result-analysis/Worker Failure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics analysis - Worker failure\n",
    "This notebook visualizes worker failures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will use input factor: 3.166667\n"
     ]
    }
   ],
   "source": [
    "NOTEBOOK_PHASE = 3\n",
    "\n",
    "if (NOTEBOOK_PHASE == 1):\n",
    "    inputfactor = 1\n",
    "elif(NOTEBOOK_PHASE == 2):\n",
    "    inputfactor = 2\n",
    "elif(NOTEBOOK_PHASE == 3):\n",
    "    inputfactor = 3.166667    \n",
    "\n",
    "print(\"Will use input factor: \" + str(inputfactor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# settings for saving plots\n",
    "saveplots = True\n",
    "dpiResolution = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyspark.sql.functions as F\n",
    "import numpy as np\n",
    "# Import to indent the plots in the notebook\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.collections as collections\n",
    "import seaborn as sns\n",
    "from IPython.core.display import display, HTML\n",
    "from PIL import Image\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import AutoMinorLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Python imports\n",
    "import pandas as pd \n",
    "from collections import defaultdict\n",
    "from datetime import timedelta\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pytz\n",
    "import math\n",
    "\n",
    "# SQL imports\n",
    "from pyspark.sql.functions import isnan, when, count, col, lead, udf, lag, to_date, avg, desc, sum, min\n",
    "from pyspark.sql import Window\n",
    "from pyspark.sql import functions\n",
    "from pyspark.sql.types import IntegerType, LongType, DoubleType, TimestampType, StringType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# settings to get plots in the right style\n",
    "plt.style.use('ggplot')\n",
    "plt.style.use('seaborn-deep')\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 20\n",
    "plt.rcParams['axes.labelweight'] = 'normal'\n",
    "plt.rcParams['axes.labelcolor'] = 'black'\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['axes.edgecolor'] = 'lightgrey'\n",
    "plt.rcParams['axes.titlesize'] = 20\n",
    "plt.rcParams['axes.titleweight'] = 'normal'\n",
    "plt.rcParams['figure.edgecolor'] = 'lightgrey'\n",
    "plt.rcParams['xtick.labelsize'] = 20\n",
    "plt.rcParams['ytick.labelsize'] = 20\n",
    "plt.rcParams['xtick.color'] = 'black'\n",
    "plt.rcParams['ytick.color'] = 'black'\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['legend.edgecolor'] = 'lightgrey'\n",
    "plt.rcParams['figure.titlesize'] = 20\n",
    "plt.rcParams['figure.titleweight'] ='bold'\n",
    "plt.rcParams['grid.color'] = 'grey'\n",
    "plt.rcParams['grid.linestyle'] = ':'\n",
    "plt.rcParams['figure.facecolor'] = 'white'\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.left'] = False\n",
    "plt.rcParams['axes.spines.bottom'] = True\n",
    "plt.rcParams['xtick.direction'] = 'out'\n",
    "plt.rcParams['ytick.direction'] ='out'\n",
    "plt.rcParams[\"date.autoformatter.minute\"] = \"%H:%M\"\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - hive</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://192.168.0.197:4050\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.3.0</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>PySparkShell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7fcce60416d8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if Spark is running\n",
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# List of which frameworks should be included in this comparison\n",
    "frameworks_that_could_be_in_the_data = [\"FLINK\", \"KAFKASTREAMS\", \"SPARK\", \"STRUCTUREDSTREAMING\"]\n",
    "frameworksPrintable = {\n",
    "    \"SPARK\": \"Spark Streaming\", \n",
    "    \"STRUCTUREDSTREAMING\":\"Structured Streaming\", \n",
    "    \"FLINK\": \"Flink\", \n",
    "    \"KAFKASTREAMS\": \"Kafka Streams\"   \n",
    "}\n",
    "stages=[\"ingest\", \"parse\", \"join\", \"aggregate\", \"window\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataPath = dict()\n",
    "for i in frameworks_that_could_be_in_the_data:\n",
    "    dataPath[i + \"_ALO\"] = \"./scalability-data/worker-failure/\" + i + \"/stage\" + str(NOTEBOOK_PHASE) + \"/5x-4cpu-20gb/atleastonce/*\"\n",
    "    if((i==\"FLINK\") | (i==\"KAFKASTREAMS\")):\n",
    "        dataPath[i + \"_EO\"] = \"./scalability-data/worker-failure/\" + i + \"/stage\" + str(NOTEBOOK_PHASE) + \"/5x-4cpu-20gb/exactlyonce/*\"\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The paths that will be read: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'FLINK_ALO': './scalability-data/worker-failure/FLINK/stage3/5x-4cpu-20gb/atleastonce/*',\n",
       " 'FLINK_EO': './scalability-data/worker-failure/FLINK/stage3/5x-4cpu-20gb/exactlyonce/*',\n",
       " 'KAFKASTREAMS_ALO': './scalability-data/worker-failure/KAFKASTREAMS/stage3/5x-4cpu-20gb/atleastonce/*',\n",
       " 'KAFKASTREAMS_EO': './scalability-data/worker-failure/KAFKASTREAMS/stage3/5x-4cpu-20gb/exactlyonce/*',\n",
       " 'SPARK_ALO': './scalability-data/worker-failure/SPARK/stage3/5x-4cpu-20gb/atleastonce/*',\n",
       " 'STRUCTUREDSTREAMING_ALO': './scalability-data/worker-failure/STRUCTUREDSTREAMING/stage3/5x-4cpu-20gb/atleastonce/*'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The paths that will be read: \")\n",
    "dataPath"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For printing the quantiles of a column:\n",
    "provide a list of quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def datetimeFromEpoch(epoch):\n",
    "    return datetime.utcfromtimestamp(epoch//1000).replace(microsecond=epoch%1000*1000)\n",
    "\n",
    "datetimeFromEpochUDF = functions.udf(datetimeFromEpoch, TimestampType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_img_colored_and_grayscale(path_colored_img):\n",
    "    if saveplots: \n",
    "        plt.savefig(path_colored_img + '.png', dpi=dpiResolution, bbox_inches=\"tight\", pad_inches = 0)\n",
    "        im = Image.open(path_colored_img + '.png').convert('L')\n",
    "        im.save(path_colored_img + '_grayscale.png', dpi=(300, 300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latency \n",
    "Read in latency data and transform in the right format for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all checks passed for FLINK_ALO\n",
      "all checks passed for FLINK_EO\n",
      "all checks passed for KAFKASTREAMS_ALO\n",
      "all checks passed for KAFKASTREAMS_EO\n",
      "all checks passed for SPARK_ALO\n",
      "framework SPARK_EO not in data\n",
      "all checks passed for STRUCTUREDSTREAMING_ALO\n",
      "framework STRUCTUREDSTREAMING_EO not in data\n"
     ]
    }
   ],
   "source": [
    "# Check if each framework had all containers running during the benchmark. To avoid including runs which had issues with some components. \n",
    "frameworks = []\n",
    "containerCheck = defaultdict(dict)\n",
    "for framework in frameworks_that_could_be_in_the_data:\n",
    "    for proc_semantic in [\"_ALO\", \"_EO\"]:\n",
    "        try:\n",
    "            if framework == \"KAFKASTREAMS\":\n",
    "                requiredAmtContainers = 5 \n",
    "            elif framework == \"FLINK\": \n",
    "                requiredAmtContainers = 6\n",
    "            else:\n",
    "                requiredAmtContainers = 7\n",
    "                \n",
    "            containerCheckPhase = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "            .csv(dataPath[framework + proc_semantic] + \"/cpu-per-container-timeseries.csv/*\")\n",
    "            amtOfContainers = containerCheckPhase.select(\"containerName\").distinct().count()\n",
    "            if amtOfContainers != requiredAmtContainers:\n",
    "                containerCheckPhase.select(\"containerName\").distinct().show()\n",
    "                print(\"WARNING FOR \" + framework + \" volume: \" + str(NOTEBOOK_PHASE) + \" amount of containers: \" + str(amtOfContainers))\n",
    "            else:\n",
    "                frameworks.append(framework + proc_semantic)\n",
    "                print(\"all checks passed for \" + framework + proc_semantic)\n",
    "        except:\n",
    "            print('framework ' + framework + proc_semantic + \" not in data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_ALO\n",
      "FLINK_EO\n",
      "KAFKASTREAMS_ALO\n",
      "KAFKASTREAMS_EO\n",
      "SPARK_ALO\n",
      "STRUCTUREDSTREAMING_ALO\n"
     ]
    }
   ],
   "source": [
    "latencyTimeseriesDataWithoutStartup = dict()\n",
    "minTimeSec = dict()\n",
    "for framework in frameworks:\n",
    "    print(framework)\n",
    "    latencyTimeseriesDataPhase = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "        .csv(dataPath[framework] + \"/latency-timeseries-data-without-startup.csv/*\") \\\n",
    "        .withColumn(\"time\", datetimeFromEpochUDF(col(\"outputBucketTime\")))\n",
    "\n",
    "    minTimeInDF = latencyTimeseriesDataPhase.select(\"startTime\").agg(min(\"startTime\")).collect()[0][0]\n",
    "    if ((framework == \"SPARK_EO\") | (framework == \"STRUCTUREDSTREAMING_EO\") | (framework == \"SPARK_ALO\") | (framework == \"STRUCTUREDSTREAMING_ALO\")):\n",
    "        minTimeSec[framework] = (int(minTimeInDF)) / 60000\n",
    "    else:\n",
    "        minTimeSec[framework] =  int(minTimeInDF) / 60000\n",
    "    \n",
    "    latencyTimeseriesDataWithoutStartup[framework] = latencyTimeseriesDataPhase.withColumn(\"timeSec\", (col(\"outputBucketTime\")/60000.0)-minTimeSec[framework])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Throughput"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 0 throughput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "throughputTimeseriesDataWithStartup = dict()\n",
    "for framework in frameworks:\n",
    "    throughputTimeseriesDataWithStartupPhase = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "        .csv(dataPath[framework] + \"/output-throughput-timeseries-second-buckets.csv/*\") \\\n",
    "        .withColumn(\"time\", datetimeFromEpochUDF(col(\"outputBucketTime\")))    \n",
    "\n",
    "    throughputTimeseriesDataWithStartup[framework] = throughputTimeseriesDataWithStartupPhase.withColumn(\"timeSec\", (col(\"outputBucketTime\")/60000.0)-minTimeSec[framework])\n",
    "\n",
    "inputThroughputTimeseriesDataWithStartup = dict()\n",
    "for framework in frameworks:\n",
    "    inputThroughputTimeseriesDataWithStartupPhase = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "        .csv(dataPath[framework] + \"/input-throughput-timeseries-second-buckets.csv/*\") \\\n",
    "        .withColumn(\"time\", datetimeFromEpochUDF(col(\"inputBucketTime\")))    \n",
    "\n",
    "    inputThroughputTimeseriesDataWithStartup[framework] = inputThroughputTimeseriesDataWithStartupPhase.withColumn(\"timeSec\", (col(\"inputBucketTime\")/60000.0)-minTimeSec[framework])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CPU "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cpuTimeseries = dict()\n",
    "for framework in frameworks:\n",
    "    try:\n",
    "        cpuTimeseries[framework] = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "            .csv(dataPath[framework] + \"/cpu-per-container-timeseries.csv/*\") \\\n",
    "            .withColumn(\"timeParsed\", datetimeFromEpochUDF(col(\"time\")))\n",
    "        \n",
    "        \n",
    "        \n",
    "    except:\n",
    "        print(\"didnt work for \" + framework)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "containersPandas = dict()\n",
    "for framework in frameworks:\n",
    "    containersPandas[framework] = cpuTimeseries[framework].select(\"containerName\").distinct().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# You can use this to assign different colors to the different workers in the plot. We didn't use this here.\n",
    "# Map label to RGB\n",
    "color_map = dict()\n",
    "for framework in frameworks:\n",
    "    #Assign different color to each container\n",
    "    rgb_values = sns.diverging_palette(255, 133, l=60, n=len(containersPandas[framework]), center=\"dark\")\n",
    "    color_map[framework] = dict(zip(containersPandas[framework]['containerName'], rgb_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAABLCAYAAABHlv13AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlEv8GyoSBwMWtdp3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.diverging_palette(255, 133, l=60, n=len(containersPandas), center=\"dark\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Map label to RGB\n",
    "color_map = defaultdict(dict)\n",
    "for framework in frameworks:\n",
    "    #Assign different color to each container:\n",
    "    rgb_values = sns.husl_palette(len(containersPandas[framework]), h=0.4, l=0.65, s=1) \n",
    "    color_map[framework] = dict(zip(containersPandas[framework]['containerName'], rgb_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CPU timeseries analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cpuTimeseriesDataWithStartup = dict()\n",
    "for framework in frameworks:\n",
    "    cpuOfPhasePerContainer = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\") \\\n",
    "        .csv(dataPath[framework] + \"/cpu-per-container-timeseries.csv/*\") \\\n",
    "        .withColumn(\"timeParsed\", datetimeFromEpochUDF(col(\"time\")))\n",
    "\n",
    "    cpuTimeseriesDataWithStartup[framework] = cpuOfPhasePerContainer \\\n",
    "        .withColumn(\"timeSec\", (col(\"time\")/60000.0)-minTimeSec[framework])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "containersPandasPerPhase = dict()\n",
    "for framework in frameworks:\n",
    "    containersPandasPerPhase[framework] = cpuTimeseriesDataWithStartup[framework] \\\n",
    "        .select(\"containerName\").distinct().orderBy(\"containerName\").toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric correlations\n",
    "Plotting different metrics for a certain stage together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For generating a chart of the four metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_ALO\n",
      "FLINK_EO\n",
      "KAFKASTREAMS_ALO\n",
      "KAFKASTREAMS_EO\n",
      "SPARK_ALO\n",
      "STRUCTUREDSTREAMING_ALO\n"
     ]
    }
   ],
   "source": [
    "stageLatencyPandasShortSample = dict()\n",
    "stageThroughputPandasShortSample = dict()\n",
    "stageInputThroughputPandasShortSample = dict()\n",
    "stageCpuPandasShortSample = dict()\n",
    "start = 8.5\n",
    "end = 10.5\n",
    "for j, framework in enumerate(frameworks):\n",
    "    print(framework)\n",
    "    stageLatencyPandasShortSample[framework] = latencyTimeseriesDataWithoutStartup[framework].orderBy(\"timeSec\") \\\n",
    "        .filter((col(\"timeSec\") >start) & (col(\"timeSec\")<end)).toPandas()\n",
