox attributes and tests (#778) * Initial errors inbox commit Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Add enduser.id field * Move validate_error_trace_attributes into validators directory * Add error callback attributes test * Add tests for enduser.id & error.group.name Co-authored-by: Timothy Pansino * Uncomment code_coverage * Drop commented out line --------- Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Error Group Callback API (#785) * Error group initial implementat… * Fix merge conflicts for develop scikitlearn (#922) * Exclude command line functionality from test coverage (#855) * FIX: resilient environment settings (#825) if the application uses generalimport to manage optional depedencies, it's possible that generalimport.MissingOptionalDependency is raised. In this case, we should not report the module as it is not actually loaded and is not a runtime dependency of the application. Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Replace drop_transaction logic by using transaction context manager (#832) * Replace drop_transaction call * [Mega-Linter] Apply linters fixes * Empty commit to start tests * Change logic in BG Wrappers --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Upgrade to Pypy38 for TypedDict (#861) * Fix base branch * Revert tox dependencies * Replace all pypy37 with pypy38 * Remove action.yml file * Push Empty Commit * Fix skip_missing_interpreters behavior * Fix skip_missing_interpreters behavior * Pin dev CI image sha * Remove unsupported Tornado tests * Add latest tests to Tornado * Remove pypy38 (for now) --------- Co-authored-by: Tim Pansino * Add profile_trace testing (#858) * Include isort stdlibs for determining stdlib modules * Use isort & sys to eliminate std & builtin modules Previously, the logic would fail to identify third party modules installed within the local user socpe. This fixes that issue by skipping builtin and stdlib modules by name, instead of attempting to identify third party modules based on file paths. * Handle importlib_metadata.version being a callable * Add isort into third party notices * [Mega-Linter] Apply linters fixes * Remove Python 2.7 and pypy2 testing (#835) * Change setup-python to @v2 for py2.7 * Remove py27 and pypy testing * Fix syntax errors * Fix comma related syntax errors * Fix more issues in tox * Remove gearman test * Containerized CI Pipeline (#836) * Revert "Remove Python 2.7 and pypy2 testing (#835)" This reverts commit abb6405d2bfd629ed83f48e8a17b4a28e3a3c352. * Containerize CI process * Publish new docker container for CI images * Rename github actions job * Copyright tag scripts * Drop debug line * Swap to new CI image * Move pip install to just main python * Remove libcurl special case from tox * Install special case packages into main image * Remove unused packages * Remove all other triggers besides manual * Add make run command * Cleanup small bugs * Fix CI Image Tagging (#838) * Correct templated CI image name * Pin pypy2.7 in image * Fix up scripting * Temporarily Restore Old CI Pipeline (#841) * Restore old pipelines * Remove python 2 from setup-python * Rework CI Pipeline (#839) Change pypy to pypy27 in tox. Fix checkout logic Pin tox requires * Fix Tests on New CI (#843) * Remove non-root user * Test new CI image * Change pypy to pypy27 in tox. * Fix checkout logic * Fetch git tags properly * Pin tox requires * Adjust default db settings for github actions * Rename elasticsearch services * Reset to new pipelines * [Mega-Linter] Apply linters fixes * Fix timezone * Fix docker networking * Pin dev image to new sha * Standardize gearman DB settings * Fix elasticsearch settings bug * Fix gearman bug * Add missing odbc headers * Add more debug messages * Swap out dev ci image * Fix required virtualenv version * Swap out dev ci image * Swap out dev ci image * Remove aioredis v1 for EOL * Add coverage paths for docker container * Unpin ci container --------- Co-authored-by: TimPansino * Trigger tests * Add testing for profile trace. * [Mega-Linter] Apply linters fixes * Ignore __call__ from coverage on profile_trace. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: hmstepanek Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Add Transaction API Tests (#857) * Test for suppress_apdex_metric * Add custom_metrics tests * Add distributed_trace_headers testing in existing tests * [Mega-Linter] Apply linters fixes * Remove redundant if-statement * Ignore deprecated transaction function from coverage * [Mega-Linter] Apply linters fixes * Push empty commit * Update newrelic/api/transaction.py --------- Co-authored-by: lrafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add tests for jinja2. (#842) * Add tests for jinja2. * [Mega-Linter] Apply linters fixes * Update tox.ini Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix starlette testing matrix for updated behavior. (#869) Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Correct Serverless Distributed Tracing Logic (#870) * Fix serverless logic for distributed tracing * Test stubs * Collapse testing changes * Add negative testing to regular DT test suite * Apply linter fixes * [Mega-Linter] Apply linters fixes --------- Co-authored-by: TimPansino * Fix Kafka CI (#863) * Reenable kafka testing * Add kafka dev lib * Sync install python with devcontainer * Fix kafka local host setting * Drop set -u flag * Pin CI image dev sha * Add parallel flag to kafka * Fix proper exit status * Build librdkafka from source * Updated dev image sha * Remove coverage exclusions * Add new options to better emulate GHA * Reconfigure kafka networking Co-authored-by: Hannah Stepanek * Fix kafka ports on GHA * Run kafka tests serially * Separate kafka consumer groups * Put CI container makefile back * Remove confluent kafka Py27 for latest * Roll back ubuntu version update * Update dev ci sha --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Change image tag to latest (#871) * Change image tag to latest * Use built sha * Fixup * Replace w/ latest * Add full version for pypy3.8 to tox (#872) * Add full version for pypy3.8 * Remove solrpy from tests * Instrument RedisCluster (#809) * Add instrumentation for RedisCluster * Add tests for redis cluster * Ignore Django instrumentation from older versions (#859) * Ignore Django instrumentation from older versions * Ignore Django instrumentation from older versions * Fix text concatenation * Update newrelic/hooks/framework_django.py Co-authored-by: Hannah Stepanek * Update newrelic/hooks/framework_django.py Co-authored-by: Hannah Stepanek --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Modify postgresql tests to include WITH query (#885) * Modify postgresql tests to include WITH * [Mega-Linter] Apply linters fixes --------- Co-authored-by: lrafeei * Develop redis addons (#888) * Added separate instrumentation for redis.asyncio.client (#808) * Added separate instrumentation for redis.asyncio.client Merge main branch updates Add tests for newrelic/config.py (#860) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Modify redis tests * removed redis.asyncio from aioredis instrumentation removed aioredis instrumentation in redis asyncio client removed redis.asyncio from aioredis instrumentation --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Lalleh Rafeei Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Redis asyncio testing (#881) * Add/modify redis asyncio tests * Change to psubscribe * Tweak redis async tests/instrumentation * [Mega-Linter] Apply linters fixes * Push empty commit * Exclude older instrumentation from coverage * Resolve requested testing changes * Tweak async pubsub test * Fix pubsub test --------- Co-authored-by: lrafeei * Remove aioredis and aredis from tox (#891) * Remove aioredis and aredis from tox * Add aredis and aioredis to coverage ignore * Push empty commit * Fix codecov ignore file --------- Co-authored-by: Ahmed Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: lrafeei * Add google firestore instrumentation (#893) * Add instrumentation for Google Firestore documents and collections (#876) * Initial GCP firestore instrumentation commit. * Add testing for documents and collections + test generators Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Trim whitespace --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Firestore CI (#877) * Add firestore CI runner * Correct hook file name * Setup emulator credentials * Swap dependency to firestore alone * Hacky setup for firestore * Fix firestore hostname * Ensure firestore connection * Fix CI issues * Refactor Firestore Hooks (#879) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Firestore Sync Client Instrumentation (#880) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Client instrumentation * Add query and aggregation query instrumentation * Fix deprecation warning * Simplify collection lookup * Combine query test files * Rename methods for clarity * Instrument Firestore batching * Add transaction instrumentation * Consumer iterators on <=Py38 * Allow better parallelization in firestore tests * Clean out unnecessary code * [Mega-Linter] Apply linters fixes * Better parallelization safeguards * Add collection group instrumentation * [Mega-Linter] Apply linters fixes * Change imports to native APIs * Swap target functions to lambdas * Convert exercise functions to fixtures --------- Co-authored-by: TimPansino * Update datastore_trace wrapper to take instance info (#883) * Update datastore trace wrapper to take instance info. * [Mega-Linter] Apply linters fixes * Make instance info args optional. * [Mega-Linter] Apply linters fixes * Add datastore trace testing. * Add background task decorator. * [Mega-Linter] Apply linters fixes * Fix typo in validator. --------- Co-authored-by: umaannamalai * Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Trace Async Wrapper Argument (#886) * Add async_wrapper to datastore_trace api * Add async wrapper argument to all trace APIs * Add testing for automatic and manual asyncwrappers * Firstore Async Instrumentation (#882) * Remove unnecessary instrumentation * Simplify existing instrumentation * Remove unnecessary settings lookups * Client instrumentation * Add query and aggregation query instrumentation * Fix deprecation warning * Simplify collection lookup * Combine query test files * Rename methods for clarity * Instrument Firestore batching * Add transaction instrumentation * Consumer iterators on <=Py38 * Add async generator wrapper * Allow better parallelization in firestore tests * Fix issue in async generator wrapper * Add async client instrumentation * Squashed commit of the following: commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests * Add async collection instrumentation * Add async document instrumentation * Async Query instrumentation * Add async batch instrumentation * Add instrumentation for AsyncTransaction * Squashed commit of the following: commit c836f8f377f9391af86452e81b59f834330b18fb Author: TimPansino Date: Thu Jul 27 19:54:35 2023 +0000 [Mega-Linter] Apply linters fixes commit 02a55a11017fd27b45f06ab719a33917cf185aac Author: Tim Pansino Date: Thu Jul 27 12:46:46 2023 -0700 Add collection group instrumentation commit ab1f4ff5d2e88e6def42fa3c99c619f9673ce918 Author: Tim Pansino Date: Thu Jul 27 12:00:33 2023 -0700 Better parallelization safeguards commit fa5f39a2b037421cf017a062901c0ea1ec2b9723 Author: TimPansino Date: Wed Jul 26 22:59:11 2023 +0000 [Mega-Linter] Apply linters fixes commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests * Remove reset_firestore * Re-merge of test_query * Use public API imports * Add async collection group instrumentation * Refactor exercise functions to fixtures * Squashed commit of the following: commit 09c5e11498b4c200057190e859f8151241c421f3 Author: Tim Pansino Date: Wed Aug 2 14:33:24 2023 -0700 Add testing for automatic and manual asyncwrappers commit fc3ef6bfb8cb2f9cd6c8ffdf7bfd953be41cc974 Author: Tim Pansino Date: Wed Aug 2 14:33:05 2023 -0700 Add async wrapper argument to all trace APIs commit 479f9e236e2212e0f9cdf51627996068027acd82 Merge: faf3cccea edd1f94b0 Author: Tim Pansino Date: Wed Aug 2 13:44:24 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-async-wrapper-argument commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit faf3ccceae127128aff81fc59d95dd3f49699a3c Author: Tim Pansino Date: Mon Jul 31 15:10:56 2023 -0700 Add async_wrapper to datastore_trace api * Remove custom wrapper code from firestore * Undo wrapper edits --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Firestore Instance Info (#887) * Add instance info testing to query * Instance info for query.stream * Squashed commit of the following: commit 1c426c84b2c8ee36c6a40bf6bbfcb862c90db1cf Author: umaannamalai Date: Mon Jul 31 23:01:49 2023 +0000 [Mega-Linter] Apply linters fixes commit 7687c0695783fe40a86e705ec9790c19248f0c1e Author: Uma Annamalai Date: Mon Jul 31 15:47:09 2023 -0700 Make instance info args optional. commit 53f8400ce0d0e8b53bfcaba4b54f898a63e3d68b Author: umaannamalai Date: Mon Jul 31 22:23:20 2023 +0000 [Mega-Linter] Apply linters fixes commit d95d477cdfd54de4490211e3c4dd7de2504057f3 Author: Uma Annamalai Date: Mon Jul 31 15:20:41 2023 -0700 Update datastore trace wrapper to take instance info. * Add instance info testing to all apis * Separate transaction instance info tests * Implement all instance info getters * Squashed commit of the following: commit db3561e54f773730f269455ae323865b6230a613 Merge: 844e556ab edd1f94b0 Author: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Date: Wed Aug 2 22:10:32 2023 +0000 Merge branch 'develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 844e556abfbca63573e51a2647141e07ce9e942f Author: Tim Pansino Date: Wed Aug 2 15:09:49 2023 -0700 Remove custom wrapper code from firestore commit ad2999ff50b6b17b5774f69ca8116ee901f47474 Author: Tim Pansino Date: Wed Aug 2 14:58:38 2023 -0700 Squashed commit of the following: commit 09c5e11498b4c200057190e859f8151241c421f3 Author: Tim Pansino Date: Wed Aug 2 14:33:24 2023 -0700 Add testing for automatic and manual asyncwrappers commit fc3ef6bfb8cb2f9cd6c8ffdf7bfd953be41cc974 Author: Tim Pansino Date: Wed Aug 2 14:33:05 2023 -0700 Add async wrapper argument to all trace APIs commit 479f9e236e2212e0f9cdf51627996068027acd82 Merge: faf3cccea edd1f94b0 Author: Tim Pansino Date: Wed Aug 2 13:44:24 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-async-wrapper-argument commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit faf3ccceae127128aff81fc59d95dd3f49699a3c Author: Tim Pansino Date: Mon Jul 31 15:10:56 2023 -0700 Add async_wrapper to datastore_trace api commit edd1f94b0f601a2674da4e594b777bae0eed6643 Author: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Date: Wed Aug 2 13:40:51 2023 -0700 Async Generator Wrapper (#884) * Add async generator wrapper * Add no harm test * Remove anext calls * Add graphql traces to decorator testing * Remove pypy generator gc logic --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> commit 29579fc2ecd8199b0227922425556d3279f17e57 Merge: 4a8a3fe04 7596fb40d Author: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Date: Wed Aug 2 19:54:09 2023 +0000 Merge branch 'develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 7596fb40dd739572a4224978173d61f5c9da9b3d Author: Uma Annamalai Date: Wed Aug 2 12:53:29 2023 -0700 Update datastore_trace wrapper to take instance info (#883) * Update datastore trace wrapper to take instance info. * [Mega-Linter] Apply linters fixes * Make instance info args optional. * [Mega-Linter] Apply linters fixes * Add datastore trace testing. * Add background task decorator. * [Mega-Linter] Apply linters fixes * Fix typo in validator. --------- Co-authored-by: umaannamalai commit 4a8a3fe0486801ab88f7ddac05e89d96b6ae6fc0 Merge: 7bf6f4978 dcc92a914 Author: Tim Pansino Date: Mon Jul 31 14:51:20 2023 -0700 Merge remote-tracking branch 'origin/develop-google-firestore-instrumentation' into feature-firstore-async-instrumentation commit 7bf6f4978f058206c3cfb2b9c0efed963ca610ef Author: Tim Pansino Date: Mon Jul 31 14:34:26 2023 -0700 Refactor exercise functions to fixtures commit d3e473204bb2d840d6a73ec1b5de897e11e193ee Author: Tim Pansino Date: Thu Jul 27 13:20:37 2023 -0700 Add async collection group instrumentation commit 5902515202f7c7985b787bd00bb66b7f89699e19 Author: Tim Pansino Date: Thu Jul 27 13:09:13 2023 -0700 Use public API imports commit 9266924d8ef965852dec415973d7d9699031f011 Author: Tim Pansino Date: Thu Jul 27 13:04:19 2023 -0700 Re-merge of test_query commit b6bc9a47f28f2da29a8c5bf77dd37273c83a3757 Author: Tim Pansino Date: Thu Jul 27 13:01:27 2023 -0700 Remove reset_firestore commit 87fbe6203def1e583ff8fea58d4d6a0e70bfa606 Author: Tim Pansino Date: Thu Jul 27 13:00:37 2023 -0700 Squashed commit of the following: commit c836f8f377f9391af86452e81b59f834330b18fb Author: TimPansino Date: Thu Jul 27 19:54:35 2023 +0000 [Mega-Linter] Apply linters fixes commit 02a55a11017fd27b45f06ab719a33917cf185aac Author: Tim Pansino Date: Thu Jul 27 12:46:46 2023 -0700 Add collection group instrumentation commit ab1f4ff5d2e88e6def42fa3c99c619f9673ce918 Author: Tim Pansino Date: Thu Jul 27 12:00:33 2023 -0700 Better parallelization safeguards commit fa5f39a2b037421cf017a062901c0ea1ec2b9723 Author: TimPansino Date: Wed Jul 26 22:59:11 2023 +0000 [Mega-Linter] Apply linters fixes commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit e04ec6f7959001558951bb0b716bf7c2f9062380 Author: Tim Pansino Date: Thu Jul 27 11:55:44 2023 -0700 Add instrumentation for AsyncTransaction commit 6b7fc79b2466bc729d07878193643f989f95bf04 Author: Tim Pansino Date: Wed Jul 26 16:56:04 2023 -0700 Add async batch instrumentation commit c392e78fba4cde9334dc7e1b40a7a6531e9b672c Author: Tim Pansino Date: Wed Jul 26 16:36:03 2023 -0700 Async Query instrumentation commit aab244bcb45cc5cb6cb2be870a8182da95128582 Author: Tim Pansino Date: Wed Jul 26 16:20:58 2023 -0700 Add async document instrumentation commit 3fb6a6cd32c3a7fcfa1874aeb68e2cf3c23ea85c Author: Tim Pansino Date: Wed Jul 26 16:11:17 2023 -0700 Add async collection instrumentation commit 7851baf92ece9d7aa85c0286b32aa8249d3b2191 Author: Tim Pansino Date: Wed Jul 26 15:58:12 2023 -0700 Squashed commit of the following: commit 9d411e00e37476be4ce0c40c7e64e71c4a09cfc6 Author: Tim Pansino Date: Wed Jul 26 15:57:39 2023 -0700 Clean out unnecessary code commit cb550bad9bb9e15edfdcef5dd361022448e0348f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit c49a1cf0b079c53f61192de589efa32044712b58 Author: Tim Pansino Date: Wed Jul 26 15:54:13 2023 -0700 Add async client instrumentation commit c857358cc89d064fa7dddb5a6a0f2069496db708 Author: Tim Pansino Date: Wed Jul 26 15:53:21 2023 -0700 Fix issue in async generator wrapper commit 5693dd2f3ca2c23bc170b1e2cd9ea87862d9d80f Author: Tim Pansino Date: Wed Jul 26 14:27:01 2023 -0700 Allow better parallelization in firestore tests commit fbe40eaf4eb9da57fd6cb881328087fedc0dc2d9 Author: Tim Pansino Date: Wed Jul 26 14:22:53 2023 -0700 Add async generator wrapper commit b9a91e574a8e183249549f223bb4090226467f80 Author: Tim Pansino Date: Wed Jul 26 12:21:25 2023 -0700 Consumer iterators on <=Py38 commit ef06df5dca7d6e6f0f7e96700544514b99e9c132 Author: Tim Pansino Date: Wed Jul 26 12:01:25 2023 -0700 Add transaction instrumentation commit 2ce45c85ebf0a6951884c675d6cad77486988b7b Author: Tim Pansino Date: Tue Jul 25 15:55:50 2023 -0700 Instrument Firestore batching commit d17b62f720c98216fe5e80df13234ab84ccd9924 Author: Tim Pansino Date: Tue Jul 25 15:31:48 2023 -0700 Rename methods for clarity commit 6214f0bc5926b0b76acdf4bad612cc2710eeb3c7 Author: Tim Pansino Date: Tue Jul 25 15:30:23 2023 -0700 Combine query test files commit b4e87005d6b15c777563dc9ba1885612b384c61e Author: Tim Pansino Date: Tue Jul 25 15:23:03 2023 -0700 Simplify collection lookup commit a0c78a22dbd4ac43d7ef0cb444614683ce76142b Author: Tim Pansino Date: Tue Jul 25 15:18:51 2023 -0700 Fix deprecation warning commit 44598cc271e4a8d5d2962284894a9547372efdbe Author: Tim Pansino Date: Tue Jul 25 15:15:13 2023 -0700 Add query and aggregation query instrumentation commit b9eaa5b35144be48243e6315b8c64ad599d6a4de Author: Tim Pansino Date: Tue Jul 25 13:33:42 2023 -0700 Client instrumentation commit 19f5a48326b6aa51c1deb7e3acc2e5e6ba6ef749 Author: Tim Pansino Date: Mon Jul 24 15:55:52 2023 -0700 Remove unnecessary settings lookups commit ba7850a06a48005612e59b44c1a509d28f99f86d Author: Tim Pansino Date: Mon Jul 24 15:44:54 2023 -0700 Simplify existing instrumentation commit e07ffc3efb351769614c67425f7352dc4217e6be Author: Tim Pansino Date: Mon Jul 24 15:44:10 2023 -0700 Remove unnecessary instrumentation * Add instance info to async client * Simplify lookup logic for instance info * Precompute closures for memory usage * Undo wrapper edits * Fix typo * Change port from int ot str * Fix Generator Wrappers (#890) * Fix async wrapper implementations * Add regression testing --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: TimPansino Co-authored-by: umaannamalai * Base Devcontainer on CI Image (#873) * Base devcontainer on ci image * Build arm image on main * Fix syntax --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add support for redis v5. (#895) * Instrumentat methods added in redis v5 release. * Update metrics in redis tests. * Use importlib.metadata first to avoid deprecation warnings (#878) * Use importlib.metadata first to avoid deprecation warnings * Use get distribution name of module before fetching its version * Add support for Python versions < 3.9 * Fix conditional for packages_distributions * Linter fixes * Remove fixture in favor of test skips --------- Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Fix Normalization Rules (#894) * Fix cross agent tests to run from anywhere * Cover failures in rules engine with testing Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Patch metrics not being properly ignored * Patch normalization rule init default arguments * Clean up to match other fixture setups --------- Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix database instance metric bug (#905) * Remove enable_datastore_instance_feature This was added in 2016 when the database instance feature was first developed. It appears to be a method of gating this feature internally within the agent at the time that it was implemented. However, it is not needed now and database instrumentations that don't call this are actually broken in that the metric that is used to create the service map (namely `Datastore/instance/MySQL//`) does not get created due to not calling this enable feature function. * Rename cross agent test * Add Database/instance metric check * Add check for both path and file (#907) * Update structlog instrumentation. (#865) * Add structlog instrumentation. (#685) * Add initial structlog instrumentation. * Cleanup. * Add processor filtering and attribute testing. * Add more filtering tests. * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei * Remove pylint codes from flake8 config (#701) * Create pytest fixtures and cleanup tests. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Hannah Stepanek * Updates to release structlog instrumentation. * Update pypy testing versions. * Update from pypy37 to pypy38 for structlog. --------- Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * GraphQL Async Instrumentation Support (#908) * Add GraphQL Server Sanic Instrumentation * Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Add co-authors Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Comment out Copyright notice message Co-authored-by: Timothy Pansino Co-authored-by: Uma Annamalai * Finalize Sanic testing * Fix flask framework details with callable * Parametrized testing for graphql-server * GraphQL Async Resolvers Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai * GraphQL Proper Coro and Promise Support (#508) * Fix GraphQL async issues * Fix nonlocal binding issues in python 2 * Fix promises with async graphql * Issues with promises * Fix promises in graphql2 * Fixed all graphql async issues * Fix Py27 quirks * Update tox * Fix importing paths of graphqlserver * Fix broken import path * Unpin pypy37 * Fix weird import issues * Fix graphql impl coros (#522) * Strawberry Async Updates (#521) * Parameterize strawberry tests * Remove duplicate functions * Fix strawberry version testing * Updates * Finalize strawberry updates * Clean out code * Ariadne Async Testing (#523) * Parameterize ariadne tests * Fixing ariadne tests * Fixing ariadne middleware * Set 0 extra spans for graphql core tests * Add spans attr to strawberry tests * Graphene Async Testing (#524) * Graphene Async Testing * Fix missing extra spans numbers * Graphene promise tests * Fix py2 imports * Removed unused __init__ * Update code level metrics validator for py2 * Unify graphql testing imports * Fix ariadne imports * Fix other imports * Fix import issues * Merge main into develop-graphql-async (#892) * Update Versioning Scheme (#651) * Update versioning scheme to 3 semver digits * Fix version indexing Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei * Remove version truncation * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei Co-authored-by: TimPansino * Fix Trace Finalizer Crashes (#652) * Patch crashes in various traces with None settings * Add tests for graphql trace types to unittests * Add test to ensure traces don't crash in finalizer * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: TimPansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Add usage tracking metrics for Kafka clients. (#658) * Add usage tracking metrics for Kafka clients. * Fix double import lint error * [Mega-Linter] Apply linters fixes * Create version util file and add metrics to consumer. * Address linting errors. * Add missing semi-colon. * [Mega-Linter] Apply linters fixes * Bump tests. Co-authored-by: Hannah Stepanek Co-authored-by: hmstepanek Co-authored-by: umaannamalai * Deprecate add_custom_parameter(s) API (#655) * Deprecate add_custom_parameter(s) API * Fix unicode tests and some pylint errors * Fix more pylint errors * Revert "Fix more pylint errors" This reverts commit 807ec1c5c40fe421300ccdcd6fedd81f288dce2c. * Edit deprecation message in add_custom_parameters * Add usage metrics for Daphne and Hypercorn. (#665) * Add usage metrics for Daphne and Hypercorn. * [Mega-Linter] Apply linters fixes Co-authored-by: umaannamalai * Fix Flask view support in Code Level Metrics (#664) * Fix Flask view support in Code Level Metrics Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * [Mega-Linter] Apply linters fixes * Bump tests * Fix CLM tests for flaskrest * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: TimPansino Co-authored-by: Uma Annamalai * Fix aioredis version crash (#661) Co-authored-by: Uma Annamalai * Add double wrapped testing for Hypercorn and Daphne and dispatcher argument to WSGI API. (#667) * Add double wrapped app tests. * Fix linting errors. * [Mega-Linter] Apply linters fixes * Add co-authors. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * Add Python 3.11 Support (#654) * Add py311 tests * Fix typo * Added 3.11 support for aiohttp framework Co-authored-by: Timothy Pansino * Set up environment to run Python 3.11 Co-authored-by: Timothy Pansino * Add Python 3.11 support for agent_features Co-authored-by: Timothy Pansino * Partial Python 3.11 support added for Tornado Co-authored-by: Timothy Pansino * Adjust postgres versions * Fix tornado install path locally * Remove aioredis py311 tests * Update 3.11 to dev in tests * Fix sanic instrumentation and imp/importlib deprecation Co-authored-by: Timothy Pansino * Simplify wheel build options * Update cibuildwheel for 3.11 * Remove falconmaster py311 test Co-authored-by: Lalleh Rafeei Co-authored-by: Timothy Pansino * Remove devcontainer submodule (#669) * Uncomment NewRelicContextFormatter from agent.py (#676) * Fix botocore tests for botocore v1.28.1+ (#675) * Fix botocore tests for botocore v1.28.1+ Co-authored-by: Timothy Pansino * Fix boto3 tests for botocore v1.28.1+ Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Fix boto3 tests for python 2.7 Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Feature increased custom event limit (#674) * Update reservoir size for custom events. * [Mega-Linter] Apply linters fixes * Increase custom event limit. (#666) * Remove duplicated CUSTOM_EVENT_RESERVOIR_SIZE Co-authored-by: Tim Pansino Co-authored-by: TimPansino Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Uma Annamalai * Add python 3.11 stable release to GHA (#671) * Double kafka test runners (#677) Co-authored-by: Hannah Stepanek * Fix failing flask_rest tests (#683) * Pin flask-restx in flask_rest tests for 2.7 flask-restx dropped support for 2.7 in 1.0.1. * Drop support for flask-restplus flask-restx replaced flask-restplus. flask-restplus's latest version supports 3.6 which we don't even support anymore. * Fix failing botocore tests (#684) * Change queue url for botocore>=1.29.0 botocore >=1.29.0 uses sqs.us-east-1.amazonaws.com url instead of queue.amazonaws.com. * Use tuple version instead of str * Change botocore129->botocore128 * Add record_log_event to public api (#681) * Add patch for sentry SDK to correct ASGI v2/v3 detection. (#680) * Add patch for sentry to correct ASGI v2/v3 detection. Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek * [Mega-Linter] Apply linters fixes Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: umaannamalai Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Update pip install command (#688) * Validator transfer from fixtures.py to validators directory, Part 1 (#672) * Move validate_transaction_metrics to validators directory * Comment out original validate_transaction_metrics from fixtures.py * Move validate_time_metrics_outside_transaction to validators directory * Move validate_internal_metrics into validators directory and fixed validate_transaction_metrics * Move validate_transaction_errors into validators directory * Move validate_application_errors into validators directory * Move validate_custom_parameters into validators directory * Move validate_synthetics_event into validators directory * Move validate_transaction_event_attributes into validators directory * Move validate_non_transaction_error_event into validators directory * Fix import issues * Fix (more) import issues * Fix validate_transaction_metrics import in aioredis * Remove commented code from fixtures.py * Initialize ExternalNode properties (#687) Co-authored-by: Hannah Stepanek * Fix package_version_utils.py logic (#689) * Fix package_version_utils.py logic Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Move description of func into func itself * typecast lists into tuples * Remove breakpoints * Empty _test_package_version_utils.py * Make changes to the test Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Pin Github Actions Runner to Ubuntu 20 for Py27 (#698) * Pin Github Actions runner to ubuntu 20 for Py27 * Upgrade setup-python * Fix Confluent Kafka Producer Arguments (#699) * Add confluentkafka test for posargs/kwargs * Fix confluent kafka topic argument bug * More sensible producer arguments * Fix tornado master tests & instrument redis 4.3.5 (#695) * Remove 3.7 testing of tornado master tornadomaster dropped support for 3.7 * Instrument new redis 4.3.5 client methods Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Remove pylint codes from flake8 config (#701) * Validator transfer from fixtures.py to validators directory, Part 2 (#690) * Move validate_transaction_metrics to validators directory * Comment out original validate_transaction_metrics from fixtures.py * Move validate_time_metrics_outside_transaction to validators directory * Move validate_internal_metrics into validators directory and fixed validate_transaction_metrics * Move validate_transaction_errors into validators directory * Move validate_application_errors into validators directory * Move validate_custom_parameters into validators directory * Move validate_synthetics_event into validators directory * Move validate_transaction_event_attributes into validators directory * Move validate_non_transaction_error_event into validators directory * Move validate_application_error_trace_count into validators directory * Move validate_application_error_event_count into validators directory * Move validate_synthetics_transaction_trace into validators directory * Move validate_tt_collector_json to validators directory * Move validate_transaction_trace_attributes into validator directory * Move validate_transaction_error_trace_attributes into validator directory * Move validate_error_trace_collector_json into validator directory * Move validate_error_event_collector_json into validator directory * Move validate_transaction_event_collector_json into validator directory * Fix import issues from merge * Fix some pylint errors * Revert 'raise ValueError' to be PY2 compatible * Delete commented lines * Fix bug in celery where works don't report data (#696) This fixes Missing information from Celery workers when using MAX_TASKS_PER_CHILD issue. Previously, if celery was run with the --loglevel=INFO flag, an agent instance would be created for the main celery process and after the first worker shutdown, all following worker's agent instances would point to that agent instance instead of creating a new instance. This was root caused to incorrectly creating an agent instance when application activate was not set. Now no agent instance will be created for the main celery process. * Reverts removal of flask_restful hooks. (#705) * Update instrumented methods in redis. (#707) Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Add TraceCache Guarded Iteration (#704) * Add MutableMapping API to TraceCache * Update trace cache usage to use guarded APIs. * [Mega-Linter] Apply linters fixes * Bump tests * Fix keys iterator * Comments for trace cache methods * Reorganize tests * Fix fixture refs * Fix testing refs * [Mega-Linter] Apply linters fixes * Bump tests * Upper case constant Co-authored-by: TimPansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> * Fix Type Constructor Classes in Code Level Metrics (#708) * Fix CLM exception catching * Reorganize CLM Tests * Add type constructor tests to CLM * Fix line number * Pin tox version * Fix lambda tests in CLM * Fix lint issues * Turn helper func into pytest fixture Co-authored-by: Hannah Stepanek * Fix sanic and starlette tests (#734) * Fix sanic tests * Tweak test fix for sanic * Remove test for v18.12 in sanic (no longer supported) * Pin starlette latest to v0.23.1 (for now) * Add comment in tox about pinned starlette version * Add methods to instrument (#738) * Add card to instrumented methods in Redis (#740) * Add DevContainer (#711) * Add devcontainer setup * Add newrelic env vars to passenv * Add default extensions * Add devcontainer instructions to contributing docs * Convert smart quotes in contributing docs. * Apply proper RST formatting * [Mega-Linter] Apply linters fixes * Add GHCR to prerequisites * [Mega-Linter] Apply linters fixes * Bump tests Co-authored-by: TimPansino * Module classmethod fix (#662) * Fix function_wrapper calls to module * Fix wrapper in pika hook * Revert elasticsearch instrumentation * Revert some wrap_function_wrappers to orig * Remove comments/breakpoints * Fix hooks in elasticsearch Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Fix log decorating to be JSON compatible (#736) * Initial addition of JSON capability * Add NR-LINKING metadata JSON combatibility * Remove breakpoint * Hardcode local log decorating tests * Tweak linking metatdata parsing/adding * Revert "Fix log decorating to be JSON compatible" (#746) * Revert "Fix log decorating to be JSON compatible (#736)" This reverts commit 0db5fee1e5d44b0791dc517ac9f5d88d1240a340. * [Mega-Linter] Apply linters fixes * Trigger tests Co-authored-by: hmstepanek * Add apdexPerfZone attribute to Transaction. (#753) Co-authored-by: Enriqueta De Leon Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Enriqueta De Leon Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Fix tests in starlette v0.23.1 (#752) * Fix tests in starlette v0.23.1 * Fix conditional tests * Add comment to bg_task test * Support `redis.asyncio` (#744) * Support `redis.asyncio` * Fix `flake8` issues Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Redis Asyncio Testing (#750) * Add standardized method for package version tuples * Adapt aioredis tests to redis.asyncio * Standardize version tuple * Refresh uninstrumented redis methods * Fix aioredis version checking * Remove aioredis version function * CodeCov Integration (#710) * Add aggregate coverage settings to tox.ini * Refactor coverage fixture for GHA * Send coverage data files * Linter fixes * Configure codecov report * Yield cov handle from fixture * Fix empty coverage fixture * Specify artifact download dir * Find coverage files with find command * Add concurrency cancelling to github actions * uncomment test deps * Fix or symbol * Fix concurrency groups * Linter fixes * Add comment for yield None in fixture * [Mega-Linter] Apply linters fixes * Bump Tests --------- Co-authored-by: TimPansino * Mergify (#761) * Add mergify config file * Remove priority * Clean up mergify rules * Add non-draft requirement for merge * Add merge method * [Mega-Linter] Apply linters fixes * Don't update draft PRs. * Remove merge rules for develop branches * Linting --------- Co-authored-by: TimPansino * Elasticsearch v8 support (#741) * Fix function_wrapper calls to module * Fix wrapper in pika hook * Revert elasticsearch instrumentation * Revert some wrap_function_wrappers to orig * Remove comments/breakpoints * Fix hooks in elasticsearch * Add new client methods from v8 and their hooks * Add elasticsearch v8 to workflow and tox * Fix indices for elasticsearch01 * Disable xpack security in elasticsearch v8.0 * Start to add try/except blocks in tests * Add support for v8 transport * add support for v8 connection * Add tests-WIP * Clean up most tests * Clean up unused instrumentation Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Remove elastic search source code * Elasticsearch v8 testing Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek * Scope ES fixture * ES v8 only supports Python3.6+ * Refactor transport tests for v8 Co-authored-by: Lalleh Rafeei Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek Co-authored-by: Kate Anderson Co-authored-by: Enriqueta De Leon * Remove extra comments * Added perform_request_kwargs to test_transport * Fix some linter issues * Remove extra newline * Group es v7 v8 process modules together * Add auto signature detection & binding * Use bind_arguments in ES * Add test for wrapped function * Add validator for datastore trace inputs * Use common bind_arguments for PY3 * Fix tests in starlette v0.23.1 (#752) * Fix tests in starlette v0.23.1 * Fix conditional tests * Add comment to bg_task test * Split below es 8 methods from es 8 methods Note the previous tests in this file to check whether a method was instrumented, did not test anything because they were checking whether the list of methods that we instrumented were instrumented instead of whether there were uninstrumented methods on the es client that we missed. Because we decided due to lack of reporting of bugs by our customers, to not support the buggy wrapping on previous es versions (below es8), we only added tests to assert all methods were wrapped from es8+. We also are only testing es8+ wrapping of methods since the previous versions wrapping behavior may not have been correct due to the signature of the methods changing without us detecting it due to lack of tests. Since our customers have not reported any issues, it seems not worth it at this time to go back and fix these bugs. * Remove signature auto detection implementation * Fixup: remove signature autodetection * Fixup: cleanup * Test method calls on all es versions * Fixup: don't run some methods on es7 --------- Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: mary-martinez Co-authored-by: enriqueta Co-authored-by: Tim Pansino Co-authored-by: Lalleh Rafeei Co-authored-by: Enriqueta De Leon Co-authored-by: Uma Annamalai Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Hannah Stepanek Co-authored-by: Hannah Stepanek Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Update contributors workspace link in CONTRIBUTING.rst. (#760) * Update link in CONTRIBUTING.rst. * Update to RST syntax. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add Retry to Pip Install (#763) * Add retry to pip install * Fix retry backoff constant * Fix script failures --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add aiohttp support for expected status codes (#735) * Add aiohttp support for expected status codes * Adjust naming convention * Fix expected tests for new validator behavior --------- Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Tim Pansino * Fix PyPy Priority Sampling Test (#766) * Fix pypy priority sampling * [Mega-Linter] Apply linters fixes * Bump tests --------- Co-authored-by: TimPansino * Config linter fixes (#768) * Fix default value and lazy logging pylint * Fix default value and lazy logging pylint * Fix unnecessary 'else' in pylint * Fix logging-not-lazy in pylint * Fix redefined built-in error in Pylint * Fix implicit string concatenation in Pylint * Fix dict() to {} in Pylint * Make sure eval is OK to use for Pylint * Fix logging format string for Pylint * Change list comprehension to generator expression * [Mega-Linter] Apply linters fixes * Rerun tests --------- Co-authored-by: lrafeei * Sync tests w/ agents/cross_agent_tests/pull/150 (#770) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Infinite Tracing Batching & Compression (#762) * Infinite Tracing Batching and Compression settings (#756) * Add compression setting * Add batching setting * Infinite Tracing Compression (#758) * Initial commit * Add compression option in StreamingRPC * Add compression default to tests * Add test to confirm compression settings * Remove commented out code * Set compression settings from settings override * Infinite Tracing Batching (#759) * Initial infinite tracing batching implementation * Add RecordSpanBatch method to mock grpc server * Span batching settings and testing. Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Add final 8t batching tests * Rename serialization test * Formatting * Guard unittests from failing due to batching * Linting * Simplify batching algorithm * Properly wire batching parametrization * Fix incorrect validator use * Data loss on reconnect regression testing Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Test stream buffer batch sizes * Fix logic in supportability metrics for spans * Clean up nested conditionals in stream buffer * Compression parametrization in serialization test * Formatting * Update 8t test_no_delay_on_ok * Update protobufs * Remove unnecessary patching from test * Fix waiting in supportability metric tests * Add sleep to waiting in test * Reorder sleep and condition check * Mark no data loss xfail for py2. * Fix conditional check * Fix flake8 linter issues --------- Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Infinite Tracing Supportability Feature Toggle Metrics (#769) * Add 8T feature toggle supportability metrics * Remove supportability metrics when 8t is disabled. * Formatting --------- Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Lalleh Rafeei Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: Hannah Stepanek * Fix DT settings for txn feature tests (#771) * Fix pyramid testing versions (#764) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Fix Ariadne Middleware Testing (#776) * Fix ariadne middleware testing Co-authored-by: Uma Annamalai Co-authored-by: Lalleh Rafeei * [Mega-Linter] Apply linters fixes * Bump tests --------- Co-authored-by: Uma Annamalai Co-authored-by: Lalleh Rafeei Co-authored-by: TimPansino * Exclude merged PRs from automatic mergify actions. (#774) Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Refactor Code Coverage (#765) * Reorder dependency of code coverage fixture * Fix tests with coverage disabled * Refactor code coverage fixture * Clean out old coverage settings * Fix missing code coverage fixture * Fix pypy priority sampling * Start coverage from pytest-cov for better tracking * Refactor coverage config file * Ripping out coverage fixtures * Move tool config to bottom of tox.ini * Disabling py27 warning * Renaming env var --------- Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Add GraphQL Introspection Setting (#783) * Add graphql introspection setting * Sort settings object hierarchy * Add test for introspection queries setting * Expand introspection queries testing * [Mega-Linter] Apply linters fixes * Adjust introspection detection for graphql --------- Co-authored-by: TimPansino * Fix instance info tests for redis. (#784) * Fix instance info tests for redis. * [Mega-Linter] Apply linters fixes --------- Co-authored-by: umaannamalai * Fix Redis Instance Info (#790) * Fix failing redis test for new default behavior * Revert "Fix instance info tests for redis. (#784)" This reverts commit f7108e3c2a54ab02a1104f6c16bd5fd799b9fc7e. * Guard GraphQL Settings Lookup (#787) * Guard graphql settings lookup * [Mega-Linter] Apply linters fixes * Bump tests * Update graphql settings test --------- Co-authored-by: TimPansino Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> * Errors Inbox Improvements (#791) * Errors inbox attributes and tests (#778) * Initial errors inbox commit Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai * Add enduser.id field * Move validate_error_trace_attributes into validators directory * Add error callback attributes test * Add tests for enduser.id & error.group.name Co-authored-by: Timothy Pansino * Uncomment code_coverage * Drop commented out line --------- Co-authored-by: Timothy Pansino Co-authored-by: Hannah Stepanek Co-authored-by: Uma Annamalai Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> * Error Group Callback API (#785) * Error group initial implementation * Rewrite… * Add third party notice for opentelemetry * Disable Sending of ML Events by Default (#923) * Disable sending of ML events by default. * Override settings for ML event testing. * Update scikit-learn tests with settings overrides. * Update all scikitlearn tests with enabled settings override. * Revert Update all scikitlearn tests with enabled settings override. * Formatting. * Disable sending of ML inference events by default. (#924) --------- Co-authored-by: Hannah Stepanek Co-authored-by: Timothy Pansino Co-authored-by: Lalleh Rafeei <84813886+lrafeei@users.noreply.github.com> Co-authored-by: Uma Annamalai Co-authored-by: hmstepanek Co-authored-by: Tim Pansino Co-authored-by: lrafeei Co-authored-by: Lalleh Rafeei Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Timothy Pansino <11214426+TimPansino@users.noreply.github.com> Co-authored-by: Ahmed Helil Co-authored-by: Ahmed Co-authored-by: Tim Pansino Co-authored-by: Renan Ivo Co-authored-by: Kevin Morey Co-authored-by: Kate Anderson <90657569+kanderson250@users.noreply.github.com> Co-authored-by: Enriqueta De Leon Co-authored-by: Kate Anderson Co-authored-by: Mary Martinez Co-authored-by: Dmitry Kolyagin Co-authored-by: mary-martinez Co-authored-by: enriqueta Co-authored-by: Mary Martinez Co-authored-by: Justin Richert Co-authored-by: Błażej Cyrzon <36710760+bc291@users.noreply.github.com> --- MANIFEST.in | 1 + THIRD_PARTY_NOTICES.md | 9 + newrelic/agent.py | 9 +- newrelic/api/application.py | 12 + newrelic/api/ml_model.py | 35 + newrelic/api/transaction.py | 102 ++- newrelic/common/agent_http.py | 143 ++-- newrelic/common/metric_utils.py | 35 + newrelic/config.py | 770 ++++++++++++++++- newrelic/core/agent.py | 34 + newrelic/core/agent_protocol.py | 91 +- newrelic/core/application.py | 86 ++ newrelic/core/attribute.py | 43 +- newrelic/core/config.py | 75 +- newrelic/core/data_collector.py | 30 +- newrelic/core/otlp_utils.py | 243 ++++++ newrelic/core/stats_engine.py | 297 ++++++- newrelic/core/transaction_node.py | 2 + newrelic/hooks/mlmodel_sklearn.py | 781 ++++++++++++++++++ .../packages/opentelemetry_proto/LICENSE.txt | 201 +++++ .../packages/opentelemetry_proto/__init__.py | 0 .../opentelemetry_proto/common_pb2.py | 87 ++ .../packages/opentelemetry_proto/logs_pb2.py | 117 +++ .../opentelemetry_proto/metrics_pb2.py | 217 +++++ .../opentelemetry_proto/resource_pb2.py | 36 + setup.py | 1 + tests/agent_features/conftest.py | 1 + tests/agent_features/test_configuration.py | 2 + .../test_dimensional_metrics.py | 224 +++++ .../agent_features/test_high_security_mode.py | 60 ++ .../test_metric_normalization.py | 78 ++ tests/agent_features/test_ml_events.py | 199 +++++ tests/agent_unittests/test_harvest_loop.py | 14 +- .../test_utilization_settings.py | 16 + tests/mlmodel_sklearn/conftest.py | 34 + .../test_calibration_models.py | 76 ++ tests/mlmodel_sklearn/test_cluster_models.py | 186 +++++ tests/mlmodel_sklearn/test_compose_models.py | 94 +++ .../mlmodel_sklearn/test_covariance_models.py | 110 +++ .../test_cross_decomposition_models.py | 81 ++ .../test_discriminant_analysis_models.py | 91 ++ tests/mlmodel_sklearn/test_dummy_models.py | 94 +++ tests/mlmodel_sklearn/test_ensemble_models.py | 303 +++++++ .../test_feature_selection_models.py | 138 ++++ .../test_gaussian_process_models.py | 83 ++ .../mlmodel_sklearn/test_inference_events.py | 429 ++++++++++ .../test_kernel_ridge_models.py | 74 ++ tests/mlmodel_sklearn/test_linear_models.py | 335 ++++++++ tests/mlmodel_sklearn/test_metric_scorers.py | 150 ++++ tests/mlmodel_sklearn/test_mixture_models.py | 85 ++ tests/mlmodel_sklearn/test_ml_model.py | 337 ++++++++ .../test_model_selection_models.py | 99 +++ .../mlmodel_sklearn/test_multiclass_models.py | 91 ++ .../test_multioutput_models.py | 129 +++ .../test_naive_bayes_models.py | 141 ++++ .../mlmodel_sklearn/test_neighbors_models.py | 172 ++++ .../test_neural_network_models.py | 96 +++ tests/mlmodel_sklearn/test_pipeline_models.py | 95 +++ .../mlmodel_sklearn/test_prediction_stats.py | 519 ++++++++++++ .../test_semi_supervised_models.py | 132 +++ tests/mlmodel_sklearn/test_svm_models.py | 110 +++ tests/mlmodel_sklearn/test_tree_models.py | 158 ++++ tests/testing_support/fixtures.py | 5 +- .../validate_dimensional_metric_payload.py | 187 +++++ ...dimensional_metrics_outside_transaction.py | 99 +++ ...validate_log_events_outside_transaction.py | 15 +- .../validators/validate_ml_event_count.py | 54 ++ ...date_ml_event_count_outside_transaction.py | 55 ++ .../validators/validate_ml_event_payload.py | 104 +++ .../validators/validate_ml_events.py | 110 +++ .../validate_ml_events_outside_transaction.py | 64 ++ .../validate_transaction_metrics.py | 37 +- tox.ini | 11 + 73 files changed, 9085 insertions(+), 149 deletions(-) create mode 100644 newrelic/api/ml_model.py create mode 100644 newrelic/common/metric_utils.py create mode 100644 newrelic/core/otlp_utils.py create mode 100644 newrelic/hooks/mlmodel_sklearn.py create mode 100644 newrelic/packages/opentelemetry_proto/LICENSE.txt create mode 100644 newrelic/packages/opentelemetry_proto/__init__.py create mode 100644 newrelic/packages/opentelemetry_proto/common_pb2.py create mode 100644 newrelic/packages/opentelemetry_proto/logs_pb2.py create mode 100644 newrelic/packages/opentelemetry_proto/metrics_pb2.py create mode 100644 newrelic/packages/opentelemetry_proto/resource_pb2.py create mode 100644 tests/agent_features/test_dimensional_metrics.py create mode 100644 tests/agent_features/test_metric_normalization.py create mode 100644 tests/agent_features/test_ml_events.py create mode 100644 tests/mlmodel_sklearn/conftest.py create mode 100644 tests/mlmodel_sklearn/test_calibration_models.py create mode 100644 tests/mlmodel_sklearn/test_cluster_models.py create mode 100644 tests/mlmodel_sklearn/test_compose_models.py create mode 100644 tests/mlmodel_sklearn/test_covariance_models.py create mode 100644 tests/mlmodel_sklearn/test_cross_decomposition_models.py create mode 100644 tests/mlmodel_sklearn/test_discriminant_analysis_models.py create mode 100644 tests/mlmodel_sklearn/test_dummy_models.py create mode 100644 tests/mlmodel_sklearn/test_ensemble_models.py create mode 100644 tests/mlmodel_sklearn/test_feature_selection_models.py create mode 100644 tests/mlmodel_sklearn/test_gaussian_process_models.py create mode 100644 tests/mlmodel_sklearn/test_inference_events.py create mode 100644 tests/mlmodel_sklearn/test_kernel_ridge_models.py create mode 100644 tests/mlmodel_sklearn/test_linear_models.py create mode 100644 tests/mlmodel_sklearn/test_metric_scorers.py create mode 100644 tests/mlmodel_sklearn/test_mixture_models.py create mode 100644 tests/mlmodel_sklearn/test_ml_model.py create mode 100644 tests/mlmodel_sklearn/test_model_selection_models.py create mode 100644 tests/mlmodel_sklearn/test_multiclass_models.py create mode 100644 tests/mlmodel_sklearn/test_multioutput_models.py create mode 100644 tests/mlmodel_sklearn/test_naive_bayes_models.py create mode 100644 tests/mlmodel_sklearn/test_neighbors_models.py create mode 100644 tests/mlmodel_sklearn/test_neural_network_models.py create mode 100644 tests/mlmodel_sklearn/test_pipeline_models.py create mode 100644 tests/mlmodel_sklearn/test_prediction_stats.py create mode 100644 tests/mlmodel_sklearn/test_semi_supervised_models.py create mode 100644 tests/mlmodel_sklearn/test_svm_models.py create mode 100644 tests/mlmodel_sklearn/test_tree_models.py create mode 100644 tests/testing_support/validators/validate_dimensional_metric_payload.py create mode 100644 tests/testing_support/validators/validate_dimensional_metrics_outside_transaction.py create mode 100644 tests/testing_support/validators/validate_ml_event_count.py create mode 100644 tests/testing_support/validators/validate_ml_event_count_outside_transaction.py create mode 100644 tests/testing_support/validators/validate_ml_event_payload.py create mode 100644 tests/testing_support/validators/validate_ml_events.py create mode 100644 tests/testing_support/validators/validate_ml_events_outside_transaction.py diff --git a/MANIFEST.in b/MANIFEST.in index bf746435c..0759bce87 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -9,3 +9,4 @@ include newrelic/packages/wrapt/LICENSE include newrelic/packages/wrapt/README include newrelic/packages/urllib3/LICENSE.txt include newrelic/packages/isort/LICENSE +include newrelic/packages/opentelemetry_proto/LICENSE.txt diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md index a1dd7e07d..7c4242cc2 100644 --- a/THIRD_PARTY_NOTICES.md +++ b/THIRD_PARTY_NOTICES.md @@ -26,6 +26,15 @@ Distributed under the following license(s): * [The MIT License](http://opensource.org/licenses/MIT) +## [opentelemetry-proto](https://pypi.org/project/opentelemetry-proto) + +Copyright (c) The OpenTelemetry Authors + +Distributed under the following license(s): + +* [The Apache License, Version 2.0 License](https://opensource.org/license/apache-2-0/) + + ## [six](https://pypi.org/project/six) Copyright (c) 2010-2013 Benjamin Peterson diff --git a/newrelic/agent.py b/newrelic/agent.py index 95a540780..2c7f0fb85 100644 --- a/newrelic/agent.py +++ b/newrelic/agent.py @@ -59,6 +59,7 @@ from newrelic.api.transaction import record_custom_metric as __record_custom_metric from newrelic.api.transaction import record_custom_metrics as __record_custom_metrics from newrelic.api.transaction import record_log_event as __record_log_event +from newrelic.api.transaction import record_ml_event as __record_ml_event from newrelic.api.transaction import set_background_task as __set_background_task from newrelic.api.transaction import set_transaction_name as __set_transaction_name from newrelic.api.transaction import suppress_apdex_metric as __suppress_apdex_metric @@ -152,6 +153,7 @@ def __asgi_application(*args, **kwargs): from newrelic.api.message_transaction import ( wrap_message_transaction as __wrap_message_transaction, ) +from newrelic.api.ml_model import wrap_mlmodel as __wrap_mlmodel from newrelic.api.profile_trace import ProfileTraceWrapper as __ProfileTraceWrapper from newrelic.api.profile_trace import profile_trace as __profile_trace from newrelic.api.profile_trace import wrap_profile_trace as __wrap_profile_trace @@ -206,11 +208,6 @@ def __asgi_application(*args, **kwargs): # EXPERIMENTAL - Generator traces are currently experimental and may not # exist in this form in future versions of the agent. - -# EXPERIMENTAL - Profile traces are currently experimental and may not -# exist in this form in future versions of the agent. - - initialize = __initialize extra_settings = __wrap_api_call(__extra_settings, "extra_settings") global_settings = __wrap_api_call(__global_settings, "global_settings") @@ -248,6 +245,7 @@ def __asgi_application(*args, **kwargs): record_custom_metrics = __wrap_api_call(__record_custom_metrics, "record_custom_metrics") record_custom_event = __wrap_api_call(__record_custom_event, "record_custom_event") record_log_event = __wrap_api_call(__record_log_event, "record_log_event") +record_ml_event = __wrap_api_call(__record_ml_event, "record_ml_event") accept_distributed_trace_payload = __wrap_api_call( __accept_distributed_trace_payload, "accept_distributed_trace_payload" ) @@ -341,3 +339,4 @@ def __asgi_application(*args, **kwargs): wrap_out_function = __wrap_api_call(__wrap_out_function, "wrap_out_function") insert_html_snippet = __wrap_api_call(__insert_html_snippet, "insert_html_snippet") verify_body_exists = __wrap_api_call(__verify_body_exists, "verify_body_exists") +wrap_mlmodel = __wrap_api_call(__wrap_mlmodel, "wrap_mlmodel") diff --git a/newrelic/api/application.py b/newrelic/api/application.py index ea57829f2..e2e7be139 100644 --- a/newrelic/api/application.py +++ b/newrelic/api/application.py @@ -142,10 +142,22 @@ def record_custom_metrics(self, metrics): if self.active and metrics: self._agent.record_custom_metrics(self._name, metrics) + def record_dimensional_metric(self, name, value, tags=None): + if self.active: + self._agent.record_dimensional_metric(self._name, name, value, tags) + + def record_dimensional_metrics(self, metrics): + if self.active and metrics: + self._agent.record_dimensional_metrics(self._name, metrics) + def record_custom_event(self, event_type, params): if self.active: self._agent.record_custom_event(self._name, event_type, params) + def record_ml_event(self, event_type, params): + if self.active: + self._agent.record_ml_event(self._name, event_type, params) + def record_transaction(self, data): if self.active: self._agent.record_transaction(self._name, data) diff --git a/newrelic/api/ml_model.py b/newrelic/api/ml_model.py new file mode 100644 index 000000000..edbcaf340 --- /dev/null +++ b/newrelic/api/ml_model.py @@ -0,0 +1,35 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +from newrelic.common.object_names import callable_name +from newrelic.hooks.mlmodel_sklearn import _nr_instrument_model + + +def wrap_mlmodel(model, name=None, version=None, feature_names=None, label_names=None, metadata=None): + model_callable_name = callable_name(model) + _class = model.__class__.__name__ + module = sys.modules[model_callable_name.split(":")[0]] + _nr_instrument_model(module, _class) + if name: + model._nr_wrapped_name = name + if version: + model._nr_wrapped_version = version + if feature_names: + model._nr_wrapped_feature_names = feature_names + if label_names: + model._nr_wrapped_label_names = label_names + if metadata: + model._nr_wrapped_metadata = metadata diff --git a/newrelic/api/transaction.py b/newrelic/api/transaction.py index d2bfc8528..988b56be6 100644 --- a/newrelic/api/transaction.py +++ b/newrelic/api/transaction.py @@ -60,11 +60,15 @@ DST_NONE, DST_TRANSACTION_TRACER, ) -from newrelic.core.config import CUSTOM_EVENT_RESERVOIR_SIZE, LOG_EVENT_RESERVOIR_SIZE +from newrelic.core.config import ( + CUSTOM_EVENT_RESERVOIR_SIZE, + LOG_EVENT_RESERVOIR_SIZE, + ML_EVENT_RESERVOIR_SIZE, +) from newrelic.core.custom_event import create_custom_event from newrelic.core.log_event_node import LogEventNode from newrelic.core.stack_trace import exception_stack -from newrelic.core.stats_engine import CustomMetrics, SampledDataSet +from newrelic.core.stats_engine import CustomMetrics, DimensionalMetrics, SampledDataSet from newrelic.core.thread_utilization import utilization_tracker from newrelic.core.trace_cache import ( TraceCacheActiveTraceError, @@ -305,6 +309,7 @@ def __init__(self, application, enabled=None, source=None): self.synthetics_header = None self._custom_metrics = CustomMetrics() + self._dimensional_metrics = DimensionalMetrics() global_settings = application.global_settings @@ -328,12 +333,14 @@ def __init__(self, application, enabled=None, source=None): self._custom_events = SampledDataSet( capacity=self._settings.event_harvest_config.harvest_limits.custom_event_data ) + self._ml_events = SampledDataSet(capacity=self._settings.event_harvest_config.harvest_limits.ml_event_data) self._log_events = SampledDataSet( capacity=self._settings.event_harvest_config.harvest_limits.log_event_data ) else: self._custom_events = SampledDataSet(capacity=CUSTOM_EVENT_RESERVOIR_SIZE) self._log_events = SampledDataSet(capacity=LOG_EVENT_RESERVOIR_SIZE) + self._ml_events = SampledDataSet(capacity=ML_EVENT_RESERVOIR_SIZE) def __del__(self): self._dead = True @@ -580,10 +587,12 @@ def __exit__(self, exc, value, tb): errors=tuple(self._errors), slow_sql=tuple(self._slow_sql), custom_events=self._custom_events, + ml_events=self._ml_events, log_events=self._log_events, apdex_t=self.apdex, suppress_apdex=self.suppress_apdex, custom_metrics=self._custom_metrics, + dimensional_metrics=self._dimensional_metrics, guid=self.guid, cpu_time=self._cpu_user_time_value, suppress_transaction_trace=self.suppress_transaction_trace, @@ -1607,6 +1616,16 @@ def record_custom_metrics(self, metrics): for name, value in metrics: self._custom_metrics.record_custom_metric(name, value) + def record_dimensional_metric(self, name, value, tags=None): + self._dimensional_metrics.record_dimensional_metric(name, value, tags) + + def record_dimensional_metrics(self, metrics): + for metric in metrics: + name, value = metric[:2] + tags = metric[2] if len(metric) >= 3 else None + + self._dimensional_metrics.record_dimensional_metric(name, value, tags) + def record_custom_event(self, event_type, params): settings = self._settings @@ -1620,6 +1639,19 @@ def record_custom_event(self, event_type, params): if event: self._custom_events.add(event, priority=self.priority) + def record_ml_event(self, event_type, params): + settings = self._settings + + if not settings: + return + + if not settings.ml_insights_events.enabled: + return + + event = create_custom_event(event_type, params) + if event: + self._ml_events.add(event, priority=self.priority) + def _intern_string(self, value): return self._string_cache.setdefault(value, value) @@ -1913,6 +1945,44 @@ def record_custom_metrics(metrics, application=None): application.record_custom_metrics(metrics) +def record_dimensional_metric(name, value, tags=None, application=None): + if application is None: + transaction = current_transaction() + if transaction: + transaction.record_dimensional_metric(name, value, tags) + else: + _logger.debug( + "record_dimensional_metric has been called but no " + "transaction was running. As a result, the following metric " + "has not been recorded. Name: %r Value: %r Tags: %r. To correct this " + "problem, supply an application object as a parameter to this " + "record_dimensional_metrics call.", + name, + value, + tags, + ) + elif application.enabled: + application.record_dimensional_metric(name, value, tags) + + +def record_dimensional_metrics(metrics, application=None): + if application is None: + transaction = current_transaction() + if transaction: + transaction.record_dimensional_metrics(metrics) + else: + _logger.debug( + "record_dimensional_metrics has been called but no " + "transaction was running. As a result, the following metrics " + "have not been recorded: %r. To correct this problem, " + "supply an application object as a parameter to this " + "record_dimensional_metric call.", + list(metrics), + ) + elif application.enabled: + application.record_dimensional_metrics(metrics) + + def record_custom_event(event_type, params, application=None): """Record a custom event. @@ -1941,6 +2011,34 @@ def record_custom_event(event_type, params, application=None): application.record_custom_event(event_type, params) +def record_ml_event(event_type, params, application=None): + """Record a machine learning custom event. + + Args: + event_type (str): The type (name) of the ml event. + params (dict): Attributes to add to the event. + application (newrelic.api.Application): Application instance. + + """ + + if application is None: + transaction = current_transaction() + if transaction: + transaction.record_ml_event(event_type, params) + else: + _logger.debug( + "record_ml_event has been called but no " + "transaction was running. As a result, the following event " + "has not been recorded. event_type: %r params: %r. To correct " + "this problem, supply an application object as a parameter to " + "this record_ml_event call.", + event_type, + params, + ) + elif application.enabled: + application.record_ml_event(event_type, params) + + def record_log_event(message, level=None, timestamp=None, application=None, priority=None): """Record a log event. diff --git a/newrelic/common/agent_http.py b/newrelic/common/agent_http.py index e1ba0b345..89876a60c 100644 --- a/newrelic/common/agent_http.py +++ b/newrelic/common/agent_http.py @@ -92,6 +92,7 @@ def __init__( compression_method="gzip", max_payload_size_in_bytes=1000000, audit_log_fp=None, + default_content_encoding_header="Identity", ): self._audit_log_fp = audit_log_fp @@ -112,9 +113,7 @@ def _supportability_request(params, payload, body, compression_time): pass @classmethod - def log_request( - cls, fp, method, url, params, payload, headers, body=None, compression_time=None - ): + def log_request(cls, fp, method, url, params, payload, headers, body=None, compression_time=None): cls._supportability_request(params, payload, body, compression_time) if not fp: @@ -126,7 +125,8 @@ def log_request( cls.AUDIT_LOG_ID += 1 print( - "TIME: %r" % time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), file=fp, + "TIME: %r" % time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), + file=fp, ) print(file=fp) print("ID: %r" % cls.AUDIT_LOG_ID, file=fp) @@ -178,9 +178,7 @@ def log_response(cls, fp, log_id, status, headers, data, connection="direct"): except Exception: result = data - print( - "TIME: %r" % time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), file=fp - ) + print("TIME: %r" % time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), file=fp) print(file=fp) print("ID: %r" % log_id, file=fp) print(file=fp) @@ -219,9 +217,7 @@ def send_request( class HttpClient(BaseClient): CONNECTION_CLS = urllib3.HTTPSConnectionPool PREFIX_SCHEME = "https://" - BASE_HEADERS = urllib3.make_headers( - keep_alive=True, accept_encoding=True, user_agent=USER_AGENT - ) + BASE_HEADERS = urllib3.make_headers(keep_alive=True, accept_encoding=True, user_agent=USER_AGENT) def __init__( self, @@ -240,6 +236,7 @@ def __init__( compression_method="gzip", max_payload_size_in_bytes=1000000, audit_log_fp=None, + default_content_encoding_header="Identity", ): self._host = host port = self._port = port @@ -248,6 +245,7 @@ def __init__( self._compression_method = compression_method self._max_payload_size_in_bytes = max_payload_size_in_bytes self._audit_log_fp = audit_log_fp + self._default_content_encoding_header = default_content_encoding_header self._prefix = "" @@ -265,9 +263,7 @@ def __init__( # required and report this condition as a supportability metric. if not verify_path.cafile and not verify_path.capath: ca_bundle_path = certs.where() - internal_metric( - "Supportability/Python/Certificate/BundleRequired", 1 - ) + internal_metric("Supportability/Python/Certificate/BundleRequired", 1) if ca_bundle_path: if os.path.isdir(ca_bundle_path): @@ -279,11 +275,13 @@ def __init__( connection_kwargs["cert_reqs"] = "NONE" proxy = self._parse_proxy( - proxy_scheme, proxy_host, proxy_port, proxy_user, proxy_pass, - ) - proxy_headers = ( - proxy and proxy.auth and urllib3.make_headers(proxy_basic_auth=proxy.auth) + proxy_scheme, + proxy_host, + proxy_port, + proxy_user, + proxy_pass, ) + proxy_headers = proxy and proxy.auth and urllib3.make_headers(proxy_basic_auth=proxy.auth) if proxy: if self.CONNECTION_CLS.scheme == "https" and proxy.scheme != "https": @@ -343,15 +341,9 @@ def _connection(self): if self._connection_attr: return self._connection_attr - retries = urllib3.Retry( - total=False, connect=None, read=None, redirect=0, status=None - ) + retries = urllib3.Retry(total=False, connect=None, read=None, redirect=0, status=None) self._connection_attr = self.CONNECTION_CLS( - self._host, - self._port, - strict=True, - retries=retries, - **self._connection_kwargs + self._host, self._port, strict=True, retries=retries, **self._connection_kwargs ) return self._connection_attr @@ -374,9 +366,7 @@ def log_request( if not self._prefix: url = self.CONNECTION_CLS.scheme + "://" + self._host + url - return super(HttpClient, self).log_request( - fp, method, url, params, payload, headers, body, compression_time - ) + return super(HttpClient, self).log_request(fp, method, url, params, payload, headers, body, compression_time) @staticmethod def _compress(data, method="gzip", level=None): @@ -419,11 +409,9 @@ def send_request( method=self._compression_method, level=self._compression_level, ) - content_encoding = self._compression_method - else: - content_encoding = "Identity" - - merged_headers["Content-Encoding"] = content_encoding + merged_headers["Content-Encoding"] = self._compression_method + elif self._default_content_encoding_header: + merged_headers["Content-Encoding"] = self._default_content_encoding_header request_id = self.log_request( self._audit_log_fp, @@ -441,16 +429,16 @@ def send_request( try: response = self._connection.request_encode_url( - method, - path, - fields=params, - body=body, - headers=merged_headers, - **self._urlopen_kwargs + method, path, fields=params, body=body, headers=merged_headers, **self._urlopen_kwargs ) except urllib3.exceptions.HTTPError as e: self.log_response( - self._audit_log_fp, request_id, 0, None, None, connection, + self._audit_log_fp, + request_id, + 0, + None, + None, + connection, ) # All urllib3 HTTP errors should be treated as a network # interface exception. @@ -489,6 +477,7 @@ def __init__( compression_method="gzip", max_payload_size_in_bytes=1000000, audit_log_fp=None, + default_content_encoding_header="Identity", ): proxy = self._parse_proxy(proxy_scheme, proxy_host, None, None, None) if proxy and proxy.scheme == "https": @@ -515,6 +504,7 @@ def __init__( compression_method, max_payload_size_in_bytes, audit_log_fp, + default_content_encoding_header, ) @@ -536,9 +526,7 @@ def _supportability_request(params, payload, body, compression_time): "Supportability/Python/Collector/%s/ZLIB/Bytes" % agent_method, len(body), ) - internal_metric( - "Supportability/Python/Collector/ZLIB/Bytes", len(body) - ) + internal_metric("Supportability/Python/Collector/ZLIB/Bytes", len(body)) internal_metric( "Supportability/Python/Collector/%s/ZLIB/Compress" % agent_method, compression_time, @@ -548,28 +536,21 @@ def _supportability_request(params, payload, body, compression_time): len(payload), ) # Top level metric to aggregate overall bytes being sent - internal_metric( - "Supportability/Python/Collector/Output/Bytes", len(payload) - ) + internal_metric("Supportability/Python/Collector/Output/Bytes", len(payload)) @staticmethod def _supportability_response(status, exc, connection="direct"): if exc or not 200 <= status < 300: internal_count_metric("Supportability/Python/Collector/Failures", 1) - internal_count_metric( - "Supportability/Python/Collector/Failures/%s" % connection, 1 - ) + internal_count_metric("Supportability/Python/Collector/Failures/%s" % connection, 1) if exc: internal_count_metric( - "Supportability/Python/Collector/Exception/" - "%s" % callable_name(exc), + "Supportability/Python/Collector/Exception/" "%s" % callable_name(exc), 1, ) else: - internal_count_metric( - "Supportability/Python/Collector/HTTPError/%d" % status, 1 - ) + internal_count_metric("Supportability/Python/Collector/HTTPError/%d" % status, 1) class ApplicationModeClient(SupportabilityMixin, HttpClient): @@ -578,33 +559,31 @@ class ApplicationModeClient(SupportabilityMixin, HttpClient): class DeveloperModeClient(SupportabilityMixin, BaseClient): RESPONSES = { - "preconnect": {u"redirect_host": u"fake-collector.newrelic.com"}, + "preconnect": {"redirect_host": "fake-collector.newrelic.com"}, "agent_settings": [], "connect": { - u"js_agent_loader": u"", - u"js_agent_file": u"fake-js-agent.newrelic.com/nr-0.min.js", - u"browser_key": u"1234567890", - u"browser_monitoring.loader_version": u"0", - u"beacon": u"fake-beacon.newrelic.com", - u"error_beacon": u"fake-jserror.newrelic.com", - u"apdex_t": 0.5, - u"encoding_key": u"1111111111111111111111111111111111111111", - u"entity_guid": u"DEVELOPERMODEENTITYGUID", - u"agent_run_id": u"1234567", - u"product_level": 50, - u"trusted_account_ids": [12345], - u"trusted_account_key": u"12345", - u"url_rules": [], - u"collect_errors": True, - u"account_id": u"12345", - u"cross_process_id": u"12345#67890", - u"messages": [ - {u"message": u"Reporting to fake collector", u"level": u"INFO"} - ], - u"sampling_rate": 0, - u"collect_traces": True, - u"collect_span_events": True, - u"data_report_period": 60, + "js_agent_loader": "", + "js_agent_file": "fake-js-agent.newrelic.com/nr-0.min.js", + "browser_key": "1234567890", + "browser_monitoring.loader_version": "0", + "beacon": "fake-beacon.newrelic.com", + "error_beacon": "fake-jserror.newrelic.com", + "apdex_t": 0.5, + "encoding_key": "1111111111111111111111111111111111111111", + "entity_guid": "DEVELOPERMODEENTITYGUID", + "agent_run_id": "1234567", + "product_level": 50, + "trusted_account_ids": [12345], + "trusted_account_key": "12345", + "url_rules": [], + "collect_errors": True, + "account_id": "12345", + "cross_process_id": "12345#67890", + "messages": [{"message": "Reporting to fake collector", "level": "INFO"}], + "sampling_rate": 0, + "collect_traces": True, + "collect_span_events": True, + "data_report_period": 60, }, "metric_data": None, "get_agent_commands": [], @@ -648,7 +627,11 @@ def send_request( payload = {"return_value": result} response_data = json_encode(payload).encode("utf-8") self.log_response( - self._audit_log_fp, request_id, 200, {}, response_data, + self._audit_log_fp, + request_id, + 200, + {}, + response_data, ) return 200, response_data diff --git a/newrelic/common/metric_utils.py b/newrelic/common/metric_utils.py new file mode 100644 index 000000000..ebffe8332 --- /dev/null +++ b/newrelic/common/metric_utils.py @@ -0,0 +1,35 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This module implements functions for creating a unique identity from a name and set of tags for use in dimensional metrics. +""" + +from newrelic.core.attribute import process_user_attribute + + +def create_metric_identity(name, tags=None): + if tags: + # Convert dicts to an iterable of tuples, other iterables should already be in this form + if isinstance(tags, dict): + tags = tags.items() + + # Apply attribute system sanitization. + # process_user_attribute returns (None, None) for results that fail sanitization. + # The filter removes these results from the iterable before creating the frozenset. + tags = frozenset(filter(lambda args: args[0] is not None, map(lambda args: process_user_attribute(*args), tags))) + + tags = tags or None # Set empty iterables after filtering to None + + return (name, tags) diff --git a/newrelic/config.py b/newrelic/config.py index 87c050d77..6816c43b5 100644 --- a/newrelic/config.py +++ b/newrelic/config.py @@ -328,6 +328,8 @@ def _process_configuration(section): _process_setting(section, "api_key", "get", None) _process_setting(section, "host", "get", None) _process_setting(section, "port", "getint", None) + _process_setting(section, "otlp_host", "get", None) + _process_setting(section, "otlp_port", "getint", None) _process_setting(section, "ssl", "getboolean", None) _process_setting(section, "proxy_scheme", "get", None) _process_setting(section, "proxy_host", "get", None) @@ -441,6 +443,7 @@ def _process_configuration(section): ) _process_setting(section, "custom_insights_events.enabled", "getboolean", None) _process_setting(section, "custom_insights_events.max_samples_stored", "getint", None) + _process_setting(section, "ml_insights_events.enabled", "getboolean", None) _process_setting(section, "distributed_tracing.enabled", "getboolean", None) _process_setting(section, "distributed_tracing.exclude_newrelic_header", "getboolean", None) _process_setting(section, "span_events.enabled", "getboolean", None) @@ -500,6 +503,7 @@ def _process_configuration(section): _process_setting(section, "debug.disable_certificate_validation", "getboolean", None) _process_setting(section, "debug.disable_harvest_until_shutdown", "getboolean", None) _process_setting(section, "debug.connect_span_stream_in_developer_mode", "getboolean", None) + _process_setting(section, "debug.otlp_content_encoding", "get", None) _process_setting(section, "cross_application_tracer.enabled", "getboolean", None) _process_setting(section, "message_tracer.segment_parameters_enabled", "getboolean", None) _process_setting(section, "process_host.display_name", "get", None) @@ -534,6 +538,7 @@ def _process_configuration(section): None, ) _process_setting(section, "event_harvest_config.harvest_limits.custom_event_data", "getint", None) + _process_setting(section, "event_harvest_config.harvest_limits.ml_event_data", "getint", None) _process_setting(section, "event_harvest_config.harvest_limits.span_event_data", "getint", None) _process_setting(section, "event_harvest_config.harvest_limits.error_event_data", "getint", None) _process_setting(section, "event_harvest_config.harvest_limits.log_event_data", "getint", None) @@ -550,6 +555,9 @@ def _process_configuration(section): _process_setting(section, "application_logging.metrics.enabled", "getboolean", None) _process_setting(section, "application_logging.local_decorating.enabled", "getboolean", None) + _process_setting(section, "machine_learning.enabled", "getboolean", None) + _process_setting(section, "machine_learning.inference_events_value.enabled", "getboolean", None) + # Loading of configuration from specified file and for specified # deployment environment. Can also indicate whether configuration @@ -881,6 +889,10 @@ def apply_local_high_security_mode_setting(settings): settings.custom_insights_events.enabled = False _logger.info(log_template, "custom_insights_events.enabled", True, False) + if settings.ml_insights_events.enabled: + settings.ml_insights_events.enabled = False + _logger.info(log_template, "ml_insights_events.enabled", True, False) + if settings.message_tracer.segment_parameters_enabled: settings.message_tracer.segment_parameters_enabled = False _logger.info(log_template, "message_tracer.segment_parameters_enabled", True, False) @@ -889,6 +901,10 @@ def apply_local_high_security_mode_setting(settings): settings.application_logging.forwarding.enabled = False _logger.info(log_template, "application_logging.forwarding.enabled", True, False) + if settings.machine_learning.inference_events_value.enabled: + settings.machine_learning.inference_events_value.enabled = False + _logger.info(log_template, "machine_learning.inference_events_value.enabled", True, False) + return settings @@ -2988,6 +3004,756 @@ def _process_module_builtin_defaults(): ) _process_module_definition("tastypie.api", "newrelic.hooks.component_tastypie", "instrument_tastypie_api") + _process_module_definition( + "sklearn.metrics", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_metrics", + ) + + _process_module_definition( + "sklearn.tree._classes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_tree_models", + ) + # In scikit-learn < 0.21 the model classes are in tree.py instead of _classes.py. + _process_module_definition( + "sklearn.tree.tree", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_tree_models", + ) + + _process_module_definition( + "sklearn.compose._column_transformer", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_compose_models", + ) + + _process_module_definition( + "sklearn.compose._target", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_compose_models", + ) + + _process_module_definition( + "sklearn.covariance._empirical_covariance", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.covariance.empirical_covariance_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.covariance.shrunk_covariance_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_shrunk_models", + ) + + _process_module_definition( + "sklearn.covariance._shrunk_covariance", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_shrunk_models", + ) + + _process_module_definition( + "sklearn.covariance.robust_covariance_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.covariance._robust_covariance", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.covariance.graph_lasso_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_graph_models", + ) + + _process_module_definition( + "sklearn.covariance._graph_lasso", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_graph_models", + ) + + _process_module_definition( + "sklearn.covariance.elliptic_envelope", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.covariance._elliptic_envelope", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_covariance_models", + ) + + _process_module_definition( + "sklearn.ensemble._bagging", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_bagging_models", + ) + + _process_module_definition( + "sklearn.ensemble.bagging", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_bagging_models", + ) + + _process_module_definition( + "sklearn.ensemble._forest", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_forest_models", + ) + + _process_module_definition( + "sklearn.ensemble.forest", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_forest_models", + ) + + _process_module_definition( + "sklearn.ensemble._iforest", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_iforest_models", + ) + + _process_module_definition( + "sklearn.ensemble.iforest", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_iforest_models", + ) + + _process_module_definition( + "sklearn.ensemble._weight_boosting", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_weight_boosting_models", + ) + + _process_module_definition( + "sklearn.ensemble.weight_boosting", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_weight_boosting_models", + ) + + _process_module_definition( + "sklearn.ensemble._gb", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_gradient_boosting_models", + ) + + _process_module_definition( + "sklearn.ensemble.gradient_boosting", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_gradient_boosting_models", + ) + + _process_module_definition( + "sklearn.ensemble._voting", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_voting_models", + ) + + _process_module_definition( + "sklearn.ensemble.voting_classifier", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_voting_models", + ) + + _process_module_definition( + "sklearn.ensemble._stacking", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_stacking_models", + ) + + _process_module_definition( + "sklearn.ensemble._hist_gradient_boosting.gradient_boosting", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_ensemble_hist_models", + ) + + _process_module_definition( + "sklearn.linear_model._base", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model.base", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model._bayes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_bayes_models", + ) + + _process_module_definition( + "sklearn.linear_model.bayes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_bayes_models", + ) + + _process_module_definition( + "sklearn.linear_model._least_angle", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_least_angle_models", + ) + + _process_module_definition( + "sklearn.linear_model.least_angle", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_least_angle_models", + ) + + _process_module_definition( + "sklearn.linear_model.coordinate_descent", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_coordinate_descent_models", + ) + + _process_module_definition( + "sklearn.linear_model._coordinate_descent", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_coordinate_descent_models", + ) + + _process_module_definition( + "sklearn.linear_model._glm", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_GLM_models", + ) + + _process_module_definition( + "sklearn.linear_model._huber", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model.huber", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model._stochastic_gradient", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_stochastic_gradient_models", + ) + + _process_module_definition( + "sklearn.linear_model.stochastic_gradient", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_stochastic_gradient_models", + ) + + _process_module_definition( + "sklearn.linear_model._ridge", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_ridge_models", + ) + + _process_module_definition( + "sklearn.linear_model.ridge", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_ridge_models", + ) + + _process_module_definition( + "sklearn.linear_model._logistic", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_logistic_models", + ) + + _process_module_definition( + "sklearn.linear_model.logistic", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_logistic_models", + ) + + _process_module_definition( + "sklearn.linear_model._omp", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_OMP_models", + ) + + _process_module_definition( + "sklearn.linear_model.omp", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_OMP_models", + ) + + _process_module_definition( + "sklearn.linear_model._passive_aggressive", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_passive_aggressive_models", + ) + + _process_module_definition( + "sklearn.linear_model.passive_aggressive", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_passive_aggressive_models", + ) + + _process_module_definition( + "sklearn.linear_model._perceptron", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model.perceptron", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model._quantile", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model._ransac", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model.ransac", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model._theil_sen", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.linear_model.theil_sen", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_linear_models", + ) + + _process_module_definition( + "sklearn.cross_decomposition._pls", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cross_decomposition_models", + ) + + _process_module_definition( + "sklearn.cross_decomposition.pls_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cross_decomposition_models", + ) + + _process_module_definition( + "sklearn.discriminant_analysis", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_discriminant_analysis_models", + ) + + _process_module_definition( + "sklearn.gaussian_process._gpc", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_gaussian_process_models", + ) + + _process_module_definition( + "sklearn.gaussian_process.gpc", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_gaussian_process_models", + ) + + _process_module_definition( + "sklearn.gaussian_process._gpr", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_gaussian_process_models", + ) + + _process_module_definition( + "sklearn.gaussian_process.gpr", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_gaussian_process_models", + ) + + _process_module_definition( + "sklearn.dummy", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_dummy_models", + ) + + _process_module_definition( + "sklearn.feature_selection._rfe", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_rfe_models", + ) + + _process_module_definition( + "sklearn.feature_selection.rfe", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_rfe_models", + ) + + _process_module_definition( + "sklearn.feature_selection._variance_threshold", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_models", + ) + + _process_module_definition( + "sklearn.feature_selection.variance_threshold", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_models", + ) + + _process_module_definition( + "sklearn.feature_selection._from_model", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_models", + ) + + _process_module_definition( + "sklearn.feature_selection.from_model", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_models", + ) + + _process_module_definition( + "sklearn.feature_selection._sequential", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_feature_selection_models", + ) + + _process_module_definition( + "sklearn.kernel_ridge", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_kernel_ridge_models", + ) + + _process_module_definition( + "sklearn.neural_network._multilayer_perceptron", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neural_network_models", + ) + + _process_module_definition( + "sklearn.neural_network.multilayer_perceptron", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neural_network_models", + ) + + _process_module_definition( + "sklearn.neural_network._rbm", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neural_network_models", + ) + + _process_module_definition( + "sklearn.neural_network.rbm", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neural_network_models", + ) + + _process_module_definition( + "sklearn.calibration", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_calibration_models", + ) + + _process_module_definition( + "sklearn.cluster._affinity_propagation", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster.affinity_propagation_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._agglomerative", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_agglomerative_models", + ) + + _process_module_definition( + "sklearn.cluster.hierarchical", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_agglomerative_models", + ) + + _process_module_definition( + "sklearn.cluster._birch", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster.birch", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._bisect_k_means", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_kmeans_models", + ) + + _process_module_definition( + "sklearn.cluster._dbscan", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster.dbscan_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._feature_agglomeration", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._kmeans", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_kmeans_models", + ) + + _process_module_definition( + "sklearn.cluster.k_means_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_kmeans_models", + ) + + _process_module_definition( + "sklearn.cluster._mean_shift", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster.mean_shift_", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._optics", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_models", + ) + + _process_module_definition( + "sklearn.cluster._spectral", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_clustering_models", + ) + + _process_module_definition( + "sklearn.cluster.spectral", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_clustering_models", + ) + + _process_module_definition( + "sklearn.cluster._bicluster", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_clustering_models", + ) + + _process_module_definition( + "sklearn.cluster.bicluster", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_cluster_clustering_models", + ) + + _process_module_definition( + "sklearn.multiclass", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_multiclass_models", + ) + + _process_module_definition( + "sklearn.multioutput", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_multioutput_models", + ) + + _process_module_definition( + "sklearn.naive_bayes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_naive_bayes_models", + ) + + _process_module_definition( + "sklearn.model_selection._search", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_model_selection_models", + ) + + _process_module_definition( + "sklearn.mixture._bayesian_mixture", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_mixture_models", + ) + + _process_module_definition( + "sklearn.mixture.bayesian_mixture", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_mixture_models", + ) + + _process_module_definition( + "sklearn.mixture._gaussian_mixture", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_mixture_models", + ) + + _process_module_definition( + "sklearn.mixture.gaussian_mixture", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_mixture_models", + ) + + _process_module_definition( + "sklearn.pipeline", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_pipeline_models", + ) + + _process_module_definition( + "sklearn.semi_supervised._label_propagation", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_semi_supervised_models", + ) + + _process_module_definition( + "sklearn.semi_supervised._self_training", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_semi_supervised_models", + ) + + _process_module_definition( + "sklearn.semi_supervised.label_propagation", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_semi_supervised_models", + ) + + _process_module_definition( + "sklearn.svm._classes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_svm_models", + ) + + _process_module_definition( + "sklearn.svm.classes", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_svm_models", + ) + + _process_module_definition( + "sklearn.neighbors._classification", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_KRadius_models", + ) + + _process_module_definition( + "sklearn.neighbors.classification", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_KRadius_models", + ) + + _process_module_definition( + "sklearn.neighbors._graph", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_KRadius_models", + ) + + _process_module_definition( + "sklearn.neighbors._kde", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors.kde", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors._lof", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors.lof", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors._nca", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors._nearest_centroid", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors.nearest_centroid", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors._regression", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_KRadius_models", + ) + + _process_module_definition( + "sklearn.neighbors.regression", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_KRadius_models", + ) + + _process_module_definition( + "sklearn.neighbors._unsupervised", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + + _process_module_definition( + "sklearn.neighbors.unsupervised", + "newrelic.hooks.mlmodel_sklearn", + "instrument_sklearn_neighbors_models", + ) + _process_module_definition( "rest_framework.views", "newrelic.hooks.component_djangorestframework", @@ -3020,9 +3786,7 @@ def _process_module_builtin_defaults(): "newrelic.hooks.application_celery", "instrument_celery_worker", ) - # _process_module_definition('celery.loaders.base', - # 'newrelic.hooks.application_celery', - # 'instrument_celery_loaders_base') + _process_module_definition( "celery.execute.trace", "newrelic.hooks.application_celery", diff --git a/newrelic/core/agent.py b/newrelic/core/agent.py index 6ab9571a4..9d9aadab1 100644 --- a/newrelic/core/agent.py +++ b/newrelic/core/agent.py @@ -524,6 +524,33 @@ def record_custom_metrics(self, app_name, metrics): application.record_custom_metrics(metrics) + def record_dimensional_metric(self, app_name, name, value, tags=None): + """Records a basic metric for the named application. If there has + been no prior request to activate the application, the metric is + discarded. + + """ + + application = self._applications.get(app_name, None) + if application is None or not application.active: + return + + application.record_dimensional_metric(name, value, tags) + + def record_dimensional_metrics(self, app_name, metrics): + """Records the metrics for the named application. If there has + been no prior request to activate the application, the metric is + discarded. The metrics should be an iterable yielding tuples + consisting of the name and value. + + """ + + application = self._applications.get(app_name, None) + if application is None or not application.active: + return + + application.record_dimensional_metrics(metrics) + def record_custom_event(self, app_name, event_type, params): application = self._applications.get(app_name, None) if application is None or not application.active: @@ -531,6 +558,13 @@ def record_custom_event(self, app_name, event_type, params): application.record_custom_event(event_type, params) + def record_ml_event(self, app_name, event_type, params): + application = self._applications.get(app_name, None) + if application is None or not application.active: + return + + application.record_ml_event(event_type, params) + def record_log_event(self, app_name, message, level=None, timestamp=None, priority=None): application = self._applications.get(app_name, None) if application is None or not application.active: diff --git a/newrelic/core/agent_protocol.py b/newrelic/core/agent_protocol.py index ba277d4de..dd4dc264f 100644 --- a/newrelic/core/agent_protocol.py +++ b/newrelic/core/agent_protocol.py @@ -38,6 +38,7 @@ global_settings_dump, ) from newrelic.core.internal_metrics import internal_count_metric +from newrelic.core.otlp_utils import OTLP_CONTENT_TYPE, otlp_encode from newrelic.network.exceptions import ( DiscardDataForRequest, ForceAgentDisconnect, @@ -143,7 +144,9 @@ class AgentProtocol(object): "transaction_tracer.record_sql", "strip_exception_messages.enabled", "custom_insights_events.enabled", + "ml_insights_events.enabled", "application_logging.forwarding.enabled", + "machine_learning.inference_events_value.enabled", ) LOGGER_FUNC_MAPPING = { @@ -215,11 +218,16 @@ def __exit__(self, exc, value, tb): def close_connection(self): self.client.close_connection() - def send(self, method, payload=()): + def send( + self, + method, + payload=(), + path="/agent_listener/invoke_raw_method", + ): params, headers, payload = self._to_http(method, payload) try: - response = self.client.send_request(params=params, headers=headers, payload=payload) + response = self.client.send_request(path=path, params=params, headers=headers, payload=payload) except NetworkInterfaceException: # All HTTP errors are currently retried raise RetryDataForRequest @@ -251,7 +259,10 @@ def send(self, method, payload=()): exception = self.STATUS_CODE_RESPONSE.get(status, DiscardDataForRequest) raise exception if status == 200: - return json_decode(data.decode("utf-8"))["return_value"] + return self.decode_response(data) + + def decode_response(self, response): + return json_decode(response.decode("utf-8"))["return_value"] def _to_http(self, method, payload=()): params = dict(self._params) @@ -514,3 +525,77 @@ def connect( # can be modified later settings.aws_lambda_metadata = aws_lambda_metadata return cls(settings, client_cls=client_cls) + + +class OtlpProtocol(AgentProtocol): + def __init__(self, settings, host=None, client_cls=ApplicationModeClient): + if settings.audit_log_file: + audit_log_fp = open(settings.audit_log_file, "a") + else: + audit_log_fp = None + + self.client = client_cls( + host=host or settings.otlp_host, + port=settings.otlp_port or 4318, + proxy_scheme=settings.proxy_scheme, + proxy_host=settings.proxy_host, + proxy_port=settings.proxy_port, + proxy_user=settings.proxy_user, + proxy_pass=settings.proxy_pass, + timeout=settings.agent_limits.data_collector_timeout, + ca_bundle_path=settings.ca_bundle_path, + disable_certificate_validation=settings.debug.disable_certificate_validation, + compression_threshold=settings.agent_limits.data_compression_threshold, + compression_level=settings.agent_limits.data_compression_level, + compression_method=settings.compressed_content_encoding, + max_payload_size_in_bytes=1000000, + audit_log_fp=audit_log_fp, + default_content_encoding_header=None, + ) + + self._params = {} + self._headers = { + "api-key": settings.license_key, + } + + # In Python 2, the JSON is loaded with unicode keys and values; + # however, the header name must be a non-unicode value when given to + # the HTTP library. This code converts the header name from unicode to + # non-unicode. + if settings.request_headers_map: + for k, v in settings.request_headers_map.items(): + if not isinstance(k, str): + k = k.encode("utf-8") + self._headers[k] = v + + # Content-Type should be protobuf, but falls back to JSON if protobuf is not installed. + self._headers["Content-Type"] = OTLP_CONTENT_TYPE + self._run_token = settings.agent_run_id + + # Logging + self._proxy_host = settings.proxy_host + self._proxy_port = settings.proxy_port + self._proxy_user = settings.proxy_user + + # Do not access configuration anywhere inside the class + self.configuration = settings + + @classmethod + def connect( + cls, + app_name, + linked_applications, + environment, + settings, + client_cls=ApplicationModeClient, + ): + with cls(settings, client_cls=client_cls) as protocol: + pass + + return protocol + + def _to_http(self, method, payload=()): + return {}, self._headers, otlp_encode(payload) + + def decode_response(self, response): + return response.decode("utf-8") diff --git a/newrelic/core/application.py b/newrelic/core/application.py index 7be217428..82cdf8a9a 100644 --- a/newrelic/core/application.py +++ b/newrelic/core/application.py @@ -510,6 +510,9 @@ def connect_to_data_collector(self, activate_agent): with self._stats_custom_lock: self._stats_custom_engine.reset_stats(configuration) + with self._stats_lock: + self._stats_engine.reset_stats(configuration) + # Record an initial start time for the reporting period and # clear record of last transaction processed. @@ -860,6 +863,50 @@ def record_custom_metrics(self, metrics): self._global_events_account += 1 self._stats_custom_engine.record_custom_metric(name, value) + def record_dimensional_metric(self, name, value, tags=None): + """Record a dimensional metric against the application independent + of a specific transaction. + + NOTE that this will require locking of the stats engine for + dimensional metrics and so under heavy use will have performance + issues. It is better to record the dimensional metric against an + active transaction as they will then be aggregated at the end of + the transaction when all other metrics are aggregated and so no + additional locking will be required. + + """ + + if not self._active_session: + return + + with self._stats_lock: + self._global_events_account += 1 + self._stats_engine.record_dimensional_metric(name, value, tags) + + def record_dimensional_metrics(self, metrics): + """Record a set of dimensional metrics against the application + independent of a specific transaction. + + NOTE that this will require locking of the stats engine for + dimensional metrics and so under heavy use will have performance + issues. It is better to record the dimensional metric against an + active transaction as they will then be aggregated at the end of + the transaction when all other metrics are aggregated and so no + additional locking will be required. + + """ + + if not self._active_session: + return + + with self._stats_lock: + for metric in metrics: + name, value = metric[:2] + tags = metric[2] if len(metric) >= 3 else None + + self._global_events_account += 1 + self._stats_engine.record_dimensional_metric(name, value, tags) + def record_custom_event(self, event_type, params): if not self._active_session: return @@ -876,6 +923,22 @@ def record_custom_event(self, event_type, params): self._global_events_account += 1 self._stats_engine.record_custom_event(event) + def record_ml_event(self, event_type, params): + if not self._active_session: + return + + settings = self._stats_engine.settings + + if settings is None or not settings.ml_insights_events.enabled: + return + + event = create_custom_event(event_type, params) + + if event: + with self._stats_custom_lock: + self._global_events_account += 1 + self._stats_engine.record_ml_event(event) + def record_log_event(self, message, level=None, timestamp=None, priority=None): if not self._active_session: return @@ -1335,6 +1398,26 @@ def harvest(self, shutdown=False, flexible=False): stats.reset_custom_events() + # Send machine learning events + + if configuration.ml_insights_events.enabled: + ml_events = stats.ml_events + + if ml_events: + if ml_events.num_samples > 0: + ml_event_samples = list(ml_events) + + _logger.debug("Sending machine learning event data for harvest of %r.", self._app_name) + + self._active_session.send_ml_events(ml_events.sampling_info, ml_event_samples) + ml_event_samples = None + + # As per spec + internal_count_metric("Supportability/Events/Customer/Seen", ml_events.num_seen) + internal_count_metric("Supportability/Events/Customer/Sent", ml_events.num_samples) + + stats.reset_ml_events() + # Send log events if ( @@ -1416,11 +1499,14 @@ def harvest(self, shutdown=False, flexible=False): _logger.debug("Normalizing metrics for harvest of %r.", self._app_name) metric_data = stats.metric_data(metric_normalizer) + dimensional_metric_data = stats.dimensional_metric_data(metric_normalizer) _logger.debug("Sending metric data for harvest of %r.", self._app_name) # Send metrics self._active_session.send_metric_data(self._period_start, period_end, metric_data) + if dimensional_metric_data: + self._active_session.send_dimensional_metric_data(self._period_start, period_end, dimensional_metric_data) _logger.debug("Done sending data for harvest of %r.", self._app_name) diff --git a/newrelic/core/attribute.py b/newrelic/core/attribute.py index 372711369..10ae8e459 100644 --- a/newrelic/core/attribute.py +++ b/newrelic/core/attribute.py @@ -180,7 +180,6 @@ def create_user_attributes(attr_dict, attribute_filter): def truncate(text, maxsize=MAX_ATTRIBUTE_LENGTH, encoding="utf-8", ending=None): - # Truncate text so that its byte representation # is no longer than maxsize bytes. @@ -225,7 +224,6 @@ def check_max_int(value, max_int=MAX_64_BIT_INT): def process_user_attribute(name, value, max_length=MAX_ATTRIBUTE_LENGTH, ending=None): - # Perform all necessary checks on a potential attribute. # # Returns: @@ -245,23 +243,22 @@ def process_user_attribute(name, value, max_length=MAX_ATTRIBUTE_LENGTH, ending= value = sanitize(value) except NameIsNotStringException: - _logger.debug("Attribute name must be a string. Dropping " "attribute: %r=%r", name, value) + _logger.debug("Attribute name must be a string. Dropping attribute: %r=%r", name, value) return FAILED_RESULT except NameTooLongException: - _logger.debug("Attribute name exceeds maximum length. Dropping " "attribute: %r=%r", name, value) + _logger.debug("Attribute name exceeds maximum length. Dropping attribute: %r=%r", name, value) return FAILED_RESULT except IntTooLargeException: - _logger.debug("Attribute value exceeds maximum integer value. " "Dropping attribute: %r=%r", name, value) + _logger.debug("Attribute value exceeds maximum integer value. Dropping attribute: %r=%r", name, value) return FAILED_RESULT except CastingFailureException: - _logger.debug("Attribute value cannot be cast to a string. " "Dropping attribute: %r=%r", name, value) + _logger.debug("Attribute value cannot be cast to a string. Dropping attribute: %r=%r", name, value) return FAILED_RESULT else: - # Check length after casting valid_types_text = (six.text_type, six.binary_type) @@ -270,7 +267,7 @@ def process_user_attribute(name, value, max_length=MAX_ATTRIBUTE_LENGTH, ending= trunc_value = truncate(value, maxsize=max_length, ending=ending) if value != trunc_value: _logger.debug( - "Attribute value exceeds maximum length " "(%r bytes). Truncating value: %r=%r.", + "Attribute value exceeds maximum length (%r bytes). Truncating value: %r=%r.", max_length, name, trunc_value, @@ -282,15 +279,31 @@ def process_user_attribute(name, value, max_length=MAX_ATTRIBUTE_LENGTH, ending= def sanitize(value): + """ + Return value unchanged, if it's a valid type that is supported by + Insights. Otherwise, convert value to a string. - # Return value unchanged, if it's a valid type that is supported by - # Insights. Otherwise, convert value to a string. - # - # Raise CastingFailureException, if str(value) somehow fails. + Raise CastingFailureException, if str(value) somehow fails. + """ valid_value_types = (six.text_type, six.binary_type, bool, float, six.integer_types) - if not isinstance(value, valid_value_types): + # When working with numpy, note that numpy has its own `int`s, `str`s, + # et cetera. `numpy.str_` and `numpy.float_` inherit from Python's native + # `str` and `float`, respectively. However, some types, such as `numpy.int_` + # and `numpy.bool_`, do not inherit from `int` and `bool` (respectively). + # In those cases, the valid_value_types check fails and it will try to + # convert these to string, which is not the desired behavior. Checking for + # `type` in lieu of `isinstance` has the potential to impact performance. + + # numpy values have an attribute "item" that returns the closest + # equivalent Python native type. Ex: numpy.int64 -> int + # This is important to utilize in cases like int and bool where + # numpy does not inherit from those classes. This logic is + # determining whether or not the value is a valid_value_type (or + # inherited from one of those types) AND whether it is a numpy + # type (by determining if it has the attribute "item"). + if not isinstance(value, valid_value_types) and not hasattr(value, "item"): original = value try: @@ -298,8 +311,6 @@ def sanitize(value): except Exception: raise CastingFailureException() else: - _logger.debug( - "Attribute value is of type: %r. Casting %r to " "string: %s", type(original), original, value - ) + _logger.debug("Attribute value is of type: %r. Casting %r to string: %s", type(original), original, value) return value diff --git a/newrelic/core/config.py b/newrelic/core/config.py index 7489be222..483e23df8 100644 --- a/newrelic/core/config.py +++ b/newrelic/core/config.py @@ -51,11 +51,14 @@ # By default, Transaction Events and Custom Events have the same size # reservoir. Error Events have a different default size. +# Slow harvest (Every 60 seconds) DEFAULT_RESERVOIR_SIZE = 1200 -CUSTOM_EVENT_RESERVOIR_SIZE = 3600 ERROR_EVENT_RESERVOIR_SIZE = 100 SPAN_EVENT_RESERVOIR_SIZE = 2000 +# Fast harvest (Every 5 seconds, so divide by 12 to get average per minute value) +CUSTOM_EVENT_RESERVOIR_SIZE = 3600 LOG_EVENT_RESERVOIR_SIZE = 10000 +ML_EVENT_RESERVOIR_SIZE = 100000 # settings that should be completely ignored if set server side IGNORED_SERVER_SIDE_SETTINGS = [ @@ -101,6 +104,7 @@ def create_settings(nested): class TopLevelSettings(Settings): _host = None + _otlp_host = None @property def host(self): @@ -112,6 +116,16 @@ def host(self): def host(self, value): self._host = value + @property + def otlp_host(self): + if self._otlp_host: + return self._otlp_host + return default_otlp_host(self.host) + + @otlp_host.setter + def otlp_host(self, value): + self._otlp_host = value + class AttributesSettings(Settings): pass @@ -121,6 +135,14 @@ class GCRuntimeMetricsSettings(Settings): enabled = False +class MachineLearningSettings(Settings): + pass + + +class MachineLearningInferenceEventsValueSettings(Settings): + pass + + class CodeLevelMetricsSettings(Settings): pass @@ -199,6 +221,10 @@ class CustomInsightsEventsSettings(Settings): pass +class MlInsightsEventsSettings(Settings): + pass + + class ProcessHostSettings(Settings): pass @@ -370,6 +396,8 @@ class EventHarvestConfigHarvestLimitSettings(Settings): _settings.application_logging.forwarding = ApplicationLoggingForwardingSettings() _settings.application_logging.local_decorating = ApplicationLoggingLocalDecoratingSettings() _settings.application_logging.metrics = ApplicationLoggingMetricsSettings() +_settings.machine_learning = MachineLearningSettings() +_settings.machine_learning.inference_events_value = MachineLearningInferenceEventsValueSettings() _settings.attributes = AttributesSettings() _settings.browser_monitoring = BrowserMonitorSettings() _settings.browser_monitoring.attributes = BrowserMonitorAttributesSettings() @@ -377,6 +405,7 @@ class EventHarvestConfigHarvestLimitSettings(Settings): _settings.console = ConsoleSettings() _settings.cross_application_tracer = CrossApplicationTracerSettings() _settings.custom_insights_events = CustomInsightsEventsSettings() +_settings.ml_insights_events = MlInsightsEventsSettings() _settings.datastore_tracer = DatastoreTracerSettings() _settings.datastore_tracer.database_name_reporting = DatastoreTracerDatabaseNameReportingSettings() _settings.datastore_tracer.instance_reporting = DatastoreTracerInstanceReportingSettings() @@ -542,6 +571,24 @@ def default_host(license_key): return host +def default_otlp_host(host): + HOST_MAP = { + "collector.newrelic.com": "otlp.nr-data.net", + "collector.eu.newrelic.com": "otlp.eu01.nr-data.net", + "gov-collector.newrelic.com": "gov-otlp.nr-data.net", + "staging-collector.newrelic.com": "staging-otlp.nr-data.net", + "staging-collector.eu.newrelic.com": "staging-otlp.eu01.nr-data.net", + "staging-gov-collector.newrelic.com": "staging-gov-otlp.nr-data.net", + "fake-collector.newrelic.com": "fake-otlp.nr-data.net", + } + otlp_host = HOST_MAP.get(host, None) + if not otlp_host: + default = HOST_MAP["collector.newrelic.com"] + _logger.warn("Unable to find corresponding OTLP host using default %s" % default) + otlp_host = default + return otlp_host + + _LOG_LEVEL = { "CRITICAL": logging.CRITICAL, "ERROR": logging.ERROR, @@ -567,7 +614,9 @@ def default_host(license_key): _settings.ssl = _environ_as_bool("NEW_RELIC_SSL", True) _settings.host = os.environ.get("NEW_RELIC_HOST") +_settings.otlp_host = os.environ.get("NEW_RELIC_OTLP_HOST") _settings.port = int(os.environ.get("NEW_RELIC_PORT", "0")) +_settings.otlp_port = int(os.environ.get("NEW_RELIC_OTLP_PORT", "0")) _settings.agent_run_id = None _settings.entity_guid = None @@ -668,6 +717,7 @@ def default_host(license_key): _settings.transaction_events.attributes.include = [] _settings.custom_insights_events.enabled = True +_settings.ml_insights_events.enabled = False _settings.distributed_tracing.enabled = _environ_as_bool("NEW_RELIC_DISTRIBUTED_TRACING_ENABLED", default=True) _settings.distributed_tracing.exclude_newrelic_header = False @@ -760,6 +810,10 @@ def default_host(license_key): "NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STORED", CUSTOM_EVENT_RESERVOIR_SIZE ) +_settings.event_harvest_config.harvest_limits.ml_event_data = _environ_as_int( + "NEW_RELIC_ML_INSIGHTS_EVENTS_MAX_SAMPLES_STORED", ML_EVENT_RESERVOIR_SIZE +) + _settings.event_harvest_config.harvest_limits.span_event_data = _environ_as_int( "NEW_RELIC_SPAN_EVENTS_MAX_SAMPLES_STORED", SPAN_EVENT_RESERVOIR_SIZE ) @@ -797,6 +851,7 @@ def default_host(license_key): _settings.debug.log_untrusted_distributed_trace_keys = False _settings.debug.disable_harvest_until_shutdown = False _settings.debug.connect_span_stream_in_developer_mode = False +_settings.debug.otlp_content_encoding = None _settings.message_tracer.segment_parameters_enabled = True @@ -839,6 +894,10 @@ def default_host(license_key): _settings.application_logging.local_decorating.enabled = _environ_as_bool( "NEW_RELIC_APPLICATION_LOGGING_LOCAL_DECORATING_ENABLED", default=False ) +_settings.machine_learning.enabled = _environ_as_bool("NEW_RELIC_MACHINE_LEARNING_ENABLED", default=False) +_settings.machine_learning.inference_events_value.enabled = _environ_as_bool( + "NEW_RELIC_MACHINE_LEARNING_INFERENCE_EVENT_VALUE_ENABLED", default=False +) def global_settings(): @@ -1083,8 +1142,8 @@ def apply_server_side_settings(server_side_config=None, settings=_settings): apply_config_setting(settings_snapshot, name, value) # Overlay with global server side configuration settings. - # global server side configuration always takes precedence over the global - # server side configuration settings. + # global server side configuration always takes precedence over the local + # agent configuration settings. for name, value in server_side_config.items(): apply_config_setting(settings_snapshot, name, value) @@ -1101,6 +1160,16 @@ def apply_server_side_settings(server_side_config=None, settings=_settings): settings_snapshot, "event_harvest_config.harvest_limits.span_event_data", span_event_harvest_limit ) + # Since the server does not override this setting as it's an OTLP setting, + # we must override it here manually by converting it into a per harvest cycle + # value. + apply_config_setting( + settings_snapshot, + "event_harvest_config.harvest_limits.ml_event_data", + # override ml_events / (60s/5s) harvest + settings_snapshot.event_harvest_config.harvest_limits.ml_event_data / 12, + ) + # This will be removed at some future point # Special case for account_id which will be sent instead of # cross_process_id in the future diff --git a/newrelic/core/data_collector.py b/newrelic/core/data_collector.py index 985e37240..269139664 100644 --- a/newrelic/core/data_collector.py +++ b/newrelic/core/data_collector.py @@ -25,21 +25,30 @@ DeveloperModeClient, ServerlessModeClient, ) -from newrelic.core.agent_protocol import AgentProtocol, ServerlessModeProtocol +from newrelic.core.agent_protocol import ( + AgentProtocol, + OtlpProtocol, + ServerlessModeProtocol, +) from newrelic.core.agent_streaming import StreamingRpc from newrelic.core.config import global_settings +from newrelic.core.otlp_utils import encode_metric_data, encode_ml_event_data _logger = logging.getLogger(__name__) class Session(object): PROTOCOL = AgentProtocol + OTLP_PROTOCOL = OtlpProtocol CLIENT = ApplicationModeClient def __init__(self, app_name, linked_applications, environment, settings): self._protocol = self.PROTOCOL.connect( app_name, linked_applications, environment, settings, client_cls=self.CLIENT ) + self._otlp_protocol = self.OTLP_PROTOCOL.connect( + app_name, linked_applications, environment, settings, client_cls=self.CLIENT + ) self._rpc = None @property @@ -112,6 +121,11 @@ def send_custom_events(self, sampling_info, custom_event_data): payload = (self.agent_run_id, sampling_info, custom_event_data) return self._protocol.send("custom_event_data", payload) + def send_ml_events(self, sampling_info, custom_event_data): + """Called to submit sample set for machine learning events.""" + payload = encode_ml_event_data(custom_event_data, str(self.agent_run_id)) + return self._otlp_protocol.send("ml_event_data", payload, path="/v1/logs") + def send_span_events(self, sampling_info, span_event_data): """Called to submit sample set for span events.""" @@ -128,6 +142,20 @@ def send_metric_data(self, start_time, end_time, metric_data): payload = (self.agent_run_id, start_time, end_time, metric_data) return self._protocol.send("metric_data", payload) + def send_dimensional_metric_data(self, start_time, end_time, metric_data): + """Called to submit dimensional metric data for specified period of time. + Time values are seconds since UNIX epoch as returned by the + time.time() function. The metric data should be iterable of + specific metrics. + + NOTE: This data is sent not sent to the normal agent endpoints but is sent + to the OTLP API endpoints to keep the entity separate. This is for use + with the machine learning integration only. + """ + + payload = encode_metric_data(metric_data, start_time, end_time) + return self._otlp_protocol.send("dimensional_metric_data", payload, path="/v1/metrics") + def send_log_events(self, sampling_info, log_event_data): """Called to submit sample set for log events.""" diff --git a/newrelic/core/otlp_utils.py b/newrelic/core/otlp_utils.py new file mode 100644 index 000000000..e78a63603 --- /dev/null +++ b/newrelic/core/otlp_utils.py @@ -0,0 +1,243 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This module provides common utilities for interacting with OTLP protocol buffers. + +The serialization implemented here attempts to use protobuf as an encoding, but falls +back to JSON when encoutering exceptions unless the content type is explicitly set in debug settings. +""" + +import logging + +from newrelic.common.encoding_utils import json_encode +from newrelic.core.config import global_settings +from newrelic.core.stats_engine import CountStats, TimeStats + +_logger = logging.getLogger(__name__) + +_settings = global_settings() +otlp_content_setting = _settings.debug.otlp_content_encoding +if not otlp_content_setting or otlp_content_setting == "protobuf": + try: + from newrelic.packages.opentelemetry_proto.common_pb2 import AnyValue, KeyValue + from newrelic.packages.opentelemetry_proto.logs_pb2 import ( + LogsData, + ResourceLogs, + ScopeLogs, + ) + from newrelic.packages.opentelemetry_proto.metrics_pb2 import ( + AggregationTemporality, + Metric, + MetricsData, + NumberDataPoint, + ResourceMetrics, + ScopeMetrics, + Sum, + Summary, + SummaryDataPoint, + ) + from newrelic.packages.opentelemetry_proto.resource_pb2 import Resource + + ValueAtQuantile = SummaryDataPoint.ValueAtQuantile + AGGREGATION_TEMPORALITY_DELTA = AggregationTemporality.AGGREGATION_TEMPORALITY_DELTA + OTLP_CONTENT_TYPE = "application/x-protobuf" + + otlp_content_setting = "protobuf" # Explicitly set to overwrite None values + except Exception: + if otlp_content_setting == "protobuf": + raise # Reraise exception if content type explicitly set + # Fallback to JSON + otlp_content_setting = "json" + + +if otlp_content_setting == "json": + AnyValue = dict + KeyValue = dict + Metric = dict + MetricsData = dict + NumberDataPoint = dict + Resource = dict + ResourceMetrics = dict + ScopeMetrics = dict + Sum = dict + Summary = dict + SummaryDataPoint = dict + ValueAtQuantile = dict + ResourceLogs = dict + ScopeLogs = dict + LogsData = dict + + AGGREGATION_TEMPORALITY_DELTA = 1 + OTLP_CONTENT_TYPE = "application/json" + + +def otlp_encode(payload): + if type(payload) is dict: # pylint: disable=C0123 + _logger.warning( + "Using OTLP integration while protobuf is not installed. This may result in larger payload sizes and data loss." + ) + return json_encode(payload).encode("utf-8") + return payload.SerializeToString() + + +def create_key_value(key, value): + if isinstance(value, bool): + return KeyValue(key=key, value=AnyValue(bool_value=value)) + elif isinstance(value, int): + return KeyValue(key=key, value=AnyValue(int_value=value)) + elif isinstance(value, float): + return KeyValue(key=key, value=AnyValue(double_value=value)) + elif isinstance(value, str): + return KeyValue(key=key, value=AnyValue(string_value=value)) + # Technically AnyValue accepts array, kvlist, and bytes however, since + # those are not valid custom attribute types according to our api spec, + # we will not bother to support them here either. + else: + _logger.warning("Unsupported attribute value type %s: %s." % (key, value)) + + +def create_key_values_from_iterable(iterable): + if not iterable: + return None + elif isinstance(iterable, dict): + iterable = iterable.items() + + # The create_key_value list may return None if the value is an unsupported type + # so filter None values out before returning. + return list( + filter( + lambda i: i is not None, + (create_key_value(key, value) for key, value in iterable), + ) + ) + + +def create_resource(attributes=None): + attributes = attributes or {"instrumentation.provider": "newrelic-opentelemetry-python-ml"} + return Resource(attributes=create_key_values_from_iterable(attributes)) + + +def TimeStats_to_otlp_data_point(self, start_time, end_time, attributes=None): + data = SummaryDataPoint( + time_unix_nano=int(end_time * 1e9), # Time of current harvest + start_time_unix_nano=int(start_time * 1e9), # Time of last harvest + attributes=attributes, + count=int(self[0]), + sum=float(self[1]), + quantile_values=[ + ValueAtQuantile(quantile=0.0, value=float(self[3])), # Min Value + ValueAtQuantile(quantile=1.0, value=float(self[4])), # Max Value + ], + ) + return data + + +def CountStats_to_otlp_data_point(self, start_time, end_time, attributes=None): + data = NumberDataPoint( + time_unix_nano=int(end_time * 1e9), # Time of current harvest + start_time_unix_nano=int(start_time * 1e9), # Time of last harvest + attributes=attributes, + as_int=int(self[0]), + ) + return data + + +def stats_to_otlp_metrics(metric_data, start_time, end_time): + """ + Generator producing protos for Summary and Sum metrics, for CountStats and TimeStats respectively. + + Individual Metric protos must be entirely one type of metric data point. For mixed metric types we have to + separate the types and report multiple metrics, one for each type. + """ + for name, metric_container in metric_data: + # Types are checked here using type() instead of isinstance, as CountStats is a subclass of TimeStats. + # Imporperly checking with isinstance will lead to count metrics being encoded and reported twice. + if any(type(metric) is CountStats for metric in metric_container.values()): # pylint: disable=C0123 + # Metric contains Sum metric data points. + yield Metric( + name=name, + sum=Sum( + aggregation_temporality=AGGREGATION_TEMPORALITY_DELTA, + is_monotonic=True, + data_points=[ + CountStats_to_otlp_data_point( + value, + start_time=start_time, + end_time=end_time, + attributes=create_key_values_from_iterable(tags), + ) + for tags, value in metric_container.items() + if type(value) is CountStats # pylint: disable=C0123 + ], + ), + ) + if any(type(metric) is TimeStats for metric in metric_container.values()): # pylint: disable=C0123 + # Metric contains Summary metric data points. + yield Metric( + name=name, + summary=Summary( + data_points=[ + TimeStats_to_otlp_data_point( + value, + start_time=start_time, + end_time=end_time, + attributes=create_key_values_from_iterable(tags), + ) + for tags, value in metric_container.items() + if type(value) is TimeStats # pylint: disable=C0123 + ] + ), + ) + + +def encode_metric_data(metric_data, start_time, end_time, resource=None, scope=None): + resource = resource or create_resource() + return MetricsData( + resource_metrics=[ + ResourceMetrics( + resource=resource, + scope_metrics=[ + ScopeMetrics( + scope=scope, + metrics=list(stats_to_otlp_metrics(metric_data, start_time, end_time)), + ) + ], + ) + ] + ) + + +def encode_ml_event_data(custom_event_data, agent_run_id): + resource = create_resource() + ml_events = [] + for event in custom_event_data: + event_info, event_attrs = event + event_attrs.update( + { + "real_agent_id": agent_run_id, + "event.domain": "newrelic.ml_events", + "event.name": event_info["type"], + } + ) + ml_attrs = create_key_values_from_iterable(event_attrs) + unix_nano_timestamp = event_info["timestamp"] * 1e6 + ml_events.append( + { + "time_unix_nano": int(unix_nano_timestamp), + "attributes": ml_attrs, + } + ) + + return LogsData(resource_logs=[ResourceLogs(resource=resource, scope_logs=[ScopeLogs(log_records=ml_events)])]) diff --git a/newrelic/core/stats_engine.py b/newrelic/core/stats_engine.py index 88ec31c6e..ebebe7dbe 100644 --- a/newrelic/core/stats_engine.py +++ b/newrelic/core/stats_engine.py @@ -35,6 +35,7 @@ from newrelic.api.settings import STRIP_EXCEPTION_MESSAGE from newrelic.api.time_trace import get_linking_metadata from newrelic.common.encoding_utils import json_encode +from newrelic.common.metric_utils import create_metric_identity from newrelic.common.object_names import parse_exc_info from newrelic.common.streaming_utils import StreamBuffer from newrelic.core.attribute import ( @@ -61,7 +62,7 @@ "reset_synthetics_events", ), "span_event_data": ("reset_span_events",), - "custom_event_data": ("reset_custom_events",), + "custom_event_data": ("reset_custom_events", "reset_ml_events"), "error_event_data": ("reset_error_events",), "log_event_data": ("reset_log_events",), } @@ -180,6 +181,11 @@ def merge_custom_metric(self, value): self.merge_raw_time_metric(value) + def merge_dimensional_metric(self, value): + """Merge data value.""" + + self.merge_raw_time_metric(value) + class CountStats(TimeStats): def merge_stats(self, other): @@ -235,6 +241,99 @@ def reset_metric_stats(self): self.