feat: wavefront fetcher (#413)

Introducing a wavefront data fetcher.
Supports simple metric based queries as well as raw queries.

---------

Signed-off-by: Avik Basu <ab93@users.noreply.github.com>
Co-authored-by: Avik Basu <ab93@users.noreply.github.com>

numalogic/connectors/__init__.py

@@ -11,6 +11,7 @@
     RDSFetcherConf,
 )
 from numalogic.connectors.prometheus import PrometheusFetcher
+from numalogic.connectors.wavefront import WavefrontFetcher
 
 __all__ = [
     "RedisConf",
@@ -23,6 +24,7 @@
     "RDSFetcher",
     "RDSConf",
     "RDSFetcherConf",
+    "WavefrontFetcher",
 ]
 
 if find_spec("boto3"):

numalogic/connectors/wavefront.py

@@ -0,0 +1,159 @@
+import os
+from datetime import datetime
+from typing import Optional
+
+import pandas as pd
+from wavefront_api_client import Configuration, QueryApi, ApiClient
+
+from numalogic.connectors._base import DataFetcher
+from numalogic.tools.exceptions import WavefrontFetcherError
+
+import logging
+
+LOGGER = logging.getLogger(__name__)
+
+
+class WavefrontFetcher(DataFetcher):
+    """
+    Fetches data from Wavefront.
+
+    Args:
+        url (str): Wavefront URL.
+        api_token (str): Wavefront API token.
+
+    Raises
+    ------
+        ValueError: If API token is not provided.
+        WavefrontFetcherError: If there is an error fetching data from Wavefront.
+    """
+
+    def __init__(self, url: str, api_token: Optional[str] = None):
+        super().__init__(url)
+        api_token = api_token or os.getenv("WAVEFRONT_API_TOKEN")
+        if not api_token:
+            raise ValueError("WAVEFRONT API token is not provided")
+        configuration = Configuration()
+        configuration.host = url
+        configuration.api_key["X-AUTH-TOKEN"] = api_token
+        self.api_client = QueryApi(
+            ApiClient(
+                configuration,
+                header_name="Authorization",
+                header_value=f"Bearer {api_token}",
+            )
+        )
+
+    def _call_api(
+        self, query: str, start: int, end: Optional[int], granularity: str
+    ) -> pd.DataFrame:
+        """Calls the Wavefront API to fetch data."""
+        return self.api_client.query_api(
+            query, start, granularity, e=end, include_obsolete_metrics=True, use_raw_qk=True
+        )
+
+    @staticmethod
+    def _format_results(res: dict) -> pd.DataFrame:
+        """Validates and formats the results from the API."""
+        if res.get("error_type") is not None:
+            raise WavefrontFetcherError(
+                f"Error fetching data from Wavefront: "
+                f"{res.get('error_type')}: {res.get('error_message')}"
+            )
+        if res.get("timeseries") is None:
+            raise WavefrontFetcherError("No timeseries data found for the query")
+        dfs = []
+        for ts in res["timeseries"]:
+            dfs.append(pd.DataFrame(ts["data"], columns=["timestamp", "value"]))
+        df = pd.concat(dfs)
+        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="s")
+        return df.set_index("timestamp").sort_index()
+
+    def fetch(
+        self,
+        metric: str,
+        start: datetime,
+        filters: Optional[dict] = None,
+        end: Optional[datetime] = None,
+        granularity: str = "m",
+    ) -> pd.DataFrame:
+        """
+        Fetches data from Wavefront as a single metric.
+
+        Args:
+            metric (str): Metric to fetch. Example: 'system.cpu.usage'.
+                Do not include the 'ts()' function.
+            start (datetime): Start time.
+            filters (dict): Filters to apply to the query.
+            end (datetime): End time. Set to None to fetch data until now.
+            granularity (str): Granularity of the data. Default is 'm' (minute).
+
+        Returns
+        -------
+            Dataframe with the fetched data in the format: timestamp (index), value (column).
+
+        Raises
+        ------
+            WavefrontFetcherError: If there is an error fetching data from Wavefront
+        """
+        start = int(start.timestamp())
+        if end:
+            end = int(end.timestamp())
+        if filters:
+            _filters = " and ".join([f'{key}="{value}"' for key, value in filters.items()])
+            query = f"ts({metric}, {_filters})"
+        else:
+            query = f"ts({metric}"
+        LOGGER.info("Fetching data from Wavefront for query: %s", query)
+        res = self._call_api(query, start, end, granularity)
+        return self._format_results(res.to_dict())
+
+    def raw_fetch(
+        self,
+        query: str,
+        start: datetime,
+        filters: Optional[dict] = None,
+        end: Optional[datetime] = None,
+        granularity: str = "m",
+    ) -> pd.DataFrame:
+        """
+        Fetches data from Wavefront using a raw query, allowing for more complex queries.
+
+        Args:
+            query (str): Raw query to fetch data.
+            start (datetime): Start time.
+            filters (dict): Filters to apply to the query.
+            end (datetime): End time. Set to None to fetch data until now.
+            granularity (str): Granularity of the data. Default is 'm' (minute).
+
+        Returns
+        -------
+            Dataframe with the fetched data in the format: timestamp (index), value (column).
+
+        Raises
+        ------
+            WavefrontFetcherError:
+                - If there is an error fetching data from Wavefront
+                - If there is a key error in the query.
+
+        >>> from datetime import datetime, timedelta
+        ...
+        >>> fetcher = WavefrontFetcher(url="https://miata.wavefront.com", api_token="6spd-manual")
+        >>> df = fetcher.raw_fetch(
+        ...     query="rawsum(ts(engine.rpm, gear='{gear}' and track='{track}'))",
+        ...     start=datetime.now() - timedelta(minutes=5),
+        ...     filters={"gear": "1", "track": "laguna_seca"},
+        ...     end=datetime.now(),
+        ... )
+        """
+        start = start.timestamp()
+        if end:
+            end = end.timestamp()
+
+        try:
+            query = query.format(**filters)
+        except KeyError as key_err:
+            raise WavefrontFetcherError(f"Key error in query: {key_err}") from key_err
+
+        LOGGER.info("Fetching data from Wavefront for query: %s", query)
+        qres = self._call_api(query, start, granularity, end)
+        return self._format_results(qres.to_dict())

numalogic/tools/exceptions.py

@@ -122,3 +122,9 @@ class RDSFetcherError(Exception):
     """Base class for all exceptions raised by the RDSFetcher class."""
 
     pass
+
+
+class WavefrontFetcherError(Exception):
+    """Base class for all exceptions raised by the WavefrontFetcher class."""
+
+    pass

pyproject.toml

@@ -36,6 +36,7 @@ pynumaflow = "~0.8"
 prometheus_client = "^0.18.0"
 structlog = "^24.1.0"
 numalogic-prometheus = { version = "^0.8", allow-prereleases = true }
+wavefront-api-client = "^2.202.2"
 
 # extras
 mlflow-skinny = { version = "^2.0", optional = true }