    "    stageThroughputPandasShortSample[framework] = throughputTimeseriesDataWithStartup[framework].orderBy(\"timeSec\") \\\n",
    "        .filter((col(\"timeSec\") >start) & (col(\"timeSec\")<end)).toPandas()\n",
    "    stageInputThroughputPandasShortSample[framework] = inputThroughputTimeseriesDataWithStartup[framework].orderBy(\"timeSec\") \\\n",
    "        .filter((col(\"timeSec\") >start) & (col(\"timeSec\")<end)).toPandas()\n",
    "    stageCpuPandasShortSample[framework] = cpuTimeseriesDataWithStartup[framework].orderBy(\"timeSec\")\\\n",
    "        .filter((col(\"timeSec\") >start) & (col(\"timeSec\")<end)).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generateWorkerFailureChart(colNum, containersPandas, latencyPandas, throughputPandas, inputThroughputPandas, cpuPandas, start, end):\n",
    "    minor_x_locator = AutoMinorLocator(5) # how many minor grid lines in between two major grid lines for x axis\n",
    "      \n",
    "    # we entered this manually based on the excel and manual computation\n",
    "    recovery_time = {\n",
    "        \"FLINK_EO\": 58,\n",
    "        \"KAFKASTREAMS_EO\": 32,\n",
    "        \"SPARK_EO\": 23,\n",
    "        \"STRUCTUREDSTREAMING_EO\": 22,\n",
    "        \"FLINK_ALO\": 53,\n",
    "        \"KAFKASTREAMS_ALO\": 21,\n",
    "        \"SPARK_ALO\": 30,\n",
    "        \"STRUCTUREDSTREAMING_ALO\": 20\n",
    "    }\n",
    "    pct50_line, = ax[0, colNum].plot(latencyPandas[\"timeSec\"], latencyPandas[\"percentile_50_second\"], color=\"#606060\", linestyle=\"solid\", label = \"50p\")\n",
    "    pct99_line, = ax[0, colNum].plot(latencyPandas[\"timeSec\"], latencyPandas[\"percentile_99_second\"], color=\"black\", linestyle=\"solid\", label = \"99p\")\n",
    "    ax[0, colNum].set_ylim(ymin=0, ymax=20000)\n",
    "    ax[0, colNum].set_xlim(xmin=start, xmax=end)\n",
    "    ax[0, colNum].xaxis.set_minor_locator(minor_x_locator)\n",
    "    minor_y_locator_1 = AutoMinorLocator(2) # how many minor grid lines in between two major grid lines for y axis\n",
    "    ax[0, colNum].yaxis.set_minor_locator(minor_y_locator_1)\n",
    "    ax[0, colNum].grid(which='minor', color='black')\n",
    "    ax[0, colNum].get_yaxis().set_major_formatter(\n",
    "        matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x/1000.0), ',').replace(',', ' ') + \" s\"))\n",
    "    \n",
    "    tp_line1 = ax[1, colNum].scatter(throughputPandas[\"timeSec\"], throughputPandas[\"outputMsgCount\"], zorder = 10, s = 3, label = \"output\", color=\"#151515\")\n",
    "    tp_line2, = ax[1, colNum].plot(inputThroughputPandas[\"timeSec\"], inputThroughputPandas[\"inputMsgCount\"].multiply(inputfactor), zorder = 10, label = \"input\", color=\"black\", linestyle = \"--\")\n",
    "    ax[1, colNum].get_yaxis().set_major_formatter(\n",
    "        matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x/1000.0), ',').replace(',', ' ')))\n",
    "    ax[1, colNum].set_ylim(ymin=0, ymax=40000)\n",
    "    minor_y_locator_2 = AutoMinorLocator(2) # how many minor grid lines in between two major grid lines for y axis\n",
    "    ax[1, colNum].yaxis.set_minor_locator(minor_y_locator_2)\n",
    "    ax[1, colNum].xaxis.set_minor_locator(minor_x_locator)\n",
    "    ax[1, colNum].set_yticks([0, 20000, 40000])\n",
    "    ax[1, colNum].grid(which='minor', color='black')\n",
    "    \n",
    "    for contNum, containerId in enumerate(containersPandasPerPhase[framework]['containerName']):\n",
    "        data = cpuPandas.loc[cpuPandas['containerName'] == containerId]\n",
    "        if \"-1\" in containerId:\n",
    "            # to know when the failure happened we look at the maximum timestamp of the first container\n",
    "            first_container_id = data[data['timeSec'] == data['timeSec'].min()][\"containerTaskId\"].iloc[0]\n",
    "            time_of_failure = cpuPandas.loc[cpuPandas['containerTaskId'] == first_container_id][\"timeSec\"].max()\n",
    "            # to know when the new container came up, we look at the minimum timestamp of the second container\n",
    "            restarted_container_id = data[data['timeSec'] == data['timeSec'].max()][\"containerTaskId\"].iloc[0]\n",
    "            time_restart_finished = cpuPandas.loc[cpuPandas['containerTaskId'] == restarted_container_id][\"timeSec\"].min()\n",
    "            \n",
    "            print(\"time of failure at for \" + framework + \" \" + str(time_of_failure*60) + \" time of restart at \" + str(time_restart_finished*60))\n",
    "            print(\"total outage duration for \" + framework + \" \" + str((time_restart_finished*60-time_of_failure*60)))\n",
    "            \n",
    "            for rowNum in [0, 1, 2]:\n",
    "                ax[rowNum, colNum].axvspan(time_of_failure, time_of_failure + recovery_time[framework]/60, facecolor='#C0C0C0', alpha=0.5)\n",
    "            cpu_worker_line_1 = ax[2, colNum].scatter(data['timeSec'], data['cpuUsagePct'], \n",
    "               c=\"black\", label=\"cpu usage worker\", s = 2, zorder = 20)\n",
    "        else:\n",
    "            cpu_worker_line_others = ax[2, colNum].scatter(data['timeSec'], data['cpuUsagePct'], \n",
    "               c=\"grey\", label=\"cpu usage worker\", s = 2, zorder = 10)\n",
    "\n",
    "\n",
    "    ax[2, colNum].set_ylim(ymin=0, ymax=110)\n",
    "    ax[2, colNum].set_yticks([0, 50, 100])\n",
    "    ax[2, colNum].xaxis.set_minor_locator(minor_x_locator)\n",
    "    minor_y_locator_3 = AutoMinorLocator(2) # how many minor grid lines in between two major grid lines for y axis\n",
    "    ax[2, colNum].yaxis.set_minor_locator(minor_y_locator_3)\n",
    "    ax[2, colNum].grid(which='minor', color='black')\n",
    "    ax[2, colNum].get_yaxis().set_major_formatter(\n",
    "        matplotlib.ticker.FuncFormatter(lambda x, p: str(int(x)) + \"%\"))\n",
    "    ax[2, colNum].set_xlabel(\"minute\")\n",
    "\n",
    "    if (colNum == 0):\n",
    "        ax[0, colNum].set_ylabel(\"latency\")\n",
    "        ax[1, colNum].set_ylabel(\"throughput\\nx1000 msg/s\")\n",
    "        ax[2, colNum].set_ylabel(\"CPU\")\n",
    "    \n",
    "    if (colNum == len(frameworks)-1):\n",
    "        ax[0, colNum].legend(loc = \"upper right\", ncol=3, bbox_to_anchor=(1, 1.45), framealpha=1.0, frameon=False)\n",
    "        ax[1, colNum].legend(handletextpad=0.1, loc = \"upper right\", ncol=2, bbox_to_anchor=(1, 1.45), framealpha=1.0, frameon=False, scatterpoints=1, markerscale=3)\n",
    "        ax[2, colNum].legend([cpu_worker_line_1, cpu_worker_line_others], [\"failed worker\", \"other workers\"], handletextpad=0.05, ncol = 2, loc = \"upper right\", bbox_to_anchor=(1, 1.45), frameon=False, scatterpoints=1, markerscale=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time of failure at for FLINK_ALO 548.4550000727177 time of restart at 550.4550001770258\n",
      "total outage duration for FLINK_ALO 2.0000001043081284\n",
      "time of failure at for FLINK_EO 546.9100000709295 time of restart at 550.9100000560284\n",
      "total outage duration for FLINK_EO 3.999999985098839\n",
      "time of failure at for KAFKASTREAMS_ALO 534.8130000382662 time of restart at 540.8129999041557\n",
      "total outage duration for KAFKASTREAMS_ALO 5.999999865889549\n",
      "time of failure at for KAFKASTREAMS_EO 534.890000000596 time of restart at 538.8899999856949\n",
      "total outage duration for KAFKASTREAMS_EO 3.999999985098839\n",
      "time of failure at for SPARK_ALO 565.6549999862909 time of restart at 571.6549998521805\n",
      "total outage duration for SPARK_ALO 5.999999865889549\n",
      "time of failure at for STRUCTUREDSTREAMING_ALO 566.5020000189543 time of restart at 570.5020000040531\n",
      "total outage duration for STRUCTUREDSTREAMING_ALO 3.999999985098839\n"
     ]
    },
    {
     "data": {
      "image/png": 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MDRs2pKKiIiHNtWvXCAC5uLhIVe/qpnnz5uTv7y+xrWPHjgSA7ty5I2z7+eef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WtlatWiErK8vod6HH2Nvba71vJfU85o7x8/PTS9zHjh2TPCqIhEvb9o0bN7B27VqtJaYs5Xex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\n9pqr1AKqpguhCj5sxVrwU1paaubPVE/Xr1+Xlf0hUFpais8//xylpaU2fZv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du3ejrq4O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\nBEEQBEEQBEEQBEEQT/h/7liHuWmdYxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frameworks=[\"FLINK\", \"KAFKASTREAMS\", \"SPARK\", \"STRUCTUREDSTREAMING\"]\n",
    "frameworksPrintable2 = {\n",
    "    \"SPARK_EO\": \"Spark Str. \\n exactly-once\", \n",
    "    \"STRUCTUREDSTREAMING_EO\":\"Structured Str. \\n exactly-once\", \n",
    "    \"FLINK_EO\": \"Flink \\n exactly-once\", \n",
    "    \"KAFKASTREAMS_EO\": \"Kafka Str. \\n exactly-once\",\n",
    "    \"SPARK_ALO\": \"Spark Str. \\n at-least-once\", \n",
    "    \"STRUCTUREDSTREAMING_ALO\":\"Structured Str. \\n at-least-once\", \n",
    "    \"FLINK_ALO\": \"Flink \\n at-least-once\", \n",
    "    \"KAFKASTREAMS_ALO\": \"Kafka Str. \\n at-least-once\"     \n",
    "}\n",
    "f, ax = plt.subplots(3, len(frameworks),figsize=(18, 7), sharey='row', sharex='col')\n",
    "pad = 5\n",
    "\n",
    "\n",
    "for j, framework in enumerate(frameworks):\n",
    "    generateWorkerFailureChart(j, containersPandas=containersPandas, \\\n",
    "                           latencyPandas=stageLatencyPandasShortSample[framework], \\\n",
    "                           throughputPandas=stageThroughputPandasShortSample[framework], \\\n",
    "                           inputThroughputPandas=stageInputThroughputPandasShortSample[framework], \\\n",
    "                           cpuPandas=stageCpuPandasShortSample[framework], start=start, end=end)\n",
    "    ax[0, j].annotate(frameworksPrintable2[framework], xy=(0.5, 1.3), xytext=(0, pad),\n",
    "            xycoords='axes fraction', textcoords='offset points',\n",
    "            size='medium', ha='center', va='baseline')\n",
    "\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.35)\n",
    "\n",
    "save_img_colored_and_grayscale(\"./figures/worker-failure/overall/phase\" + str(NOTEBOOK_PHASE) + \"/worker_failure_\" + str(NOTEBOOK_PHASE) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_ALO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   3.0|              73016|\n",
      "|   4.0|             345600|\n",
      "|   5.0|             345600|\n",
      "|   6.0|             345600|\n",
      "|   7.0|             345600|\n",
      "|   8.0|             345600|\n",
      "|   9.0|             345600|\n",
      "|  10.0|             204088|\n",
      "|  11.0|             498632|\n",
      "|  12.0|             345600|\n",
      "|  13.0|             345600|\n",
      "|  14.0|             345600|\n",
      "|  15.0|             345600|\n",
      "|  16.0|             345600|\n",
      "|  17.0|              69120|\n",
      "+------+-------------------+\n",
      "\n",
      "FLINK_EO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   3.0|              74880|\n",
      "|   4.0|             345600|\n",
      "|   5.0|             345600|\n",
      "|   6.0|             345600|\n",
      "|   7.0|             345600|\n",
      "|   8.0|             345600|\n",
      "|   9.0|             345600|\n",
      "|  10.0|              40320|\n",
      "|  11.0|             650880|\n",
      "|  12.0|             345600|\n",
      "|  13.0|             345600|\n",
      "|  14.0|             345600|\n",
      "|  15.0|             345600|\n",
      "|  16.0|             345600|\n",
      "|  17.0|              51840|\n",
      "+------+-------------------+\n",
      "\n",
      "KAFKASTREAMS_ALO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   3.0|              21896|\n",
      "|   4.0|             345445|\n",
      "|   5.0|             346201|\n",
      "|   6.0|             346262|\n",
      "|   7.0|             344105|\n",
      "|   8.0|             345394|\n",
      "|   9.0|             333406|\n",
      "|  10.0|             358941|\n",
      "|  11.0|             343788|\n",
      "|  12.0|             345836|\n",
      "|  13.0|             346061|\n",
      "|  14.0|             346744|\n",
      "|  15.0|             345953|\n",
      "|  16.0|             338100|\n",
      "+------+-------------------+\n",
      "\n",
      "KAFKASTREAMS_EO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   3.0|             101537|\n",
      "|   4.0|             347187|\n",
      "|   5.0|             343010|\n",
      "|   6.0|             346982|\n",
      "|   7.0|             347151|\n",
      "|   8.0|             342796|\n",
      "|   9.0|             329140|\n",
      "|  10.0|             364118|\n",
      "|  11.0|             345718|\n",
      "|  12.0|             344207|\n",
      "|  13.0|             346497|\n",
      "|  14.0|             346586|\n",
      "|  15.0|             344855|\n",
      "|  16.0|             345227|\n",
      "|  17.0|               9560|\n",
      "+------+-------------------+\n",
      "\n",
      "SPARK_ALO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   4.0|             267008|\n",
      "|   5.0|             345504|\n",
      "|   6.0|             345600|\n",
      "|   7.0|             345600|\n",
      "|   8.0|             345600|\n",
      "|   9.0|             345089|\n",
      "|  10.0|             345408|\n",
      "|  11.0|             345455|\n",
      "|  12.0|             345600|\n",
      "|  13.0|             345600|\n",
      "|  14.0|             345600|\n",
      "|  15.0|             345600|\n",
      "|  16.0|             345600|\n",
      "|  17.0|             179549|\n",
      "+------+-------------------+\n",
      "\n",
      "STRUCTUREDSTREAMING_ALO\n",
      "+------+-------------------+\n",
      "|minute|sum(outputMsgCount)|\n",
      "+------+-------------------+\n",
      "|   4.0|             256320|\n",
      "|   5.0|             346027|\n",
      "|   6.0|             345173|\n",
      "|   7.0|             345600|\n",
      "|   8.0|             351360|\n",
      "|   9.0|             345600|\n",
      "|  10.0|             339840|\n",
      "|  11.0|             345600|\n",
      "|  12.0|             348281|\n",
      "|  13.0|             342919|\n",
      "|  14.0|             351360|\n",
      "|  15.0|             339840|\n",
      "|  16.0|             351360|\n",
      "|  17.0|             184320|\n",
      "+------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = 0\n",
    "end = 25\n",
    "for j, framework in enumerate(frameworks):\n",
    "    print(framework)\n",
    "\n",
    "    throughputAroundFailure = throughputTimeseriesDataWithStartup[framework].orderBy(\"timeSec\") \\\n",
    "        .filter((col(\"timeSec\") >start) & (col(\"timeSec\")<end))\\\n",
    "        .withColumn(\"minute\", F.round(col(\"timeSec\")+0.5))\\\n",