__stats_table = {} +class DimensionalMetrics(object): + + """Nested dictionary table for collecting a set of metrics broken down by tags.""" + + def __init__(self): + self.__stats_table = {} + + def __contains__(self, key): + if isinstance(key, tuple): + if not isinstance(key[1], frozenset): + # Convert tags dict to a frozen set for proper comparisons + name, tags = create_metric_identity(*key) + else: + name, tags = key + + # Check that both metric name and tags are already present. + stats_container = self.__stats_table.get(name) + return stats_container and tags in stats_container + else: + # Only look for metric name + return key in self.__stats_table + + def record_dimensional_metric(self, name, value, tags=None): + """Record a single value metric, merging the data with any data + from prior value metrics with the same name and tags. + """ + name, tags = create_metric_identity(name, tags) + + if isinstance(value, dict): + if len(value) == 1 and "count" in value: + new_stats = CountStats(call_count=value["count"]) + else: + new_stats = TimeStats(*c2t(**value)) + else: + new_stats = TimeStats(1, value, value, value, value, value**2) + + stats_container = self.__stats_table.get(name) + if stats_container is None: + # No existing metrics with this name. Set up new stats container. + self.__stats_table[name] = {tags: new_stats} + else: + # Existing metric container found. + stats = stats_container.get(tags) + if stats is None: + # No data points for this set of tags. Add new data. + stats_container[tags] = new_stats + else: + # Existing data points found, merge stats. + stats.merge_stats(new_stats) + + return (name, tags) + + def metrics(self): + """Returns an iterator over the set of value metrics. + The items returned are a dictionary of tags for each metric value. + Metric values are each a tuple consisting of the metric name and accumulated + stats for the metric. + """ + + return six.iteritems(self.__stats_table) + + def metrics_count(self): + """Returns a count of the number of unique metrics currently + recorded for apdex, time and value metrics. + """ + + return sum(len(metric) for metric in self.__stats_table.values()) + + def reset_metric_stats(self): + """Resets the accumulated statistics back to initial state for + metric data. + """ + self.__stats_table = {} + + def get(self, key, default=None): + return self.__stats_table.get(key, default) + + def __setitem__(self, key, value): + self.__stats_table[key] = value + + def __getitem__(self, key): + return self.__stats_table[key] + + def __str__(self): + return str(self.__stats_table) + + def __repr__(self): + return "%s(%s)" % (__class__.__name__, repr(self.__stats_table)) + + def items(self): + return self.metrics() + + class SlowSqlStats(list): def __init__(self): super(SlowSqlStats, self).__init__([0, 0, 0, 0, None]) @@ -433,9 +532,11 @@ class StatsEngine(object): def __init__(self): self.__settings = None self.__stats_table = {} + self.__dimensional_stats_table = DimensionalMetrics() self._transaction_events = SampledDataSet() self._error_events = SampledDataSet() self._custom_events = SampledDataSet() + self._ml_events = SampledDataSet() self._span_events = SampledDataSet() self._log_events = SampledDataSet() self._span_stream = None @@ -456,6 +557,10 @@ def settings(self): def stats_table(self): return self.__stats_table + @property + def dimensional_stats_table(self): + return self.__dimensional_stats_table + @property def transaction_events(self): return self._transaction_events @@ -464,6 +569,10 @@ def transaction_events(self): def custom_events(self): return self._custom_events + @property + def ml_events(self): + return self._ml_events + @property def span_events(self): return self._span_events @@ -494,7 +603,7 @@ def metrics_count(self): """ - return len(self.__stats_table) + return len(self.__stats_table) + self.__dimensional_stats_table.metrics_count() def record_apdex_metric(self, metric): """Record a single apdex metric, merging the data with any data @@ -716,7 +825,6 @@ def notice_error(self, error=None, attributes=None, expected=None, ignore=None, user_attributes = create_user_attributes(custom_attributes, settings.attribute_filter) - # Extract additional details about the exception as agent attributes agent_attributes = {} @@ -728,28 +836,37 @@ def notice_error(self, error=None, attributes=None, expected=None, ignore=None, error_group_name = None try: # Call callback to obtain error group name - error_group_name_raw = settings.error_collector.error_group_callback(value, { - "traceback": tb, - "error.class": exc, - "error.message": message_raw, - "error.expected": is_expected, - "custom_params": attributes, - # Transaction specific items should be set to None - "transactionName": None, - "response.status": None, - "request.method": None, - "request.uri": None, - }) + error_group_name_raw = settings.error_collector.error_group_callback( + value, + { + "traceback": tb, + "error.class": exc, + "error.message": message_raw, + "error.expected": is_expected, + "custom_params": attributes, + # Transaction specific items should be set to None + "transactionName": None, + "response.status": None, + "request.method": None, + "request.uri": None, + }, + ) if error_group_name_raw: _, error_group_name = process_user_attribute("error.group.name", error_group_name_raw) if error_group_name is None or not isinstance(error_group_name, six.string_types): - raise ValueError("Invalid attribute value for error.group.name. Expected string, got: %s" % repr(error_group_name_raw)) + raise ValueError( + "Invalid attribute value for error.group.name. Expected string, got: %s" + % repr(error_group_name_raw) + ) else: agent_attributes["error.group.name"] = error_group_name except Exception: - _logger.error("Encountered error when calling error group callback:\n%s", "".join(traceback.format_exception(*sys.exc_info()))) - + _logger.error( + "Encountered error when calling error group callback:\n%s", + "".join(traceback.format_exception(*sys.exc_info())), + ) + agent_attributes = create_agent_attributes(agent_attributes, settings.attribute_filter) # Record the exception details. @@ -774,7 +891,7 @@ def notice_error(self, error=None, attributes=None, expected=None, ignore=None, for attr in agent_attributes: if attr.destinations & DST_ERROR_COLLECTOR: attributes["agentAttributes"][attr.name] = attr.value - + error_details = TracedError( start_time=time.time(), path="Exception", message=message, type=fullname, parameters=attributes ) @@ -829,6 +946,15 @@ def record_custom_event(self, event): if settings.collect_custom_events and settings.custom_insights_events.enabled: self._custom_events.add(event) + def record_ml_event(self, event): + settings = self.__settings + + if not settings: + return + + if settings.ml_insights_events.enabled: + self._ml_events.add(event) + def record_custom_metric(self, name, value): """Record a single value metric, merging the data with any data from prior value metrics with the same name. @@ -865,6 +991,28 @@ def record_custom_metrics(self, metrics): for name, value in metrics: self.record_custom_metric(name, value) + def record_dimensional_metric(self, name, value, tags=None): + """Record a single value metric, merging the data with any data + from prior value metrics with the same name and tags. + """ + return self.__dimensional_stats_table.record_dimensional_metric(name, value, tags) + + def record_dimensional_metrics(self, metrics): + """Record the value metrics supplied by the iterable, merging + the data with any data from prior value metrics with the same + name. + + """ + + if not self.__settings: + return + + for metric in metrics: + name, value = metric[:2] + tags = metric[2] if len(metric) >= 3 else None + + self.record_dimensional_metric(name, value, tags) + def record_slow_sql_node(self, node): """Record a single sql metric, merging the data with any data from prior sql metrics for the same sql key. @@ -975,6 +1123,8 @@ def record_transaction(self, transaction): self.merge_custom_metrics(transaction.custom_metrics.metrics()) + self.merge_dimensional_metrics(transaction.dimensional_metrics.metrics()) + self.record_time_metrics(transaction.time_metrics(self)) # Capture any errors if error collection is enabled. @@ -1042,6 +1192,11 @@ def record_transaction(self, transaction): if settings.collect_custom_events and settings.custom_insights_events.enabled: self.custom_events.merge(transaction.custom_events) + # Merge in machine learning events + + if settings.ml_insights_events.enabled: + self.ml_events.merge(transaction.ml_events) + # Merge in span events if settings.distributed_tracing.enabled and settings.span_events.enabled and settings.collect_span_events: @@ -1163,6 +1318,66 @@ def metric_data_count(self): return len(self.__stats_table) + def dimensional_metric_data(self, normalizer=None): + """Returns a list containing the low level metric data for + sending to the core application pertaining to the reporting + period. This consists of tuple pairs where first is dictionary + with name and scope keys with corresponding values, or integer + identifier if metric had an entry in dictionary mapping metric + (name, tags) as supplied from core application. The second is + the list of accumulated metric data, the list always being of + length 6. + + """ + + if not self.__settings: + return [] + + result = [] + normalized_stats = {} + + # Metric Renaming and Re-Aggregation. After applying the metric + # renaming rules, the metrics are re-aggregated to collapse the + # metrics with same names after the renaming. + + if self.__settings.debug.log_raw_metric_data: + _logger.info( + "Raw dimensional metric data for harvest of %r is %r.", + self.__settings.app_name, + list(self.__dimensional_stats_table.metrics()), + ) + + if normalizer is not None: + for key, value in self.__dimensional_stats_table.metrics(): + key = normalizer(key)[0] + stats = normalized_stats.get(key) + if stats is None: + normalized_stats[key] = copy.copy(value) + else: + stats.merge_stats(value) + else: + normalized_stats = self.__dimensional_stats_table + + if self.__settings.debug.log_normalized_metric_data: + _logger.info( + "Normalized metric data for harvest of %r is %r.", + self.__settings.app_name, + list(normalized_stats.metrics()), + ) + + for key, value in normalized_stats.items(): + result.append((key, value)) + + return result + + def dimensional_metric_data_count(self): + """Returns a count of the number of unique metrics.""" + + if not self.__settings: + return 0 + + return self.__dimensional_stats_table.metrics_count() + def error_data(self): """Returns a to a list containing any errors collected during the reporting period. @@ -1440,7 +1655,6 @@ def reset_stats(self, settings, reset_stream=False): """ self.__settings = settings - self.__stats_table = {} self.__sql_stats_table = {} self.__slow_transaction = None self.__slow_transaction_map = {} @@ -1448,9 +1662,11 @@ def reset_stats(self, settings, reset_stream=False): self.__transaction_errors = [] self.__synthetics_transactions = [] + self.reset_metric_stats() self.reset_transaction_events() self.reset_error_events() self.reset_custom_events() + self.reset_ml_events() self.reset_span_events() self.reset_log_events() self.reset_synthetics_events() @@ -1467,6 +1683,7 @@ def reset_metric_stats(self): """ self.__stats_table = {} + self.__dimensional_stats_table.reset_metric_stats() def reset_transaction_events(self): """Resets the accumulated statistics back to initial state for @@ -1493,6 +1710,12 @@ def reset_custom_events(self): else: self._custom_events = SampledDataSet() + def reset_ml_events(self): + if self.__settings is not None: + self._ml_events = SampledDataSet(self.__settings.event_harvest_config.harvest_limits.ml_event_data) + else: + self._ml_events = SampledDataSet() + def reset_span_events(self): if self.__settings is not None: self._span_events = SampledDataSet(self.__settings.event_harvest_config.harvest_limits.span_event_data) @@ -1626,6 +1849,7 @@ def merge(self, snapshot): self._merge_error_events(snapshot) self._merge_error_traces(snapshot) self._merge_custom_events(snapshot) + self._merge_ml_events(snapshot) self._merge_span_events(snapshot) self._merge_log_events(snapshot) self._merge_sql(snapshot) @@ -1651,6 +1875,7 @@ def rollback(self, snapshot): self._merge_synthetics_events(snapshot, rollback=True) self._merge_error_events(snapshot) self._merge_custom_events(snapshot, rollback=True) + self._merge_ml_events(snapshot, rollback=True) self._merge_span_events(snapshot, rollback=True) self._merge_log_events(snapshot, rollback=True) @@ -1720,6 +1945,12 @@ def _merge_custom_events(self, snapshot, rollback=False): return self._custom_events.merge(events) + def _merge_ml_events(self, snapshot, rollback=False): + events = snapshot.ml_events + if not events: + return + self._ml_events.merge(events) + def _merge_span_events(self, snapshot, rollback=False): events = snapshot.span_events if not events: @@ -1789,6 +2020,29 @@ def merge_custom_metrics(self, metrics): else: stats.merge_stats(other) + def merge_dimensional_metrics(self, metrics): + """ + Merges in a set of dimensional metrics. The metrics should be + provide as an iterable where each item is a tuple of the metric + key and the accumulated stats for the metric. The metric key should + also be a tuple, containing a name and attribute filtered frozenset of tags. + """ + + if not self.__settings: + return + + for key, other in metrics: + stats_container = self.__dimensional_stats_table.get(key) + if not stats_container: + self.__dimensional_stats_table[key] = other + else: + for tags, other_value in other.items(): + stats = stats_container.get(tags) + if not stats: + stats_container[tags] = other_value + else: + stats.merge_stats(other_value) + def _snapshot(self): copy = object.__new__(StatsEngineSnapshot) copy.__dict__.update(self.__dict__) @@ -1802,6 +2056,9 @@ def reset_transaction_events(self): def reset_custom_events(self): self._custom_events = None + def reset_ml_events(self): + self._ml_events = None + def reset_span_events(self): self._span_events = None diff --git a/newrelic/core/transaction_node.py b/newrelic/core/transaction_node.py index 0faae3790..d63d7f9b6 100644 --- a/newrelic/core/transaction_node.py +++ b/newrelic/core/transaction_node.py @@ -60,10 +60,12 @@ "errors", "slow_sql", "custom_events", + "ml_events", "log_events", "apdex_t", "suppress_apdex", "custom_metrics", + "dimensional_metrics", "guid", "cpu_time", "suppress_transaction_trace", diff --git a/newrelic/hooks/mlmodel_sklearn.py b/newrelic/hooks/mlmodel_sklearn.py new file mode 100644 index 000000000..bdfeccfc8 --- /dev/null +++ b/newrelic/hooks/mlmodel_sklearn.py @@ -0,0 +1,781 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import sys +import uuid + +from newrelic.api.function_trace import FunctionTrace +from newrelic.api.time_trace import current_trace +from newrelic.api.transaction import current_transaction +from newrelic.common.object_wrapper import ObjectProxy, wrap_function_wrapper +from newrelic.core.config import global_settings + +METHODS_TO_WRAP = ("predict", "fit", "fit_predict", "predict_log_proba", "predict_proba", "transform", "score") +METRIC_SCORERS = ( + "accuracy_score", + "balanced_accuracy_score", + "f1_score", + "precision_score", + "recall_score", + "roc_auc_score", + "r2_score", +) +PY2 = sys.version_info[0] == 2 +_logger = logging.getLogger(__name__) + + +def isnumeric(column): + import numpy as np + + try: + column.astype(np.float64) + return [True] * len(column) + except: + pass + return [False] * len(column) + + +class PredictReturnTypeProxy(ObjectProxy): + def __init__(self, wrapped, model_name, training_step): + super(ObjectProxy, self).__init__(wrapped) + self._nr_model_name = model_name + self._nr_training_step = training_step + + +def _wrap_method_trace(module, class_, method, name=None, group=None): + def _nr_wrapper_method(wrapped, instance, args, kwargs): + transaction = current_transaction() + trace = current_trace() + + if transaction is None: + return wrapped(*args, **kwargs) + + settings = transaction.settings if transaction.settings is not None else global_settings() + + if settings and not settings.machine_learning.enabled: + return wrapped(*args, **kwargs) + + wrapped_attr_name = "_nr_wrapped_%s" % method + + # If the method has already been wrapped do not wrap it again. This happens + # when one class inherits from another and they both implement the method. + if getattr(trace, wrapped_attr_name, False): + return wrapped(*args, **kwargs) + + trace = FunctionTrace(name=name, group=group, source=wrapped) + + try: + # Set the _nr_wrapped attribute to denote that this method is being wrapped. + setattr(trace, wrapped_attr_name, True) + + with trace: + return_val = wrapped(*args, **kwargs) + finally: + # Set the _nr_wrapped attribute to denote that this method is no longer wrapped. + setattr(trace, wrapped_attr_name, False) + + # If this is the fit method, increment the training_step counter. + if method in ("fit", "fit_predict"): + training_step = getattr(instance, "_nr_wrapped_training_step", -1) + setattr(instance, "_nr_wrapped_training_step", training_step + 1) + + # If this is the predict method, wrap the return type in an nr type with + # _nr_wrapped attrs that will attach model info to the data. + if method in ("predict", "fit_predict"): + training_step = getattr(instance, "_nr_wrapped_training_step", "Unknown") + create_prediction_event(transaction, class_, instance, args, kwargs, return_val) + return PredictReturnTypeProxy(return_val, model_name=class_, training_step=training_step) + return return_val + + wrap_function_wrapper(module, "%s.%s" % (class_, method), _nr_wrapper_method) + + +def _calc_prediction_feature_stats(prediction_input, class_, feature_column_names, tags): + import numpy as np + + # Drop any feature columns that are not numeric since we can't compute stats + # on non-numeric columns. + x = np.array(prediction_input) + isnumeric_features = np.apply_along_axis(isnumeric, 0, x) + numeric_features = x[isnumeric_features] + + # Drop any feature column names that are not numeric since we can't compute stats + # on non-numeric columns. + feature_column_names = feature_column_names[isnumeric_features[0]] + + # Only compute stats for features if we have any feature columns left after dropping + # non-numeric columns. + num_cols = len(feature_column_names) + if num_cols > 0: + # Boolean selection of numpy array values reshapes the array to a single + # dimension so we have to reshape it back into a 2D array. + features = np.reshape(numeric_features, (len(numeric_features) // num_cols, num_cols)) + features = features.astype(dtype=np.float64) + + _record_stats(features, feature_column_names, class_, "Feature", tags) + + +def _record_stats(data, column_names, class_, column_type, tags): + import numpy as np + + mean = np.mean(data, axis=0) + percentile25 = np.percentile(data, q=0.25, axis=0) + percentile50 = np.percentile(data, q=0.50, axis=0) + percentile75 = np.percentile(data, q=0.75, axis=0) + standard_deviation = np.std(data, axis=0) + _min = np.min(data, axis=0) + _max = np.max(data, axis=0) + _count = data.shape[0] + + transaction = current_transaction() + + # Currently record_metric only supports a subset of these stats so we have + # to upload them one at a time instead of as a dictionary of stats per + # feature column. + for index, col_name in enumerate(column_names): + metric_name = "MLModel/Sklearn/Named/%s/Predict/%s/%s" % (class_, column_type, col_name) + + transaction.record_dimensional_metrics( + [ + ("%s/%s" % (metric_name, "Mean"), float(mean[index]), tags), + ("%s/%s" % (metric_name, "Percentile25"), float(percentile25[index]), tags), + ("%s/%s" % (metric_name, "Percentile50"), float(percentile50[index]), tags), + ("%s/%s" % (metric_name, "Percentile75"), float(percentile75[index]), tags), + ("%s/%s" % (metric_name, "StandardDeviation"), float(standard_deviation[index]), tags), + ("%s/%s" % (metric_name, "Min"), float(_min[index]), tags), + ("%s/%s" % (metric_name, "Max"), float(_max[index]), tags), + ("%s/%s" % (metric_name, "Count"), _count, tags), + ] + ) + + +def _calc_prediction_label_stats(labels, class_, label_column_names, tags): + import numpy as np + + labels = np.array(labels, dtype=np.float64) + _record_stats(labels, label_column_names, class_, "Label", tags) + + +def _get_label_names(user_defined_label_names, prediction_array): + import numpy as np + + if user_defined_label_names is None: + return np.array(range(prediction_array.shape[1])) + if user_defined_label_names and len(user_defined_label_names) != prediction_array.shape[1]: + _logger.warning( + "The number of label names passed to the ml_model wrapper function is not equal to the number of predictions in the data set. Please supply the correct number of label names." + ) + return np.array(range(prediction_array.shape[1])) + else: + return user_defined_label_names + + +def find_type_category(data_set, row_index, column_index): + # If pandas DataFrame, return type of column. + pd = sys.modules.get("pandas", None) + if pd and isinstance(data_set, pd.DataFrame): + value_type = data_set.iloc[:, column_index].dtype.name + if value_type == "category": + return "categorical" + categorized_value_type = categorize_data_type(value_type) + return categorized_value_type + # If it's not a pandas DataFrame then it is a list or numpy array. + python_type = str(type(data_set[column_index][row_index])) + return categorize_data_type(python_type) + + +def categorize_data_type(python_type): + if "int" in python_type or "float" in python_type or "complex" in python_type: + return "numeric" + if "bool" in python_type: + return "bool" + if "str" in python_type or "unicode" in python_type: + return "str" + else: + return python_type + + +def _get_feature_column_names(user_provided_feature_names, features): + import numpy as np + + num_feature_columns = np.array(features).shape[1] + + # If the user provided feature names are the correct size, return the user provided feature + # names. + if user_provided_feature_names and len(user_provided_feature_names) == num_feature_columns: + return np.array(user_provided_feature_names) + + # If the user provided feature names aren't the correct size, log a warning and do not use the user provided feature names. + if user_provided_feature_names: + _logger.warning( + "The number of feature names passed to the ml_model wrapper function is not equal to the number of columns in the data set. Please supply the correct number of feature names." + ) + + # If the user doesn't provide the feature names or they were provided but the size was incorrect and the features are a pandas data frame, return the column names from the pandas data frame. + pd = sys.modules.get("pandas", None) + if pd and isinstance(features, pd.DataFrame): + return features.columns + + # If the user doesn't provide the feature names or they were provided but the size was incorrect and the features are not a pandas data frame, return the column indexes as the feature names. + return np.array(range(num_feature_columns)) + + +def bind_predict(X, *args, **kwargs): + return X + + +def create_prediction_event(transaction, class_, instance, args, kwargs, return_val): + import numpy as np + + data_set = bind_predict(*args, **kwargs) + model_name = getattr(instance, "_nr_wrapped_name", class_) + model_version = getattr(instance, "_nr_wrapped_version", "0.0.0") + user_provided_feature_names = getattr(instance, "_nr_wrapped_feature_names", None) + label_names = getattr(instance, "_nr_wrapped_label_names", None) + metadata = getattr(instance, "_nr_wrapped_metadata", {}) + settings = transaction.settings if transaction.settings is not None else global_settings() + + prediction_id = uuid.uuid4() + + labels = [] + if return_val is not None: + if not hasattr(return_val, "__iter__"): + labels = np.array([return_val]) + else: + labels = np.array(return_val) + if len(labels.shape) == 1: + labels = np.reshape(labels, (len(labels) // 1, 1)) + + label_names_list = _get_label_names(label_names, labels) + _calc_prediction_label_stats( + labels, + class_, + label_names_list, + tags={ + "prediction_id": prediction_id, + "model_version": model_version, + # The following are used for entity synthesis. + "modelName": model_name, + }, + ) + + final_feature_names = _get_feature_column_names(user_provided_feature_names, data_set) + np_casted_data_set = np.array(data_set) + _calc_prediction_feature_stats( + data_set, + class_, + final_feature_names, + tags={ + "prediction_id": prediction_id, + "model_version": model_version, + # The following are used for entity synthesis. + "modelName": model_name, + }, + ) + features, predictions = np_casted_data_set.shape + for prediction_index, prediction in enumerate(np_casted_data_set): + inference_id = uuid.uuid4() + + event = { + "inference_id": inference_id, + "prediction_id": prediction_id, + "model_version": model_version, + "new_relic_data_schema_version": 2, + # The following are used for entity synthesis. + "modelName": model_name, + } + if metadata and isinstance(metadata, dict): + event.update(metadata) + # Don't include the raw value when inference_event_value is disabled. + if settings and settings.machine_learning and settings.machine_learning.inference_events_value.enabled: + event.update( + { + "feature.%s" % str(final_feature_names[feature_col_index]): value + for feature_col_index, value in enumerate(prediction) + } + ) + event.update( + { + "label.%s" % str(label_names_list[index]): str(value) + for index, value in enumerate(labels[prediction_index]) + } + ) + transaction.record_ml_event("InferenceData", event) + + +def _nr_instrument_model(module, model_class): + for method_name in METHODS_TO_WRAP: + if hasattr(getattr(module, model_class), method_name): + # Function/MLModel/Sklearn/Named/. + name = "MLModel/Sklearn/Named/%s.%s" % (model_class, method_name) + _wrap_method_trace(module, model_class, method_name, name=name) + + +def _instrument_sklearn_models(module, model_classes): + for model_cls in model_classes: + if hasattr(module, model_cls): + _nr_instrument_model(module, model_cls) + + +def _bind_scorer(y_true, y_pred, *args, **kwargs): + return y_true, y_pred, args, kwargs + + +def wrap_metric_scorer(wrapped, instance, args, kwargs): + transaction = current_transaction() + # If there is no transaction, do not wrap anything. + if not transaction: + return wrapped(*args, **kwargs) + + settings = transaction.settings if transaction.settings is not None else global_settings() + + if settings and not settings.machine_learning.enabled: + return wrapped(*args, **kwargs) + + score = wrapped(*args, **kwargs) + + y_true, y_pred, args, kwargs = _bind_scorer(*args, **kwargs) + model_name = "Unknown" + training_step = "Unknown" + if hasattr(y_pred, "_nr_model_name"): + model_name = y_pred._nr_model_name + if hasattr(y_pred, "_nr_training_step"): + training_step = y_pred._nr_training_step + # Attribute values must be int, float, str, or boolean. If it's not one of these + # types and an iterable add the values as separate attributes. + if not isinstance(score, (str, int, float, bool)): + if hasattr(score, "__iter__"): + for i, s in enumerate(score): + transaction._add_agent_attribute( + "%s/TrainingStep/%s/%s[%s]" % (model_name, training_step, wrapped.__name__, i), s + ) + else: + transaction._add_agent_attribute("%s/TrainingStep/%s/%s" % (model_name, training_step, wrapped.__name__), score) + return score + + +def instrument_sklearn_tree_models(module): + model_classes = ( + "DecisionTreeClassifier", + "DecisionTreeRegressor", + "ExtraTreeClassifier", + "ExtraTreeRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_bagging_models(module): + model_classes = ( + "BaggingClassifier", + "BaggingRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_forest_models(module): + model_classes = ( + "ExtraTreesClassifier", + "ExtraTreesRegressor", + "RandomForestClassifier", + "RandomForestRegressor", + "RandomTreesEmbedding", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_iforest_models(module): + model_classes = ("IsolationForest",) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_weight_boosting_models(module): + model_classes = ( + "AdaBoostClassifier", + "AdaBoostRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_gradient_boosting_models(module): + model_classes = ( + "GradientBoostingClassifier", + "GradientBoostingRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_voting_models(module): + model_classes = ( + "VotingClassifier", + "VotingRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_ensemble_stacking_models(module): + module_classes = ( + "StackingClassifier", + "StackingRegressor", + ) + _instrument_sklearn_models(module, module_classes) + + +def instrument_sklearn_ensemble_hist_models(module): + model_classes = ( + "HistGradientBoostingClassifier", + "HistGradientBoostingRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_coordinate_descent_models(module): + model_classes = ( + "Lasso", + "LassoCV", + "ElasticNet", + "ElasticNetCV", + "MultiTaskLasso", + "MultiTaskLassoCV", + "MultiTaskElasticNet", + "MultiTaskElasticNetCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_compose_models(module): + model_classes = ( + "ColumnTransformer", + "TransformedTargetRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_covariance_shrunk_models(module): + model_classes = ( + "ShrunkCovariance", + "LedoitWolf", + "OAS", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_cross_decomposition_models(module): + model_classes = ( + "PLSRegression", + "PLSSVD", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_covariance_graph_models(module): + model_classes = ( + "GraphicalLasso", + "GraphicalLassoCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_discriminant_analysis_models(module): + model_classes = ( + "LinearDiscriminantAnalysis", + "QuadraticDiscriminantAnalysis", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_covariance_models(module): + model_classes = ( + "EmpiricalCovariance", + "MinCovDet", + "EllipticEnvelope", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_gaussian_process_models(module): + model_classes = ( + "GaussianProcessClassifier", + "GaussianProcessRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_dummy_models(module): + model_classes = ( + "DummyClassifier", + "DummyRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_feature_selection_rfe_models(module): + model_classes = ( + "RFE", + "RFECV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_kernel_ridge_models(module): + model_classes = ("KernelRidge",) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_calibration_models(module): + model_classes = ("CalibratedClassifierCV",) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_cluster_models(module): + model_classes = ( + "AffinityPropagation", + "Birch", + "DBSCAN", + "MeanShift", + "OPTICS", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_least_angle_models(module): + model_classes = ( + "Lars", + "LarsCV", + "LassoLars", + "LassoLarsCV", + "LassoLarsIC", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_feature_selection_models(module): + model_classes = ( + "VarianceThreshold", + "SelectFromModel", + "SequentialFeatureSelector", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_cluster_agglomerative_models(module): + model_classes = ( + "AgglomerativeClustering", + "FeatureAgglomeration", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_GLM_models(module): + model_classes = ( + "PoissonRegressor", + "GammaRegressor", + "TweedieRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_cluster_clustering_models(module): + model_classes = ( + "SpectralBiclustering", + "SpectralCoclustering", + "SpectralClustering", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_stochastic_gradient_models(module): + model_classes = ( + "SGDClassifier", + "SGDRegressor", + "SGDOneClassSVM", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_ridge_models(module): + model_classes = ( + "Ridge", + "RidgeCV", + "RidgeClassifier", + "RidgeClassifierCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_logistic_models(module): + model_classes = ( + "LogisticRegression", + "LogisticRegressionCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_OMP_models(module): + model_classes = ( + "OrthogonalMatchingPursuit", + "OrthogonalMatchingPursuitCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_passive_aggressive_models(module): + model_classes = ( + "PassiveAggressiveClassifier", + "PassiveAggressiveRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_bayes_models(module): + model_classes = ( + "ARDRegression", + "BayesianRidge", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_linear_models(module): + model_classes = ( + "HuberRegressor", + "LinearRegression", + "Perceptron", + "QuantileRegressor", + "TheilSenRegressor", + "RANSACRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_cluster_kmeans_models(module): + model_classes = ( + "BisectingKMeans", + "KMeans", + "MiniBatchKMeans", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_multiclass_models(module): + model_classes = ( + "OneVsRestClassifier", + "OneVsOneClassifier", + "OutputCodeClassifier", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_multioutput_models(module): + model_classes = ( + "MultiOutputEstimator", + "MultiOutputClassifier", + "ClassifierChain", + "RegressorChain", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_naive_bayes_models(module): + model_classes = ( + "GaussianNB", + "MultinomialNB", + "ComplementNB", + "BernoulliNB", + "CategoricalNB", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_model_selection_models(module): + model_classes = ( + "GridSearchCV", + "RandomizedSearchCV", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_mixture_models(module): + model_classes = ( + "GaussianMixture", + "BayesianGaussianMixture", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_neural_network_models(module): + model_classes = ( + "BernoulliRBM", + "MLPClassifier", + "MLPRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_neighbors_KRadius_models(module): + model_classes = ( + "KNeighborsClassifier", + "RadiusNeighborsClassifier", + "KNeighborsTransformer", + "RadiusNeighborsTransformer", + "KNeighborsRegressor", + "RadiusNeighborsRegressor", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_svm_models(module): + model_classes = ( + "LinearSVC", + "LinearSVR", + "SVC", + "NuSVC", + "SVR", + "NuSVR", + "OneClassSVM", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_semi_supervised_models(module): + model_classes = ( + "LabelPropagation", + "LabelSpreading", + "SelfTrainingClassifier", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_pipeline_models(module): + model_classes = ( + "Pipeline", + "FeatureUnion", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_neighbors_models(module): + model_classes = ( + "KernelDensity", + "LocalOutlierFactor", + "NeighborhoodComponentsAnalysis", + "NearestCentroid", + "NearestNeighbors", + ) + _instrument_sklearn_models(module, model_classes) + + +def instrument_sklearn_metrics(module): + for scorer in METRIC_SCORERS: + if hasattr(module, scorer): + wrap_function_wrapper(module, scorer, wrap_metric_scorer) diff --git a/newrelic/packages/opentelemetry_proto/LICENSE.txt b/newrelic/packages/opentelemetry_proto/LICENSE.txt new file mode 100644 index 000000000..261eeb9e9 --- /dev/null +++ b/newrelic/packages/opentelemetry_proto/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/newrelic/packages/opentelemetry_proto/__init__.py b/newrelic/packages/opentelemetry_proto/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/newrelic/packages/opentelemetry_proto/common_pb2.py b/newrelic/packages/opentelemetry_proto/common_pb2.py new file mode 100644 index 000000000..a38431a58 --- /dev/null +++ b/newrelic/packages/opentelemetry_proto/common_pb2.py @@ -0,0 +1,87 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: opentelemetry/proto/common/v1/common.