metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "import pyspark\n",
    "import pyspark.sql.functions as F\n",
    "import json\n",
    "\n",
    "spark = pyspark.sql.SparkSession.builder.getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = spark.read.json(\"./criteo-logs/app-20201008161252-0083\")\n",
    "# metrics.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_and_dictify(df):\n",
    "    return [json.loads(row[0]) for row in df.selectExpr(\"to_json(*)\").collect()]\n",
    "\n",
    "def executor_info(df):\n",
    "    info = df.select(\"Executor Info\").dropna()\n",
    "    return collect_and_dictify(info)\n",
    "\n",
    "def plan_info(df):\n",
    "    return collect_and_dictify(df.select(\"sparkPlanInfo\").dropna())\n",
    "\n",
    "def stageInfo(df):\n",
    "    return collect_and_dictify(df.select(\"Stage Info\").dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plan_info(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def wide_metrics(df, mcol='Task Info', idcol='Task ID', interesting_metrics=None):\n",
    "    acc_cols = [F.col('Accumulable.%s' % s).alias('Metric %s' % s) for s in ['ID', 'Name', 'Value']]\n",
    "    obs = df.select(mcol).dropna().select('%s.*' % mcol)\n",
    "    cols = [F.col(elt) for elt in sorted(set(obs.columns) - set([idcol, 'Accumulables']))]\n",
    "    \n",
    "    if interesting_metrics is None:\n",
    "        interesting_metrics = F.col('Metric Name').isin(\n",
    "            'internal.metrics.resultSerializationTime',\n",
    "            'write time',\n",
    "            'shuffle write time',\n",
    "            'join time',\n",
    "            'GPU time',\n",
    "            'GPU decode time',\n",
    "            'fetch wait time',\n",
    "            'internal.metrics.executorCpuTime',\n",
    "            'internal.metrics.executorDeserializeTime',\n",
    "            'internal.metrics.jvmGCTime',\n",
    "            'internal.metrics.jvmGCTime'\n",
    "        )\n",
    "    \n",
    "    return obs.select(\n",
    "        idcol, \n",
    "        F.explode('Accumulables').alias('Accumulable'), \n",
    "        *cols\n",
    "    ).select(\n",
    "        idcol, \n",
    "        *(cols + acc_cols)\n",
    "    ).where(interesting_metrics)\n",
    "\n",
    "def wide_tasks(df):\n",
    "    return wide_metrics(df, 'Task Info', 'Task ID')\n",
    "\n",
    "def wide_stages(df):\n",
    "    return wide_metrics(df, 'Stage Info', 'Stage ID')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_metrics = wide_tasks(metrics).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "MaxRowsError",
     "evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMaxRowsError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\altair\\vegalite\\v4\\api.py\u001b[0m in \u001b[0;36mto_dict\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m         \u001b[0moriginal_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"data\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mcopy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\altair\\vegalite\\v4\\api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[1;34m(data, context)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;31m# convert dataframes  or objects with __geo_interface__ to dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__geo_interface__\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_pipe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;31m# convert string input to a URLData\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\toolz\\functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[1;34m(data, *funcs)\u001b[0m\n\u001b[0;32m    632\u001b[0m     \"\"\"\n\u001b[0;32m    633\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 634\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    635\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\toolz\\functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\altair\\vegalite\\data.py\u001b[0m in \u001b[0;36mdefault_data_transformer\u001b[1;34m(data, max_rows)\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mcurried\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcurry\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdefault_data_transformer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcurried\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit_rows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\toolz\\functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[1;34m(data, *funcs)\u001b[0m\n\u001b[0;32m    632\u001b[0m     \"\"\"\n\u001b[0;32m    633\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 634\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    635\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\toolz\\functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Miniconda3\\envs\\ds38\\lib\\site-packages\\altair\\utils\\data.py\u001b[0m in \u001b[0;36mlimit_rows\u001b[1;34m(data, max_rows)\u001b[0m\n\u001b[0;32m     78\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmax_rows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m         raise MaxRowsError(\n\u001b[0m\u001b[0;32m     81\u001b[0m             \u001b[1;34m\"The number of rows in your dataset is greater \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;34m\"than the maximum allowed ({}). \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation"