    "        .groupBy(\"minute\").agg(sum(\"outputMsgCount\")).orderBy(\"minute\").show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_ALO\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
       "      <th>percentile_50_second</th>\n",
       "      <th>percentile_99_second</th>\n",
       "      <th>timeSinceFailure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>flink</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>flink</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>flink</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.381345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flink</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>flink</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.572935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>flink</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.882733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>flink</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>flink</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.255845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>flink</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.232385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>flink</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>flink</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.355910</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>flink</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.300746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>flink</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>flink</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.171705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>flink</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>flink</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.285826</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>flink</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.268967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>flink</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>flink</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.232128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>flink</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>flink</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.307139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>flink</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.209438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>flink</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>flink</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.299738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>flink</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.347457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>flink</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>flink</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.228076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>flink</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>flink</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.139310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>flink</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>flink</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.136550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flink</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.662201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>flink</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>flink</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.107004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>flink</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>flink</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.115748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>flink</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.109906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>flink</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>flink</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.116514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flink</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.104428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>flink</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>flink</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.123171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>flink</td>\n",
       "      <td>40.0</td>\n",
       "      <td>27.517018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>flink</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>flink</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.473366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>flink</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>flink</td>\n",
       "      <td>44.0</td>\n",
       "      <td>101.799762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>flink</td>\n",
       "      <td>45.0</td>\n",
       "      <td>97.974122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>flink</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>flink</td>\n",
       "      <td>47.0</td>\n",
       "      <td>99.305910</td>\n",
       "      <td>2529.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:34</td>\n",
       "      <td>9.957617</td>\n",
       "      <td>51996.0</td>\n",
       "      <td>52499.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>flink</td>\n",
       "      <td>48.0</td>\n",
       "      <td>100.706029</td>\n",
       "      <td>54121.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:35</td>\n",
       "      <td>9.974283</td>\n",
       "      <td>47969.0</td>\n",
       "      <td>52878.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>flink</td>\n",
       "      <td>49.0</td>\n",
       "      <td>100.755382</td>\n",
       "      <td>101358.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:36</td>\n",
       "      <td>9.990950</td>\n",
       "      <td>35180.0</td>\n",
       "      <td>46485.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>flink</td>\n",
       "      <td>50.0</td>\n",
       "      <td>96.557063</td>\n",
       "      <td>158770.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:37</td>\n",
       "      <td>10.007617</td>\n",
       "      <td>13402.0</td>\n",
       "      <td>33510.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>flink</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5782.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:38</td>\n",
       "      <td>10.024283</td>\n",
       "      <td>823.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>flink</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.738893</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:39</td>\n",
       "      <td>10.040950</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>flink</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:40</td>\n",
       "      <td>10.057617</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>flink</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.025693</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:41</td>\n",
       "      <td>10.074283</td>\n",
       "      <td>802.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>flink</td>\n",
       "      <td>55.0</td>\n",
       "      <td>32.056722</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:42</td>\n",
       "      <td>10.090950</td>\n",
       "      <td>808.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>flink</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:43</td>\n",
       "      <td>10.107617</td>\n",
       "      <td>793.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>flink</td>\n",
       "      <td>57.0</td>\n",
       "      <td>33.473885</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 13:47:44</td>\n",
       "      <td>10.124283</td>\n",
       "      <td>804.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   framework  timeSinceRestart  cpuUsagePct  outputMsgCount  inputMsgCount  \\\n",
       "0      flink              -2.0          NaN             NaN           5760   \n",
       "1      flink              -1.0          NaN             NaN           5760   \n",
       "2      flink               0.0    46.381345             NaN           5760   \n",
       "3      flink               1.0          NaN             NaN           5760   \n",
       "4      flink               2.0    59.572935             NaN           5760   \n",
       "5      flink               3.0    19.882733             NaN           5760   \n",
       "6      flink               4.0          NaN             NaN           5760   \n",
       "7      flink               5.0     0.255845             NaN           5760   \n",
       "8      flink               6.0     0.232385             NaN           5760   \n",
       "9      flink               7.0          NaN             NaN           5760   \n",
       "10     flink               8.0     0.355910             NaN           5760   \n",
       "11     flink               9.0     0.300746             NaN           5760   \n",
       "12     flink              10.0          NaN             NaN           5760   \n",
       "13     flink              11.0     0.171705             NaN           5760   \n",
       "14     flink              12.0          NaN             NaN           5760   \n",
       "15     flink              13.0     0.285826             NaN           5760   \n",
       "16     flink              14.0     0.268967             NaN           5760   \n",
       "17     flink              15.0          NaN             NaN           5760   \n",
       "18     flink              16.0     0.232128             NaN           5760   \n",
       "19     flink              17.0          NaN             NaN           5760   \n",
       "20     flink              18.0     0.307139             NaN           5760   \n",
       "21     flink              19.0     0.209438             NaN           5760   \n",
       "22     flink              20.0          NaN             NaN           5760   \n",
       "23     flink              21.0     0.299738             NaN           5760   \n",
       "24     flink              22.0     0.347457             NaN           5760   \n",
       "25     flink              23.0          NaN             NaN           5760   \n",
       "26     flink              24.0     0.228076             NaN           5760   \n",
       "27     flink              25.0          NaN             NaN           5760   \n",
       "28     flink              26.0     0.139310             NaN           5760   \n",
       "29     flink              27.0          NaN             NaN           5760   \n",
       "30     flink              28.0     0.136550             NaN           5760   \n",
       "31     flink              29.0     0.662201             NaN           5760   \n",
       "32     flink              30.0          NaN             NaN           5760   \n",
       "33     flink              31.0     0.107004             NaN           5760   \n",
       "34     flink              32.0          NaN             NaN           5760   \n",
       "35     flink              33.0     0.115748             NaN           5760   \n",
       "36     flink              34.0     0.109906             NaN           5760   \n",
       "37     flink              35.0          NaN             NaN           5760   \n",
       "38     flink              36.0     0.116514             NaN           5760   \n",
       "39     flink              37.0     0.104428             NaN           5760   \n",
       "40     flink              38.0          NaN             NaN           5760   \n",
       "41     flink              39.0     0.123171             NaN           5760   \n",
       "42     flink              40.0    27.517018             NaN           5760   \n",
       "43     flink              41.0          NaN             NaN           5760   \n",
       "44     flink              42.0    84.473366             NaN           5760   \n",
       "45     flink              43.0          NaN             NaN           5760   \n",
       "46     flink              44.0   101.799762             NaN           5760   \n",
       "47     flink              45.0    97.974122             NaN           5760   \n",
       "48     flink              46.0          NaN             NaN           5760   \n",
       "49     flink              47.0    99.305910          2529.0           5760   \n",
       "50     flink              48.0   100.706029         54121.0           5760   \n",
       "51     flink              49.0   100.755382        101358.0           5760   \n",
       "52     flink              50.0    96.557063        158770.0           5760   \n",
       "53     flink              51.0          NaN          5782.0           5760   \n",
       "54     flink              52.0    58.738893          5760.0           5760   \n",
       "55     flink              53.0          NaN          5760.0           5760   \n",
       "56     flink              54.0    40.025693          5760.0           5760   \n",
       "57     flink              55.0    32.056722          5760.0           5760   \n",
       "58     flink              56.0          NaN          5760.0           5760   \n",
       "59     flink              57.0    33.473885          5760.0           5760   \n",
       "\n",
       "                  time    timeSec  percentile_50_second  percentile_99_second  \\\n",
       "0                  NaT        NaN                   NaN                   NaN   \n",
       "1                  NaT        NaN                   NaN                   NaN   \n",
       "2                  NaT        NaN                   NaN                   NaN   \n",
       "3                  NaT        NaN                   NaN                   NaN   \n",
       "4                  NaT        NaN                   NaN                   NaN   \n",
       "5                  NaT        NaN                   NaN                   NaN   \n",
       "6                  NaT        NaN                   NaN                   NaN   \n",
       "7                  NaT        NaN                   NaN                   NaN   \n",
       "8                  NaT        NaN                   NaN                   NaN   \n",
       "9                  NaT        NaN                   NaN                   NaN   \n",
       "10                 NaT        NaN                   NaN                   NaN   \n",
       "11                 NaT        NaN                   NaN                   NaN   \n",
       "12                 NaT        NaN                   NaN                   NaN   \n",
       "13                 NaT        NaN                   NaN                   NaN   \n",
       "14                 NaT        NaN                   NaN                   NaN   \n",