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*opentelemetry/proto/common/v1/common.proto\x12\x1dopentelemetry.proto.common.v1\"\x8c\x02\n\x08\x41nyValue\x12\x16\n\x0cstring_value\x18\x01 \x01(\tH\x00\x12\x14\n\nbool_value\x18\x02 \x01(\x08H\x00\x12\x13\n\tint_value\x18\x03 \x01(\x03H\x00\x12\x16\n\x0c\x64ouble_value\x18\x04 \x01(\x01H\x00\x12@\n\x0b\x61rray_value\x18\x05 \x01(\x0b\x32).opentelemetry.proto.common.v1.ArrayValueH\x00\x12\x43\n\x0ckvlist_value\x18\x06 \x01(\x0b\x32+.opentelemetry.proto.common.v1.KeyValueListH\x00\x12\x15\n\x0b\x62ytes_value\x18\x07 \x01(\x0cH\x00\x42\x07\n\x05value\"E\n\nArrayValue\x12\x37\n\x06values\x18\x01 \x03(\x0b\x32\'.opentelemetry.proto.common.v1.AnyValue\"G\n\x0cKeyValueList\x12\x37\n\x06values\x18\x01 \x03(\x0b\x32\'.opentelemetry.proto.common.v1.KeyValue\"O\n\x08KeyValue\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x36\n\x05value\x18\x02 \x01(\x0b\x32\'.opentelemetry.proto.common.v1.AnyValue\";\n\x16InstrumentationLibrary\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t:\x02\x18\x01\"5\n\x14InstrumentationScope\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\tB[\n io.opentelemetry.proto.common.v1B\x0b\x43ommonProtoP\x01Z(go.opentelemetry.io/proto/otlp/common/v1b\x06proto3') + + + +_ANYVALUE = DESCRIPTOR.message_types_by_name['AnyValue'] +_ARRAYVALUE = DESCRIPTOR.message_types_by_name['ArrayValue'] +_KEYVALUELIST = DESCRIPTOR.message_types_by_name['KeyValueList'] +_KEYVALUE = DESCRIPTOR.message_types_by_name['KeyValue'] +_INSTRUMENTATIONLIBRARY = DESCRIPTOR.message_types_by_name['InstrumentationLibrary'] +_INSTRUMENTATIONSCOPE = DESCRIPTOR.message_types_by_name['InstrumentationScope'] +AnyValue = _reflection.GeneratedProtocolMessageType('AnyValue', (_message.Message,), { + 'DESCRIPTOR' : _ANYVALUE, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.AnyValue) + }) +_sym_db.RegisterMessage(AnyValue) + +ArrayValue = _reflection.GeneratedProtocolMessageType('ArrayValue', (_message.Message,), { + 'DESCRIPTOR' : _ARRAYVALUE, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.ArrayValue) + }) +_sym_db.RegisterMessage(ArrayValue) + +KeyValueList = _reflection.GeneratedProtocolMessageType('KeyValueList', (_message.Message,), { + 'DESCRIPTOR' : _KEYVALUELIST, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.KeyValueList) + }) +_sym_db.RegisterMessage(KeyValueList) + +KeyValue = _reflection.GeneratedProtocolMessageType('KeyValue', (_message.Message,), { + 'DESCRIPTOR' : _KEYVALUE, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.KeyValue) + }) +_sym_db.RegisterMessage(KeyValue) + +InstrumentationLibrary = _reflection.GeneratedProtocolMessageType('InstrumentationLibrary', (_message.Message,), { + 'DESCRIPTOR' : _INSTRUMENTATIONLIBRARY, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.InstrumentationLibrary) + }) +_sym_db.RegisterMessage(InstrumentationLibrary) + +InstrumentationScope = _reflection.GeneratedProtocolMessageType('InstrumentationScope', (_message.Message,), { + 'DESCRIPTOR' : _INSTRUMENTATIONSCOPE, + '__module__' : 'opentelemetry.proto.common.v1.common_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.common.v1.InstrumentationScope) + }) +_sym_db.RegisterMessage(InstrumentationScope) + +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n io.opentelemetry.proto.common.v1B\013CommonProtoP\001Z(go.opentelemetry.io/proto/otlp/common/v1' + _INSTRUMENTATIONLIBRARY._options = None + _INSTRUMENTATIONLIBRARY._serialized_options = b'\030\001' + _ANYVALUE._serialized_start=78 + _ANYVALUE._serialized_end=346 + _ARRAYVALUE._serialized_start=348 + _ARRAYVALUE._serialized_end=417 + _KEYVALUELIST._serialized_start=419 + _KEYVALUELIST._serialized_end=490 + _KEYVALUE._serialized_start=492 + _KEYVALUE._serialized_end=571 + _INSTRUMENTATIONLIBRARY._serialized_start=573 + _INSTRUMENTATIONLIBRARY._serialized_end=632 + _INSTRUMENTATIONSCOPE._serialized_start=634 + _INSTRUMENTATIONSCOPE._serialized_end=687 +# @@protoc_insertion_point(module_scope) diff --git a/newrelic/packages/opentelemetry_proto/logs_pb2.py b/newrelic/packages/opentelemetry_proto/logs_pb2.py new file mode 100644 index 000000000..bb6a55d66 --- /dev/null +++ b/newrelic/packages/opentelemetry_proto/logs_pb2.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: opentelemetry/proto/logs/v1/logs.proto +"""Generated protocol buffer code.""" +from google.protobuf.internal import enum_type_wrapper +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from . import common_pb2 as opentelemetry_dot_proto_dot_common_dot_v1_dot_common__pb2 +from . import resource_pb2 as opentelemetry_dot_proto_dot_resource_dot_v1_dot_resource__pb2 + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n&opentelemetry/proto/logs/v1/logs.proto\x12\x1bopentelemetry.proto.logs.v1\x1a*opentelemetry/proto/common/v1/common.proto\x1a.opentelemetry/proto/resource/v1/resource.proto\"L\n\x08LogsData\x12@\n\rresource_logs\x18\x01 \x03(\x0b\x32).opentelemetry.proto.logs.v1.ResourceLogs\"\xff\x01\n\x0cResourceLogs\x12;\n\x08resource\x18\x01 \x01(\x0b\x32).opentelemetry.proto.resource.v1.Resource\x12:\n\nscope_logs\x18\x02 \x03(\x0b\x32&.opentelemetry.proto.logs.v1.ScopeLogs\x12\x62\n\x1cinstrumentation_library_logs\x18\xe8\x07 \x03(\x0b\x32\x37.opentelemetry.proto.logs.v1.InstrumentationLibraryLogsB\x02\x18\x01\x12\x12\n\nschema_url\x18\x03 \x01(\t\"\xa0\x01\n\tScopeLogs\x12\x42\n\x05scope\x18\x01 \x01(\x0b\x32\x33.opentelemetry.proto.common.v1.InstrumentationScope\x12;\n\x0blog_records\x18\x02 \x03(\x0b\x32&.opentelemetry.proto.logs.v1.LogRecord\x12\x12\n\nschema_url\x18\x03 \x01(\t\"\xc9\x01\n\x1aInstrumentationLibraryLogs\x12V\n\x17instrumentation_library\x18\x01 \x01(\x0b\x32\x35.opentelemetry.proto.common.v1.InstrumentationLibrary\x12;\n\x0blog_records\x18\x02 \x03(\x0b\x32&.opentelemetry.proto.logs.v1.LogRecord\x12\x12\n\nschema_url\x18\x03 \x01(\t:\x02\x18\x01\"\xef\x02\n\tLogRecord\x12\x16\n\x0etime_unix_nano\x18\x01 \x01(\x06\x12\x1f\n\x17observed_time_unix_nano\x18\x0b \x01(\x06\x12\x44\n\x0fseverity_number\x18\x02 \x01(\x0e\x32+.opentelemetry.proto.logs.v1.SeverityNumber\x12\x15\n\rseverity_text\x18\x03 \x01(\t\x12\x35\n\x04\x62ody\x18\x05 \x01(\x0b\x32\'.opentelemetry.proto.common.v1.AnyValue\x12;\n\nattributes\x18\x06 \x03(\x0b\x32\'.opentelemetry.proto.common.v1.KeyValue\x12 \n\x18\x64ropped_attributes_count\x18\x07 \x01(\r\x12\r\n\x05\x66lags\x18\x08 \x01(\x07\x12\x10\n\x08trace_id\x18\t \x01(\x0c\x12\x0f\n\x07span_id\x18\n \x01(\x0cJ\x04\x08\x04\x10\x05*\xc3\x05\n\x0eSeverityNumber\x12\x1f\n\x1bSEVERITY_NUMBER_UNSPECIFIED\x10\x00\x12\x19\n\x15SEVERITY_NUMBER_TRACE\x10\x01\x12\x1a\n\x16SEVERITY_NUMBER_TRACE2\x10\x02\x12\x1a\n\x16SEVERITY_NUMBER_TRACE3\x10\x03\x12\x1a\n\x16SEVERITY_NUMBER_TRACE4\x10\x04\x12\x19\n\x15SEVERITY_NUMBER_DEBUG\x10\x05\x12\x1a\n\x16SEVERITY_NUMBER_DEBUG2\x10\x06\x12\x1a\n\x16SEVERITY_NUMBER_DEBUG3\x10\x07\x12\x1a\n\x16SEVERITY_NUMBER_DEBUG4\x10\x08\x12\x18\n\x14SEVERITY_NUMBER_INFO\x10\t\x12\x19\n\x15SEVERITY_NUMBER_INFO2\x10\n\x12\x19\n\x15SEVERITY_NUMBER_INFO3\x10\x0b\x12\x19\n\x15SEVERITY_NUMBER_INFO4\x10\x0c\x12\x18\n\x14SEVERITY_NUMBER_WARN\x10\r\x12\x19\n\x15SEVERITY_NUMBER_WARN2\x10\x0e\x12\x19\n\x15SEVERITY_NUMBER_WARN3\x10\x0f\x12\x19\n\x15SEVERITY_NUMBER_WARN4\x10\x10\x12\x19\n\x15SEVERITY_NUMBER_ERROR\x10\x11\x12\x1a\n\x16SEVERITY_NUMBER_ERROR2\x10\x12\x12\x1a\n\x16SEVERITY_NUMBER_ERROR3\x10\x13\x12\x1a\n\x16SEVERITY_NUMBER_ERROR4\x10\x14\x12\x19\n\x15SEVERITY_NUMBER_FATAL\x10\x15\x12\x1a\n\x16SEVERITY_NUMBER_FATAL2\x10\x16\x12\x1a\n\x16SEVERITY_NUMBER_FATAL3\x10\x17\x12\x1a\n\x16SEVERITY_NUMBER_FATAL4\x10\x18*X\n\x0eLogRecordFlags\x12\x1f\n\x1bLOG_RECORD_FLAG_UNSPECIFIED\x10\x00\x12%\n LOG_RECORD_FLAG_TRACE_FLAGS_MASK\x10\xff\x01\x42U\n\x1eio.opentelemetry.proto.logs.v1B\tLogsProtoP\x01Z&go.opentelemetry.io/proto/otlp/logs/v1b\x06proto3') + +_SEVERITYNUMBER = DESCRIPTOR.enum_types_by_name['SeverityNumber'] +SeverityNumber = enum_type_wrapper.EnumTypeWrapper(_SEVERITYNUMBER) +_LOGRECORDFLAGS = DESCRIPTOR.enum_types_by_name['LogRecordFlags'] +LogRecordFlags = enum_type_wrapper.EnumTypeWrapper(_LOGRECORDFLAGS) +SEVERITY_NUMBER_UNSPECIFIED = 0 +SEVERITY_NUMBER_TRACE = 1 +SEVERITY_NUMBER_TRACE2 = 2 +SEVERITY_NUMBER_TRACE3 = 3 +SEVERITY_NUMBER_TRACE4 = 4 +SEVERITY_NUMBER_DEBUG = 5 +SEVERITY_NUMBER_DEBUG2 = 6 +SEVERITY_NUMBER_DEBUG3 = 7 +SEVERITY_NUMBER_DEBUG4 = 8 +SEVERITY_NUMBER_INFO = 9 +SEVERITY_NUMBER_INFO2 = 10 +SEVERITY_NUMBER_INFO3 = 11 +SEVERITY_NUMBER_INFO4 = 12 +SEVERITY_NUMBER_WARN = 13 +SEVERITY_NUMBER_WARN2 = 14 +SEVERITY_NUMBER_WARN3 = 15 +SEVERITY_NUMBER_WARN4 = 16 +SEVERITY_NUMBER_ERROR = 17 +SEVERITY_NUMBER_ERROR2 = 18 +SEVERITY_NUMBER_ERROR3 = 19 +SEVERITY_NUMBER_ERROR4 = 20 +SEVERITY_NUMBER_FATAL = 21 +SEVERITY_NUMBER_FATAL2 = 22 +SEVERITY_NUMBER_FATAL3 = 23 +SEVERITY_NUMBER_FATAL4 = 24 +LOG_RECORD_FLAG_UNSPECIFIED = 0 +LOG_RECORD_FLAG_TRACE_FLAGS_MASK = 255 + + +_LOGSDATA = DESCRIPTOR.message_types_by_name['LogsData'] +_RESOURCELOGS = DESCRIPTOR.message_types_by_name['ResourceLogs'] +_SCOPELOGS = DESCRIPTOR.message_types_by_name['ScopeLogs'] +_INSTRUMENTATIONLIBRARYLOGS = DESCRIPTOR.message_types_by_name['InstrumentationLibraryLogs'] +_LOGRECORD = DESCRIPTOR.message_types_by_name['LogRecord'] +LogsData = _reflection.GeneratedProtocolMessageType('LogsData', (_message.Message,), { + 'DESCRIPTOR' : _LOGSDATA, + '__module__' : 'opentelemetry.proto.logs.v1.logs_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.logs.v1.LogsData) + }) +_sym_db.RegisterMessage(LogsData) + +ResourceLogs = _reflection.GeneratedProtocolMessageType('ResourceLogs', (_message.Message,), { + 'DESCRIPTOR' : _RESOURCELOGS, + '__module__' : 'opentelemetry.proto.logs.v1.logs_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.logs.v1.ResourceLogs) + }) +_sym_db.RegisterMessage(ResourceLogs) + +ScopeLogs = _reflection.GeneratedProtocolMessageType('ScopeLogs', (_message.Message,), { + 'DESCRIPTOR' : _SCOPELOGS, + '__module__' : 'opentelemetry.proto.logs.v1.logs_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.logs.v1.ScopeLogs) + }) +_sym_db.RegisterMessage(ScopeLogs) + +InstrumentationLibraryLogs = _reflection.GeneratedProtocolMessageType('InstrumentationLibraryLogs', (_message.Message,), { + 'DESCRIPTOR' : _INSTRUMENTATIONLIBRARYLOGS, + '__module__' : 'opentelemetry.proto.logs.v1.logs_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.logs.v1.InstrumentationLibraryLogs) + }) +_sym_db.RegisterMessage(InstrumentationLibraryLogs) + +LogRecord = _reflection.GeneratedProtocolMessageType('LogRecord', (_message.Message,), { + 'DESCRIPTOR' : _LOGRECORD, + '__module__' : 'opentelemetry.proto.logs.v1.logs_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.logs.v1.LogRecord) + }) +_sym_db.RegisterMessage(LogRecord) + +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\036io.opentelemetry.proto.logs.v1B\tLogsProtoP\001Z&go.opentelemetry.io/proto/otlp/logs/v1' + _RESOURCELOGS.fields_by_name['instrumentation_library_logs']._options = None + _RESOURCELOGS.fields_by_name['instrumentation_library_logs']._serialized_options = b'\030\001' + _INSTRUMENTATIONLIBRARYLOGS._options = None + _INSTRUMENTATIONLIBRARYLOGS._serialized_options = b'\030\001' + _SEVERITYNUMBER._serialized_start=1237 + _SEVERITYNUMBER._serialized_end=1944 + _LOGRECORDFLAGS._serialized_start=1946 + _LOGRECORDFLAGS._serialized_end=2034 + _LOGSDATA._serialized_start=163 + _LOGSDATA._serialized_end=239 + _RESOURCELOGS._serialized_start=242 + _RESOURCELOGS._serialized_end=497 + _SCOPELOGS._serialized_start=500 + _SCOPELOGS._serialized_end=660 + _INSTRUMENTATIONLIBRARYLOGS._serialized_start=663 + _INSTRUMENTATIONLIBRARYLOGS._serialized_end=864 + _LOGRECORD._serialized_start=867 + _LOGRECORD._serialized_end=1234 +# @@protoc_insertion_point(module_scope) diff --git a/newrelic/packages/opentelemetry_proto/metrics_pb2.py b/newrelic/packages/opentelemetry_proto/metrics_pb2.py new file mode 100644 index 000000000..dea77c7de --- /dev/null +++ b/newrelic/packages/opentelemetry_proto/metrics_pb2.py @@ -0,0 +1,217 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: opentelemetry/proto/metrics/v1/metrics.proto +"""Generated protocol buffer code.""" +from google.protobuf.internal import enum_type_wrapper +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from . import common_pb2 as opentelemetry_dot_proto_dot_common_dot_v1_dot_common__pb2 +from . import resource_pb2 as opentelemetry_dot_proto_dot_resource_dot_v1_dot_resource__pb2 + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,opentelemetry/proto/metrics/v1/metrics.proto\x12\x1eopentelemetry.proto.metrics.v1\x1a*opentelemetry/proto/common/v1/common.proto\x1a.opentelemetry/proto/resource/v1/resource.proto\"X\n\x0bMetricsData\x12I\n\x10resource_metrics\x18\x01 \x03(\x0b\x32/.opentelemetry.proto.metrics.v1.ResourceMetrics\"\x94\x02\n\x0fResourceMetrics\x12;\n\x08resource\x18\x01 \x01(\x0b\x32).opentelemetry.proto.resource.v1.Resource\x12\x43\n\rscope_metrics\x18\x02 \x03(\x0b\x32,.opentelemetry.proto.metrics.v1.ScopeMetrics\x12k\n\x1finstrumentation_library_metrics\x18\xe8\x07 \x03(\x0b\x32=.opentelemetry.proto.metrics.v1.InstrumentationLibraryMetricsB\x02\x18\x01\x12\x12\n\nschema_url\x18\x03 \x01(\t\"\x9f\x01\n\x0cScopeMetrics\x12\x42\n\x05scope\x18\x01 \x01(\x0b\x32\x33.opentelemetry.proto.common.v1.InstrumentationScope\x12\x37\n\x07metrics\x18\x02 \x03(\x0b\x32&.opentelemetry.proto.metrics.v1.Metric\x12\x12\n\nschema_url\x18\x03 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\x03(\x0b\x32\'.opentelemetry.proto.common.v1.KeyValue\x12\x16\n\x0etime_unix_nano\x18\x02 \x01(\x06\x12\x13\n\tas_double\x18\x03 \x01(\x01H\x00\x12\x10\n\x06\x61s_int\x18\x06 \x01(\x10H\x00\x12\x0f\n\x07span_id\x18\x04 \x01(\x0c\x12\x10\n\x08trace_id\x18\x05 \x01(\x0c\x42\x07\n\x05valueJ\x04\x08\x01\x10\x02*\x8c\x01\n\x16\x41ggregationTemporality\x12\'\n#AGGREGATION_TEMPORALITY_UNSPECIFIED\x10\x00\x12!\n\x1d\x41GGREGATION_TEMPORALITY_DELTA\x10\x01\x12&\n\"AGGREGATION_TEMPORALITY_CUMULATIVE\x10\x02*;\n\x0e\x44\x61taPointFlags\x12\r\n\tFLAG_NONE\x10\x00\x12\x1a\n\x16\x46LAG_NO_RECORDED_VALUE\x10\x01\x42^\n!io.opentelemetry.proto.metrics.v1B\x0cMetricsProtoP\x01Z)go.opentelemetry.io/proto/otlp/metrics/v1b\x06proto3') + +_AGGREGATIONTEMPORALITY = DESCRIPTOR.enum_types_by_name['AggregationTemporality'] +AggregationTemporality = enum_type_wrapper.EnumTypeWrapper(_AGGREGATIONTEMPORALITY) +_DATAPOINTFLAGS = DESCRIPTOR.enum_types_by_name['DataPointFlags'] +DataPointFlags = enum_type_wrapper.EnumTypeWrapper(_DATAPOINTFLAGS) +AGGREGATION_TEMPORALITY_UNSPECIFIED = 0 +AGGREGATION_TEMPORALITY_DELTA = 1 +AGGREGATION_TEMPORALITY_CUMULATIVE = 2 +FLAG_NONE = 0 +FLAG_NO_RECORDED_VALUE = 1 + + +_METRICSDATA = DESCRIPTOR.message_types_by_name['MetricsData'] +_RESOURCEMETRICS = DESCRIPTOR.message_types_by_name['ResourceMetrics'] +_SCOPEMETRICS = DESCRIPTOR.message_types_by_name['ScopeMetrics'] +_INSTRUMENTATIONLIBRARYMETRICS = DESCRIPTOR.message_types_by_name['InstrumentationLibraryMetrics'] +_METRIC = DESCRIPTOR.message_types_by_name['Metric'] +_GAUGE = DESCRIPTOR.message_types_by_name['Gauge'] +_SUM = DESCRIPTOR.message_types_by_name['Sum'] +_HISTOGRAM = DESCRIPTOR.message_types_by_name['Histogram'] +_EXPONENTIALHISTOGRAM = DESCRIPTOR.message_types_by_name['ExponentialHistogram'] +_SUMMARY = DESCRIPTOR.message_types_by_name['Summary'] +_NUMBERDATAPOINT = DESCRIPTOR.message_types_by_name['NumberDataPoint'] +_HISTOGRAMDATAPOINT = DESCRIPTOR.message_types_by_name['HistogramDataPoint'] +_EXPONENTIALHISTOGRAMDATAPOINT = DESCRIPTOR.message_types_by_name['ExponentialHistogramDataPoint'] +_EXPONENTIALHISTOGRAMDATAPOINT_BUCKETS = _EXPONENTIALHISTOGRAMDATAPOINT.nested_types_by_name['Buckets'] +_SUMMARYDATAPOINT = DESCRIPTOR.message_types_by_name['SummaryDataPoint'] +_SUMMARYDATAPOINT_VALUEATQUANTILE = _SUMMARYDATAPOINT.nested_types_by_name['ValueAtQuantile'] +_EXEMPLAR = DESCRIPTOR.message_types_by_name['Exemplar'] +MetricsData = _reflection.GeneratedProtocolMessageType('MetricsData', (_message.Message,), { + 'DESCRIPTOR' : _METRICSDATA, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.MetricsData) + }) +_sym_db.RegisterMessage(MetricsData) + +ResourceMetrics = _reflection.GeneratedProtocolMessageType('ResourceMetrics', (_message.Message,), { + 'DESCRIPTOR' : _RESOURCEMETRICS, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.ResourceMetrics) + }) +_sym_db.RegisterMessage(ResourceMetrics) + +ScopeMetrics = _reflection.GeneratedProtocolMessageType('ScopeMetrics', (_message.Message,), { + 'DESCRIPTOR' : _SCOPEMETRICS, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.ScopeMetrics) + }) +_sym_db.RegisterMessage(ScopeMetrics) + +InstrumentationLibraryMetrics = _reflection.GeneratedProtocolMessageType('InstrumentationLibraryMetrics', (_message.Message,), { + 'DESCRIPTOR' : _INSTRUMENTATIONLIBRARYMETRICS, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.InstrumentationLibraryMetrics) + }) +_sym_db.RegisterMessage(InstrumentationLibraryMetrics) + +Metric = _reflection.GeneratedProtocolMessageType('Metric', (_message.Message,), { + 'DESCRIPTOR' : _METRIC, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Metric) + }) +_sym_db.RegisterMessage(Metric) + +Gauge = _reflection.GeneratedProtocolMessageType('Gauge', (_message.Message,), { + 'DESCRIPTOR' : _GAUGE, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Gauge) + }) +_sym_db.RegisterMessage(Gauge) + +Sum = _reflection.GeneratedProtocolMessageType('Sum', (_message.Message,), { + 'DESCRIPTOR' : _SUM, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Sum) + }) +_sym_db.RegisterMessage(Sum) + +Histogram = _reflection.GeneratedProtocolMessageType('Histogram', (_message.Message,), { + 'DESCRIPTOR' : _HISTOGRAM, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Histogram) + }) +_sym_db.RegisterMessage(Histogram) + +ExponentialHistogram = _reflection.GeneratedProtocolMessageType('ExponentialHistogram', (_message.Message,), { + 'DESCRIPTOR' : _EXPONENTIALHISTOGRAM, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.ExponentialHistogram) + }) +_sym_db.RegisterMessage(ExponentialHistogram) + +Summary = _reflection.GeneratedProtocolMessageType('Summary', (_message.Message,), { + 'DESCRIPTOR' : _SUMMARY, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Summary) + }) +_sym_db.RegisterMessage(Summary) + +NumberDataPoint = _reflection.GeneratedProtocolMessageType('NumberDataPoint', (_message.Message,), { + 'DESCRIPTOR' : _NUMBERDATAPOINT, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.NumberDataPoint) + }) +_sym_db.RegisterMessage(NumberDataPoint) + +HistogramDataPoint = _reflection.GeneratedProtocolMessageType('HistogramDataPoint', (_message.Message,), { + 'DESCRIPTOR' : _HISTOGRAMDATAPOINT, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.HistogramDataPoint) + }) +_sym_db.RegisterMessage(HistogramDataPoint) + +ExponentialHistogramDataPoint = _reflection.GeneratedProtocolMessageType('ExponentialHistogramDataPoint', (_message.Message,), { + + 'Buckets' : _reflection.GeneratedProtocolMessageType('Buckets', (_message.Message,), { + 'DESCRIPTOR' : _EXPONENTIALHISTOGRAMDATAPOINT_BUCKETS, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint.Buckets) + }) + , + 'DESCRIPTOR' : _EXPONENTIALHISTOGRAMDATAPOINT, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.ExponentialHistogramDataPoint) + }) +_sym_db.RegisterMessage(ExponentialHistogramDataPoint) +_sym_db.RegisterMessage(ExponentialHistogramDataPoint.Buckets) + +SummaryDataPoint = _reflection.GeneratedProtocolMessageType('SummaryDataPoint', (_message.Message,), { + + 'ValueAtQuantile' : _reflection.GeneratedProtocolMessageType('ValueAtQuantile', (_message.Message,), { + 'DESCRIPTOR' : _SUMMARYDATAPOINT_VALUEATQUANTILE, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.SummaryDataPoint.ValueAtQuantile) + }) + , + 'DESCRIPTOR' : _SUMMARYDATAPOINT, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.SummaryDataPoint) + }) +_sym_db.RegisterMessage(SummaryDataPoint) +_sym_db.RegisterMessage(SummaryDataPoint.ValueAtQuantile) + +Exemplar = _reflection.GeneratedProtocolMessageType('Exemplar', (_message.Message,), { + 'DESCRIPTOR' : _EXEMPLAR, + '__module__' : 'opentelemetry.proto.metrics.v1.metrics_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.metrics.v1.Exemplar) + }) +_sym_db.RegisterMessage(Exemplar) + +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n!io.opentelemetry.proto.metrics.v1B\014MetricsProtoP\001Z)go.opentelemetry.io/proto/otlp/metrics/v1' + _RESOURCEMETRICS.fields_by_name['instrumentation_library_metrics']._options = None + _RESOURCEMETRICS.fields_by_name['instrumentation_library_metrics']._serialized_options = b'\030\001' + _INSTRUMENTATIONLIBRARYMETRICS._options = None + _INSTRUMENTATIONLIBRARYMETRICS._serialized_options = b'\030\001' + _AGGREGATIONTEMPORALITY._serialized_start=3754 + _AGGREGATIONTEMPORALITY._serialized_end=3894 + _DATAPOINTFLAGS._serialized_start=3896 + _DATAPOINTFLAGS._serialized_end=3955 + _METRICSDATA._serialized_start=172 + _METRICSDATA._serialized_end=260 + _RESOURCEMETRICS._serialized_start=263 + _RESOURCEMETRICS._serialized_end=539 + _SCOPEMETRICS._serialized_start=542 + _SCOPEMETRICS._serialized_end=701 + _INSTRUMENTATIONLIBRARYMETRICS._serialized_start=704 + _INSTRUMENTATIONLIBRARYMETRICS._serialized_end=904 + _METRIC._serialized_start=907 + _METRIC._serialized_end=1309 + _GAUGE._serialized_start=1311 + _GAUGE._serialized_end=1388 + _SUM._serialized_start=1391 + _SUM._serialized_end=1577 + _HISTOGRAM._serialized_start=1580 + _HISTOGRAM._serialized_end=1753 + _EXPONENTIALHISTOGRAM._serialized_start=1756 + _EXPONENTIALHISTOGRAM._serialized_end=1951 + _SUMMARY._serialized_start=1953 + _SUMMARY._serialized_end=2033 + _NUMBERDATAPOINT._serialized_start=2036 + _NUMBERDATAPOINT._serialized_end=2298 + _HISTOGRAMDATAPOINT._serialized_start=2301 + _HISTOGRAMDATAPOINT._serialized_end=2659 + _EXPONENTIALHISTOGRAMDATAPOINT._serialized_start=2662 + _EXPONENTIALHISTOGRAMDATAPOINT._serialized_end=3227 + _EXPONENTIALHISTOGRAMDATAPOINT_BUCKETS._serialized_start=3163 + _EXPONENTIALHISTOGRAMDATAPOINT_BUCKETS._serialized_end=3211 + _SUMMARYDATAPOINT._serialized_start=3230 + _SUMMARYDATAPOINT._serialized_end=3555 + _SUMMARYDATAPOINT_VALUEATQUANTILE._serialized_start=3499 + _SUMMARYDATAPOINT_VALUEATQUANTILE._serialized_end=3549 + _EXEMPLAR._serialized_start=3558 + _EXEMPLAR._serialized_end=3751 +# @@protoc_insertion_point(module_scope) \ No newline at end of file diff --git a/newrelic/packages/opentelemetry_proto/resource_pb2.py b/newrelic/packages/opentelemetry_proto/resource_pb2.py new file mode 100644 index 000000000..8cc64e352 --- /dev/null +++ b/newrelic/packages/opentelemetry_proto/resource_pb2.py @@ -0,0 +1,36 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: opentelemetry/proto/resource/v1/resource.proto +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from . import common_pb2 as opentelemetry_dot_proto_dot_common_dot_v1_dot_common__pb2 + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n.opentelemetry/proto/resource/v1/resource.proto\x12\x1fopentelemetry.proto.resource.v1\x1a*opentelemetry/proto/common/v1/common.proto\"i\n\x08Resource\x12;\n\nattributes\x18\x01 \x03(\x0b\x32\'.opentelemetry.proto.common.v1.KeyValue\x12 \n\x18\x64ropped_attributes_count\x18\x02 \x01(\rBa\n\"io.opentelemetry.proto.resource.v1B\rResourceProtoP\x01Z*go.opentelemetry.io/proto/otlp/resource/v1b\x06proto3') + + + +_RESOURCE = DESCRIPTOR.message_types_by_name['Resource'] +Resource = _reflection.GeneratedProtocolMessageType('Resource', (_message.Message,), { + 'DESCRIPTOR' : _RESOURCE, + '__module__' : 'opentelemetry.proto.resource.v1.resource_pb2' + # @@protoc_insertion_point(class_scope:opentelemetry.proto.resource.v1.Resource) + }) +_sym_db.RegisterMessage(Resource) + +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + DESCRIPTOR._serialized_options = b'\n\"io.opentelemetry.proto.resource.v1B\rResourceProtoP\001Z*go.opentelemetry.io/proto/otlp/resource/v1' + _RESOURCE._serialized_start=127 + _RESOURCE._serialized_end=232 +# @@protoc_insertion_point(module_scope) diff --git a/setup.py b/setup.py index 2b1e5191e..ed8dbfb84 100644 --- a/setup.py +++ b/setup.py @@ -111,6 +111,7 @@ def build_extension(self, ext): "newrelic/packages/urllib3/packages", "newrelic/packages/urllib3/packages/backports", "newrelic/packages/wrapt", + "newrelic/packages/opentelemetry_proto", "newrelic.samplers", ] diff --git a/tests/agent_features/conftest.py b/tests/agent_features/conftest.py index 57263238b..bd6aa6c2a 100644 --- a/tests/agent_features/conftest.py +++ b/tests/agent_features/conftest.py @@ -30,6 +30,7 @@ "debug.record_transaction_failure": True, "debug.log_autorum_middleware": True, "agent_limits.errors_per_harvest": 100, + "ml_insights_events.enabled": True } collector_agent_registration = collector_agent_registration_fixture( diff --git a/tests/agent_features/test_configuration.py b/tests/agent_features/test_configuration.py index 79f2a41f1..1a311e693 100644 --- a/tests/agent_features/test_configuration.py +++ b/tests/agent_features/test_configuration.py @@ -591,6 +591,8 @@ def test_translate_deprecated_ignored_params_with_new_setting(): ("agent_run_id", None), ("entity_guid", None), ("distributed_tracing.exclude_newrelic_header", False), + ("otlp_host", "otlp.nr-data.net"), + ("otlp_port", 0), ), ) def test_default_values(name, expected_value): diff --git a/tests/agent_features/test_dimensional_metrics.py b/tests/agent_features/test_dimensional_metrics.py new file mode 100644 index 000000000..ef9e98418 --- /dev/null +++ b/tests/agent_features/test_dimensional_metrics.py @@ -0,0 +1,224 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.fixtures import reset_core_stats_engine +from testing_support.validators.validate_dimensional_metric_payload import ( + validate_dimensional_metric_payload, +) +from testing_support.validators.validate_dimensional_metrics_outside_transaction import ( + validate_dimensional_metrics_outside_transaction, +) +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +import newrelic.core.otlp_utils +from newrelic.api.application import application_instance +from newrelic.api.background_task import background_task +from newrelic.api.transaction import ( + record_dimensional_metric, + record_dimensional_metrics, +) +from newrelic.common.metric_utils import create_metric_identity +from newrelic.core.config import global_settings +from newrelic.packages import six + +try: + # python 2.x + reload +except NameError: + # python 3.x + from importlib import reload + + +@pytest.fixture(scope="module", autouse=True, params=["protobuf", "json"]) +def otlp_content_encoding(request): + if six.PY2 and request.param == "protobuf": + pytest.skip("OTLP protos are not compatible with Python 2.") + + _settings = global_settings() + prev = _settings.debug.otlp_content_encoding + _settings.debug.otlp_content_encoding = request.param + reload(newrelic.core.otlp_utils) + assert newrelic.core.otlp_utils.otlp_content_setting == request.param, "Content encoding mismatch." + + yield + + _settings.debug.otlp_content_encoding = prev + + +_test_tags_examples = [ + (None, None), + ({}, None), + ([], None), + ({"str": "a"}, frozenset({("str", "a")})), + ({"int": 1}, frozenset({("int", 1)})), + ({"float": 1.0}, frozenset({("float", 1.0)})), + ({"bool": True}, frozenset({("bool", True)})), + ({"list": [1]}, frozenset({("list", "[1]")})), + ({"dict": {"subtag": 1}}, frozenset({("dict", "{'subtag': 1}")})), + ([("tags-as-list", 1)], frozenset({("tags-as-list", 1)})), +] + + +@pytest.mark.parametrize("tags,expected", _test_tags_examples) +def test_create_metric_identity(tags, expected): + name = "Metric" + output_name, output_tags = create_metric_identity(name, tags=tags) + assert output_name == name, "Name does not match." + assert output_tags == expected, "Output tags do not match." + + +@pytest.mark.parametrize("tags,expected", _test_tags_examples) +@reset_core_stats_engine() +def test_record_dimensional_metric_inside_transaction(tags, expected): + @validate_transaction_metrics( + "test_record_dimensional_metric_inside_transaction", + background_task=True, + dimensional_metrics=[ + ("Metric", expected, 1), + ], + ) + @background_task(name="test_record_dimensional_metric_inside_transaction") + def _test(): + record_dimensional_metric("Metric", 1, tags=tags) + + _test() + + +@pytest.mark.parametrize("tags,expected", _test_tags_examples) +@reset_core_stats_engine() +def test_record_dimensional_metric_outside_transaction(tags, expected): + @validate_dimensional_metrics_outside_transaction([("Metric", expected, 1)]) + def _test(): + app = application_instance() + record_dimensional_metric("Metric", 1, tags=tags, application=app) + + _test() + + +@pytest.mark.parametrize("tags,expected", _test_tags_examples) +@reset_core_stats_engine() +def test_record_dimensional_metrics_inside_transaction(tags, expected): + @validate_transaction_metrics( + "test_record_dimensional_metrics_inside_transaction", + background_task=True, + dimensional_metrics=[("Metric.1", expected, 1), ("Metric.2", expected, 1)], + ) + @background_task(name="test_record_dimensional_metrics_inside_transaction") + def _test(): + record_dimensional_metrics([("Metric.1", 1, tags), ("Metric.2", 1, tags)]) + + _test() + + +@pytest.mark.parametrize("tags,expected", _test_tags_examples) +@reset_core_stats_engine() +def test_record_dimensional_metrics_outside_transaction(tags, expected): + @validate_dimensional_metrics_outside_transaction([("Metric.1", expected, 1), ("Metric.2", expected, 1)]) + def _test(): + app = application_instance() + record_dimensional_metrics([("Metric.1", 1, tags), ("Metric.2", 1, tags)], application=app) + + _test() + + +@reset_core_stats_engine() +def test_dimensional_metrics_different_tags(): + @validate_transaction_metrics( + "test_dimensional_metrics_different_tags", + background_task=True, + dimensional_metrics=[ + ("Metric", frozenset({("tag", 1)}), 1), + ("Metric", frozenset({("tag", 2)}), 2), + ], + ) + @background_task(name="test_dimensional_metrics_different_tags") + def _test(): + record_dimensional_metrics( + [ + ("Metric", 1, {"tag": 1}), + ("Metric", 1, {"tag": 2}), + ] + ) + record_dimensional_metric("Metric", 1, {"tag": 2}) + + _test() + + +@reset_core_stats_engine() +@validate_dimensional_metric_payload( + summary_metrics=[ + ("Metric.Summary", {"tag": 1}, 1), + ("Metric.Summary", {"tag": 2}, 1), + ("Metric.Summary", None, 1), + ("Metric.Mixed", {"tag": 1}, 1), + ("Metric.NotPresent", None, None), + ], + count_metrics=[ + ("Metric.Count", {"tag": 1}, 1), + ("Metric.Count", {"tag": 2}, 2), + ("Metric.Count", None, 3), + ("Metric.Mixed", {"tag": 2}, 2), + ("Metric.NotPresent", None, None), + ], +) +def test_dimensional_metrics_payload(): + @background_task(name="test_dimensional_metric_payload") + def _test(): + record_dimensional_metrics( + [ + ("Metric.Summary", 1, {"tag": 1}), + ("Metric.Summary", 2, {"tag": 2}), + ("Metric.Summary", 3), # No tags + ("Metric.Count", {"count": 1}, {"tag": 1}), + ("Metric.Count", {"count": 2}, {"tag": 2}), + ("Metric.Count", {"count": 3}), # No tags + ("Metric.Mixed", 1, {"tag": 1}), + ("Metric.Mixed", {"count": 2}, {"tag": 2}), + ] + ) + + _test() + app = application_instance() + core_app = app._agent.application(app.name) + core_app.harvest() + + +@reset_core_stats_engine() +@validate_dimensional_metric_payload( + summary_metrics=[ + ("Metric.Summary", None, 1), + ("Metric.Count", None, None), # Should NOT be present + ], + count_metrics=[ + ("Metric.Count", None, 1), + ("Metric.Summary", None, None), # Should NOT be present + ], +) +def test_dimensional_metrics_no_duplicate_encodings(): + @background_task(name="test_dimensional_metric_payload") + def _test(): + record_dimensional_metrics( + [ + ("Metric.Summary", 1), + ("Metric.Count", {"count": 1}), + ] + ) + + _test() + app = application_instance() + core_app = app._agent.application(app.name) + core_app.harvest() diff --git a/tests/agent_features/test_high_security_mode.py b/tests/agent_features/test_high_security_mode.py index 20d997837..d2ded9308 100644 --- a/tests/agent_features/test_high_security_mode.py +++ b/tests/agent_features/test_high_security_mode.py @@ -79,8 +79,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "high_security": False, @@ -88,8 +90,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "high_security": False, @@ -97,8 +101,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": False, @@ -106,8 +112,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "off", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "message_tracer.segment_parameters_enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, ] @@ -118,8 +126,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": True, @@ -127,8 +137,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": True, @@ -136,8 +148,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": True, @@ -145,8 +159,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "high_security": True, @@ -154,8 +170,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "high_security": True, @@ -163,8 +181,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "off", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "message_tracer.segment_parameters_enabled": False, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "high_security": True, @@ -172,8 +192,10 @@ def test_hsm_configuration_default(): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "message_tracer.segment_parameters_enabled": False, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, ] @@ -196,8 +218,10 @@ def test_local_config_file_override_hsm_disabled(settings): original_record_sql = settings.transaction_tracer.record_sql original_strip_messages = settings.strip_exception_messages.enabled original_custom_events = settings.custom_insights_events.enabled + original_ml_events = settings.ml_insights_events.enabled original_message_segment_params_enabled = settings.message_tracer.segment_parameters_enabled original_application_logging_forwarding_enabled = settings.application_logging.forwarding.enabled + original_machine_learning_inference_event_value_enabled = settings.machine_learning.inference_events_value.enabled apply_local_high_security_mode_setting(settings) @@ -205,8 +229,13 @@ def test_local_config_file_override_hsm_disabled(settings): assert settings.transaction_tracer.record_sql == original_record_sql assert settings.strip_exception_messages.enabled == original_strip_messages assert settings.custom_insights_events.enabled == original_custom_events + assert settings.ml_insights_events.enabled == original_ml_events assert settings.message_tracer.segment_parameters_enabled == original_message_segment_params_enabled assert settings.application_logging.forwarding.enabled == original_application_logging_forwarding_enabled + assert ( + settings.machine_learning.inference_events_value.enabled + == original_machine_learning_inference_event_value_enabled + ) @parameterize_hsm_local_config(_hsm_local_config_file_settings_enabled) @@ -217,8 +246,10 @@ def test_local_config_file_override_hsm_enabled(settings): assert settings.transaction_tracer.record_sql in ("off", "obfuscated") assert settings.strip_exception_messages.enabled assert settings.custom_insights_events.enabled is False + assert settings.ml_insights_events.enabled is False assert settings.message_tracer.segment_parameters_enabled is False assert settings.application_logging.forwarding.enabled is False + assert settings.machine_learning.inference_events_value.enabled is False _server_side_config_settings_hsm_disabled = [ @@ -229,7 +260,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "agent_config": { @@ -237,7 +270,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, }, ), @@ -248,7 +283,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, { "agent_config": { @@ -256,7 +293,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "off", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, }, ), @@ -270,7 +309,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": True, @@ -278,13 +319,17 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, "agent_config": { "capture_params": False, "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, }, ), @@ -295,7 +340,9 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, }, { "high_security": True, @@ -303,13 +350,17 @@ def test_local_config_file_override_hsm_enabled(settings): "transaction_tracer.record_sql": "obfuscated", "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, "application_logging.forwarding.enabled": False, + "machine_learning.inference_events_value.enabled": False, "agent_config": { "capture_params": True, "transaction_tracer.record_sql": "raw", "strip_exception_messages.enabled": False, "custom_insights_events.enabled": True, + "ml_insights_events.enabled": True, "application_logging.forwarding.enabled": True, + "machine_learning.inference_events_value.enabled": True, }, }, ), @@ -329,7 +380,9 @@ def test_remote_config_fixups_hsm_disabled(local_settings, server_settings): original_record_sql = agent_config["transaction_tracer.record_sql"] original_strip_messages = agent_config["strip_exception_messages.enabled"] original_custom_events = agent_config["custom_insights_events.enabled"] + original_ml_events = agent_config["ml_insights_events.enabled"] original_log_forwarding = agent_config["application_logging.forwarding.enabled"] + original_machine_learning_events = agent_config["machine_learning.inference_events_value.enabled"] _settings = global_settings() settings = override_generic_settings(_settings, local_settings)(AgentProtocol._apply_high_security_mode_fixups)( @@ -344,7 +397,9 @@ def test_remote_config_fixups_hsm_disabled(local_settings, server_settings): assert agent_config["transaction_tracer.record_sql"] == original_record_sql assert agent_config["strip_exception_messages.enabled"] == original_strip_messages assert agent_config["custom_insights_events.enabled"] == original_custom_events + assert agent_config["ml_insights_events.enabled"] == original_ml_events assert agent_config["application_logging.forwarding.enabled"] == original_log_forwarding + assert agent_config["machine_learning.inference_events_value.enabled"] == original_machine_learning_events @pytest.mark.parametrize("local_settings,server_settings", _server_side_config_settings_hsm_enabled) @@ -366,13 +421,17 @@ def test_remote_config_fixups_hsm_enabled(local_settings, server_settings): assert "transaction_tracer.record_sql" not in settings assert "strip_exception_messages.enabled" not in settings assert "custom_insights_events.enabled" not in settings + assert "ml_insights_events.enabled" not in settings assert "application_logging.forwarding.enabled" not in settings + assert "machine_learning.inference_events_value.enabled" not in settings assert "capture_params" not in agent_config assert "transaction_tracer.record_sql" not in agent_config assert "strip_exception_messages.enabled" not in agent_config assert "custom_insights_events.enabled" not in agent_config + assert "ml_insights_events.enabled" not in agent_config assert "application_logging.forwarding.enabled" not in agent_config + assert "machine_learning.inference_events_value.enabled" not in agent_config def test_remote_config_hsm_fixups_server_side_disabled(): @@ -397,6 +456,7 @@ def test_remote_config_hsm_fixups_server_side_disabled(): "high_security": True, "strip_exception_messages.enabled": True, "custom_insights_events.enabled": False, + "ml_insights_events.enabled": False, } diff --git a/tests/agent_features/test_metric_normalization.py b/tests/agent_features/test_metric_normalization.py new file mode 100644 index 000000000..65f2903ae --- /dev/null +++ b/tests/agent_features/test_metric_normalization.py @@ -0,0 +1,78 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.fixtures import reset_core_stats_engine +from testing_support.validators.validate_dimensional_metric_payload import ( + validate_dimensional_metric_payload, +) +from testing_support.validators.validate_metric_payload import validate_metric_payload + +from newrelic.api.application import application_instance +from newrelic.api.background_task import background_task +from newrelic.api.transaction import record_custom_metric, record_dimensional_metric +from newrelic.core.rules_engine import NormalizationRule, RulesEngine + +RULES = [{"match_expression": "(replace)", "replacement": "expected", "ignore": False, "eval_order": 0}] +EXPECTED_TAGS = frozenset({"tag": 1}.items()) + + +def _prepare_rules(test_rules): + # ensure all keys are present, if not present set to an empty string + for rule in test_rules: + for key in NormalizationRule._fields: + rule[key] = rule.get(key, "") + return test_rules + + +@pytest.fixture(scope="session") +def core_app(collector_agent_registration): + app = collector_agent_registration + return app._agent.application(app.name) + + +@pytest.fixture(scope="function") +def rules_engine_fixture(core_app): + rules_engine = core_app._rules_engine + previous_rules = rules_engine["metric"] + + rules_engine["metric"] = RulesEngine(_prepare_rules(RULES)) + yield + rules_engine["metric"] = previous_rules # Restore after test run + + +@validate_dimensional_metric_payload(summary_metrics=[("Metric/expected", EXPECTED_TAGS, 1)]) +@validate_metric_payload([("Metric/expected", 1)]) +@reset_core_stats_engine() +def test_metric_normalization_inside_transaction(core_app, rules_engine_fixture): + @background_task(name="test_record_dimensional_metric_inside_transaction") + def _test(): + record_dimensional_metric("Metric/replace", 1, tags={"tag": 1}) + record_custom_metric("Metric/replace", 1) + + _test() + core_app.harvest() + + +@validate_dimensional_metric_payload(summary_metrics=[("Metric/expected", EXPECTED_TAGS, 1)]) +@validate_metric_payload([("Metric/expected", 1)]) +@reset_core_stats_engine() +def test_metric_normalization_outside_transaction(core_app, rules_engine_fixture): + def _test(): + app = application_instance() + record_dimensional_metric("Metric/replace", 1, tags={"tag": 1}, application=app) + record_custom_metric("Metric/replace", 1, application=app) + + _test() + core_app.harvest() diff --git a/tests/agent_features/test_ml_events.py b/tests/agent_features/test_ml_events.py new file mode 100644 index 000000000..5720224bb --- /dev/null +++ b/tests/agent_features/test_ml_events.py @@ -0,0 +1,199 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import time + +import pytest +from testing_support.fixtures import ( # function_not_called,; override_application_settings, + function_not_called, + override_application_settings, + reset_core_stats_engine, +) +from testing_support.validators.validate_ml_event_count import validate_ml_event_count +from testing_support.validators.validate_ml_event_payload import ( + validate_ml_event_payload, +) +from testing_support.validators.validate_ml_events import validate_ml_events +from testing_support.validators.validate_ml_events_outside_transaction import ( + validate_ml_events_outside_transaction, +) + +import newrelic.core.otlp_utils +from newrelic.api.application import application_instance as application +from newrelic.api.background_task import background_task +from newrelic.api.transaction import record_ml_event +from newrelic.core.config import global_settings +from newrelic.packages import six + +try: + # python 2.x + reload +except NameError: + # python 3.x + from importlib import reload + +_now = time.time() + +_intrinsics = { + "type": "LabelEvent", + "timestamp": _now, +} + + +@pytest.fixture(scope="session") +def core_app(collector_agent_registration): + app = collector_agent_registration + return app._agent.application(app.name) + + +@validate_ml_event_payload( + [{"foo": "bar", "real_agent_id": "1234567", "event.domain": "newrelic.ml_events", "event.name": "InferenceEvent"}] +) +@reset_core_stats_engine() +def test_ml_event_payload_inside_transaction(core_app): + @background_task(name="test_ml_event_payload_inside_transaction") + def _test(): + record_ml_event("InferenceEvent", {"foo": "bar"}) + + _test() + core_app.harvest() + + +@validate_ml_event_payload( + [{"foo": "bar", "real_agent_id": "1234567", "event.domain": "newrelic.ml_events", "event.name": "InferenceEvent"}] +) +@reset_core_stats_engine() +def test_ml_event_payload_outside_transaction(core_app): + def _test(): + app = application() + record_ml_event("InferenceEvent", {"foo": "bar"}, application=app) + + _test() + core_app.harvest() + + +@pytest.mark.parametrize( + "params,expected", + [ + ({"foo": "bar"}, [(_intrinsics, {"foo": "bar"})]), + ({"foo": "bar", 123: "bad key"}, [(_intrinsics, {"foo": "bar"})]), + ({"foo": "bar", "*" * 256: "too long"}, [(_intrinsics, {"foo": "bar"})]), + ], + ids=["Valid key/value", "Bad key", "Value too long"], +) +@reset_core_stats_engine() +def test_record_ml_event_inside_transaction(params, expected): + @validate_ml_events(expected) + @background_task() + def _test(): + record_ml_event("LabelEvent", params) + + _test() + + +@pytest.mark.parametrize( + "params,expected", + [ + ({"foo": "bar"}, [(_intrinsics, {"foo": "bar"})]), + ({"foo": "bar", 123: "bad key"}, [(_intrinsics, {"foo": "bar"})]), + ({"foo": "bar", "*" * 256: "too long"}, [(_intrinsics, {"foo": "bar"})]), + ], + ids=["Valid key/value", "Bad key", "Value too long"], +) +@reset_core_stats_engine() +def test_record_ml_event_outside_transaction(params, expected): + @validate_ml_events_outside_transaction(expected) + def _test(): + app = application() + record_ml_event("LabelEvent", params, application=app) + + _test() + + +@reset_core_stats_engine() +@validate_ml_event_count(count=0) +@background_task() +def test_record_ml_event_inside_transaction_bad_event_type(): + record_ml_event("!