     ]
    }
   ],
   "source": [
    "alt.Chart(task_metrics).mark_bar().encode(\n",
    "    x='Task ID:N',\n",
    "    y='Metric Value:Q',\n",
    "    color='Metric Name:N',\n",
    "    tooltip=['Metric Name', 'Metric Value', 'Task ID']\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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\"Number of Tasks\": 136, \"Parent IDs\": [], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 136, [33], 34, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"MapPartitionsRDD\", 0, 136, [28], 29, \"{\\\"id\\\":\\\"71\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"FileScanRDD\", 0, 136, [], 28, \"{\\\"id\\\":\\\"71\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:173\", 0, 0, \"MapPartitionsRDD\", 0, 136, [29], 30, \"{\\\"id\\\":\\\"70\\\",\\\"name\\\":\\\"GpuFilter\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 136, [30], 31, 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\"Number of Tasks\": 136, \"Parent IDs\": [], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 136, [33], 34, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"MapPartitionsRDD\", 0, 136, [28], 29, \"{\\\"id\\\":\\\"71\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"FileScanRDD\", 0, 136, [], 28, \"{\\\"id\\\":\\\"71\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:173\", 0, 0, \"MapPartitionsRDD\", 0, 136, [29], 30, \"{\\\"id\\\":\\\"70\\\",\\\"name\\\":\\\"GpuFilter\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 136, [30], 31, 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\"Number of Tasks\": 48, \"Parent IDs\": [6], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 48, [37], 38, \"{\\\"id\\\":\\\"63\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [35], 36, \"{\\\"id\\\":\\\"65\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at aggregate.scala:286\", 0, 0, \"MapPartitionsRDD\", 0, 48, [36], 37, \"{\\\"id\\\":\\\"64\\\",\\\"name\\\":\\\"GpuHashAggregate\\\"}\", [false, 1, false, false]], [false, \"ShuffledBatchRDD at GpuShuffleExchangeExec.scala:102\", 0, 0, \"ShuffledBatchRDD\", 0, 48, [34], 35, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"mapPartitions at GpuShuffleExchangeExec.scala:159\", \"Submission Time\": 1602174101572, \"Metric ID\": 559, \"Metric Name\": \"internal.metrics.executorDeserializeTime\", \"Metric Value\": \"567\"}, {\"Stage ID\": 7, \"Completion Time\": 1602174101653, \"Details\": 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\"Number of Tasks\": 48, \"Parent IDs\": [6], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 48, [37], 38, \"{\\\"id\\\":\\\"63\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [35], 36, \"{\\\"id\\\":\\\"65\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at aggregate.scala:286\", 0, 0, \"MapPartitionsRDD\", 0, 48, [36], 37, \"{\\\"id\\\":\\\"64\\\",\\\"name\\\":\\\"GpuHashAggregate\\\"}\", [false, 1, false, false]], [false, \"ShuffledBatchRDD at GpuShuffleExchangeExec.scala:102\", 0, 0, \"ShuffledBatchRDD\", 0, 48, [34], 35, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"mapPartitions at GpuShuffleExchangeExec.scala:159\", \"Submission Time\": 1602174101572, \"Metric ID\": 402, \"Metric Name\": \"shuffle write time\", \"Metric Value\": \"49344340\"}, {\"Stage ID\": 7, \"Completion Time\": 1602174101653, \"Details\": 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\"Number of Tasks\": 48, \"Parent IDs\": [6], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 48, [37], 38, \"{\\\"id\\\":\\\"63\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [35], 36, \"{\\\"id\\\":\\\"65\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at aggregate.scala:286\", 0, 0, \"MapPartitionsRDD\", 0, 48, [36], 37, \"{\\\"id\\\":\\\"64\\\",\\\"name\\\":\\\"GpuHashAggregate\\\"}\", [false, 1, false, false]], [false, \"ShuffledBatchRDD at GpuShuffleExchangeExec.scala:102\", 0, 0, \"ShuffledBatchRDD\", 0, 48, [34], 35, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"mapPartitions at GpuShuffleExchangeExec.scala:159\", \"Submission Time\": 1602174101572, \"Metric ID\": 384, \"Metric Name\": \"fetch wait time\", \"Metric Value\": \"3\"}, {\"Stage ID\": 7, \"Completion Time\": 1602174101653, \"Details\": 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\"Number of Tasks\": 