       "15                 NaT        NaN                   NaN                   NaN   \n",
       "16                 NaT        NaN                   NaN                   NaN   \n",
       "17                 NaT        NaN                   NaN                   NaN   \n",
       "18                 NaT        NaN                   NaN                   NaN   \n",
       "19                 NaT        NaN                   NaN                   NaN   \n",
       "20                 NaT        NaN                   NaN                   NaN   \n",
       "21                 NaT        NaN                   NaN                   NaN   \n",
       "22                 NaT        NaN                   NaN                   NaN   \n",
       "23                 NaT        NaN                   NaN                   NaN   \n",
       "24                 NaT        NaN                   NaN                   NaN   \n",
       "25                 NaT        NaN                   NaN                   NaN   \n",
       "26                 NaT        NaN                   NaN                   NaN   \n",
       "27                 NaT        NaN                   NaN                   NaN   \n",
       "28                 NaT        NaN                   NaN                   NaN   \n",
       "29                 NaT        NaN                   NaN                   NaN   \n",
       "30                 NaT        NaN                   NaN                   NaN   \n",
       "31                 NaT        NaN                   NaN                   NaN   \n",
       "32                 NaT        NaN                   NaN                   NaN   \n",
       "33                 NaT        NaN                   NaN                   NaN   \n",
       "34                 NaT        NaN                   NaN                   NaN   \n",
       "35                 NaT        NaN                   NaN                   NaN   \n",
       "36                 NaT        NaN                   NaN                   NaN   \n",
       "37                 NaT        NaN                   NaN                   NaN   \n",
       "38                 NaT        NaN                   NaN                   NaN   \n",
       "39                 NaT        NaN                   NaN                   NaN   \n",
       "40                 NaT        NaN                   NaN                   NaN   \n",
       "41                 NaT        NaN                   NaN                   NaN   \n",
       "42                 NaT        NaN                   NaN                   NaN   \n",
       "43                 NaT        NaN                   NaN                   NaN   \n",
       "44                 NaT        NaN                   NaN                   NaN   \n",
       "45                 NaT        NaN                   NaN                   NaN   \n",
       "46                 NaT        NaN                   NaN                   NaN   \n",
       "47                 NaT        NaN                   NaN                   NaN   \n",
       "48                 NaT        NaN                   NaN                   NaN   \n",
       "49 2020-09-20 13:47:34   9.957617               51996.0               52499.0   \n",
       "50 2020-09-20 13:47:35   9.974283               47969.0               52878.0   \n",
       "51 2020-09-20 13:47:36   9.990950               35180.0               46485.0   \n",
       "52 2020-09-20 13:47:37  10.007617               13402.0               33510.0   \n",
       "53 2020-09-20 13:47:38  10.024283                 823.0                1431.0   \n",
       "54 2020-09-20 13:47:39  10.040950                 800.0                1306.0   \n",
       "55 2020-09-20 13:47:40  10.057617                 805.0                1309.0   \n",
       "56 2020-09-20 13:47:41  10.074283                 802.0                1308.0   \n",
       "57 2020-09-20 13:47:42  10.090950                 808.0                1315.0   \n",
       "58 2020-09-20 13:47:43  10.107617                 793.0                1299.0   \n",
       "59 2020-09-20 13:47:44  10.124283                 804.0                1310.0   \n",
       "\n",
       "    timeSinceFailure  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "10               NaN  \n",
       "11               NaN  \n",
       "12               NaN  \n",
       "13               NaN  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16               NaN  \n",
       "17               NaN  \n",
       "18               NaN  \n",
       "19               NaN  \n",
       "20               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "23               NaN  \n",
       "24               NaN  \n",
       "25               NaN  \n",
       "26               NaN  \n",
       "27               NaN  \n",
       "28               NaN  \n",
       "29               NaN  \n",
       "30               NaN  \n",
       "31               NaN  \n",
       "32               NaN  \n",
       "33               NaN  \n",
       "34               NaN  \n",
       "35               NaN  \n",
       "36               NaN  \n",
       "37               NaN  \n",
       "38               NaN  \n",
       "39               NaN  \n",
       "40               NaN  \n",
       "41               NaN  \n",
       "42               NaN  \n",
       "43               NaN  \n",
       "44               NaN  \n",
       "45               NaN  \n",
       "46               NaN  \n",
       "47               NaN  \n",
       "48               NaN  \n",
       "49              49.0  \n",
       "50              50.0  \n",
       "51              51.0  \n",
       "52              52.0  \n",
       "53              53.0  \n",
       "54              54.0  \n",
       "55              55.0  \n",
       "56              56.0  \n",
       "57              57.0  \n",
       "58              58.0  \n",
       "59              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_EO\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
       "      <th>percentile_50_second</th>\n",
       "      <th>percentile_99_second</th>\n",
       "      <th>timeSinceFailure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>flink</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:06:40</td>\n",
       "      <td>9.115200</td>\n",
       "      <td>837.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>flink</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>flink</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flink</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>flink</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.473519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>flink</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>flink</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.316327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>flink</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.973424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>flink</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>flink</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.276944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>flink</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.400981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>flink</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>flink</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.411481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>flink</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>flink</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.349977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>flink</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.187932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>flink</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.313376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>flink</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>flink</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.392321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>flink</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.243492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>flink</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>flink</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.192114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>flink</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.174152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>flink</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>flink</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.240999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>flink</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.321348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>flink</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.194750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>flink</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>flink</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.247825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>flink</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>flink</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.130288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flink</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.124151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>flink</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>flink</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.365686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>flink</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.330572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>flink</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.104618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>flink</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.136518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>flink</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>flink</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.182558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flink</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>flink</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.129377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>flink</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>flink</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.128136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>flink</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.132178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>flink</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>flink</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.150001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>flink</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.103305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>flink</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>flink</td>\n",
       "      <td>44.0</td>\n",
       "      <td>63.404071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>flink</td>\n",
       "      <td>45.0</td>\n",
       "      <td>86.560076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>flink</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>flink</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.065768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>flink</td>\n",
       "      <td>48.0</td>\n",
       "      <td>98.660475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>flink</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>flink</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.301630</td>\n",
       "      <td>93638.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:34</td>\n",
       "      <td>10.015200</td>\n",
       "      <td>44349.0</td>\n",
       "      <td>54452.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>flink</td>\n",
       "      <td>51.0</td>\n",
       "      <td>101.414037</td>\n",
       "      <td>164243.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:35</td>\n",
       "      <td>10.031867</td>\n",
       "      <td>20840.0</td>\n",
       "      <td>54528.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>flink</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61230.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:36</td>\n",
       "      <td>10.048533</td>\n",
       "      <td>13766.0</td>\n",
       "      <td>43526.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>flink</td>\n",
       "      <td>53.0</td>\n",
       "      <td>67.394775</td>\n",
       "      <td>9209.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:37</td>\n",
       "      <td>10.065200</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>flink</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:38</td>\n",
       "      <td>10.081867</td>\n",
       "      <td>826.0</td>\n",
       "      <td>1335.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>flink</td>\n",
       "      <td>55.0</td>\n",
       "      <td>40.885544</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 12:07:39</td>\n",
       "      <td>10.098533</td>\n",
       "      <td>814.0</td>\n",
       "      <td>1319.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   framework  timeSinceRestart  cpuUsagePct  outputMsgCount  inputMsgCount  \\\n",
       "0      flink              -4.0          NaN          5760.0           5760   \n",
       "1      flink              -3.0          NaN             NaN           5760   \n",
       "2      flink              -2.0          NaN             NaN           5760   \n",
       "3      flink              -1.0          NaN             NaN           5760   \n",
       "4      flink               0.0    45.473519             NaN           5760   \n",
       "5      flink               1.0          NaN             NaN           5760   \n",
       "6      flink               2.0    57.316327             NaN           5760   \n",
       "7      flink               3.0    21.973424             NaN           5760   \n",
       "8      flink               4.0          NaN             NaN           5760   \n",
       "9      flink               5.0     0.276944             NaN           5760   \n",
       "10     flink               6.0     0.400981             NaN           5760   \n",
       "11     flink               7.0          NaN             NaN           5760   \n",
       "12     flink               8.0     0.411481             NaN           5760   \n",
       "13     flink               9.0          NaN             NaN           5760   \n",
       "14     flink              10.0     0.349977             NaN           5760   \n",
       "15     flink              11.0     0.187932             NaN           5760   \n",