@#$%^&*()", {"foo": "bar"}) + + +@reset_core_stats_engine() +@validate_ml_event_count(count=0) +def test_record_ml_event_outside_transaction_bad_event_type(): + app = application() + record_ml_event("!@#$%^&*()", {"foo": "bar"}, application=app) + + +@reset_core_stats_engine() +@validate_ml_event_count(count=0) +@background_task() +def test_record_ml_event_inside_transaction_params_not_a_dict(): + record_ml_event("ParamsListEvent", ["not", "a", "dict"]) + + +@reset_core_stats_engine() +@validate_ml_event_count(count=0) +def test_record_ml_event_outside_transaction_params_not_a_dict(): + app = application() + record_ml_event("ParamsListEvent", ["not", "a", "dict"], application=app) + + +# Tests for ML Events configuration settings + +@override_application_settings({"ml_insights_events.enabled": False}) +@reset_core_stats_engine() +@validate_ml_event_count(count=0) +@background_task() +def test_ml_event_settings_check_ml_insights_disabled(): + record_ml_event("FooEvent", {"foo": "bar"}) + + +# Test that record_ml_event() methods will short-circuit. +# +# If the ml_insights_events setting is False, verify that the +# `create_ml_event()` function is not called, in order to avoid the +# event_type and attribute processing. + + +@override_application_settings({"ml_insights_events.enabled": False}) +@reset_core_stats_engine() +@function_not_called("newrelic.api.transaction", "create_custom_event") +@background_task() +def test_transaction_create_ml_event_not_called(): + record_ml_event("FooEvent", {"foo": "bar"}) + + +@override_application_settings({"ml_insights_events.enabled": False}) +@reset_core_stats_engine() +@function_not_called("newrelic.core.application", "create_custom_event") +@background_task() +def test_application_create_ml_event_not_called(): + app = application() + record_ml_event("FooEvent", {"foo": "bar"}, application=app) + + +@pytest.fixture(scope="module", autouse=True, params=["protobuf", "json"]) +def otlp_content_encoding(request): + if six.PY2 and request.param == "protobuf": + pytest.skip("OTLP protos are not compatible with Python 2.") + + _settings = global_settings() + prev = _settings.debug.otlp_content_encoding + _settings.debug.otlp_content_encoding = request.param + reload(newrelic.core.otlp_utils) + assert newrelic.core.otlp_utils.otlp_content_setting == request.param, "Content encoding mismatch." + + yield + + _settings.debug.otlp_content_encoding = prev diff --git a/tests/agent_unittests/test_harvest_loop.py b/tests/agent_unittests/test_harvest_loop.py index 305622107..15b67a81e 100644 --- a/tests/agent_unittests/test_harvest_loop.py +++ b/tests/agent_unittests/test_harvest_loop.py @@ -32,7 +32,7 @@ from newrelic.core.function_node import FunctionNode from newrelic.core.log_event_node import LogEventNode from newrelic.core.root_node import RootNode -from newrelic.core.stats_engine import CustomMetrics, SampledDataSet +from newrelic.core.stats_engine import CustomMetrics, SampledDataSet, DimensionalMetrics from newrelic.core.transaction_node import TransactionNode from newrelic.network.exceptions import RetryDataForRequest @@ -49,6 +49,11 @@ def transaction_node(request): event = create_custom_event("Custom", {}) custom_events.add(event) + ml_events = SampledDataSet(capacity=num_events) + for _ in range(num_events): + event = create_custom_event("Custom", {}) + ml_events.add(event) + log_events = SampledDataSet(capacity=num_events) for _ in range(num_events): event = LogEventNode(1653609717, "WARNING", "A", {}) @@ -122,10 +127,12 @@ def transaction_node(request): errors=errors, slow_sql=(), custom_events=custom_events, + ml_events=ml_events, log_events=log_events, apdex_t=0.5, suppress_apdex=False, custom_metrics=CustomMetrics(), + dimensional_metrics=DimensionalMetrics(), guid="4485b89db608aece", cpu_time=0.0, suppress_transaction_trace=False, @@ -818,6 +825,7 @@ def test_flexible_events_harvested(allowlist_event): app._stats_engine.log_events.add(LogEventNode(1653609717, "WARNING", "A", {})) app._stats_engine.span_events.add("span event") app._stats_engine.record_custom_metric("CustomMetric/Int", 1) + app._stats_engine.record_dimensional_metric("DimensionalMetric/Int", 1, tags={"tag": "tag"}) assert app._stats_engine.transaction_events.num_seen == 1 assert app._stats_engine.error_events.num_seen == 1 @@ -825,6 +833,7 @@ def test_flexible_events_harvested(allowlist_event): assert app._stats_engine.log_events.num_seen == 1 assert app._stats_engine.span_events.num_seen == 1 assert app._stats_engine.record_custom_metric("CustomMetric/Int", 1) + assert app._stats_engine.record_dimensional_metric("DimensionalMetric/Int", 1, tags={"tag": "tag"}) app.harvest(flexible=True) @@ -844,7 +853,8 @@ def test_flexible_events_harvested(allowlist_event): assert app._stats_engine.span_events.num_seen == num_seen assert ("CustomMetric/Int", "") in app._stats_engine.stats_table - assert app._stats_engine.metrics_count() > 1 + assert ("DimensionalMetric/Int", frozenset({("tag", "tag")})) in app._stats_engine.dimensional_stats_table + assert app._stats_engine.metrics_count() > 3 @pytest.mark.parametrize( diff --git a/tests/agent_unittests/test_utilization_settings.py b/tests/agent_unittests/test_utilization_settings.py index 8af4bcbf1..96cf47669 100644 --- a/tests/agent_unittests/test_utilization_settings.py +++ b/tests/agent_unittests/test_utilization_settings.py @@ -118,6 +118,22 @@ def reset(wrapped, instance, args, kwargs): return reset +@reset_agent_config(INI_FILE_WITHOUT_UTIL_CONF, ENV_WITHOUT_UTIL_CONF) +def test_otlp_host_port_default(): + settings = global_settings() + assert settings.otlp_host == "otlp.nr-data.net" + assert settings.otlp_port == 0 + + +@reset_agent_config( + INI_FILE_WITHOUT_UTIL_CONF, {"NEW_RELIC_OTLP_HOST": "custom-otlp.nr-data.net", "NEW_RELIC_OTLP_PORT": 443} +) +def test_otlp_port_override(): + settings = global_settings() + assert settings.otlp_host == "custom-otlp.nr-data.net" + assert settings.otlp_port == 443 + + @reset_agent_config(INI_FILE_WITHOUT_UTIL_CONF, ENV_WITHOUT_UTIL_CONF) def test_heroku_default(): settings = global_settings() diff --git a/tests/mlmodel_sklearn/conftest.py b/tests/mlmodel_sklearn/conftest.py new file mode 100644 index 000000000..d91eb549a --- /dev/null +++ b/tests/mlmodel_sklearn/conftest.py @@ -0,0 +1,34 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from testing_support.fixtures import ( # noqa: F401, pylint: disable=W0611 + collector_agent_registration_fixture, + collector_available_fixture, +) + +_default_settings = { + "transaction_tracer.explain_threshold": 0.0, + "transaction_tracer.transaction_threshold": 0.0, + "transaction_tracer.stack_trace_threshold": 0.0, + "debug.log_data_collector_payloads": True, + "debug.record_transaction_failure": True, + "machine_learning.enabled": True, + "machine_learning.inference_events_value.enabled": True, + "ml_insights_events.enabled": True +} +collector_agent_registration = collector_agent_registration_fixture( + app_name="Python Agent Test (mlmodel_sklearn)", + default_settings=_default_settings, + linked_applications=["Python Agent Test (mlmodel_sklearn)"], +) diff --git a/tests/mlmodel_sklearn/test_calibration_models.py b/tests/mlmodel_sklearn/test_calibration_models.py new file mode 100644 index 000000000..39ac34cb2 --- /dev/null +++ b/tests/mlmodel_sklearn/test_calibration_models.py @@ -0,0 +1,76 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +def test_model_methods_wrapped_in_function_trace(calibration_model_name, run_calibration_model): + expected_scoped_metrics = { + "CalibratedClassifierCV": [ + ("Function/MLModel/Sklearn/Named/CalibratedClassifierCV.fit", 1), + ("Function/MLModel/Sklearn/Named/CalibratedClassifierCV.predict", 1), + ("Function/MLModel/Sklearn/Named/CalibratedClassifierCV.predict_proba", 2), + ], + } + + expected_transaction_name = "test_calibration_models:_test" + if six.PY3: + expected_transaction_name = ( + "test_calibration_models:test_model_methods_wrapped_in_function_trace.._test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[calibration_model_name], + rollup_metrics=expected_scoped_metrics[calibration_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_calibration_model() + + _test() + + +@pytest.fixture(params=["CalibratedClassifierCV"]) +def calibration_model_name(request): + return request.param + + +@pytest.fixture +def run_calibration_model(calibration_model_name): + def _run(): + import sklearn.calibration + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.calibration, calibration_model_name)() + + model = clf.fit(x_train, y_train) + model.predict(x_test) + + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_cluster_models.py b/tests/mlmodel_sklearn/test_cluster_models.py new file mode 100644 index 000000000..906995c22 --- /dev/null +++ b/tests/mlmodel_sklearn/test_cluster_models.py @@ -0,0 +1,186 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn import __version__ # noqa: this is needed for get_package_version +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "cluster_model_name", + [ + "AffinityPropagation", + "AgglomerativeClustering", + "Birch", + "DBSCAN", + "FeatureAgglomeration", + "KMeans", + "MeanShift", + "MiniBatchKMeans", + "SpectralBiclustering", + "SpectralCoclustering", + "SpectralClustering", + ], +) +def test_below_v1_1_model_methods_wrapped_in_function_trace(cluster_model_name, run_cluster_model): + expected_scoped_metrics = { + "AffinityPropagation": [ + ("Function/MLModel/Sklearn/Named/AffinityPropagation.fit", 2), + ("Function/MLModel/Sklearn/Named/AffinityPropagation.predict", 1), + ("Function/MLModel/Sklearn/Named/AffinityPropagation.fit_predict", 1), + ], + "AgglomerativeClustering": [ + ("Function/MLModel/Sklearn/Named/AgglomerativeClustering.fit", 2), + ("Function/MLModel/Sklearn/Named/AgglomerativeClustering.fit_predict", 1), + ], + "Birch": [ + ("Function/MLModel/Sklearn/Named/Birch.fit", 2), + ( + "Function/MLModel/Sklearn/Named/Birch.predict", + 1 if SKLEARN_VERSION >= (1, 0, 0) else 3, + ), + ("Function/MLModel/Sklearn/Named/Birch.fit_predict", 1), + ("Function/MLModel/Sklearn/Named/Birch.transform", 1), + ], + "DBSCAN": [ + ("Function/MLModel/Sklearn/Named/DBSCAN.fit", 2), + ("Function/MLModel/Sklearn/Named/DBSCAN.fit_predict", 1), + ], + "FeatureAgglomeration": [ + ("Function/MLModel/Sklearn/Named/FeatureAgglomeration.fit", 1), + ("Function/MLModel/Sklearn/Named/FeatureAgglomeration.transform", 1), + ], + "KMeans": [ + ("Function/MLModel/Sklearn/Named/KMeans.fit", 2), + ("Function/MLModel/Sklearn/Named/KMeans.predict", 1), + ("Function/MLModel/Sklearn/Named/KMeans.fit_predict", 1), + ("Function/MLModel/Sklearn/Named/KMeans.transform", 1), + ], + "MeanShift": [ + ("Function/MLModel/Sklearn/Named/MeanShift.fit", 2), + ("Function/MLModel/Sklearn/Named/MeanShift.predict", 1), + ("Function/MLModel/Sklearn/Named/MeanShift.fit_predict", 1), + ], + "MiniBatchKMeans": [ + ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.fit", 2), + ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.predict", 1), + ("Function/MLModel/Sklearn/Named/MiniBatchKMeans.fit_predict", 1), + ], + "SpectralBiclustering": [ + ("Function/MLModel/Sklearn/Named/SpectralBiclustering.fit", 1), + ], + "SpectralCoclustering": [ + ("Function/MLModel/Sklearn/Named/SpectralCoclustering.fit", 1), + ], + "SpectralClustering": [ + ("Function/MLModel/Sklearn/Named/SpectralClustering.fit", 2), + ("Function/MLModel/Sklearn/Named/SpectralClustering.fit_predict", 1), + ], + } + expected_transaction_name = "test_cluster_models:_test" + if six.PY3: + expected_transaction_name = ( + "test_cluster_models:test_below_v1_1_model_methods_wrapped_in_function_trace.._test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[cluster_model_name], + rollup_metrics=expected_scoped_metrics[cluster_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_cluster_model(cluster_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 1, 0), reason="Requires sklearn > 1.1") +@pytest.mark.parametrize( + "cluster_model_name", + [ + "BisectingKMeans", + "OPTICS", + ], +) +def test_above_v1_1_model_methods_wrapped_in_function_trace(cluster_model_name, run_cluster_model): + expected_scoped_metrics = { + "BisectingKMeans": [ + ("Function/MLModel/Sklearn/Named/BisectingKMeans.fit", 2), + ("Function/MLModel/Sklearn/Named/BisectingKMeans.predict", 1), + ("Function/MLModel/Sklearn/Named/BisectingKMeans.fit_predict", 1), + ], + "OPTICS": [ + ("Function/MLModel/Sklearn/Named/OPTICS.fit", 2), + ("Function/MLModel/Sklearn/Named/OPTICS.fit_predict", 1), + ], + } + expected_transaction_name = "test_cluster_models:_test" + if six.PY3: + expected_transaction_name = ( + "test_cluster_models:test_above_v1_1_model_methods_wrapped_in_function_trace.._test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[cluster_model_name], + rollup_metrics=expected_scoped_metrics[cluster_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_cluster_model(cluster_model_name) + + _test() + + +@pytest.fixture +def run_cluster_model(): + def _run(cluster_model_name): + import sklearn.cluster + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.cluster, cluster_model_name)() + + model = clf.fit(x_train, y_train) + + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "fit_predict"): + model.fit_predict(x_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_compose_models.py b/tests/mlmodel_sklearn/test_compose_models.py new file mode 100644 index 000000000..eab076fc3 --- /dev/null +++ b/tests/mlmodel_sklearn/test_compose_models.py @@ -0,0 +1,94 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import Normalizer +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "compose_model_name", + [ + "ColumnTransformer", + "TransformedTargetRegressor", + ], +) +def test_model_methods_wrapped_in_function_trace(compose_model_name, run_compose_model): + expected_scoped_metrics = { + "ColumnTransformer": [ + ("Function/MLModel/Sklearn/Named/ColumnTransformer.fit", 1), + ("Function/MLModel/Sklearn/Named/ColumnTransformer.transform", 1), + ], + "TransformedTargetRegressor": [ + ("Function/MLModel/Sklearn/Named/TransformedTargetRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/TransformedTargetRegressor.predict", 1), + ], + } + + expected_transaction_name = ( + "test_compose_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_compose_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[compose_model_name], + rollup_metrics=expected_scoped_metrics[compose_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_compose_model(compose_model_name) + + _test() + + +@pytest.fixture +def run_compose_model(): + def _run(compose_model_name): + import numpy as np + import sklearn.compose + + if compose_model_name == "TransformedTargetRegressor": + kwargs = {"regressor": LinearRegression()} + X = np.arange(4).reshape(-1, 1) + y = np.exp(2 * X).ravel() + else: + X = [[0.0, 1.0, 2.0, 2.0], [1.0, 1.0, 0.0, 1.0]] + y = None + kwargs = { + "transformers": [ + ("norm1", Normalizer(norm="l1"), [0, 1]), + ("norm2", Normalizer(norm="l1"), slice(2, 4)), + ] + } + + clf = getattr(sklearn.compose, compose_model_name)(**kwargs) + + model = clf.fit(X, y) + if hasattr(model, "predict"): + model.predict(X) + if hasattr(model, "transform"): + model.transform(X) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_covariance_models.py b/tests/mlmodel_sklearn/test_covariance_models.py new file mode 100644 index 000000000..afa5c31c2 --- /dev/null +++ b/tests/mlmodel_sklearn/test_covariance_models.py @@ -0,0 +1,110 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "covariance_model_name", + [ + "EllipticEnvelope", + "EmpiricalCovariance", + "GraphicalLasso", + "GraphicalLassoCV", + "MinCovDet", + "ShrunkCovariance", + "LedoitWolf", + "OAS", + ], +) +def test_model_methods_wrapped_in_function_trace(covariance_model_name, run_covariance_model): + expected_scoped_metrics = { + "EllipticEnvelope": [ + ("Function/MLModel/Sklearn/Named/EllipticEnvelope.fit", 1), + ("Function/MLModel/Sklearn/Named/EllipticEnvelope.predict", 2), + ("Function/MLModel/Sklearn/Named/EllipticEnvelope.score", 1), + ], + "EmpiricalCovariance": [ + ("Function/MLModel/Sklearn/Named/EmpiricalCovariance.fit", 1), + ("Function/MLModel/Sklearn/Named/EmpiricalCovariance.score", 1), + ], + "GraphicalLasso": [ + ("Function/MLModel/Sklearn/Named/GraphicalLasso.fit", 1), + ], + "GraphicalLassoCV": [ + ("Function/MLModel/Sklearn/Named/GraphicalLassoCV.fit", 1), + ], + "MinCovDet": [ + ("Function/MLModel/Sklearn/Named/MinCovDet.fit", 1), + ], + "ShrunkCovariance": [ + ("Function/MLModel/Sklearn/Named/ShrunkCovariance.fit", 1), + ], + "LedoitWolf": [ + ("Function/MLModel/Sklearn/Named/LedoitWolf.fit", 1), + ], + "OAS": [ + ("Function/MLModel/Sklearn/Named/OAS.fit", 1), + ], + } + expected_transaction_name = ( + "test_covariance_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_covariance_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[covariance_model_name], + rollup_metrics=expected_scoped_metrics[covariance_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_covariance_model(covariance_model_name) + + _test() + + +@pytest.fixture +def run_covariance_model(): + def _run(covariance_model_name): + import sklearn.covariance + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {} + if covariance_model_name in ["EllipticEnvelope", "MinCovDet"]: + kwargs = {"random_state": 0} + + clf = getattr(sklearn.covariance, covariance_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_cross_decomposition_models.py b/tests/mlmodel_sklearn/test_cross_decomposition_models.py new file mode 100644 index 000000000..6a053350f --- /dev/null +++ b/tests/mlmodel_sklearn/test_cross_decomposition_models.py @@ -0,0 +1,81 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "cross_decomposition_model_name", + [ + "PLSRegression", + "PLSSVD", + ], +) +def test_model_methods_wrapped_in_function_trace(cross_decomposition_model_name, run_cross_decomposition_model): + expected_scoped_metrics = { + "PLSRegression": [ + ("Function/MLModel/Sklearn/Named/PLSRegression.fit", 1), + ], + "PLSSVD": [ + ("Function/MLModel/Sklearn/Named/PLSSVD.fit", 1), + ("Function/MLModel/Sklearn/Named/PLSSVD.transform", 1), + ], + } + expected_transaction_name = ( + "test_cross_decomposition_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_cross_decomposition_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[cross_decomposition_model_name], + rollup_metrics=expected_scoped_metrics[cross_decomposition_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_cross_decomposition_model(cross_decomposition_model_name) + + _test() + + +@pytest.fixture +def run_cross_decomposition_model(): + def _run(cross_decomposition_model_name): + import sklearn.cross_decomposition + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, _ = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {} + if cross_decomposition_model_name == "PLSSVD": + kwargs = {"n_components": 1} + clf = getattr(sklearn.cross_decomposition, cross_decomposition_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_discriminant_analysis_models.py b/tests/mlmodel_sklearn/test_discriminant_analysis_models.py new file mode 100644 index 000000000..de1182696 --- /dev/null +++ b/tests/mlmodel_sklearn/test_discriminant_analysis_models.py @@ -0,0 +1,91 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "discriminant_analysis_model_name", + [ + "LinearDiscriminantAnalysis", + "QuadraticDiscriminantAnalysis", + ], +) +def test_model_methods_wrapped_in_function_trace(discriminant_analysis_model_name, run_discriminant_analysis_model): + expected_scoped_metrics = { + "LinearDiscriminantAnalysis": [ + ("Function/MLModel/Sklearn/Named/LinearDiscriminantAnalysis.fit", 1), + ("Function/MLModel/Sklearn/Named/LinearDiscriminantAnalysis.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/LinearDiscriminantAnalysis.predict_proba", 2), + ("Function/MLModel/Sklearn/Named/LinearDiscriminantAnalysis.transform", 1), + ], + "QuadraticDiscriminantAnalysis": [ + ("Function/MLModel/Sklearn/Named/QuadraticDiscriminantAnalysis.fit", 1), + ("Function/MLModel/Sklearn/Named/QuadraticDiscriminantAnalysis.predict", 1), + ("Function/MLModel/Sklearn/Named/QuadraticDiscriminantAnalysis.predict_proba", 2), + ("Function/MLModel/Sklearn/Named/QuadraticDiscriminantAnalysis.predict_log_proba", 1), + ], + } + + expected_transaction_name = ( + "test_discriminant_analysis_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_discriminant_analysis_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[discriminant_analysis_model_name], + rollup_metrics=expected_scoped_metrics[discriminant_analysis_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_discriminant_analysis_model(discriminant_analysis_model_name) + + _test() + + +@pytest.fixture +def run_discriminant_analysis_model(): + def _run(discriminant_analysis_model_name): + import sklearn.discriminant_analysis + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {} + clf = getattr(sklearn.discriminant_analysis, discriminant_analysis_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_dummy_models.py b/tests/mlmodel_sklearn/test_dummy_models.py new file mode 100644 index 000000000..d1059add1 --- /dev/null +++ b/tests/mlmodel_sklearn/test_dummy_models.py @@ -0,0 +1,94 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn import __init__ # noqa: needed for get_package_version +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "dummy_model_name", + [ + "DummyClassifier", + "DummyRegressor", + ], +) +def test_model_methods_wrapped_in_function_trace(dummy_model_name, run_dummy_model): + expected_scoped_metrics = { + "DummyClassifier": [ + ("Function/MLModel/Sklearn/Named/DummyClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/DummyClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/DummyClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/DummyClassifier.predict_proba", 2 if SKLEARN_VERSION > (1, 0, 0) else 4), + ("Function/MLModel/Sklearn/Named/DummyClassifier.score", 1), + ], + "DummyRegressor": [ + ("Function/MLModel/Sklearn/Named/DummyRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/DummyRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/DummyRegressor.score", 1), + ], + } + + expected_transaction_name = ( + "test_dummy_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_dummy_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[dummy_model_name], + rollup_metrics=expected_scoped_metrics[dummy_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_dummy_model(dummy_model_name) + + _test() + + +@pytest.fixture +def run_dummy_model(): + def _run(dummy_model_name): + import sklearn.dummy + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.dummy, dummy_model_name)() + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_ensemble_models.py b/tests/mlmodel_sklearn/test_ensemble_models.py new file mode 100644 index 000000000..4093edf76 --- /dev/null +++ b/tests/mlmodel_sklearn/test_ensemble_models.py @@ -0,0 +1,303 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "ensemble_model_name", + [ + "AdaBoostClassifier", + "AdaBoostRegressor", + "BaggingClassifier", + "BaggingRegressor", + "ExtraTreesClassifier", + "ExtraTreesRegressor", + "GradientBoostingClassifier", + "GradientBoostingRegressor", + "IsolationForest", + "RandomForestClassifier", + "RandomForestRegressor", + "RandomTreesEmbedding", + "VotingClassifier", + ], +) +def test_below_v1_0_model_methods_wrapped_in_function_trace(ensemble_model_name, run_ensemble_model): + expected_scoped_metrics = { + "AdaBoostClassifier": [ + ("Function/MLModel/Sklearn/Named/AdaBoostClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/AdaBoostClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/AdaBoostClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/AdaBoostClassifier.predict_proba", 2), + ("Function/MLModel/Sklearn/Named/AdaBoostClassifier.score", 1), + ], + "AdaBoostRegressor": [ + ("Function/MLModel/Sklearn/Named/AdaBoostRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/AdaBoostRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/AdaBoostRegressor.score", 1), + ], + "BaggingClassifier": [ + ("Function/MLModel/Sklearn/Named/BaggingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/BaggingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/BaggingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/BaggingClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/BaggingClassifier.predict_proba", 3), + ], + "BaggingRegressor": [ + ("Function/MLModel/Sklearn/Named/BaggingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/BaggingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/BaggingRegressor.score", 1), + ], + "ExtraTreesClassifier": [ + ("Function/MLModel/Sklearn/Named/ExtraTreesClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreesClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreesClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreesClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreesClassifier.predict_proba", 4), + ], + "ExtraTreesRegressor": [ + ("Function/MLModel/Sklearn/Named/ExtraTreesRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreesRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreesRegressor.score", 1), + ], + "GradientBoostingClassifier": [ + ("Function/MLModel/Sklearn/Named/GradientBoostingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/GradientBoostingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/GradientBoostingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/GradientBoostingClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/GradientBoostingClassifier.predict_proba", 2), + ], + "GradientBoostingRegressor": [ + ("Function/MLModel/Sklearn/Named/GradientBoostingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/GradientBoostingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/GradientBoostingRegressor.score", 1), + ], + "IsolationForest": [ + ("Function/MLModel/Sklearn/Named/IsolationForest.fit", 1), + ("Function/MLModel/Sklearn/Named/IsolationForest.predict", 1), + ], + "RandomForestClassifier": [ + ("Function/MLModel/Sklearn/Named/RandomForestClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/RandomForestClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/RandomForestClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/RandomForestClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/RandomForestClassifier.predict_proba", 4), + ], + "RandomForestRegressor": [ + ("Function/MLModel/Sklearn/Named/RandomForestRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/RandomForestRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/RandomForestRegressor.score", 1), + ], + "RandomTreesEmbedding": [ + ("Function/MLModel/Sklearn/Named/RandomTreesEmbedding.fit", 1), + ("Function/MLModel/Sklearn/Named/RandomTreesEmbedding.transform", 1), + ], + "VotingClassifier": [ + ("Function/MLModel/Sklearn/Named/VotingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/VotingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/VotingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/VotingClassifier.transform", 1), + ("Function/MLModel/Sklearn/Named/VotingClassifier.predict_proba", 3), + ], + } + + expected_transaction_name = ( + "test_ensemble_models:test_below_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_ensemble_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[ensemble_model_name], + rollup_metrics=expected_scoped_metrics[ensemble_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_ensemble_model(ensemble_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 0, 0) or SKLEARN_VERSION >= (1, 1, 0), reason="Requires 1.0 <= sklearn < 1.1") +@pytest.mark.parametrize( + "ensemble_model_name", + [ + "HistGradientBoostingClassifier", + "HistGradientBoostingRegressor", + "StackingClassifier", + "StackingRegressor", + "VotingRegressor", + ], +) +def test_between_v1_0_and_v1_1_model_methods_wrapped_in_function_trace(ensemble_model_name, run_ensemble_model): + expected_scoped_metrics = { + "HistGradientBoostingClassifier": [ + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.predict_proba", 3), + ], + "HistGradientBoostingRegressor": [ + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.score", 1), + ], + "StackingClassifier": [ + ("Function/MLModel/Sklearn/Named/StackingClassifier.fit", 1), + ], + "StackingRegressor": [ + ("Function/MLModel/Sklearn/Named/StackingRegressor.fit", 1), + ], + "VotingRegressor": [ + ("Function/MLModel/Sklearn/Named/VotingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/VotingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/VotingRegressor.score", 1), + ("Function/MLModel/Sklearn/Named/VotingRegressor.transform", 1), + ], + } + expected_transaction_name = ( + "test_ensemble_models:test_between_v1_0_and_v1_1_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_ensemble_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[ensemble_model_name], + rollup_metrics=expected_scoped_metrics[ensemble_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_ensemble_model(ensemble_model_name) + + _test() + + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 1, 0), reason="Requires sklearn >= 1.1") +@pytest.mark.parametrize( + "ensemble_model_name", + [ + "HistGradientBoostingClassifier", + "HistGradientBoostingRegressor", + "StackingClassifier", + "StackingRegressor", + "VotingRegressor", + ], +) +def test_above_v1_1_model_methods_wrapped_in_function_trace(ensemble_model_name, run_ensemble_model): + expected_scoped_metrics = { + "StackingClassifier": [ + ("Function/MLModel/Sklearn/Named/StackingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/StackingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/StackingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/StackingClassifier.predict_proba", 1), + ("Function/MLModel/Sklearn/Named/StackingClassifier.transform", 4), + ], + "StackingRegressor": [ + ("Function/MLModel/Sklearn/Named/StackingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/StackingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/StackingRegressor.score", 1), + ], + "VotingRegressor": [ + ("Function/MLModel/Sklearn/Named/VotingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/VotingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/VotingRegressor.score", 1), + ("Function/MLModel/Sklearn/Named/VotingRegressor.transform", 1), + ], + "HistGradientBoostingClassifier": [ + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingClassifier.predict_proba", 3), + ], + "HistGradientBoostingRegressor": [ + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/HistGradientBoostingRegressor.score", 1), + ], + } + expected_transaction_name = ( + "test_ensemble_models:test_above_v1_1_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_ensemble_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[ensemble_model_name], + rollup_metrics=expected_scoped_metrics[ensemble_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_ensemble_model(ensemble_model_name) + + _test() + + +@pytest.fixture +def run_ensemble_model(): + def _run(ensemble_model_name): + import sklearn.ensemble + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {"random_state": 0} + if ensemble_model_name == "StackingClassifier": + kwargs = {"estimators": [("rf", RandomForestClassifier())], "final_estimator": RandomForestClassifier()} + elif ensemble_model_name == "VotingClassifier": + kwargs = { + "estimators": [("rf", RandomForestClassifier())], + "voting": "soft", + } + elif ensemble_model_name == "VotingRegressor": + x_train = x_test = [[1, 1]] + y_train = y_test = [0] + kwargs = {"estimators": [("rf", RandomForestRegressor())]} + elif ensemble_model_name == "StackingRegressor": + kwargs = {"estimators": [("rf", RandomForestRegressor())]} + clf = getattr(sklearn.ensemble, ensemble_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_feature_selection_models.py b/tests/mlmodel_sklearn/test_feature_selection_models.py new file mode 100644 index 000000000..f4d601d32 --- /dev/null +++ b/tests/mlmodel_sklearn/test_feature_selection_models.py @@ -0,0 +1,138 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.ensemble import AdaBoostClassifier +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "feature_selection_model_name", + [ + "VarianceThreshold", + "RFE", + "RFECV", + "SelectFromModel", + ], +) +def test_below_v1_0_model_methods_wrapped_in_function_trace(feature_selection_model_name, run_feature_selection_model): + expected_scoped_metrics = { + "VarianceThreshold": [ + ("Function/MLModel/Sklearn/Named/VarianceThreshold.fit", 1), + ], + "RFE": [ + ("Function/MLModel/Sklearn/Named/RFE.fit", 1), + ("Function/MLModel/Sklearn/Named/RFE.predict", 1), + ("Function/MLModel/Sklearn/Named/RFE.score", 1), + ("Function/MLModel/Sklearn/Named/RFE.