48, \"Parent IDs\": [6], \"RDD Info\": [[false, \"mapPartitions at GpuShuffleExchangeExec.scala:159\", 0, 0, \"MapPartitionsRDD\", 0, 48, [37], 38, \"{\\\"id\\\":\\\"63\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [35], 36, \"{\\\"id\\\":\\\"65\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at aggregate.scala:286\", 0, 0, \"MapPartitionsRDD\", 0, 48, [36], 37, \"{\\\"id\\\":\\\"64\\\",\\\"name\\\":\\\"GpuHashAggregate\\\"}\", [false, 1, false, false]], [false, \"ShuffledBatchRDD at GpuShuffleExchangeExec.scala:102\", 0, 0, \"ShuffledBatchRDD\", 0, 48, [34], 35, \"{\\\"id\\\":\\\"66\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"mapPartitions at GpuShuffleExchangeExec.scala:159\", \"Submission Time\": 1602174101572, \"Metric ID\": 562, \"Metric Name\": \"internal.metrics.executorCpuTime\", \"Metric Value\": \"401471762\"}, {\"Stage ID\": 8, \"Completion Time\": 1602174102071, \"Details\": \"org.apache.spark.sql.rapids.execution.GpuBroadcastExchangeExec$$anon$1.call(GpuBroadcastExchangeExec.scala:257)\\njava.util.concurrent.FutureTask.run(FutureTask.java:266)\\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\\njava.lang.Thread.run(Thread.java:748)\", \"Number of Tasks\": 48, \"Parent IDs\": [7], \"RDD Info\": [[false, \"call at FutureTask.java:266\", 0, 0, \"MapPartitionsRDD\", 0, 48, [47], 48, \"{\\\"id\\\":\\\"49\\\",\\\"name\\\":\\\"WholeStageCodegen (2)\\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:173\", 0, 0, \"MapPartitionsRDD\", 0, 48, [44], 45, \"{\\\"id\\\":\\\"53\\\",\\\"name\\\":\\\"GpuFilter\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [39], 40, \"{\\\"id\\\":\\\"62\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuRowToColumnarExec.scala:498\", 0, 0, \"MapPartitionsRDD\", 0, 48, [43], 44, \"{\\\"id\\\":\\\"54\\\",\\\"name\\\":\\\"GpuRowToColumnar\\\"}\", [false, 1, false, false]], [false, \"execute at GpuRowToColumnarExec.scala:497\", 0, 0, \"MapPartitionsRDD\", 0, 48, [42], 43, \"{\\\"id\\\":\\\"55\\\",\\\"name\\\":\\\"Window\\\"}\", [false, 1, false, false]], [false, \"ShuffledBatchRDD at GpuShuffleExchangeExec.scala:102\", 0, 0, \"ShuffledBatchRDD\", 0, 48, [38], 39, \"{\\\"id\\\":\\\"63\\\",\\\"name\\\":\\\"GpuColumnarExchange\\\"}\", [false, 1, false, false]], [false, \"execute at GpuRowToColumnarExec.scala:497\", 0, 0, \"MapPartitionsRDD\", 0, 48, [41], 42, \"{\\\"id\\\":\\\"56\\\",\\\"name\\\":\\\"WholeStageCodegen (1)\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuSortExec.scala:88\", 0, 0, \"MapPartitionsRDD\", 0, 48, [40], 41, \"{\\\"id\\\":\\\"61\\\",\\\"name\\\":\\\"GpuSort\\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 48, [46], 47, \"{\\\"id\\\":\\\"51\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [45], 46, \"{\\\"id\\\":\\\"52\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"call at FutureTask.java:266\", \"Submission Time\": 1602174101656, \"Metric ID\": 584, \"Metric Name\": \"internal.metrics.executorDeserializeTime\", \"Metric Value\": \"2112\"}, {\"Stage ID\": 8, \"Completion Time\": 1602174102071, \"Details\": 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\"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 48, [46], 47, \"{\\\"id\\\":\\\"51\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [45], 46, \"{\\\"id\\\":\\\"52\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"call at FutureTask.java:266\", \"Submission Time\": 1602174101656, \"Metric ID\": 587, \"Metric Name\": \"internal.metrics.executorCpuTime\", \"Metric Value\": \"5693659818\"}, {\"Stage ID\": 8, \"Completion Time\": 1602174102071, \"Details\": 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\"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 48, [46], 47, \"{\\\"id\\\":\\\"51\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 48, [45], 46, \"{\\\"id\\\":\\\"52\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"call at FutureTask.java:266\", \"Submission Time\": 1602174101656, \"Metric ID\": 398, \"Metric Name\": \"fetch wait time\", \"Metric Value\": \"0\"}, {\"Stage ID\": 9, \"Completion Time\": 1602174108953, \"Details\": \"org.apache.spark.sql.rapids.GpuInsertIntoHadoopFsRelationCommand.runColumnar(GpuInsertIntoHadoopFsRelationCommand.scala:166)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.sideEffectResult$lzycompute(GpuDataWritingCommandExec.scala:61)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.sideEffectResult(GpuDataWritingCommandExec.scala:60)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.doExecuteColumnar(GpuDataWritingCommandExec.scala:84)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeColumnar$1(SparkPlan.scala:202)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\norg.