       "16     flink              12.0     0.313376             NaN           5760   \n",
       "17     flink              13.0          NaN             NaN           5760   \n",
       "18     flink              14.0     0.392321             NaN           5760   \n",
       "19     flink              15.0     0.243492             NaN           5760   \n",
       "20     flink              16.0          NaN             NaN           5760   \n",
       "21     flink              17.0     0.192114             NaN           5760   \n",
       "22     flink              18.0     0.174152             NaN           5760   \n",
       "23     flink              19.0          NaN             NaN           5760   \n",
       "24     flink              20.0     0.240999             NaN           5760   \n",
       "25     flink              21.0     0.321348             NaN           5760   \n",
       "26     flink              22.0     0.194750             NaN           5760   \n",
       "27     flink              23.0          NaN             NaN           5760   \n",
       "28     flink              24.0     0.247825             NaN           5760   \n",
       "29     flink              25.0          NaN             NaN           5760   \n",
       "30     flink              26.0     0.130288             NaN           5760   \n",
       "31     flink              27.0     0.124151             NaN           5760   \n",
       "32     flink              28.0          NaN             NaN           5760   \n",
       "33     flink              29.0     0.365686             NaN           5760   \n",
       "34     flink              30.0     0.330572             NaN           5760   \n",
       "35     flink              31.0     0.104618             NaN           5760   \n",
       "36     flink              32.0     0.136518             NaN           5760   \n",
       "37     flink              33.0          NaN             NaN           5760   \n",
       "38     flink              34.0     0.182558             NaN           5760   \n",
       "39     flink              35.0          NaN             NaN           5760   \n",
       "40     flink              36.0     0.129377             NaN           5760   \n",
       "41     flink              37.0          NaN             NaN           5760   \n",
       "42     flink              38.0     0.128136             NaN           5760   \n",
       "43     flink              39.0     0.132178             NaN           5760   \n",
       "44     flink              40.0          NaN             NaN           5760   \n",
       "45     flink              41.0     0.150001             NaN           5760   \n",
       "46     flink              42.0     0.103305             NaN           5760   \n",
       "47     flink              43.0          NaN             NaN           5760   \n",
       "48     flink              44.0    63.404071             NaN           5760   \n",
       "49     flink              45.0    86.560076             NaN           5760   \n",
       "50     flink              46.0          NaN             NaN           5760   \n",
       "51     flink              47.0   100.065768             NaN           5760   \n",
       "52     flink              48.0    98.660475             NaN           5760   \n",
       "53     flink              49.0          NaN             NaN           5760   \n",
       "54     flink              50.0    99.301630         93638.0           5760   \n",
       "55     flink              51.0   101.414037        164243.0           5760   \n",
       "56     flink              52.0          NaN         61230.0           5760   \n",
       "57     flink              53.0    67.394775          9209.0           5760   \n",
       "58     flink              54.0          NaN          5760.0           5760   \n",
       "59     flink              55.0    40.885544          5760.0           5760   \n",
       "\n",
       "                  time    timeSec  percentile_50_second  percentile_99_second  \\\n",
       "0  2020-09-20 12:06:40   9.115200                 837.0                1343.0   \n",
       "1                  NaT        NaN                   NaN                   NaN   \n",
       "2                  NaT        NaN                   NaN                   NaN   \n",
       "3                  NaT        NaN                   NaN                   NaN   \n",
       "4                  NaT        NaN                   NaN                   NaN   \n",
       "5                  NaT        NaN                   NaN                   NaN   \n",
       "6                  NaT        NaN                   NaN                   NaN   \n",
       "7                  NaT        NaN                   NaN                   NaN   \n",
       "8                  NaT        NaN                   NaN                   NaN   \n",
       "9                  NaT        NaN                   NaN                   NaN   \n",
       "10                 NaT        NaN                   NaN                   NaN   \n",
       "11                 NaT        NaN                   NaN                   NaN   \n",
       "12                 NaT        NaN                   NaN                   NaN   \n",
       "13                 NaT        NaN                   NaN                   NaN   \n",
       "14                 NaT        NaN                   NaN                   NaN   \n",
       "15                 NaT        NaN                   NaN                   NaN   \n",
       "16                 NaT        NaN                   NaN                   NaN   \n",
       "17                 NaT        NaN                   NaN                   NaN   \n",
       "18                 NaT        NaN                   NaN                   NaN   \n",
       "19                 NaT        NaN                   NaN                   NaN   \n",
       "20                 NaT        NaN                   NaN                   NaN   \n",
       "21                 NaT        NaN                   NaN                   NaN   \n",
       "22                 NaT        NaN                   NaN                   NaN   \n",
       "23                 NaT        NaN                   NaN                   NaN   \n",
       "24                 NaT        NaN                   NaN                   NaN   \n",
       "25                 NaT        NaN                   NaN                   NaN   \n",
       "26                 NaT        NaN                   NaN                   NaN   \n",
       "27                 NaT        NaN                   NaN                   NaN   \n",
       "28                 NaT        NaN                   NaN                   NaN   \n",
       "29                 NaT        NaN                   NaN                   NaN   \n",
       "30                 NaT        NaN                   NaN                   NaN   \n",
       "31                 NaT        NaN                   NaN                   NaN   \n",
       "32                 NaT        NaN                   NaN                   NaN   \n",
       "33                 NaT        NaN                   NaN                   NaN   \n",
       "34                 NaT        NaN                   NaN                   NaN   \n",
       "35                 NaT        NaN                   NaN                   NaN   \n",
       "36                 NaT        NaN                   NaN                   NaN   \n",
       "37                 NaT        NaN                   NaN                   NaN   \n",
       "38                 NaT        NaN                   NaN                   NaN   \n",
       "39                 NaT        NaN                   NaN                   NaN   \n",
       "40                 NaT        NaN                   NaN                   NaN   \n",
       "41                 NaT        NaN                   NaN                   NaN   \n",
       "42                 NaT        NaN                   NaN                   NaN   \n",
       "43                 NaT        NaN                   NaN                   NaN   \n",
       "44                 NaT        NaN                   NaN                   NaN   \n",
       "45                 NaT        NaN                   NaN                   NaN   \n",
       "46                 NaT        NaN                   NaN                   NaN   \n",
       "47                 NaT        NaN                   NaN                   NaN   \n",
       "48                 NaT        NaN                   NaN                   NaN   \n",
       "49                 NaT        NaN                   NaN                   NaN   \n",
       "50                 NaT        NaN                   NaN                   NaN   \n",
       "51                 NaT        NaN                   NaN                   NaN   \n",
       "52                 NaT        NaN                   NaN                   NaN   \n",
       "53                 NaT        NaN                   NaN                   NaN   \n",
       "54 2020-09-20 12:07:34  10.015200               44349.0               54452.0   \n",
       "55 2020-09-20 12:07:35  10.031867               20840.0               54528.0   \n",
       "56 2020-09-20 12:07:36  10.048533               13766.0               43526.0   \n",
       "57 2020-09-20 12:07:37  10.065200                1047.0                4600.0   \n",
       "58 2020-09-20 12:07:38  10.081867                 826.0                1335.0   \n",
       "59 2020-09-20 12:07:39  10.098533                 814.0                1319.0   \n",
       "\n",
       "    timeSinceFailure  \n",
       "0                0.0  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "10               NaN  \n",
       "11               NaN  \n",
       "12               NaN  \n",
       "13               NaN  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16               NaN  \n",
       "17               NaN  \n",
       "18               NaN  \n",
       "19               NaN  \n",
       "20               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "23               NaN  \n",
       "24               NaN  \n",
       "25               NaN  \n",
       "26               NaN  \n",
       "27               NaN  \n",
       "28               NaN  \n",
       "29               NaN  \n",
       "30               NaN  \n",
       "31               NaN  \n",
       "32               NaN  \n",
       "33               NaN  \n",
       "34               NaN  \n",
       "35               NaN  \n",
       "36               NaN  \n",
       "37               NaN  \n",
       "38               NaN  \n",
       "39               NaN  \n",
       "40               NaN  \n",
       "41               NaN  \n",
       "42               NaN  \n",
       "43               NaN  \n",
       "44               NaN  \n",
       "45               NaN  \n",
       "46               NaN  \n",
       "47               NaN  \n",
       "48               NaN  \n",
       "49               NaN  \n",
       "50               NaN  \n",
       "51               NaN  \n",
       "52               NaN  \n",
       "53               NaN  \n",
       "54              54.0  \n",
       "55              55.0  \n",
       "56              56.0  \n",
       "57              57.0  \n",
       "58              58.0  \n",
       "59              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KAFKASTREAMS_ALO\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
       "      <th>percentile_50_second</th>\n",
       "      <th>percentile_99_second</th>\n",
       "      <th>timeSinceFailure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5676.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:43</td>\n",
       "      <td>8.913583</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5747.0</td>\n",
       "      <td>5714</td>\n",
       "      <td>2020-09-19 07:05:44</td>\n",
       "      <td>8.930250</td>\n",
       "      <td>716.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3027.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:45</td>\n",
       "      <td>8.946917</td>\n",
       "      <td>776.0</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2660.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:46</td>\n",
       "      <td>8.963583</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2588.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:47</td>\n",
       "      <td>8.980250</td>\n",
       "      <td>705.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:48</td>\n",
       "      <td>8.996917</td>\n",
       "      <td>713.0</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kafka</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.315147</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:49</td>\n",
       "      <td>9.013583</td>\n",
       "      <td>711.0</td>\n",
       "      <td>1322.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kafka</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2622.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:50</td>\n",
       "      <td>9.030250</td>\n",
       "      <td>703.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kafka</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.560337</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:51</td>\n",
       "      <td>9.046917</td>\n",
       "      <td>704.0</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>kafka</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.787643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>kafka</td>\n",
       "      <td>4.0</td>\n",
       "      <td>99.939311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kafka</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9882.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:54</td>\n",
       "      <td>9.096917</td>\n",
       "      <td>2258.0</td>\n",
       "      <td>4906.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kafka</td>\n",
       "      <td>6.0</td>\n",
       "      <td>95.217359</td>\n",
       "      <td>2958.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:55</td>\n",
       "      <td>9.113583</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>kafka</td>\n",
       "      <td>7.0</td>\n",
       "      <td>61.257344</td>\n",
       "      <td>2609.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:56</td>\n",
       "      <td>9.130250</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>kafka</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2699.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:57</td>\n",
       "      <td>9.146917</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>kafka</td>\n",
       "      <td>9.0</td>\n",
       "      <td>46.246117</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:58</td>\n",
       "      <td>9.163583</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kafka</td>\n",