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/RFE.predict_proba", 1), + ], + "RFECV": [ + ("Function/MLModel/Sklearn/Named/RFECV.fit", 1), + ], + "SelectFromModel": [ + ("Function/MLModel/Sklearn/Named/SelectFromModel.fit", 1), + ], + } + + expected_transaction_name = ( + "test_feature_selection_models:test_below_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_feature_selection_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[feature_selection_model_name], + rollup_metrics=expected_scoped_metrics[feature_selection_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_feature_selection_model(feature_selection_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 0, 0), reason="Requires sklearn >= 1.0") +@pytest.mark.parametrize( + "feature_selection_model_name", + [ + "SequentialFeatureSelector", + ], +) +def test_above_v1_0_model_methods_wrapped_in_function_trace(feature_selection_model_name, run_feature_selection_model): + expected_scoped_metrics = { + "SequentialFeatureSelector": [ + ("Function/MLModel/Sklearn/Named/SequentialFeatureSelector.fit", 1), + ], + } + expected_transaction_name = ( + "test_feature_selection_models:test_above_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_feature_selection_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[feature_selection_model_name], + rollup_metrics=expected_scoped_metrics[feature_selection_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_feature_selection_model(feature_selection_model_name) + + _test() + + +@pytest.fixture +def run_feature_selection_model(): + def _run(feature_selection_model_name): + import sklearn.feature_selection + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {} + if feature_selection_model_name in ["RFE", "SequentialFeatureSelector", "SelectFromModel", "RFECV"]: + # This is an example of a model that has all the available attributes + # We could have choosen any estimator that has predict, score, + # predict_log_proba, and predict_proba + kwargs = {"estimator": AdaBoostClassifier()} + clf = getattr(sklearn.feature_selection, feature_selection_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_gaussian_process_models.py b/tests/mlmodel_sklearn/test_gaussian_process_models.py new file mode 100644 index 000000000..7a78fc703 --- /dev/null +++ b/tests/mlmodel_sklearn/test_gaussian_process_models.py @@ -0,0 +1,83 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "gaussian_process_model_name", + [ + "GaussianProcessClassifier", + "GaussianProcessRegressor", + ], +) +def test_model_methods_wrapped_in_function_trace(gaussian_process_model_name, run_gaussian_process_model): + expected_scoped_metrics = { + "GaussianProcessClassifier": [ + ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.predict", 1), + ("Function/MLModel/Sklearn/Named/GaussianProcessClassifier.predict_proba", 1), + ], + "GaussianProcessRegressor": [ + ("Function/MLModel/Sklearn/Named/GaussianProcessRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/GaussianProcessRegressor.predict", 1), + ], + } + + expected_transaction_name = ( + "test_gaussian_process_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_gaussian_process_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[gaussian_process_model_name], + rollup_metrics=expected_scoped_metrics[gaussian_process_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_gaussian_process_model(gaussian_process_model_name) + + _test() + + +@pytest.fixture +def run_gaussian_process_model(): + def _run(gaussian_process_model_name): + import sklearn.gaussian_process + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.gaussian_process, gaussian_process_model_name)(random_state=0) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_inference_events.py b/tests/mlmodel_sklearn/test_inference_events.py new file mode 100644 index 000000000..0a3677019 --- /dev/null +++ b/tests/mlmodel_sklearn/test_inference_events.py @@ -0,0 +1,429 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import sys + +import numpy as np +import pandas +from testing_support.fixtures import ( + override_application_settings, + reset_core_stats_engine, +) +from testing_support.fixtures import override_application_settings +from testing_support.validators.validate_ml_event_count import validate_ml_event_count +from testing_support.validators.validate_ml_events import validate_ml_events + +from newrelic.api.background_task import background_task + +pandas_df_category_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "DecisionTreeClassifier", + "model_version": "0.0.0", + "feature.col1": 2.0, + "feature.col2": 4.0, + "label.0": "27.0", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_pandas_df_categorical_feature_event(): + @validate_ml_events(pandas_df_category_recorded_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import sklearn.tree + + clf = getattr(sklearn.tree, "DecisionTreeClassifier")(random_state=0) + model = clf.fit( + pandas.DataFrame({"col1": [27.0, 24.0], "col2": [23.0, 25.0]}, dtype="category"), + pandas.DataFrame({"label": [27.0, 28.0]}), + ) + + labels = model.predict(pandas.DataFrame({"col1": [2.0], "col2": [4.0]}, dtype="category")) + return model + + _test() + + +label_type = "bool" if sys.version_info < (3, 8) else "numeric" +true_label_value = "True" if sys.version_info < (3, 8) else "1.0" +false_label_value = "False" if sys.version_info < (3, 8) else "0.0" +pandas_df_bool_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "DecisionTreeClassifier", + "model_version": "0.0.0", + "feature.col1": True, + "feature.col2": True, + "label.0": true_label_value, + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_pandas_df_bool_feature_event(): + @validate_ml_events(pandas_df_bool_recorded_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import sklearn.tree + + dtype_name = "bool" if sys.version_info < (3, 8) else "boolean" + x_train = pandas.DataFrame({"col1": [True, False], "col2": [True, False]}, dtype=dtype_name) + y_train = pandas.DataFrame({"label": [True, False]}, dtype=dtype_name) + x_test = pandas.DataFrame({"col1": [True], "col2": [True]}, dtype=dtype_name) + + clf = getattr(sklearn.tree, "DecisionTreeClassifier")(random_state=0) + model = clf.fit(x_train, y_train) + + labels = model.predict(x_test) + return model + + _test() + + +pandas_df_float_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "DecisionTreeRegressor", + "model_version": "0.0.0", + "feature.col1": 100.0, + "feature.col2": 300.0, + "label.0": "345.6", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_pandas_df_float_feature_event(): + @validate_ml_events(pandas_df_float_recorded_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import sklearn.tree + + x_train = pandas.DataFrame({"col1": [120.0, 254.0], "col2": [236.9, 234.5]}, dtype="float64") + y_train = pandas.DataFrame({"label": [345.6, 456.7]}, dtype="float64") + x_test = pandas.DataFrame({"col1": [100.0], "col2": [300.0]}, dtype="float64") + + clf = getattr(sklearn.tree, "DecisionTreeRegressor")(random_state=0) + + model = clf.fit(x_train, y_train) + labels = model.predict(x_test) + + return model + + _test() + + +int_list_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "ExtraTreeRegressor", + "model_version": "0.0.0", + "feature.0": 1, + "feature.1": 2, + "label.0": "1.0", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_int_list(): + @validate_ml_events(int_list_recorded_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import sklearn.tree + + x_train = [[0, 0], [1, 1]] + y_train = [0, 1] + x_test = [[1, 2]] + + clf = getattr(sklearn.tree, "ExtraTreeRegressor")(random_state=0) + model = clf.fit(x_train, y_train) + + labels = model.predict(x_test) + return model + + _test() + + +numpy_int_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "ExtraTreeRegressor", + "model_version": "0.0.0", + "feature.0": 12, + "feature.1": 13, + "label.0": "11.0", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_numpy_int_array(): + @validate_ml_events(numpy_int_recorded_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import sklearn.tree + + x_train = np.array([[10, 10], [11, 11]], dtype="int") + y_train = np.array([10, 11], dtype="int") + x_test = np.array([[12, 13]], dtype="int") + + clf = getattr(sklearn.tree, "ExtraTreeRegressor")(random_state=0) + model = clf.fit(x_train, y_train) + + labels = model.predict(x_test) + return model + + _test() + + +numpy_str_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "DecisionTreeClassifier", + "model_version": "0.0.0", + "feature.0": "20", + "feature.1": "21", + "label.0": "21", + "new_relic_data_schema_version": 2, + }, + ), + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "DecisionTreeClassifier", + "model_version": "0.0.0", + "feature.0": "22", + "feature.1": "23", + "label.0": "21", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_numpy_str_array_multiple_features(): + @validate_ml_events(numpy_str_recorded_custom_events) + @validate_ml_event_count(count=2) + @background_task() + def _test(): + import sklearn.tree + + x_train = np.array([[20, 20], [21, 21]], dtype="._test" + if six.PY3 + else "test_kernel_ridge_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[kernel_ridge_model_name], + rollup_metrics=expected_scoped_metrics[kernel_ridge_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_kernel_ridge_model(kernel_ridge_model_name) + + _test() + + +@pytest.fixture +def run_kernel_ridge_model(): + def _run(kernel_ridge_model_name): + import sklearn.kernel_ridge + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, _ = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.kernel_ridge, kernel_ridge_model_name)() + + model = clf.fit(x_train, y_train) + model.predict(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_linear_models.py b/tests/mlmodel_sklearn/test_linear_models.py new file mode 100644 index 000000000..582a4750e --- /dev/null +++ b/tests/mlmodel_sklearn/test_linear_models.py @@ -0,0 +1,335 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "linear_model_name", + [ + "ARDRegression", + "BayesianRidge", + "ElasticNet", + "ElasticNetCV", + "HuberRegressor", + "Lars", + "LarsCV", + "Lasso", + "LassoCV", + "LassoLars", + "LassoLarsCV", + "LassoLarsIC", + "LinearRegression", + "LogisticRegression", + "LogisticRegressionCV", + "MultiTaskElasticNet", + "MultiTaskElasticNetCV", + "MultiTaskLasso", + "MultiTaskLassoCV", + "OrthogonalMatchingPursuit", + "OrthogonalMatchingPursuitCV", + "PassiveAggressiveClassifier", + "PassiveAggressiveRegressor", + "Perceptron", + "Ridge", + "RidgeCV", + "RidgeClassifier", + "RidgeClassifierCV", + "TheilSenRegressor", + "RANSACRegressor", + ], +) +def test_model_methods_wrapped_in_function_trace(linear_model_name, run_linear_model): + expected_scoped_metrics = { + "ARDRegression": [ + ("Function/MLModel/Sklearn/Named/ARDRegression.fit", 1), + ("Function/MLModel/Sklearn/Named/ARDRegression.predict", 2), + ("Function/MLModel/Sklearn/Named/ARDRegression.score", 1), + ], + "BayesianRidge": [ + ("Function/MLModel/Sklearn/Named/BayesianRidge.fit", 1), + ("Function/MLModel/Sklearn/Named/BayesianRidge.predict", 2), + ("Function/MLModel/Sklearn/Named/BayesianRidge.score", 1), + ], + "ElasticNet": [ + ("Function/MLModel/Sklearn/Named/ElasticNet.fit", 1), + ("Function/MLModel/Sklearn/Named/ElasticNet.predict", 2), + ("Function/MLModel/Sklearn/Named/ElasticNet.score", 1), + ], + "ElasticNetCV": [ + ("Function/MLModel/Sklearn/Named/ElasticNetCV.fit", 1), + ("Function/MLModel/Sklearn/Named/ElasticNetCV.predict", 2), + ("Function/MLModel/Sklearn/Named/ElasticNetCV.score", 1), + ], + "HuberRegressor": [ + ("Function/MLModel/Sklearn/Named/HuberRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/HuberRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/HuberRegressor.score", 1), + ], + "Lars": [ + ("Function/MLModel/Sklearn/Named/Lars.fit", 1), + ("Function/MLModel/Sklearn/Named/Lars.predict", 2), + ("Function/MLModel/Sklearn/Named/Lars.score", 1), + ], + "LarsCV": [ + ("Function/MLModel/Sklearn/Named/LarsCV.fit", 1), + ("Function/MLModel/Sklearn/Named/LarsCV.predict", 2), + ("Function/MLModel/Sklearn/Named/LarsCV.score", 1), + ], + "Lasso": [ + ("Function/MLModel/Sklearn/Named/Lasso.fit", 1), + ("Function/MLModel/Sklearn/Named/Lasso.predict", 2), + ("Function/MLModel/Sklearn/Named/Lasso.score", 1), + ], + "LassoCV": [ + ("Function/MLModel/Sklearn/Named/LassoCV.fit", 1), + ("Function/MLModel/Sklearn/Named/LassoCV.predict", 2), + ("Function/MLModel/Sklearn/Named/LassoCV.score", 1), + ], + "LassoLars": [ + ("Function/MLModel/Sklearn/Named/LassoLars.fit", 1), + ("Function/MLModel/Sklearn/Named/LassoLars.predict", 2), + ("Function/MLModel/Sklearn/Named/LassoLars.score", 1), + ], + "LassoLarsCV": [ + ("Function/MLModel/Sklearn/Named/LassoLarsCV.fit", 1), + ("Function/MLModel/Sklearn/Named/LassoLarsCV.predict", 2), + ("Function/MLModel/Sklearn/Named/LassoLarsCV.score", 1), + ], + "LassoLarsIC": [ + ("Function/MLModel/Sklearn/Named/LassoLarsIC.fit", 1), + ("Function/MLModel/Sklearn/Named/LassoLarsIC.predict", 2), + ("Function/MLModel/Sklearn/Named/LassoLarsIC.score", 1), + ], + "LinearRegression": [ + ("Function/MLModel/Sklearn/Named/LinearRegression.fit", 1), + ("Function/MLModel/Sklearn/Named/LinearRegression.predict", 2), + ("Function/MLModel/Sklearn/Named/LinearRegression.score", 1), + ], + "LogisticRegression": [ + ("Function/MLModel/Sklearn/Named/LogisticRegression.fit", 1), + ("Function/MLModel/Sklearn/Named/LogisticRegression.predict", 2), + ("Function/MLModel/Sklearn/Named/LogisticRegression.score", 1), + ], + "LogisticRegressionCV": [ + ("Function/MLModel/Sklearn/Named/LogisticRegressionCV.fit", 1), + ("Function/MLModel/Sklearn/Named/LogisticRegressionCV.predict", 2), + ("Function/MLModel/Sklearn/Named/LogisticRegressionCV.score", 1), + ], + "MultiTaskElasticNet": [ + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNet.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNet.predict", 2), + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNet.score", 1), + ], + "MultiTaskElasticNetCV": [ + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNetCV.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNetCV.predict", 2), + ("Function/MLModel/Sklearn/Named/MultiTaskElasticNetCV.score", 1), + ], + "MultiTaskLasso": [ + ("Function/MLModel/Sklearn/Named/MultiTaskLasso.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiTaskLasso.predict", 2), + ("Function/MLModel/Sklearn/Named/MultiTaskLasso.score", 1), + ], + "MultiTaskLassoCV": [ + ("Function/MLModel/Sklearn/Named/MultiTaskLassoCV.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiTaskLassoCV.predict", 2), + ("Function/MLModel/Sklearn/Named/MultiTaskLassoCV.score", 1), + ], + "OrthogonalMatchingPursuit": [ + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuit.fit", 1), + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuit.predict", 2), + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuit.score", 1), + ], + "OrthogonalMatchingPursuitCV": [ + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuitCV.fit", 1), + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuitCV.predict", 2), + ("Function/MLModel/Sklearn/Named/OrthogonalMatchingPursuitCV.score", 1), + ], + "PassiveAggressiveClassifier": [ + ("Function/MLModel/Sklearn/Named/PassiveAggressiveClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/PassiveAggressiveClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/PassiveAggressiveClassifier.score", 1), + ], + "PassiveAggressiveRegressor": [ + ("Function/MLModel/Sklearn/Named/PassiveAggressiveRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/PassiveAggressiveRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/PassiveAggressiveRegressor.score", 1), + ], + "Perceptron": [ + ("Function/MLModel/Sklearn/Named/Perceptron.fit", 1), + ("Function/MLModel/Sklearn/Named/Perceptron.predict", 2), + ("Function/MLModel/Sklearn/Named/Perceptron.score", 1), + ], + "Ridge": [ + ("Function/MLModel/Sklearn/Named/Ridge.fit", 1), + ("Function/MLModel/Sklearn/Named/Ridge.predict", 2), + ("Function/MLModel/Sklearn/Named/Ridge.score", 1), + ], + "RidgeCV": [ + ("Function/MLModel/Sklearn/Named/RidgeCV.fit", 1), + ("Function/MLModel/Sklearn/Named/RidgeCV.predict", 2), + ("Function/MLModel/Sklearn/Named/RidgeCV.score", 1), + ], + "RidgeClassifier": [ + ("Function/MLModel/Sklearn/Named/RidgeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/RidgeClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/RidgeClassifier.score", 1), + ], + "RidgeClassifierCV": [ + ("Function/MLModel/Sklearn/Named/RidgeClassifierCV.fit", 1), + ("Function/MLModel/Sklearn/Named/RidgeClassifierCV.predict", 2), + ("Function/MLModel/Sklearn/Named/RidgeClassifierCV.score", 1), + ], + "TheilSenRegressor": [ + ("Function/MLModel/Sklearn/Named/TheilSenRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/TheilSenRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/TheilSenRegressor.score", 1), + ], + "RANSACRegressor": [ + ("Function/MLModel/Sklearn/Named/RANSACRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/RANSACRegressor.predict", 1), + ("Function/MLModel/Sklearn/Named/RANSACRegressor.score", 1), + ], + } + expected_transaction_name = "test_linear_models:_test" + if six.PY3: + expected_transaction_name = "test_linear_models:test_model_methods_wrapped_in_function_trace.._test" + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[linear_model_name], + rollup_metrics=expected_scoped_metrics[linear_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_linear_model(linear_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 1, 0), reason="Requires sklearn >= v1.1") +@pytest.mark.parametrize( + "linear_model_name", + [ + "PoissonRegressor", + "GammaRegressor", + "TweedieRegressor", + "QuantileRegressor", + "SGDClassifier", + "SGDRegressor", + "SGDOneClassSVM", + ], +) +def test_above_v1_1_model_methods_wrapped_in_function_trace(linear_model_name, run_linear_model): + expected_scoped_metrics = { + "PoissonRegressor": [ + ("Function/MLModel/Sklearn/Named/PoissonRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/PoissonRegressor.predict", 1), + ("Function/MLModel/Sklearn/Named/PoissonRegressor.score", 1), + ], + "GammaRegressor": [ + ("Function/MLModel/Sklearn/Named/GammaRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/GammaRegressor.predict", 1), + ("Function/MLModel/Sklearn/Named/GammaRegressor.score", 1), + ], + "TweedieRegressor": [ + ("Function/MLModel/Sklearn/Named/TweedieRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/TweedieRegressor.predict", 1), + ("Function/MLModel/Sklearn/Named/TweedieRegressor.score", 1), + ], + "QuantileRegressor": [ + ("Function/MLModel/Sklearn/Named/QuantileRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/QuantileRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/QuantileRegressor.score", 1), + ], + "SGDClassifier": [ + ("Function/MLModel/Sklearn/Named/SGDClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/SGDClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/SGDClassifier.score", 1), + ], + "SGDRegressor": [ + ("Function/MLModel/Sklearn/Named/SGDRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/SGDRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/SGDRegressor.score", 1), + ], + "SGDOneClassSVM": [ + ("Function/MLModel/Sklearn/Named/SGDOneClassSVM.fit", 1), + ("Function/MLModel/Sklearn/Named/SGDOneClassSVM.predict", 1), + ], + } + expected_transaction_name = "test_linear_models:_test" + if six.PY3: + expected_transaction_name = ( + "test_linear_models:test_above_v1_1_model_methods_wrapped_in_function_trace.._test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[linear_model_name], + rollup_metrics=expected_scoped_metrics[linear_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_linear_model(linear_model_name) + + _test() + + +@pytest.fixture +def run_linear_model(): + def _run(linear_model_name): + import sklearn.linear_model + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + if linear_model_name == "GammaRegressor": + x_train = [[1, 2], [2, 3], [3, 4], [4, 3]] + y_train = [19, 26, 33, 30] + x_test = [[1, 2], [2, 3], [3, 4], [4, 3]] + y_test = [19, 26, 33, 30] + elif linear_model_name in [ + "MultiTaskElasticNet", + "MultiTaskElasticNetCV", + "MultiTaskLasso", + "MultiTaskLassoCV", + ]: + y_train = x_train + y_test = x_test + + clf = getattr(sklearn.linear_model, linear_model_name)() + + model = clf.fit(x_train, y_train) + model.predict(x_test) + + if hasattr(model, "score"): + model.score(x_test, y_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_metric_scorers.py b/tests/mlmodel_sklearn/test_metric_scorers.py new file mode 100644 index 000000000..50557b882 --- /dev/null +++ b/tests/mlmodel_sklearn/test_metric_scorers.py @@ -0,0 +1,150 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pytest +from testing_support.fixtures import validate_attributes + +from newrelic.api.background_task import background_task +from newrelic.hooks.mlmodel_sklearn import PredictReturnTypeProxy + + +@pytest.mark.parametrize( + "metric_scorer_name", + ( + "accuracy_score", + "balanced_accuracy_score", + "f1_score", + "precision_score", + "recall_score", + "roc_auc_score", + "r2_score", + ), +) +def test_metric_scorer_attributes(metric_scorer_name, run_metric_scorer): + @validate_attributes("agent", ["DecisionTreeClassifier/TrainingStep/0/%s" % metric_scorer_name]) + @background_task() + def _test(): + run_metric_scorer(metric_scorer_name) + + _test() + + +@pytest.mark.parametrize( + "metric_scorer_name", + ( + "accuracy_score", + "balanced_accuracy_score", + "f1_score", + "precision_score", + "recall_score", + "roc_auc_score", + "r2_score", + ), +) +def test_metric_scorer_training_steps_attributes(metric_scorer_name, run_metric_scorer): + @validate_attributes( + "agent", + [ + "DecisionTreeClassifier/TrainingStep/0/%s" % metric_scorer_name, + "DecisionTreeClassifier/TrainingStep/1/%s" % metric_scorer_name, + ], + ) + @background_task() + def _test(): + run_metric_scorer(metric_scorer_name, training_steps=[0, 1]) + + _test() + + +@pytest.mark.parametrize( + "metric_scorer_name,kwargs", + [ + ("f1_score", {"average": None}), + ("precision_score", {"average": None}), + ("recall_score", {"average": None}), + ], +) +def test_metric_scorer_iterable_score_attributes(metric_scorer_name, kwargs, run_metric_scorer): + @validate_attributes( + "agent", + [ + "DecisionTreeClassifier/TrainingStep/0/%s[0]" % metric_scorer_name, + "DecisionTreeClassifier/TrainingStep/0/%s[1]" % metric_scorer_name, + ], + ) + @background_task() + def _test(): + run_metric_scorer(metric_scorer_name, kwargs) + + _test() + + +@pytest.mark.parametrize( + "metric_scorer_name", + [ + "accuracy_score", + "balanced_accuracy_score", + "f1_score", + "precision_score", + "recall_score", + "roc_auc_score", + "r2_score", + ], +) +def test_metric_scorer_attributes_unknown_model(metric_scorer_name): + @validate_attributes("agent", ["Unknown/TrainingStep/Unknown/%s" % metric_scorer_name]) + @background_task() + def _test(): + from sklearn import metrics + + y_pred = [1, 0] + y_test = [1, 0] + + getattr(metrics, metric_scorer_name)(y_test, y_pred) + + _test() + + +@pytest.mark.parametrize("data", (np.array([0, 1]), "foo", 1, 1.0, True, [0, 1], {"foo": "bar"}, (0, 1), np.str_("F"))) +def test_PredictReturnTypeProxy(data): + wrapped_data = PredictReturnTypeProxy(data, "ModelName", 0) + + assert wrapped_data._nr_model_name == "ModelName" + assert wrapped_data._nr_training_step == 0 + + +@pytest.fixture +def run_metric_scorer(): + def _run(metric_scorer_name, metric_scorer_kwargs=None, training_steps=None): + from sklearn import metrics, tree + + x_train = [[0, 0], [1, 1]] + y_train = [0, 1] + x_test = [[2.0, 2.0], [0, 0.5]] + y_test = [1, 0] + + if not training_steps: + training_steps = [0] + + clf = tree.DecisionTreeClassifier(random_state=0) + for step in training_steps: + model = clf.fit(x_train, y_train) + + labels = model.predict(x_test) + + metric_scorer_kwargs = metric_scorer_kwargs or {} + getattr(metrics, metric_scorer_name)(y_test, labels, **metric_scorer_kwargs) + + return _run diff --git a/tests/mlmodel_sklearn/test_mixture_models.py b/tests/mlmodel_sklearn/test_mixture_models.py new file mode 100644 index 000000000..7ef838126 --- /dev/null +++ b/tests/mlmodel_sklearn/test_mixture_models.py @@ -0,0 +1,85 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "mixture_model_name", + [ + "GaussianMixture", + "BayesianGaussianMixture", + ], +) +def test_model_methods_wrapped_in_function_trace(mixture_model_name, run_mixture_model): + expected_scoped_metrics = { + "GaussianMixture": [ + ("Function/MLModel/Sklearn/Named/GaussianMixture.fit", 1), + ("Function/MLModel/Sklearn/Named/GaussianMixture.predict", 1), + ("Function/MLModel/Sklearn/Named/GaussianMixture.predict_proba", 1), + ("Function/MLModel/Sklearn/Named/GaussianMixture.score", 1), + ], + "BayesianGaussianMixture": [ + ("Function/MLModel/Sklearn/Named/BayesianGaussianMixture.fit", 1), + ("Function/MLModel/Sklearn/Named/BayesianGaussianMixture.predict", 1), + ("Function/MLModel/Sklearn/Named/BayesianGaussianMixture.predict_proba", 1), + ("Function/MLModel/Sklearn/Named/BayesianGaussianMixture.score", 1), + ], + } + + expected_transaction_name = ( + "test_mixture_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_mixture_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[mixture_model_name], + rollup_metrics=expected_scoped_metrics[mixture_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_mixture_model(mixture_model_name) + + _test() + + +@pytest.fixture +def run_mixture_model(): + def _run(mixture_model_name): + import sklearn.mixture + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.mixture, mixture_model_name)() + + model = clf.fit(x_train, y_train) + model.predict(x_test) + model.score(x_test, y_test) + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_ml_model.py b/tests/mlmodel_sklearn/test_ml_model.py new file mode 100644 index 000000000..cfb8e79a6 --- /dev/null +++ b/tests/mlmodel_sklearn/test_ml_model.py @@ -0,0 +1,337 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import pandas +from testing_support.fixtures import reset_core_stats_engine +from testing_support.validators.validate_ml_event_count import validate_ml_event_count +from testing_support.validators.validate_ml_events import validate_ml_events + +from newrelic.api.background_task import background_task +from newrelic.api.ml_model import wrap_mlmodel + +try: + from sklearn.tree._classes import BaseDecisionTree +except ImportError: + from sklearn.tree.tree import BaseDecisionTree + +_logger = logging.getLogger(__name__) + + +# Create custom model that isn't auto-instrumented to validate ml_model wrapper functionality +class CustomTestModel(BaseDecisionTree): + def __init__( + self, + criterion="poisson", + splitter="random", + max_depth=None, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_features=None, + random_state=0, + max_leaf_nodes=None, + min_impurity_decrease=0.0, + class_weight=None, + ccp_alpha=0.0, + ): + super().__init__( + criterion=criterion, + splitter=splitter, + max_depth=max_depth, + min_samples_split=min_samples_split, + min_samples_leaf=min_samples_leaf, + min_weight_fraction_leaf=min_weight_fraction_leaf, + max_features=max_features, + max_leaf_nodes=max_leaf_nodes, + class_weight=class_weight, + random_state=random_state, + min_impurity_decrease=min_impurity_decrease, + ccp_alpha=ccp_alpha, + ) + + def fit(self, X, y, sample_weight=None, check_input=True): + if hasattr(super(CustomTestModel, self), "_fit"): + return self._fit( + X, + y, + sample_weight=sample_weight, + check_input=check_input, + ) + else: + return super(CustomTestModel, self).fit( + X, + y, + sample_weight=sample_weight, + check_input=check_input, + ) + + def predict(self, X, check_input=True): + return super(CustomTestModel, self).predict(X, check_input=check_input) + + +int_list_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "MyCustomModel", + "model_version": "1.2.3", + "feature.0": 1.0, + "feature.1": 2.0, + "label.0": "0.5", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_custom_model_int_list_no_features_and_labels(): + @validate_ml_event_count(count=1) + @validate_ml_events(int_list_recorded_custom_events) + @background_task() + def _test(): + x_train = [[0, 0], [1, 1]] + y_train = [0, 1] + x_test = [[1.0, 2.0]] + + model = CustomTestModel().fit(x_train, y_train) + wrap_mlmodel(model, name="MyCustomModel", version="1.2.3") + + labels = model.predict(x_test) + + return model + + _test() + + +int_list_recorded_custom_events_with_metadata = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "MyCustomModel", + "model_version": "1.2.3", + "feature.0": 1.0, + "feature.1": 2.0, + "label.0": "0.5", + "new_relic_data_schema_version": 2, + "metadata1": "value1", + "metadata2": "value2", + }, + ), +] + + +@reset_core_stats_engine() +def test_custom_model_int_list_with_metadata(): + @validate_ml_event_count(count=1) + @validate_ml_events(int_list_recorded_custom_events_with_metadata) + @background_task() + def _test(): + x_train = [[0, 0], [1, 1]] + y_train = [0, 1] + x_test = [[1.0, 2.0]] + + model = CustomTestModel().fit(x_train, y_train) + wrap_mlmodel( + model, + name="MyCustomModel", + version="1.2.3", + metadata={"metadata1": "value1", "metadata2": "value2"}, + ) + + labels = model.predict(x_test) + + return model + + _test() + + +pandas_df_recorded_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "PandasTestModel", + "model_version": "1.5.0b1", + "feature.feature1": 0, + "feature.feature2": 0, + "feature.feature3": 1, + "label.label1": "0.5", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_wrapper_attrs_custom_model_pandas_df(): + @validate_ml_event_count(count=1) + @validate_ml_events(pandas_df_recorded_custom_events) + @background_task() + def _test(): + x_train = pandas.DataFrame({"col1": [0, 1], "col2": [0, 1], "col3": [1, 2]}, dtype="category") + y_train = [0, 1] + x_test = pandas.DataFrame({"col1": [0], "col2": [0], "col3": [1]}, dtype="category") + + model = CustomTestModel(random_state=0).fit(x_train, y_train) + wrap_mlmodel( + model, + name="PandasTestModel", + version="1.5.0b1", + feature_names=["feature1", "feature2", "feature3"], + label_names=["label1"], + ) + model.predict(x_test) + return model + + _test() + + +pandas_df_recorded_builtin_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "MyDecisionTreeClassifier", + "model_version": "1.5.0b1", + "feature.feature1": 12, + "feature.feature2": 14, + "label.label1": "0", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_wrapper_attrs_builtin_model(): + @validate_ml_event_count(count=1) + @validate_ml_events(pandas_df_recorded_builtin_events) + @background_task() + def _test(): + import sklearn.tree + + x_train = pandas.DataFrame({"col1": [0, 0], "col2": [1, 1]}, dtype="int") + y_train = pandas.DataFrame({"label": [0, 1]}, dtype="int") + x_test = pandas.DataFrame({"col1": [12], "col2": [14]}, dtype="int") + + clf = getattr(sklearn.tree, "DecisionTreeClassifier")(random_state=0) + + model = clf.fit(x_train, y_train) + wrap_mlmodel( + model, + name="MyDecisionTreeClassifier", + version="1.5.0b1", + feature_names=["feature1", "feature2"], + label_names=["label1"], + ) + model.predict(x_test) + + return model + + _test() + + +pandas_df_mismatched_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "MyDecisionTreeClassifier", + "model_version": "1.5.0b1", + "feature.col1": 12, + "feature.col2": 14, + "feature.col3": 16, + "label.0": "1", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_wrapper_mismatched_features_and_labels_df(): + @validate_ml_event_count(count=1) + @validate_ml_events(pandas_df_mismatched_custom_events) + @background_task() + def _test(): + import sklearn.tree + + x_train = pandas.DataFrame({"col1": [7, 8], "col2": [9, 10], "col3": [24, 25]}, dtype="int") + y_train = pandas.DataFrame({"label": [0, 1]}, dtype="int") + x_test = pandas.DataFrame({"col1": [12], "col2": [14], "col3": [16]}, dtype="int") + + clf = getattr(sklearn.tree, "DecisionTreeClassifier")(random_state=0) + + model = clf.fit(x_train, y_train) + wrap_mlmodel( + model, + name="MyDecisionTreeClassifier", + version="1.5.0b1", + feature_names=["feature1", "feature2"], + label_names=["label1", "label2"], + ) + model.predict(x_test) + return model + + _test() + + +numpy_str_mismatched_custom_events = [ + ( + {"type": "InferenceData"}, + { + "inference_id": None, + "prediction_id": None, + "modelName": "MyDecisionTreeClassifier", + "model_version": "0.0.1", + "feature.0": "20", + "feature.1": "21", + "label.0": "21", + "new_relic_data_schema_version": 2, + }, + ), +] + + +@reset_core_stats_engine() +def test_wrapper_mismatched_features_and_labels_np_array(): + @validate_ml_events(numpy_str_mismatched_custom_events) + @validate_ml_event_count(count=1) + @background_task() + def _test(): + import numpy as np + import sklearn.tree + + x_train = np.array([[20, 20], [21, 21]], dtype="._test" + if six.PY3 + else "test_model_selection_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[model_selection_model_name], + rollup_metrics=expected_scoped_metrics[model_selection_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_model_selection_model(model_selection_model_name) + + _test() + + +@pytest.fixture +def run_model_selection_model(): + def _run(model_selection_model_name): + import sklearn.model_selection + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + if model_selection_model_name == "GridSearchCV": + kwargs = {"estimator": AdaBoostClassifier(), "param_grid": {}} + else: + kwargs = {"estimator": AdaBoostClassifier(), "param_distributions": {}} + clf = getattr(sklearn.model_selection, model_selection_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_multiclass_models.py b/tests/mlmodel_sklearn/test_multiclass_models.py new file mode 100644 index 000000000..dd10d76f1 --- /dev/null +++ b/tests/mlmodel_sklearn/test_multiclass_models.py @@ -0,0 +1,91 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.ensemble import AdaBoostClassifier +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +@pytest.mark.parametrize( + "multiclass_model_name", + [ + "OneVsRestClassifier", + "OneVsOneClassifier", + "OutputCodeClassifier", + ], +) +def test_model_methods_wrapped_in_function_trace(multiclass_model_name, run_multiclass_model): + expected_scoped_metrics = { + "OneVsRestClassifier": [ + ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.predict", 1), + ("Function/MLModel/Sklearn/Named/OneVsRestClassifier.predict_proba", 1), + ], + "OneVsOneClassifier": [ + ("Function/MLModel/Sklearn/Named/OneVsOneClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/OneVsOneClassifier.predict", 1), + ], + "OutputCodeClassifier": [ + ("Function/MLModel/Sklearn/Named/OutputCodeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/OutputCodeClassifier.predict", 1), + ], + } + + expected_transaction_name = ( + "test_multiclass_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_multiclass_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[multiclass_model_name], + rollup_metrics=expected_scoped_metrics[multiclass_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_multiclass_model(multiclass_model_name) + + _test() + + +@pytest.fixture +def run_multiclass_model(): + def _run(multiclass_model_name): + import sklearn.multiclass + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + # This is an example of a model that has all the available attributes + # We could have choosen any estimator that has predict, score, + # predict_log_proba, and predict_proba + clf = getattr(sklearn.multiclass, multiclass_model_name)(estimator=AdaBoostClassifier()) + + model = clf.fit(x_train, y_train) + model.predict(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_multioutput_models.py b/tests/mlmodel_sklearn/test_multioutput_models.py new file mode 100644 index 000000000..392328f28 --- /dev/null +++ b/tests/mlmodel_sklearn/test_multioutput_models.py @@ -0,0 +1,129 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn import __init__ # noqa: Needed for get_package_version +from sklearn.ensemble import AdaBoostClassifier +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +# Python 2 will not allow instantiation of abstract class +# (abstract method is __init__ here) +@pytest.mark.skipif(SKLEARN_VERSION >= (1, 0, 0) or six.PY2, reason="Requires sklearn < 1.0 and Python3") +@pytest.mark.parametrize( + "multioutput_model_name", + [ + "MultiOutputEstimator", + ], +) +def test_below_v1_0_model_methods_wrapped_in_function_trace(multioutput_model_name, run_multioutput_model): + expected_scoped_metrics = { + "MultiOutputEstimator": [ + ("Function/MLModel/Sklearn/Named/MultiOutputEstimator.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiOutputEstimator.predict", 2), + ], + } + expected_transaction_name = ( + "test_multioutput_models:test_below_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_multioutput_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[multioutput_model_name], + rollup_metrics=expected_scoped_metrics[multioutput_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_multioutput_model(multioutput_model_name) + + _test() + + +@pytest.mark.parametrize( + "multioutput_model_name", + [ + "MultiOutputClassifier", + "ClassifierChain", + "RegressorChain", + ], +) +def test_above_v1_0_model_methods_wrapped_in_function_trace(multioutput_model_name, run_multioutput_model): + expected_scoped_metrics = { + "MultiOutputClassifier": [ + ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.predict_proba", 1), + ("Function/MLModel/Sklearn/Named/MultiOutputClassifier.score", 1), + ], + "ClassifierChain": [ + ("Function/MLModel/Sklearn/Named/ClassifierChain.fit", 1), + ("Function/MLModel/Sklearn/Named/ClassifierChain.predict_proba", 1), + ], + "RegressorChain": [ + ("Function/MLModel/Sklearn/Named/RegressorChain.fit", 1), + ], + } + expected_transaction_name = ( + "test_multioutput_models:test_above_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_multioutput_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[multioutput_model_name], + rollup_metrics=expected_scoped_metrics[multioutput_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_multioutput_model(multioutput_model_name) + + _test() + + +@pytest.fixture +def run_multioutput_model(): + def _run(multioutput_model_name): + import sklearn.multioutput + from sklearn.datasets import make_multilabel_classification + + X, y = make_multilabel_classification(n_classes=3, random_state=0) + + kwargs = {"estimator": AdaBoostClassifier()} + if multioutput_model_name in ["RegressorChain", "ClassifierChain"]: + kwargs = {"base_estimator": AdaBoostClassifier()} + clf = getattr(sklearn.multioutput, multioutput_model_name)(**kwargs) + + model = clf.fit(X, y) + if hasattr(model, "predict"): + model.predict(X) + if hasattr(model, "score"): + model.score(X, y) + if hasattr(model, "predict_proba"): + model.predict_proba(X) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_naive_bayes_models.py b/tests/mlmodel_sklearn/test_naive_bayes_models.py new file mode 100644 index 000000000..22dc6db1b --- /dev/null +++ b/tests/mlmodel_sklearn/test_naive_bayes_models.py @@ -0,0 +1,141 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn import __init__ # noqa: needed for get_package_version +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 0, 0), reason="Requires sklearn >= 1.0") +@pytest.mark.parametrize( + "naive_bayes_model_name", + [ + "CategoricalNB", + ], +) +def test_above_v1_0_model_methods_wrapped_in_function_trace(naive_bayes_model_name, run_naive_bayes_model): + expected_scoped_metrics = { + "CategoricalNB": [ + ("Function/MLModel/Sklearn/Named/CategoricalNB.fit", 1), + ("Function/MLModel/Sklearn/Named/CategoricalNB.predict", 1), + ("Function/MLModel/Sklearn/Named/CategoricalNB.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/CategoricalNB.predict_proba", 1), + ], + } + expected_transaction_name = ( + "test_naive_bayes_models:test_above_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_naive_bayes_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[naive_bayes_model_name], + rollup_metrics=expected_scoped_metrics[naive_bayes_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_naive_bayes_model(naive_bayes_model_name) + + _test() + + +@pytest.mark.parametrize( + "naive_bayes_model_name", + [ + "GaussianNB", + "MultinomialNB", + "ComplementNB", + "BernoulliNB", + ], +) +def test_model_methods_wrapped_in_function_trace(naive_bayes_model_name, run_naive_bayes_model): + expected_scoped_metrics = { + "GaussianNB": [ + ("Function/MLModel/Sklearn/Named/GaussianNB.fit", 1), + ("Function/MLModel/Sklearn/Named/GaussianNB.predict", 1), + ("Function/MLModel/Sklearn/Named/GaussianNB.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/GaussianNB.predict_proba", 1), + ], + "MultinomialNB": [ + ("Function/MLModel/Sklearn/Named/MultinomialNB.fit", 1), + ("Function/MLModel/Sklearn/Named/MultinomialNB.predict", 1), + ("Function/MLModel/Sklearn/Named/MultinomialNB.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/MultinomialNB.predict_proba", 1), + ], + "ComplementNB": [ + ("Function/MLModel/Sklearn/Named/ComplementNB.fit", 1), + ("Function/MLModel/Sklearn/Named/ComplementNB.predict", 1), + ("Function/MLModel/Sklearn/Named/ComplementNB.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/ComplementNB.predict_proba", 1), + ], + "BernoulliNB": [ + ("Function/MLModel/Sklearn/Named/BernoulliNB.fit", 1), + ("Function/MLModel/Sklearn/Named/BernoulliNB.predict", 1), + ("Function/MLModel/Sklearn/Named/BernoulliNB.