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)\\norg.apache.spark.sql.execution.SparkPlan.executeColumnar(SparkPlan.scala:198)\\norg.apache.spark.sql.execution.InputAdapter.doExecuteColumnar(WholeStageCodegenExec.scala:519)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeColumnar$1(SparkPlan.scala:202)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\norg.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)\\norg.apache.spark.sql.execution.SparkPlan.executeColumnar(SparkPlan.scala:198)\\ncom.nvidia.spark.rapids.GpuColumnarToRowExec.inputRDDs(GpuColumnarToRowExec.scala:59)\\norg.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:720)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\", \"Number of Tasks\": 136, \"Parent IDs\": [], \"RDD Info\": [[false, \"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 136, [54], 55, \"{\\\"id\\\":\\\"72\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 136, [51], 52, \"{\\\"id\\\":\\\"96\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"MapPartitionsRDD\", 0, 136, [49], 50, \"{\\\"id\\\":\\\"98\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 136, [52], 53, \"{\\\"id\\\":\\\"95\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"FileScanRDD\", 0, 136, [], 49, \"{\\\"id\\\":\\\"98\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:84\", 0, 0, \"MapPartitionsRDD\", 0, 136, [53], 54, \"{\\\"id\\\":\\\"73\\\",\\\"name\\\":\\\"GpuBroadcastHashJoin\\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:173\", 0, 0, \"MapPartitionsRDD\", 0, 136, [50], 51, \"{\\\"id\\\":\\\"97\\\",\\\"name\\\":\\\"GpuFilter\\\"}\", [false, 1, false, false]]], \"Stage Attempt ID\": 0, \"Stage Name\": \"runColumnar at GpuDataWritingCommandExec.scala:61\", \"Submission Time\": 1602174102149, \"Metric ID\": 412, \"Metric Name\": \"write time\", \"Metric Value\": \"7654285565\"}, {\"Stage ID\": 9, \"Completion Time\": 1602174108953, \"Details\": \"org.apache.spark.sql.rapids.GpuInsertIntoHadoopFsRelationCommand.runColumnar(GpuInsertIntoHadoopFsRelationCommand.scala:166)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.sideEffectResult$lzycompute(GpuDataWritingCommandExec.scala:61)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.sideEffectResult(GpuDataWritingCommandExec.scala:60)\\ncom.nvidia.spark.rapids.GpuDataWritingCommandExec.doExecuteColumnar(GpuDataWritingCommandExec.scala:84)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeColumnar$1(SparkPlan.scala:202)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\norg.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)\\norg.apache.spark.sql.execution.SparkPlan.executeColumnar(SparkPlan.scala:198)\\norg.apache.spark.sql.execution.InputAdapter.doExecuteColumnar(WholeStageCodegenExec.scala:519)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeColumnar$1(SparkPlan.scala:202)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\norg.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)\\norg.apache.spark.sql.execution.SparkPlan.executeColumnar(SparkPlan.scala:198)\\ncom.nvidia.spark.rapids.GpuColumnarToRowExec.inputRDDs(GpuColumnarToRowExec.scala:59)\\norg.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:720)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:175)\\norg.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:213)\\norg.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\", \"Number of Tasks\": 136, \"Parent IDs\": [], \"RDD Info\": [[false, \"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 136, [54], 55, \"{\\\"id\\\":\\\"72\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"mapPartitions at GpuCoalesceBatches.scala:448\", 0, 0, \"MapPartitionsRDD\", 0, 136, [51], 52, \"{\\\"id\\\":\\\"96\\\",\\\"name\\\":\\\"GpuCoalesceBatches\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"MapPartitionsRDD\", 0, 136, [49], 50, \"{\\\"id\\\":\\\"98\\\",\\\"name\\\":\\\"GpuScan parquet \\\"}\", [false, 1, false, false]], [false, \"map at basicPhysicalOperators.scala:85\", 0, 0, \"MapPartitionsRDD\", 0, 136, [52], 53, \"{\\\"id\\\":\\\"95\\\",\\\"name\\\":\\\"GpuProject\\\"}\", [false, 1, false, false]], [false, \"executeColumnar at basicPhysicalOperators.scala:171\", 0, 0, \"FileScanRDD\", 0, 136, [], 49, \"{\\\"id\\\":\\\"98\\\",\\\"name\\\":\\\"GpuScan parquet 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       "alt.Chart(...)"
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     "metadata": {},
     "execution_count": 8
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   ],
   "source": [
    "stage_metrics = wide_stages(metrics).toPandas()\n",
    "alt.Chart(stage_metrics).mark_bar().encode(\n",
    "    x='Stage ID:N',\n",
    "    y='Metric Value:Q',\n",
    "    color='Metric Name:N',\n",
    "    tooltip=['Details', 'Metric Name', 'Metric Value', 'Stage ID']\n",
    ").interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stage_metrics"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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