       "      <td>10.0</td>\n",
       "      <td>99.800193</td>\n",
       "      <td>4885.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:05:59</td>\n",
       "      <td>9.180250</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>15793.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kafka</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17084.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:00</td>\n",
       "      <td>9.196917</td>\n",
       "      <td>9056.0</td>\n",
       "      <td>16613.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>kafka</td>\n",
       "      <td>12.0</td>\n",
       "      <td>99.033469</td>\n",
       "      <td>22542.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:01</td>\n",
       "      <td>9.213583</td>\n",
       "      <td>7833.0</td>\n",
       "      <td>17620.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>kafka</td>\n",
       "      <td>13.0</td>\n",
       "      <td>100.778842</td>\n",
       "      <td>21151.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:02</td>\n",
       "      <td>9.230250</td>\n",
       "      <td>3876.0</td>\n",
       "      <td>18481.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>kafka</td>\n",
       "      <td>14.0</td>\n",
       "      <td>68.903118</td>\n",
       "      <td>7355.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:03</td>\n",
       "      <td>9.246917</td>\n",
       "      <td>913.0</td>\n",
       "      <td>18885.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>kafka</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5822.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:04</td>\n",
       "      <td>9.263583</td>\n",
       "      <td>754.0</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kafka</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70.706266</td>\n",
       "      <td>5713.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:05</td>\n",
       "      <td>9.280250</td>\n",
       "      <td>748.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>kafka</td>\n",
       "      <td>17.0</td>\n",
       "      <td>67.510366</td>\n",
       "      <td>5806.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:06</td>\n",
       "      <td>9.296917</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1419.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>kafka</td>\n",
       "      <td>18.0</td>\n",
       "      <td>54.872650</td>\n",
       "      <td>5836.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:07</td>\n",
       "      <td>9.313583</td>\n",
       "      <td>754.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>kafka</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5724.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:08</td>\n",
       "      <td>9.330250</td>\n",
       "      <td>766.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>kafka</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.594736</td>\n",
       "      <td>6008.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:09</td>\n",
       "      <td>9.346917</td>\n",
       "      <td>754.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>kafka</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5753.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:10</td>\n",
       "      <td>9.363583</td>\n",
       "      <td>732.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>kafka</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.524931</td>\n",
       "      <td>5814.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:11</td>\n",
       "      <td>9.380250</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>kafka</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5768.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:12</td>\n",
       "      <td>9.396917</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>kafka</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.309804</td>\n",
       "      <td>5783.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:13</td>\n",
       "      <td>9.413583</td>\n",
       "      <td>728.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>kafka</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.992410</td>\n",
       "      <td>5768.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:14</td>\n",
       "      <td>9.430250</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1419.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>kafka</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5799.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:15</td>\n",
       "      <td>9.446917</td>\n",
       "      <td>732.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>kafka</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.016134</td>\n",
       "      <td>5727.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:16</td>\n",
       "      <td>9.463583</td>\n",
       "      <td>724.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>kafka</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.834639</td>\n",
       "      <td>5866.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:17</td>\n",
       "      <td>9.480250</td>\n",
       "      <td>733.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>kafka</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:18</td>\n",
       "      <td>9.496917</td>\n",
       "      <td>712.0</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>kafka</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.154437</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:19</td>\n",
       "      <td>9.513583</td>\n",
       "      <td>724.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>kafka</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.958792</td>\n",
       "      <td>5766.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:20</td>\n",
       "      <td>9.530250</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>kafka</td>\n",
       "      <td>32.0</td>\n",
       "      <td>25.142682</td>\n",
       "      <td>5780.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:21</td>\n",
       "      <td>9.546917</td>\n",
       "      <td>732.0</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>kafka</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5779.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:22</td>\n",
       "      <td>9.563583</td>\n",
       "      <td>746.0</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>kafka</td>\n",
       "      <td>34.0</td>\n",
       "      <td>23.024382</td>\n",
       "      <td>5768.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:23</td>\n",
       "      <td>9.580250</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>kafka</td>\n",
       "      <td>35.0</td>\n",
       "      <td>24.669422</td>\n",
       "      <td>5783.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:24</td>\n",
       "      <td>9.596917</td>\n",
       "      <td>724.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>kafka</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5772.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:25</td>\n",
       "      <td>9.613583</td>\n",
       "      <td>733.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>kafka</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21.730767</td>\n",
       "      <td>5710.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:26</td>\n",
       "      <td>9.630250</td>\n",
       "      <td>736.0</td>\n",
       "      <td>1334.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>kafka</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5788.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:27</td>\n",
       "      <td>9.646917</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>kafka</td>\n",
       "      <td>39.0</td>\n",
       "      <td>21.796866</td>\n",
       "      <td>5577.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:28</td>\n",
       "      <td>9.663583</td>\n",
       "      <td>746.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>kafka</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.953900</td>\n",
       "      <td>5986.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:29</td>\n",
       "      <td>9.680250</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>kafka</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.586559</td>\n",
       "      <td>5753.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:30</td>\n",
       "      <td>9.696917</td>\n",
       "      <td>731.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>kafka</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5796.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:31</td>\n",
       "      <td>9.713583</td>\n",
       "      <td>733.0</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>kafka</td>\n",
       "      <td>43.0</td>\n",
       "      <td>22.532254</td>\n",
       "      <td>5777.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:32</td>\n",
       "      <td>9.730250</td>\n",
       "      <td>733.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>kafka</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20.643800</td>\n",
       "      <td>5781.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:33</td>\n",
       "      <td>9.746917</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>kafka</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5567.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:34</td>\n",
       "      <td>9.763583</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>kafka</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.223092</td>\n",
       "      <td>5905.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:35</td>\n",
       "      <td>9.780250</td>\n",
       "      <td>742.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>kafka</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5679.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:36</td>\n",
       "      <td>9.796917</td>\n",
       "      <td>737.0</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>kafka</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.538775</td>\n",
       "      <td>5645.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:37</td>\n",
       "      <td>9.813583</td>\n",
       "      <td>753.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>kafka</td>\n",
       "      <td>49.0</td>\n",
       "      <td>21.284030</td>\n",
       "      <td>5839.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:38</td>\n",
       "      <td>9.830250</td>\n",
       "      <td>760.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>kafka</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:39</td>\n",
       "      <td>9.846917</td>\n",
       "      <td>764.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>kafka</td>\n",
       "      <td>51.0</td>\n",
       "      <td>20.700237</td>\n",
       "      <td>5871.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:40</td>\n",
       "      <td>9.863583</td>\n",
       "      <td>745.0</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>kafka</td>\n",
       "      <td>52.0</td>\n",
       "      <td>23.700942</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:41</td>\n",
       "      <td>9.880250</td>\n",
       "      <td>745.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>kafka</td>\n",
       "      <td>53.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5641.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-19 07:06:42</td>\n",
       "      <td>9.896917</td>\n",
       "      <td>753.0</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   framework  timeSinceRestart  cpuUsagePct  outputMsgCount  inputMsgCount  \\\n",
       "0      kafka              -6.0          NaN          5676.0           5760   \n",
       "1      kafka              -5.0          NaN          5747.0           5714   \n",
       "2      kafka              -4.0          NaN          3027.0           5760   \n",
       "3      kafka              -3.0          NaN          2660.0           5760   \n",
       "4      kafka              -2.0          NaN          2588.0           5760   \n",
       "5      kafka              -1.0          NaN          2607.0           5760   \n",
       "6      kafka               0.0    48.315147          2610.0           5760   \n",
       "7      kafka               1.0          NaN          2622.0           5760   \n",
       "8      kafka               2.0     0.560337          1094.0           5760   \n",
       "9      kafka               3.0    40.787643             NaN           5760   \n",
       "10     kafka               4.0    99.939311             NaN           5760   \n",
       "11     kafka               5.0          NaN          9882.0           5760   \n",
       "12     kafka               6.0    95.217359          2958.0           5760   \n",
       "13     kafka               7.0    61.257344          2609.0           5760   \n",
       "14     kafka               8.0          NaN          2699.0           5760   \n",
       "15     kafka               9.0    46.246117          2572.0           5760   \n",
       "16     kafka              10.0    99.800193          4885.0           5760   \n",
       "17     kafka              11.0          NaN         17084.0           5760   \n",
       "18     kafka              12.0    99.033469         22542.0           5760   \n",
       "19     kafka              13.0   100.778842         21151.0           5760   \n",
       "20     kafka              14.0    68.903118          7355.0           5760   \n",
       "21     kafka              15.0          NaN          5822.0           5760   \n",
       "22     kafka              16.0    70.706266          5713.0           5760   \n",
       "23     kafka              17.0    67.510366          5806.0           5760   \n",
       "24     kafka              18.0    54.872650          5836.0           5760   \n",
       "25     kafka              19.0          NaN          5724.0           5760   \n",
       "26     kafka              20.0    27.594736          6008.0           5760   \n",
       "27     kafka              21.0          NaN          5753.0           5760   \n",
       "28     kafka              22.0    23.524931          5814.0           5760   \n",
       "29     kafka              23.0          NaN          5768.0           5760   \n",
       "30     kafka              24.0    31.309804          5783.0           5760   \n",
       "31     kafka              25.0    22.992410          5768.0           5760   \n",
       "32     kafka              26.0          NaN          5799.0           5760   \n",
       "33     kafka              27.0    24.016134          5727.0           5760   \n",
       "34     kafka              28.0    21.834639          5866.0           5760   \n",