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/BernoulliNB.predict_proba", 1), + ], + } + + expected_transaction_name = ( + "test_naive_bayes_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_naive_bayes_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[naive_bayes_model_name], + rollup_metrics=expected_scoped_metrics[naive_bayes_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_naive_bayes_model(naive_bayes_model_name) + + _test() + + +@pytest.fixture +def run_naive_bayes_model(): + def _run(naive_bayes_model_name): + import sklearn.naive_bayes + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.naive_bayes, naive_bayes_model_name)() + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_neighbors_models.py b/tests/mlmodel_sklearn/test_neighbors_models.py new file mode 100644 index 000000000..53a521157 --- /dev/null +++ b/tests/mlmodel_sklearn/test_neighbors_models.py @@ -0,0 +1,172 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.neighbors import __init__ # noqa: Needed for get_package_version +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "neighbors_model_name", + [ + "KNeighborsClassifier", + "RadiusNeighborsClassifier", + "KernelDensity", + "LocalOutlierFactor", + "NearestCentroid", + "KNeighborsRegressor", + "RadiusNeighborsRegressor", + "NearestNeighbors", + ], +) +def test_model_methods_wrapped_in_function_trace(neighbors_model_name, run_neighbors_model): + expected_scoped_metrics = { + "KNeighborsClassifier": [ + ("Function/MLModel/Sklearn/Named/KNeighborsClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/KNeighborsClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/KNeighborsClassifier.predict_proba", 1), + ], + "RadiusNeighborsClassifier": [ + ("Function/MLModel/Sklearn/Named/RadiusNeighborsClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/RadiusNeighborsClassifier.predict", 2), + ], + "KernelDensity": [ + ("Function/MLModel/Sklearn/Named/KernelDensity.fit", 1), + ("Function/MLModel/Sklearn/Named/KernelDensity.score", 1), + ], + "LocalOutlierFactor": [ + ("Function/MLModel/Sklearn/Named/LocalOutlierFactor.fit", 1), + ("Function/MLModel/Sklearn/Named/LocalOutlierFactor.predict", 1), + ], + "NearestCentroid": [ + ("Function/MLModel/Sklearn/Named/NearestCentroid.fit", 1), + ("Function/MLModel/Sklearn/Named/NearestCentroid.predict", 2), + ], + "KNeighborsRegressor": [ + ("Function/MLModel/Sklearn/Named/KNeighborsRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/KNeighborsRegressor.predict", 2), + ], + "RadiusNeighborsRegressor": [ + ("Function/MLModel/Sklearn/Named/RadiusNeighborsRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/RadiusNeighborsRegressor.predict", 2), + ], + "NearestNeighbors": [ + ("Function/MLModel/Sklearn/Named/NearestNeighbors.fit", 1), + ], + } + + expected_transaction_name = ( + "test_neighbors_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_neighbors_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[neighbors_model_name], + rollup_metrics=expected_scoped_metrics[neighbors_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_neighbors_model(neighbors_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 0, 0), reason="Requires sklearn >= 1.0") +@pytest.mark.parametrize( + "neighbors_model_name", + [ + "KNeighborsTransformer", + "RadiusNeighborsTransformer", + "NeighborhoodComponentsAnalysis", + "RadiusNeighborsClassifier", + ], +) +def test_above_v1_0_model_methods_wrapped_in_function_trace(neighbors_model_name, run_neighbors_model): + expected_scoped_metrics = { + "KNeighborsTransformer": [ + ("Function/MLModel/Sklearn/Named/KNeighborsTransformer.fit", 1), + ("Function/MLModel/Sklearn/Named/KNeighborsTransformer.transform", 1), + ], + "RadiusNeighborsTransformer": [ + ("Function/MLModel/Sklearn/Named/RadiusNeighborsTransformer.fit", 1), + ("Function/MLModel/Sklearn/Named/RadiusNeighborsTransformer.transform", 1), + ], + "NeighborhoodComponentsAnalysis": [ + ("Function/MLModel/Sklearn/Named/NeighborhoodComponentsAnalysis.fit", 1), + ("Function/MLModel/Sklearn/Named/NeighborhoodComponentsAnalysis.transform", 1), + ], + "RadiusNeighborsClassifier": [ + ("Function/MLModel/Sklearn/Named/RadiusNeighborsClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/RadiusNeighborsClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/RadiusNeighborsClassifier.predict_proba", 3), # Added in v1.0 + ], + } + expected_transaction_name = ( + "test_neighbors_models:test_above_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_neighbors_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[neighbors_model_name], + rollup_metrics=expected_scoped_metrics[neighbors_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_neighbors_model(neighbors_model_name) + + _test() + + +@pytest.fixture +def run_neighbors_model(): + def _run(neighbors_model_name): + import sklearn.neighbors + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {} + if neighbors_model_name == "LocalOutlierFactor": + kwargs = {"novelty": True} + clf = getattr(sklearn.neighbors, neighbors_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_neural_network_models.py b/tests/mlmodel_sklearn/test_neural_network_models.py new file mode 100644 index 000000000..468bfb4b9 --- /dev/null +++ b/tests/mlmodel_sklearn/test_neural_network_models.py @@ -0,0 +1,96 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "neural_network_model_name", + [ + "MLPClassifier", + "MLPRegressor", + "BernoulliRBM", + ], +) +def test_model_methods_wrapped_in_function_trace(neural_network_model_name, run_neural_network_model): + expected_scoped_metrics = { + "MLPClassifier": [ + ("Function/MLModel/Sklearn/Named/MLPClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/MLPClassifier.predict", 1), + ("Function/MLModel/Sklearn/Named/MLPClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/MLPClassifier.predict_proba", 2), + ], + "MLPRegressor": [ + ("Function/MLModel/Sklearn/Named/MLPRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/MLPRegressor.predict", 1), + ], + "BernoulliRBM": [ + ("Function/MLModel/Sklearn/Named/BernoulliRBM.fit", 1), + ("Function/MLModel/Sklearn/Named/BernoulliRBM.transform", 1), + ], + } + + expected_transaction_name = ( + "test_neural_network_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_neural_network_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[neural_network_model_name], + rollup_metrics=expected_scoped_metrics[neural_network_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_neural_network_model(neural_network_model_name) + + _test() + + +@pytest.fixture +def run_neural_network_model(): + def _run(neural_network_model_name): + import sklearn.neural_network + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + clf = getattr(sklearn.neural_network, neural_network_model_name)() + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_pipeline_models.py b/tests/mlmodel_sklearn/test_pipeline_models.py new file mode 100644 index 000000000..ac9b918f4 --- /dev/null +++ b/tests/mlmodel_sklearn/test_pipeline_models.py @@ -0,0 +1,95 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from sklearn.decomposition import TruncatedSVD +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVC +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "pipeline_model_name", + [ + "Pipeline", + "FeatureUnion", + ], +) +def test_model_methods_wrapped_in_function_trace(pipeline_model_name, run_pipeline_model): + expected_scoped_metrics = { + "Pipeline": [ + ("Function/MLModel/Sklearn/Named/Pipeline.fit", 1), + ("Function/MLModel/Sklearn/Named/Pipeline.predict", 1), + ("Function/MLModel/Sklearn/Named/Pipeline.score", 1), + ], + "FeatureUnion": [ + ("Function/MLModel/Sklearn/Named/FeatureUnion.fit", 1), + ("Function/MLModel/Sklearn/Named/FeatureUnion.transform", 1), + ], + } + + expected_transaction_name = ( + "test_pipeline_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_pipeline_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[pipeline_model_name], + rollup_metrics=expected_scoped_metrics[pipeline_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_pipeline_model(pipeline_model_name) + + _test() + + +@pytest.fixture +def run_pipeline_model(): + def _run(pipeline_model_name): + import sklearn.pipeline + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + if pipeline_model_name == "Pipeline": + kwargs = {"steps": [("scaler", StandardScaler()), ("svc", SVC())]} + else: + kwargs = {"transformer_list": [("scaler", StandardScaler()), ("svd", TruncatedSVD(n_components=2))]} + clf = getattr(sklearn.pipeline, pipeline_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "transform"): + model.transform(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_prediction_stats.py b/tests/mlmodel_sklearn/test_prediction_stats.py new file mode 100644 index 000000000..5538119e7 --- /dev/null +++ b/tests/mlmodel_sklearn/test_prediction_stats.py @@ -0,0 +1,519 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import uuid + +import numpy as np +import pandas as pd +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task + +# This will act as the UUID for `prediction_id` +ML_METRIC_FORCED_UUID = "0b59992f-2349-4a46-8de1-696d3fe1088b" + + +@pytest.fixture(scope="function") +def force_uuid(monkeypatch): + monkeypatch.setattr(uuid, "uuid4", lambda *a, **k: ML_METRIC_FORCED_UUID) + + +_test_prediction_stats_tags = frozenset( + {("modelName", "DummyClassifier"), ("prediction_id", ML_METRIC_FORCED_UUID), ("model_version", "0.0.0")} +) + + +@pytest.mark.parametrize( + "x_train,y_train,x_test,metrics", + [ + ( + [[0, 0], [1, 1]], + [0, 1], + [[2.0, 2.0], [0, 0.5]], + [ + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Mean", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile25", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile50", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile75", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Count", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Mean", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile25", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile50", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile75", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Count", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Mean", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile25", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile50", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile75", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Count", _test_prediction_stats_tags, 1), + ], + ), + ( + np.array([[0, 0], [1, 1]]), + [0, 1], + np.array([[2.0, 2.0], [0, 0.5]]), + [ + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Mean", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile25", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile50", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Percentile75", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/0/Count", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Mean", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile25", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile50", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Percentile75", + _test_prediction_stats_tags, + 1, + ), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Feature/1/Count", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Mean", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile25", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile50", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Percentile75", _test_prediction_stats_tags, 1), + ( + "MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/StandardDeviation", + _test_prediction_stats_tags, + 1, + ), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Min", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Max", _test_prediction_stats_tags, 1), + ("MLModel/Sklearn/Named/DummyClassifier/Predict/Label/0/Count", _test_prediction_stats_tags, 1), + ], + ), + ( + np.array([["a", 0, 4], ["b", 1, 3]], dtype="._test" + if six.PY3 + else "test_semi_supervised_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[semi_supervised_model_name], + rollup_metrics=expected_scoped_metrics[semi_supervised_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_semi_supervised_model(semi_supervised_model_name) + + _test() + + +@pytest.mark.skipif(SKLEARN_VERSION < (1, 0, 0), reason="Requires sklearn <= 1.0") +@pytest.mark.parametrize( + "semi_supervised_model_name", + [ + "SelfTrainingClassifier", + ], +) +def test_above_v1_0_model_methods_wrapped_in_function_trace(semi_supervised_model_name, run_semi_supervised_model): + expected_scoped_metrics = { + "SelfTrainingClassifier": [ + ("Function/MLModel/Sklearn/Named/SelfTrainingClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/SelfTrainingClassifier.predict", 1), + ("Function/MLModel/Sklearn/Named/SelfTrainingClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/SelfTrainingClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/SelfTrainingClassifier.predict_proba", 1), + ], + } + expected_transaction_name = ( + "test_semi_supervised_models:test_above_v1_0_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_semi_supervised_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[semi_supervised_model_name], + rollup_metrics=expected_scoped_metrics[semi_supervised_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_semi_supervised_model(semi_supervised_model_name) + + _test() + + +@pytest.fixture +def run_semi_supervised_model(): + def _run(semi_supervised_model_name): + import sklearn.semi_supervised + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + if semi_supervised_model_name == "SelfTrainingClassifier": + kwargs = {"base_estimator": AdaBoostClassifier()} + else: + kwargs = {} + clf = getattr(sklearn.semi_supervised, semi_supervised_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + if hasattr(model, "predict"): + model.predict(x_test) + if hasattr(model, "score"): + model.score(x_test, y_test) + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_svm_models.py b/tests/mlmodel_sklearn/test_svm_models.py new file mode 100644 index 000000000..fe95f2f46 --- /dev/null +++ b/tests/mlmodel_sklearn/test_svm_models.py @@ -0,0 +1,110 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.common.package_version_utils import get_package_version +from newrelic.packages import six + +SKLEARN_VERSION = tuple(map(int, get_package_version("sklearn").split("."))) + + +@pytest.mark.parametrize( + "svm_model_name", + [ + "LinearSVC", + "LinearSVR", + "SVC", + "NuSVC", + "SVR", + "NuSVR", + "OneClassSVM", + ], +) +def test_model_methods_wrapped_in_function_trace(svm_model_name, run_svm_model): + expected_scoped_metrics = { + "LinearSVC": [ + ("Function/MLModel/Sklearn/Named/LinearSVC.fit", 1), + ("Function/MLModel/Sklearn/Named/LinearSVC.predict", 1), + ], + "LinearSVR": [ + ("Function/MLModel/Sklearn/Named/LinearSVR.fit", 1), + ("Function/MLModel/Sklearn/Named/LinearSVR.predict", 1), + ], + "SVC": [ + ("Function/MLModel/Sklearn/Named/SVC.fit", 1), + ("Function/MLModel/Sklearn/Named/SVC.predict", 1), + ], + "NuSVC": [ + ("Function/MLModel/Sklearn/Named/NuSVC.fit", 1), + ("Function/MLModel/Sklearn/Named/NuSVC.predict", 1), + ], + "SVR": [ + ("Function/MLModel/Sklearn/Named/SVR.fit", 1), + ("Function/MLModel/Sklearn/Named/SVR.predict", 1), + ], + "NuSVR": [ + ("Function/MLModel/Sklearn/Named/NuSVR.fit", 1), + ("Function/MLModel/Sklearn/Named/NuSVR.predict", 1), + ], + "OneClassSVM": [ + ("Function/MLModel/Sklearn/Named/OneClassSVM.fit", 1), + ("Function/MLModel/Sklearn/Named/OneClassSVM.predict", 1), + ], + } + + expected_transaction_name = ( + "test_svm_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_svm_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[svm_model_name], + rollup_metrics=expected_scoped_metrics[svm_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_svm_model(svm_model_name) + + _test() + + +@pytest.fixture +def run_svm_model(): + def _run(svm_model_name): + import sklearn.svm + from sklearn.datasets import load_iris + from sklearn.model_selection import train_test_split + + X, y = load_iris(return_X_y=True) + x_train, x_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) + + kwargs = {"random_state": 0} + if svm_model_name in ["SVR", "NuSVR", "OneClassSVM"]: + kwargs = {} + clf = getattr(sklearn.svm, svm_model_name)(**kwargs) + + model = clf.fit(x_train, y_train) + model.predict(x_test) + + return model + + return _run diff --git a/tests/mlmodel_sklearn/test_tree_models.py b/tests/mlmodel_sklearn/test_tree_models.py new file mode 100644 index 000000000..b30b7e2ea --- /dev/null +++ b/tests/mlmodel_sklearn/test_tree_models.py @@ -0,0 +1,158 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +from testing_support.validators.validate_transaction_metrics import ( + validate_transaction_metrics, +) + +from newrelic.api.background_task import background_task +from newrelic.packages import six + + +def test_model_methods_wrapped_in_function_trace(tree_model_name, run_tree_model): + # Note: in the following expected metrics, predict and predict_proba are called by + # score and predict_log_proba so they are expected to be called twice instead of + # once like the rest of the methods. + expected_scoped_metrics = { + "ExtraTreeRegressor": [ + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.score", 1), + ], + "DecisionTreeClassifier": [ + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict_proba", 2), + ], + "ExtraTreeClassifier": [ + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.score", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict_log_proba", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict_proba", 2), + ], + "DecisionTreeRegressor": [ + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.predict", 2), + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.score", 1), + ], + } + expected_transaction_name = ( + "test_tree_models:test_model_methods_wrapped_in_function_trace.._test" + if six.PY3 + else "test_tree_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[tree_model_name], + rollup_metrics=expected_scoped_metrics[tree_model_name], + background_task=True, + ) + @background_task() + def _test(): + run_tree_model() + + _test() + + +def test_multiple_calls_to_model_methods(tree_model_name, run_tree_model): + # Note: in the following expected metrics, predict and predict_proba are called by + # score and predict_log_proba so they are expected to be called twice as often as + # the other methods. + expected_scoped_metrics = { + "ExtraTreeRegressor": [ + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.predict", 4), + ("Function/MLModel/Sklearn/Named/ExtraTreeRegressor.score", 2), + ], + "DecisionTreeClassifier": [ + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict", 4), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.score", 2), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/DecisionTreeClassifier.predict_proba", 4), + ], + "ExtraTreeClassifier": [ + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.fit", 1), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict", 4), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.score", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict_log_proba", 2), + ("Function/MLModel/Sklearn/Named/ExtraTreeClassifier.predict_proba", 4), + ], + "DecisionTreeRegressor": [ + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.fit", 1), + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.predict", 4), + ("Function/MLModel/Sklearn/Named/DecisionTreeRegressor.score", 2), + ], + } + expected_transaction_name = ( + "test_tree_models:test_multiple_calls_to_model_methods.._test" if six.PY3 else "test_tree_models:_test" + ) + + @validate_transaction_metrics( + expected_transaction_name, + scoped_metrics=expected_scoped_metrics[tree_model_name], + rollup_metrics=expected_scoped_metrics[tree_model_name], + background_task=True, + ) + @background_task() + def _test(): + x_test = [[2.0, 2.0], [2.0, 1.0]] + y_test = [1, 1] + + model = run_tree_model() + + model.predict(x_test) + model.score(x_test, y_test) + # Some models don't have these methods. + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + + _test() + + +@pytest.fixture(params=["ExtraTreeRegressor", "DecisionTreeClassifier", "ExtraTreeClassifier", "DecisionTreeRegressor"]) +def tree_model_name(request): + return request.param + + +@pytest.fixture +def run_tree_model(tree_model_name): + def _run(): + import sklearn.tree + + x_train = [[0, 0], [1, 1]] + y_train = [0, 1] + x_test = [[2.0, 2.0], [2.0, 1.0]] + y_test = [1, 1] + + clf = getattr(sklearn.tree, tree_model_name)(random_state=0) + model = clf.fit(x_train, y_train) + + labels = model.predict(x_test) + model.score(x_test, y_test) + # Some models don't have these methods. + if hasattr(model, "predict_log_proba"): + model.predict_log_proba(x_test) + if hasattr(model, "predict_proba"): + model.predict_proba(x_test) + return model + + return _run diff --git a/tests/testing_support/fixtures.py b/tests/testing_support/fixtures.py index ce6166f0b..883c3ec59 100644 --- a/tests/testing_support/fixtures.py +++ b/tests/testing_support/fixtures.py @@ -166,7 +166,10 @@ def wrap_shutdown_agent(wrapped, instance, args, kwargs): def wrap_record_custom_metric(wrapped, instance, args, kwargs): def _bind_params(name, value, *args, **kwargs): - return name + if isinstance(name, tuple): + return name[0] + else: + return name metric_name = _bind_params(*args, **kwargs) if ( diff --git a/tests/testing_support/validators/validate_dimensional_metric_payload.py b/tests/testing_support/validators/validate_dimensional_metric_payload.py new file mode 100644 index 000000000..2f4f48c07 --- /dev/null +++ b/tests/testing_support/validators/validate_dimensional_metric_payload.py @@ -0,0 +1,187 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper +from newrelic.core.otlp_utils import otlp_content_setting + +if otlp_content_setting == "protobuf": + from google.protobuf.json_format import MessageToDict +else: + MessageToDict = None + + +def data_points_to_dict(data_points): + return { + frozenset( + {attr["key"]: attribute_to_value(attr["value"]) for attr in (data_point.get("attributes") or [])}.items() + ) + or None: data_point + for data_point in data_points + } + + +def attribute_to_value(attribute): + attribute_type, attribute_value = next(iter(attribute.items())) + if attribute_type == "int_value": + return int(attribute_value) + elif attribute_type == "double_value": + return float(attribute_value) + elif attribute_type == "bool_value": + return bool(attribute_value) + elif attribute_type == "string_value": + return str(attribute_value) + else: + raise TypeError("Invalid attribute type: %s" % attribute_type) + + +def payload_to_metrics(payload): + if type(payload) is not dict: + message = MessageToDict(payload, use_integers_for_enums=True, preserving_proto_field_name=True) + else: + message = payload + + resource_metrics = message.get("resource_metrics") + assert len(resource_metrics) == 1 + resource_metrics = resource_metrics[0] + + resource = resource_metrics.get("resource") + assert resource and resource.get("attributes")[0] == { + "key": "instrumentation.provider", + "value": {"string_value": "newrelic-opentelemetry-python-ml"}, + } + scope_metrics = resource_metrics.get("scope_metrics") + assert len(scope_metrics) == 1 + scope_metrics = scope_metrics[0] + + scope = scope_metrics.get("scope") + assert scope is None + metrics = scope_metrics.get("metrics") + + sent_summary_metrics = {} + sent_count_metrics = {} + for metric in metrics: + metric_name = metric["name"] + if metric.get("sum"): + sent_count_metrics[metric_name] = metric + elif metric.get("summary"): + sent_summary_metrics[metric_name] = metric + else: + raise TypeError("Unknown metrics type for metric: %s" % metric) + + return sent_summary_metrics, sent_count_metrics + + +def validate_dimensional_metric_payload(summary_metrics=None, count_metrics=None): + # Validates OTLP metrics as they are sent to the collector. + + summary_metrics = summary_metrics or [] + count_metrics = count_metrics or [] + + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + recorded_metrics = [] + + @transient_function_wrapper("newrelic.core.agent_protocol", "OtlpProtocol.send") + def send_request_wrapper(wrapped, instance, args, kwargs): + def _bind_params(method, payload=(), *args, **kwargs): + return method, payload + + method, payload = _bind_params(*args, **kwargs) + + if method == "dimensional_metric_data" and payload: + recorded_metrics.append(payload) + + return wrapped(*args, **kwargs) + + wrapped = send_request_wrapper(wrapped) + val = wrapped(*args, **kwargs) + assert recorded_metrics + + decoded_payloads = [payload_to_metrics(payload) for payload in recorded_metrics] + for sent_summary_metrics, sent_count_metrics in decoded_payloads: + for metric, tags, count in summary_metrics: + if isinstance(tags, dict): + tags = frozenset(tags.items()) + + if not count: + if metric in sent_summary_metrics: + data_points = data_points_to_dict(sent_summary_metrics[metric]["summary"]["data_points"]) + assert tags not in data_points, "(%s, %s) Unexpected but found." % (metric, tags and dict(tags)) + else: + assert metric in sent_summary_metrics, "%s Not Found. Got: %s" % ( + metric, + list(sent_summary_metrics.keys()), + ) + data_points = data_points_to_dict(sent_summary_metrics[metric]["summary"]["data_points"]) + assert tags in data_points, "(%s, %s) Not Found. Got: %s" % ( + metric, + tags and dict(tags), + list(data_points.keys()), + ) + + # Validate metric format + metric_container = data_points[tags] + for key in ("start_time_unix_nano", "time_unix_nano", "count", "sum", "quantile_values"): + assert key in metric_container, "Invalid metric format. Missing key: %s" % key + quantile_values = metric_container["quantile_values"] + assert len(quantile_values) == 2 # Min and Max + + # Validate metric count + if count != "present": + assert int(metric_container["count"]) == count, "(%s, %s): Expected: %s Got: %s" % ( + metric, + tags and dict(tags), + count, + metric_container["count"], + ) + + for metric, tags, count in count_metrics: + if isinstance(tags, dict): + tags = frozenset(tags.items()) + + if not count: + if metric in sent_count_metrics: + data_points = data_points_to_dict(sent_count_metrics[metric]["sum"]["data_points"]) + assert tags not in data_points, "(%s, %s) Unexpected but found." % (metric, tags and dict(tags)) + else: + assert metric in sent_count_metrics, "%s Not Found. Got: %s" % ( + metric, + list(sent_count_metrics.keys()), + ) + data_points = data_points_to_dict(sent_count_metrics[metric]["sum"]["data_points"]) + assert tags in data_points, "(%s, %s) Not Found. Got: %s" % ( + metric, + tags and dict(tags), + list(data_points.keys()), + ) + + # Validate metric format + assert sent_count_metrics[metric]["sum"].get("is_monotonic") + assert sent_count_metrics[metric]["sum"].get("aggregation_temporality") == 1 + metric_container = data_points[tags] + for key in ("start_time_unix_nano", "time_unix_nano", "as_int"): + assert key in metric_container, "Invalid metric format. Missing key: %s" % key + + # Validate metric count + if count != "present": + assert int(metric_container["as_int"]) == count, "(%s, %s): Expected: %s Got: %s" % ( + metric, + tags and dict(tags), + count, + metric_container["count"], + ) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_dimensional_metrics_outside_transaction.py b/tests/testing_support/validators/validate_dimensional_metrics_outside_transaction.py new file mode 100644 index 000000000..2854a7478 --- /dev/null +++ b/tests/testing_support/validators/validate_dimensional_metrics_outside_transaction.py @@ -0,0 +1,99 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from testing_support.fixtures import catch_background_exceptions +from newrelic.common.object_wrapper import transient_function_wrapper, function_wrapper + + +def validate_dimensional_metrics_outside_transaction(dimensional_metrics=None): + dimensional_metrics = dimensional_metrics or [] + + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + + record_dimensional_metric_called = [] + recorded_metrics = [None] + + @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_dimensional_metric") + @catch_background_exceptions + def _validate_dimensional_metrics_outside_transaction(wrapped, instance, args, kwargs): + record_dimensional_metric_called.append(True) + try: + result = wrapped(*args, **kwargs) + except: + raise + else: + metrics = instance.dimensional_stats_table.metrics() + # Record a copy of the metric value so that the values aren't + # merged in the future + _metrics = {} + for k, v in metrics: + _metrics[k] = copy.copy(v) + recorded_metrics[0] = _metrics + + return result + + def _validate(metrics, name, tags, count): + key = (name, tags) + # Dimensional metric lookup + metric_container = metrics.get(name, {}) + metric = metric_container.get(tags) + + def _metrics_table(): + out = [""] + out.append("Expected: {0}: {1}".format(key, count)) + for metric_key, metric_container in metrics.items(): + if isinstance(metric_container, dict): + for metric_tags, metric_value in metric_container.items(): + out.append("{0}: {1}".format((metric_key, metric_tags), metric_value[0])) + else: + out.append("{0}: {1}".format(metric_key, metric_container[0])) + return "\n".join(out) + + def _metric_details(): + return "metric=%r, count=%r" % (key, metric.call_count) + + if count is not None: + assert metric is not None, _metrics_table() + if count == "present": + assert metric.call_count > 0, _metric_details() + else: + assert metric.call_count == count, _metric_details() + + assert metric.total_call_time >= 0, (key, metric) + assert metric.total_exclusive_call_time >= 0, (key, metric) + assert metric.min_call_time >= 0, (key, metric) + assert metric.sum_of_squares >= 0, (key, metric) + + else: + assert metric is None, _metrics_table() + + _new_wrapper = _validate_dimensional_metrics_outside_transaction(wrapped) + val = _new_wrapper(*args, **kwargs) + assert record_dimensional_metric_called + metrics = recorded_metrics[0] + + record_dimensional_metric_called[:] = [] + recorded_metrics[:] = [] + + for dimensional_metric, dimensional_tags, count in dimensional_metrics: + if isinstance(dimensional_tags, dict): + dimensional_tags = frozenset(dimensional_tags.items()) + _validate(metrics, dimensional_metric, dimensional_tags, count) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_log_events_outside_transaction.py b/tests/testing_support/validators/validate_log_events_outside_transaction.py index f46b6e843..4bc941965 100644 --- a/tests/testing_support/validators/validate_log_events_outside_transaction.py +++ b/tests/testing_support/validators/validate_log_events_outside_transaction.py @@ -14,11 +14,11 @@ import copy +from testing_support.fixtures import catch_background_exceptions + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper from newrelic.packages import six -from newrelic.common.object_wrapper import (transient_function_wrapper, - function_wrapper) -from testing_support.fixtures import catch_background_exceptions def validate_log_events_outside_transaction(events): @function_wrapper @@ -35,18 +35,16 @@ def _validate_log_events_outside_transaction(wrapped, instance, args, kwargs): result = wrapped(*args, **kwargs) except: raise - else: - recorded_logs[:] = [] - recorded_logs.extend(list(instance._log_events)) + recorded_logs[:] = [] + recorded_logs.extend(list(instance._log_events)) return result - _new_wrapper = _validate_log_events_outside_transaction(wrapped) val = _new_wrapper(*args, **kwargs) assert record_called logs = copy.copy(recorded_logs) - + record_called[:] = [] recorded_logs[:] = [] @@ -60,7 +58,6 @@ def _validate_log_events_outside_transaction(wrapped, instance, args, kwargs): return val - def _check_log_attributes(expected, captured, mismatches): for key, value in six.iteritems(expected): if hasattr(captured, key): diff --git a/tests/testing_support/validators/validate_ml_event_count.py b/tests/testing_support/validators/validate_ml_event_count.py new file mode 100644 index 000000000..ec5de8dcf --- /dev/null +++ b/tests/testing_support/validators/validate_ml_event_count.py @@ -0,0 +1,54 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from testing_support.fixtures import catch_background_exceptions + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper + + +def validate_ml_event_count(count=1): + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + + record_called = [] + recorded_events = [] + + @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_transaction") + @catch_background_exceptions + def _validate_ml_event_count(wrapped, instance, args, kwargs): + record_called.append(True) + try: + result = wrapped(*args, **kwargs) + except: + raise + recorded_events.extend(list(instance._ml_events)) + + return result + + _new_wrapper = _validate_ml_event_count(wrapped) + val = _new_wrapper(*args, **kwargs) + if count: + assert record_called + events = copy.copy(recorded_events) + + record_called[:] = [] + recorded_events[:] = [] + + assert count == len(events), len(events) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_ml_event_count_outside_transaction.py b/tests/testing_support/validators/validate_ml_event_count_outside_transaction.py new file mode 100644 index 000000000..6ac764d1a --- /dev/null +++ b/tests/testing_support/validators/validate_ml_event_count_outside_transaction.py @@ -0,0 +1,55 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from testing_support.fixtures import catch_background_exceptions + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper + + +def validate_ml_event_count_outside_transaction(count=1): + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + + record_called = [] + recorded_events = [] + + @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_ml_event") + @catch_background_exceptions + def _validate_ml_event_count_outside_transaction(wrapped, instance, args, kwargs): + record_called.append(True) + try: + result = wrapped(*args, **kwargs) + except: + raise + recorded_events[:] = [] + recorded_events.extend(list(instance._ml_events)) + + return result + + _new_wrapper = _validate_ml_event_count_outside_transaction(wrapped) + val = _new_wrapper(*args, **kwargs) + if count: + assert record_called + events = copy.copy(recorded_events) + + record_called[:] = [] + recorded_events[:] = [] + + assert count == len(events), len(events) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_ml_event_payload.py b/tests/testing_support/validators/validate_ml_event_payload.py new file mode 100644 index 000000000..4d43cbb22 --- /dev/null +++ b/tests/testing_support/validators/validate_ml_event_payload.py @@ -0,0 +1,104 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper +from newrelic.core.otlp_utils import otlp_content_setting + +if otlp_content_setting == "protobuf": + from google.protobuf.json_format import MessageToDict +else: + MessageToDict = None + + +def attribute_to_value(attribute): + attribute_type, attribute_value = next(iter(attribute.items())) + if attribute_type == "int_value": + return int(attribute_value) + elif attribute_type == "double_value": + return float(attribute_value) + elif attribute_type == "bool_value": + return bool(attribute_value) + elif attribute_type == "string_value": + return str(attribute_value) + else: + raise TypeError("Invalid attribute type: %s" % attribute_type) + + +def payload_to_ml_events(payload): + if type(payload) is not dict: + message = MessageToDict(payload, use_integers_for_enums=True, preserving_proto_field_name=True) + else: + message = payload + + resource_logs = message.get("resource_logs") + assert len(resource_logs) == 1 + resource_logs = resource_logs[0] + resource = resource_logs.get("resource") + assert resource and resource.get("attributes")[0] == { + "key": "instrumentation.provider", + "value": {"string_value": "newrelic-opentelemetry-python-ml"}, + } + scope_logs = resource_logs.get("scope_logs") + assert len(scope_logs) == 1 + scope_logs = scope_logs[0] + + scope = scope_logs.get("scope") + assert scope is None + logs = scope_logs.get("log_records") + + return logs + + +def validate_ml_event_payload(ml_events=None): + # Validates OTLP events as they are sent to the collector. + + ml_events = ml_events or [] + + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + recorded_ml_events = [] + + @transient_function_wrapper("newrelic.core.agent_protocol", "OtlpProtocol.send") + def send_request_wrapper(wrapped, instance, args, kwargs): + def _bind_params(method, payload=(), *args, **kwargs): + return method, payload + + method, payload = _bind_params(*args, **kwargs) + + if method == "ml_event_data" and payload: + recorded_ml_events.append(payload) + + return wrapped(*args, **kwargs) + + wrapped = send_request_wrapper(wrapped) + val = wrapped(*args, **kwargs) + assert recorded_ml_events + + decoded_payloads = [payload_to_ml_events(payload) for payload in recorded_ml_events] + all_logs = [] + for sent_logs in decoded_payloads: + for data_point in sent_logs: + for key in ("time_unix_nano",): + assert key in data_point, "Invalid log format. Missing key: %s" % key + + all_logs.append( + {attr["key"]: attribute_to_value(attr["value"]) for attr in (data_point.get("attributes") or [])} + ) + + for expected_event in ml_events: + assert expected_event in all_logs, "%s Not Found. Got: %s" % (expected_event, all_logs) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_ml_events.py b/tests/testing_support/validators/validate_ml_events.py new file mode 100644 index 000000000..251e8dbe7 --- /dev/null +++ b/tests/testing_support/validators/validate_ml_events.py @@ -0,0 +1,110 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import time + +from testing_support.fixtures import catch_background_exceptions + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper +from newrelic.packages import six + + +def validate_ml_events(events): + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + + record_called = [] + recorded_events = [] + + @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_transaction") + @catch_background_exceptions + def _validate_ml_events(wrapped, instance, args, kwargs): + record_called.append(True) + try: + result = wrapped(*args, **kwargs) + except: + raise + recorded_events[:] = [] + recorded_events.extend(list(instance._ml_events)) + + return result + + _new_wrapper = _validate_ml_events(wrapped) + val = _new_wrapper(*args, **kwargs) + assert record_called + found_events = copy.copy(recorded_events) + + record_called[:] = [] + recorded_events[:] = [] + + for expected in events: + matching_ml_events = 0 + mismatches = [] + for captured in found_events: + if _check_event_attributes(expected, captured, mismatches): + matching_ml_events += 1 + assert matching_ml_events == 1, _event_details(matching_ml_events, events, mismatches) + + return val + + return _validate_wrapper + + +def _check_event_attributes(expected, captured, mismatches): + assert len(captured) == 2 # [intrinsic, user attributes] + + intrinsics = captured[0] + + if intrinsics["type"] != expected[0]["type"]: + mismatches.append("key: type, value:<%s><%s>" % (expected[0]["type"], captured[0].get("type", None))) + return False + + now = time.time() + + if not (isinstance(intrinsics["timestamp"], int) and intrinsics["timestamp"] <= 1000.0 * now): + mismatches.append("key: timestamp, value:<%s>" % intrinsics["timestamp"]) + return False + + captured_keys = set(six.iterkeys(captured[1])) + expected_keys = set(six.iterkeys(expected[1])) + extra_keys = captured_keys - expected_keys + + if extra_keys: + mismatches.append("extra_keys: %s" % str(tuple(extra_keys))) + return False + + for key, value in six.iteritems(expected[1]): + if key in captured[1]: + captured_value = captured[1].get(key, None) + else: + mismatches.append("key: %s, value:<%s><%s>" % (key, value, captured[1].get(key, None))) + return False + + if value is not None: + if value != captured_value: + mismatches.append("key: %s, value:<%s><%s>" % (key, value, captured_value)) + return False + + return True + + +def _event_details(matching_ml_events, captured, mismatches): + details = [ + "matching_ml_events=%d" % matching_ml_events, + "mismatches=%s" % mismatches, + "captured_events=%s" % captured, + ] + + return "\n".join(details) diff --git a/tests/testing_support/validators/validate_ml_events_outside_transaction.py b/tests/testing_support/validators/validate_ml_events_outside_transaction.py new file mode 100644 index 000000000..107771442 --- /dev/null +++ b/tests/testing_support/validators/validate_ml_events_outside_transaction.py @@ -0,0 +1,64 @@ +# Copyright 2010 New Relic, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy + +from testing_support.fixtures import catch_background_exceptions +from testing_support.validators.validate_ml_events import ( + _check_event_attributes, + _event_details, +) + +from newrelic.common.object_wrapper import function_wrapper, transient_function_wrapper + + +def validate_ml_events_outside_transaction(events): + @function_wrapper + def _validate_wrapper(wrapped, instance, args, kwargs): + + record_called = [] + recorded_events = [] + + @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_ml_event") + @catch_background_exceptions + def _validate_ml_events_outside_transaction(wrapped, instance, args, kwargs): + record_called.append(True) + try: + result = wrapped(*args, **kwargs) + except: + raise + recorded_events[:] = [] + recorded_events.extend(list(instance._ml_events)) + + return result + + _new_wrapper = _validate_ml_events_outside_transaction(wrapped) + val = _new_wrapper(*args, **kwargs) + assert record_called + events = copy.copy(recorded_events) + + record_called[:] = [] + recorded_events[:] = [] + + for expected in events: + matching_ml_events = 0 + mismatches = [] + for captured in events: + if _check_event_attributes(expected, captured, mismatches): + matching_ml_events += 1 + assert matching_ml_events == 1, _event_details(matching_ml_events, events, mismatches) + + return val + + return _validate_wrapper diff --git a/tests/testing_support/validators/validate_transaction_metrics.py b/tests/testing_support/validators/validate_transaction_metrics.py index 7122b009a..0cb569d29 100644 --- a/tests/testing_support/validators/validate_transaction_metrics.py +++ b/tests/testing_support/validators/validate_transaction_metrics.py @@ -27,11 +27,13 @@ def validate_transaction_metrics( scoped_metrics=None, rollup_metrics=None, custom_metrics=None, + dimensional_metrics=None, index=-1, ): scoped_metrics = scoped_metrics or [] rollup_metrics = rollup_metrics or [] custom_metrics = custom_metrics or [] + dimensional_metrics = dimensional_metrics or [] if background_task: unscoped_metrics = [ @@ -56,6 +58,7 @@ def _validate_wrapper(wrapped, instance, args, kwargs): record_transaction_called = [] recorded_metrics = [] + recorded_dimensional_metrics = [] @transient_function_wrapper("newrelic.core.stats_engine", "StatsEngine.record_transaction") @catch_background_exceptions @@ -74,17 +77,36 @@ def _validate_transaction_metrics(wrapped, instance, args, kwargs): _metrics[k] = copy.copy(v) recorded_metrics.append(_metrics) + metrics = instance.dimensional_stats_table.metrics() + # Record a copy of the metric value so that the values aren't + # merged in the future + _metrics = {} + for k, v in metrics: + _metrics[k] = copy.copy(v) + recorded_dimensional_metrics.append(_metrics) + return result def _validate(metrics, name, scope, count): key = (name, scope) - metric = metrics.get(key) + + if isinstance(scope, str): + # Normal metric lookup + metric = metrics.get(key) + else: + # Dimensional metric lookup + metric_container = metrics.get(name, {}) + metric = metric_container.get(scope) def _metrics_table(): out = [""] out.append("Expected: {0}: {1}".format(key, count)) - for metric_key, metric_value in metrics.items(): - out.append("{0}: {1}".format(metric_key, metric_value[0])) + for metric_key, metric_container in metrics.items(): + if isinstance(metric_container, dict): + for metric_tags, metric_value in metric_container.items(): + out.append("{0}: {1}".format((metric_key, metric_tags), metric_value[0])) + else: + out.append("{0}: {1}".format(metric_key, metric_container[0])) return "\n".join(out) def _metric_details(): @@ -109,9 +131,11 @@ def _metric_details(): val = _new_wrapper(*args, **kwargs) assert record_transaction_called metrics = recorded_metrics[index] + captured_dimensional_metrics = recorded_dimensional_metrics[index] record_transaction_called[:] = [] recorded_metrics[:] = [] + recorded_dimensional_metrics[:] = [] for unscoped_metric in unscoped_metrics: _validate(metrics, unscoped_metric, "", 1) @@ -125,6 +149,11 @@ def _metric_details(): for custom_name, custom_count in custom_metrics: _validate(metrics, custom_name, "", custom_count) + for dimensional_name, dimensional_tags, dimensional_count in dimensional_metrics: + if isinstance(dimensional_tags, dict): + dimensional_tags = frozenset(dimensional_tags.items()) + _validate(captured_dimensional_metrics, dimensional_name, dimensional_tags, dimensional_count) + custom_metric_names = {name for name, _ in custom_metrics} for name, _ in metrics: if name not in custom_metric_names: @@ -132,4 +161,4 @@ def _metric_details(): return val - return _validate_wrapper \ No newline at end of file + return _validate_wrapper diff --git a/tox.ini b/tox.ini index 76eba20c5..b142cd840 100644 --- a/tox.ini +++ b/tox.ini @@ -63,6 +63,8 @@ envlist = python-agent_unittests-{pypy27,pypy38}-without_extensions, python-application_celery-{py27,py37,py38,py39,py310,py311,pypy27,pypy38}, gearman-application_gearman-{py27,pypy27}, + python-mlmodel_sklearn-{py38,py39,py310,py311}-scikitlearnlatest, + python-mlmodel_sklearn-{py37}-scikitlearn0101, python-component_djangorestframework-py27-djangorestframework0300, python-component_djangorestframework-{py37,py38,py39,py310,py311}-djangorestframeworklatest, python-component_flask_rest-{py37,py38,py39,pypy38}-flaskrestxlatest, @@ -186,9 +188,17 @@ deps = adapter_waitress-waitress02: waitress<2.1 adapter_waitress-waitresslatest: waitress agent_features: beautifulsoup4 + agent_features-{py37,py38,py39,py310,py311,pypy38}: protobuf + agent_features-{py27,pypy27}: protobuf<3.18.0 application_celery: celery<6.0 application_celery-{py37,pypy38}: importlib-metadata<5.0 application_gearman: gearman<3.0.0 + mlmodel_sklearn: pandas + mlmodel_sklearn: protobuf + mlmodel_sklearn: numpy + mlmodel_sklearn: scipy<1.11.0 + mlmodel_sklearn-scikitlearnlatest: scikit-learn + mlmodel_sklearn-scikitlearn0101: scikit-learn<1.1 component_djangorestframework-djangorestframework0300: Django<1.9 component_djangorestframework-djangorestframework0300: djangorestframework<3.1 component_djangorestframework-djangorestframeworklatest: Django @@ -399,6 +409,7 @@ changedir = agent_unittests: tests/agent_unittests application_celery: tests/application_celery application_gearman: tests/application_gearman + mlmodel_sklearn: tests/mlmodel_sklearn component_djangorestframework: tests/component_djangorestframework component_flask_rest: tests/component_flask_rest component_graphqlserver: tests/component_graphqlserver