       "35     kafka              29.0          NaN          5755.0           5760   \n",
       "36     kafka              30.0    22.154437          5755.0           5760   \n",
       "37     kafka              31.0    21.958792          5766.0           5760   \n",
       "38     kafka              32.0    25.142682          5780.0           5760   \n",
       "39     kafka              33.0          NaN          5779.0           5760   \n",
       "40     kafka              34.0    23.024382          5768.0           5760   \n",
       "41     kafka              35.0    24.669422          5783.0           5760   \n",
       "42     kafka              36.0          NaN          5772.0           5760   \n",
       "43     kafka              37.0    21.730767          5710.0           5760   \n",
       "44     kafka              38.0          NaN          5788.0           5760   \n",
       "45     kafka              39.0    21.796866          5577.0           5760   \n",
       "46     kafka              40.0    21.953900          5986.0           5760   \n",
       "47     kafka              41.0    20.586559          5753.0           5760   \n",
       "48     kafka              42.0          NaN          5796.0           5760   \n",
       "49     kafka              43.0    22.532254          5777.0           5760   \n",
       "50     kafka              44.0    20.643800          5781.0           5760   \n",
       "51     kafka              45.0          NaN          5567.0           5760   \n",
       "52     kafka              46.0    20.223092          5905.0           5760   \n",
       "53     kafka              47.0          NaN          5679.0           5760   \n",
       "54     kafka              48.0    19.538775          5645.0           5760   \n",
       "55     kafka              49.0    21.284030          5839.0           5760   \n",
       "56     kafka              50.0          NaN          5750.0           5760   \n",
       "57     kafka              51.0    20.700237          5871.0           5760   \n",
       "58     kafka              52.0    23.700942          5778.0           5760   \n",
       "59     kafka              53.0          NaN          5641.0           5760   \n",
       "\n",
       "                  time   timeSec  percentile_50_second  percentile_99_second  \\\n",
       "0  2020-09-19 07:05:43  8.913583                 700.0                1340.0   \n",
       "1  2020-09-19 07:05:44  8.930250                 716.0                1350.0   \n",
       "2  2020-09-19 07:05:45  8.946917                 776.0                1341.0   \n",
       "3  2020-09-19 07:05:46  8.963583                 678.0                1346.0   \n",
       "4  2020-09-19 07:05:47  8.980250                 705.0                1390.0   \n",
       "5  2020-09-19 07:05:48  8.996917                 713.0                1351.0   \n",
       "6  2020-09-19 07:05:49  9.013583                 711.0                1322.0   \n",
       "7  2020-09-19 07:05:50  9.030250                 703.0                1350.0   \n",
       "8  2020-09-19 07:05:51  9.046917                 704.0                1326.0   \n",
       "9                  NaT       NaN                   NaN                   NaN   \n",
       "10                 NaT       NaN                   NaN                   NaN   \n",
       "11 2020-09-19 07:05:54  9.096917                2258.0                4906.0   \n",
       "12 2020-09-19 07:05:55  9.113583                 790.0                1531.0   \n",
       "13 2020-09-19 07:05:56  9.130250                 726.0                1332.0   \n",
       "14 2020-09-19 07:05:57  9.146917                 743.0                1422.0   \n",
       "15 2020-09-19 07:05:58  9.163583                 726.0                1327.0   \n",
       "16 2020-09-19 07:05:59  9.180250                1161.0               15793.0   \n",
       "17 2020-09-19 07:06:00  9.196917                9056.0               16613.0   \n",
       "18 2020-09-19 07:06:01  9.213583                7833.0               17620.0   \n",
       "19 2020-09-19 07:06:02  9.230250                3876.0               18481.0   \n",
       "20 2020-09-19 07:06:03  9.246917                 913.0               18885.0   \n",
       "21 2020-09-19 07:06:04  9.263583                 754.0                1370.0   \n",
       "22 2020-09-19 07:06:05  9.280250                 748.0                1369.0   \n",
       "23 2020-09-19 07:06:06  9.296917                 767.0                1419.0   \n",
       "24 2020-09-19 07:06:07  9.313583                 754.0                1407.0   \n",
       "25 2020-09-19 07:06:08  9.330250                 766.0                1395.0   \n",
       "26 2020-09-19 07:06:09  9.346917                 754.0                1512.0   \n",
       "27 2020-09-19 07:06:10  9.363583                 732.0                1409.0   \n",
       "28 2020-09-19 07:06:11  9.380250                 725.0                1350.0   \n",
       "29 2020-09-19 07:06:12  9.396917                 726.0                1384.0   \n",
       "30 2020-09-19 07:06:13  9.413583                 728.0                1348.0   \n",
       "31 2020-09-19 07:06:14  9.430250                 725.0                1419.0   \n",
       "32 2020-09-19 07:06:15  9.446917                 732.0                1362.0   \n",
       "33 2020-09-19 07:06:16  9.463583                 724.0                1320.0   \n",
       "34 2020-09-19 07:06:17  9.480250                 733.0                1357.0   \n",
       "35 2020-09-19 07:06:18  9.496917                 712.0                1351.0   \n",
       "36 2020-09-19 07:06:19  9.513583                 724.0                1379.0   \n",
       "37 2020-09-19 07:06:20  9.530250                 725.0                1347.0   \n",
       "38 2020-09-19 07:06:21  9.546917                 732.0                1325.0   \n",
       "39 2020-09-19 07:06:22  9.563583                 746.0                1333.0   \n",
       "40 2020-09-19 07:06:23  9.580250                 726.0                1347.0   \n",
       "41 2020-09-19 07:06:24  9.596917                 724.0                1356.0   \n",
       "42 2020-09-19 07:06:25  9.613583                 733.0                1358.0   \n",
       "43 2020-09-19 07:06:26  9.630250                 736.0                1334.0   \n",
       "44 2020-09-19 07:06:27  9.646917                 743.0                1360.0   \n",
       "45 2020-09-19 07:06:28  9.663583                 746.0                1422.0   \n",
       "46 2020-09-19 07:06:29  9.680250                 738.0                1395.0   \n",
       "47 2020-09-19 07:06:30  9.696917                 731.0                1379.0   \n",
       "48 2020-09-19 07:06:31  9.713583                 733.0                1373.0   \n",
       "49 2020-09-19 07:06:32  9.730250                 733.0                1357.0   \n",
       "50 2020-09-19 07:06:33  9.746917                 738.0                1369.0   \n",
       "51 2020-09-19 07:06:34  9.763583                 750.0                1383.0   \n",
       "52 2020-09-19 07:06:35  9.780250                 742.0                1354.0   \n",
       "53 2020-09-19 07:06:36  9.796917                 737.0                1378.0   \n",
       "54 2020-09-19 07:06:37  9.813583                 753.0                1383.0   \n",
       "55 2020-09-19 07:06:38  9.830250                 760.0                1357.0   \n",
       "56 2020-09-19 07:06:39  9.846917                 764.0                1354.0   \n",
       "57 2020-09-19 07:06:40  9.863583                 745.0                1370.0   \n",
       "58 2020-09-19 07:06:41  9.880250                 745.0                1357.0   \n",
       "59 2020-09-19 07:06:42  9.896917                 753.0                1339.0   \n",
       "\n",
       "    timeSinceFailure  \n",
       "0                0.0  \n",
       "1                1.0  \n",
       "2                2.0  \n",
       "3                3.0  \n",
       "4                4.0  \n",
       "5                5.0  \n",
       "6                6.0  \n",
       "7                7.0  \n",
       "8                8.0  \n",
       "9                NaN  \n",
       "10               NaN  \n",
       "11              11.0  \n",
       "12              12.0  \n",
       "13              13.0  \n",
       "14              14.0  \n",
       "15              15.0  \n",
       "16              16.0  \n",
       "17              17.0  \n",
       "18              18.0  \n",
       "19              19.0  \n",
       "20              20.0  \n",
       "21              21.0  \n",
       "22              22.0  \n",
       "23              23.0  \n",
       "24              24.0  \n",
       "25              25.0  \n",
       "26              26.0  \n",
       "27              27.0  \n",
       "28              28.0  \n",
       "29              29.0  \n",
       "30              30.0  \n",
       "31              31.0  \n",
       "32              32.0  \n",
       "33              33.0  \n",
       "34              34.0  \n",
       "35              35.0  \n",
       "36              36.0  \n",
       "37              37.0  \n",
       "38              38.0  \n",
       "39              39.0  \n",
       "40              40.0  \n",
       "41              41.0  \n",
       "42              42.0  \n",
       "43              43.0  \n",
       "44              44.0  \n",
       "45              45.0  \n",
       "46              46.0  \n",
       "47              47.0  \n",
       "48              48.0  \n",
       "49              49.0  \n",
       "50              50.0  \n",
       "51              51.0  \n",
       "52              52.0  \n",
       "53              53.0  \n",
       "54              54.0  \n",
       "55              55.0  \n",
       "56              56.0  \n",
       "57              57.0  \n",
       "58              58.0  \n",
       "59              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KAFKASTREAMS_EO\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
       "      <th>percentile_50_second</th>\n",
       "      <th>percentile_99_second</th>\n",
       "      <th>timeSinceFailure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5891.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:41</td>\n",
       "      <td>8.914867</td>\n",
       "      <td>2568.0</td>\n",
       "      <td>3201.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5818.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:42</td>\n",
       "      <td>8.931533</td>\n",
       "      <td>2554.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4561.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:43</td>\n",
       "      <td>8.948200</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>3131.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kafka</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:44</td>\n",
       "      <td>8.964867</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>3003.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kafka</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.663201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kafka</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.441301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kafka</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kafka</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.039858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kafka</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>kafka</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.929439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>kafka</td>\n",
       "      <td>6.0</td>\n",
       "      <td>102.006721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kafka</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.948993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kafka</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>814.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:53</td>\n",
       "      <td>9.114867</td>\n",
       "      <td>11769.0</td>\n",
       "      <td>12339.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>kafka</td>\n",
       "      <td>9.0</td>\n",
       "      <td>97.456658</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:54</td>\n",
       "      <td>9.131533</td>\n",
       "      <td>11941.0</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>kafka</td>\n",
       "      <td>10.0</td>\n",
       "      <td>98.225373</td>\n",
       "      <td>5578.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:55</td>\n",
       "      <td>9.148200</td>\n",
       "      <td>10026.0</td>\n",
       "      <td>13281.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>kafka</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6028.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:56</td>\n",
       "      <td>9.164867</td>\n",
       "      <td>6734.0</td>\n",
       "      <td>14212.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kafka</td>\n",
       "      <td>12.0</td>\n",
       "      <td>95.883942</td>\n",
       "      <td>7147.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:57</td>\n",
       "      <td>9.181533</td>\n",
       "      <td>5439.0</td>\n",
       "      <td>17261.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kafka</td>\n",
       "      <td>13.0</td>\n",
       "      <td>88.989371</td>\n",
       "      <td>9519.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:58</td>\n",
       "      <td>9.198200</td>\n",
       "      <td>10709.0</td>\n",
       "      <td>17683.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>kafka</td>\n",
       "      <td>14.0</td>\n",
       "      <td>87.657624</td>\n",
       "      <td>7595.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:33:59</td>\n",
       "      <td>9.214867</td>\n",
       "      <td>10471.0</td>\n",
       "      <td>18241.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>kafka</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10517.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:00</td>\n",
       "      <td>9.231533</td>\n",
       "      <td>10844.0</td>\n",
       "      <td>18865.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>kafka</td>\n",
       "      <td>16.0</td>\n",
       "      <td>86.880988</td>\n",
       "      <td>9693.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:01</td>\n",
       "      <td>9.248200</td>\n",
       "      <td>12824.0</td>\n",
       "      <td>20329.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>kafka</td>\n",
       "      <td>17.0</td>\n",
       "      <td>81.924200</td>\n",
       "      <td>8922.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:02</td>\n",
       "      <td>9.264867</td>\n",
       "      <td>13130.0</td>\n",
       "      <td>21395.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kafka</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:03</td>\n",
       "      <td>9.281533</td>\n",
       "      <td>10294.0</td>\n",
       "      <td>22291.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>kafka</td>\n",
       "      <td>19.0</td>\n",
       "      <td>56.487399</td>\n",
       "      <td>10063.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:04</td>\n",
       "      <td>9.298200</td>\n",
       "      <td>11561.0</td>\n",
       "      <td>22562.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>kafka</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11852.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:05</td>\n",
       "      <td>9.314867</td>\n",
       "      <td>10889.0</td>\n",
       "      <td>23652.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>kafka</td>\n",
       "      <td>21.0</td>\n",
       "      <td>47.338012</td>\n",
       "      <td>11136.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:06</td>\n",
       "      <td>9.331533</td>\n",
       "      <td>8572.0</td>\n",
       "      <td>24083.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>kafka</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.948936</td>\n",
       "      <td>8104.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:07</td>\n",
       "      <td>9.348200</td>\n",
       "      <td>5818.0</td>\n",
       "      <td>25030.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>kafka</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:08</td>\n",
       "      <td>9.364867</td>\n",
       "      <td>6872.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>kafka</td>\n",
       "      <td>24.0</td>\n",
       "      <td>34.798777</td>\n",
       "      <td>10837.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:09</td>\n",
       "      <td>9.381533</td>\n",
       "      <td>4015.0</td>\n",
       "      <td>13331.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>kafka</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.066087</td>\n",
       "      <td>10896.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:10</td>\n",
       "      <td>9.398200</td>\n",
       "      <td>3456.0</td>\n",
       "      <td>11419.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>kafka</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.851811</td>\n",
       "      <td>7934.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:11</td>\n",
       "      <td>9.414867</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>kafka</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6272.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:12</td>\n",
       "      <td>9.431533</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>3357.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>kafka</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.293300</td>\n",
       "      <td>5869.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:13</td>\n",
       "      <td>9.448200</td>\n",
       "      <td>2374.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>kafka</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5753.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:14</td>\n",
       "      <td>9.464867</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>3232.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>kafka</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.967444</td>\n",
       "      <td>5699.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:15</td>\n",
       "      <td>9.481533</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>3229.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>kafka</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.351393</td>\n",
       "      <td>5894.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:16</td>\n",
       "      <td>9.498200</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>3187.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>kafka</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5714.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:17</td>\n",
       "      <td>9.514867</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>3230.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>kafka</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.664015</td>\n",
       "      <td>5588.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:18</td>\n",
       "      <td>9.531533</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>3201.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>kafka</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5806.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:19</td>\n",
       "      <td>9.548200</td>\n",
       "      <td>2434.0</td>\n",
       "      <td>3242.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>kafka</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.262300</td>\n",
       "      <td>6025.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:20</td>\n",
       "      <td>9.564867</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>3268.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>kafka</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.484170</td>\n",
       "      <td>5861.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:21</td>\n",
       "      <td>9.581533</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>3155.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>kafka</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.999951</td>\n",
       "      <td>5914.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:22</td>\n",
       "      <td>9.598200</td>\n",
       "      <td>2355.0</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>kafka</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5672.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:23</td>\n",
       "      <td>9.614867</td>\n",
       "      <td>2349.0</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>kafka</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.713693</td>\n",
       "      <td>5782.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:24</td>\n",
       "      <td>9.631533</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>3183.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>kafka</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:25</td>\n",
       "      <td>9.648200</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>kafka</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.987065</td>\n",
       "      <td>5960.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:26</td>\n",
       "      <td>9.664867</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>3166.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>kafka</td>\n",
       "      <td>42.0</td>\n",
       "      <td>31.150216</td>\n",
       "      <td>5716.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:27</td>\n",
       "      <td>9.681533</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>3159.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>kafka</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.153877</td>\n",
       "      <td>5707.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:28</td>\n",
       "      <td>9.698200</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>3203.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>kafka</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6069.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:29</td>\n",
       "      <td>9.714867</td>\n",
       "      <td>2386.0</td>\n",
       "      <td>3259.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>kafka</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.030521</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:30</td>\n",
       "      <td>9.731533</td>\n",
       "      <td>2394.0</td>\n",
       "      <td>3238.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>kafka</td>\n",
       "      <td>46.0</td>\n",
       "      <td>31.151429</td>\n",
       "      <td>6205.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:31</td>\n",
       "      <td>9.748200</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>3213.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>kafka</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:32</td>\n",
       "      <td>9.764867</td>\n",
       "      <td>2349.0</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>kafka</td>\n",
       "      <td>48.0</td>\n",
       "      <td>30.066934</td>\n",
       "      <td>5793.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:33</td>\n",
       "      <td>9.781533</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>3254.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>kafka</td>\n",
       "      <td>49.0</td>\n",
       "      <td>30.669367</td>\n",
       "      <td>5209.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:34</td>\n",
       "      <td>9.798200</td>\n",
       "      <td>2378.0</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>kafka</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.582060</td>\n",
       "      <td>5840.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:35</td>\n",
       "      <td>9.814867</td>\n",
       "      <td>2359.0</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>kafka</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5835.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:36</td>\n",
       "      <td>9.831533</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>3188.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>kafka</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30.953467</td>\n",
       "      <td>5759.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:37</td>\n",
       "      <td>9.848200</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>3174.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>kafka</td>\n",
       "      <td>53.0</td>\n",
       "      <td>30.571929</td>\n",
       "      <td>5731.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:38</td>\n",
       "      <td>9.864867</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>3221.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>kafka</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5945.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:39</td>\n",
       "      <td>9.881533</td>\n",
       "      <td>2374.0</td>\n",
       "      <td>3244.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>kafka</td>\n",
       "      <td>55.0</td>\n",
       "      <td>30.220520</td>\n",
       "      <td>5672.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-20 11:34:40</td>\n",
       "      <td>9.898200</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   framework  timeSinceRestart  cpuUsagePct  outputMsgCount  inputMsgCount  \\\n",
       "0      kafka              -4.0          NaN          5891.0           5760   \n",
       "1      kafka              -3.0          NaN          5818.0           5760   \n",
       "2      kafka              -2.0          NaN          4561.0           5760   \n",
       "3      kafka              -1.0          NaN            53.0           5760   \n",
       "4      kafka               0.0    46.663201             NaN           5760   \n",
       "5      kafka               1.0    10.441301             NaN           5760   \n",
       "6      kafka               2.0          NaN             NaN           5760   \n",
       "7      kafka               3.0     3.039858             NaN           5760   \n",
       "8      kafka               4.0          NaN             NaN           5760   \n",
       "9      kafka               5.0   100.929439             NaN           5760   \n",
       "10     kafka               6.0   102.006721             NaN           5760   \n",
       "11     kafka               7.0    71.948993             NaN           5760   \n",
       "12     kafka               8.0          NaN           814.0           5760   \n",
       "13     kafka               9.0    97.456658          2220.0           5760   \n",
       "14     kafka              10.0    98.225373          5578.0           5760   \n",
       "15     kafka              11.0          NaN          6028.0           5760   \n",
       "16     kafka              12.0    95.883942          7147.0           5760   \n",
       "17     kafka              13.0    88.989371          9519.0           5760   \n",
       "18     kafka              14.0    87.657624          7595.0           5760   \n",
       "19     kafka              15.0          NaN         10517.0           5760   \n",
       "20     kafka              16.0    86.880988          9693.0           5760   \n",
       "21     kafka              17.0    81.924200          8922.0           5760   \n",
       "22     kafka              18.0          NaN          8700.0           5760   \n",
       "23     kafka              19.0    56.487399         10063.0           5760   \n",
       "24     kafka              20.0          NaN         11852.0           5760   \n",
       "25     kafka              21.0    47.338012         11136.0           5760   \n",
       "26     kafka              22.0    31.948936          8104.0           5760   \n",
       "27     kafka              23.0          NaN         13673.0           5760   \n",
       "28     kafka              24.0    34.798777         10837.0           5760   \n",
       "29     kafka              25.0    31.066087         10896.0           5760   \n",
       "30     kafka              26.0    30.851811          7934.0           5760   \n",
       "31     kafka              27.0          NaN          6272.0           5760   \n",
       "32     kafka              28.0    31.293300          5869.0           5760   \n",
       "33     kafka              29.0          NaN          5753.0           5760   \n",
       "34     kafka              30.0    30.967444          5699.0           5760   \n",
       "35     kafka              31.0    31.351393          5894.0           5760   \n",
       "36     kafka              32.0          NaN          5714.0           5760   \n",
       "37     kafka              33.0    33.664015          5588.0           5760   \n",
       "38     kafka              34.0          NaN          5806.0           5760   \n",
       "39     kafka              35.0    30.262300          6025.0           5760   \n",
       "40     kafka              36.0    30.484170          5861.0           5760   \n",
       "41     kafka              37.0    35.999951          5914.0           5760   \n",
       "42     kafka              38.0          NaN          5672.0           5760   \n",
       "43     kafka              39.0    36.713693          5782.0           5760   \n",
       "44     kafka              40.0          NaN          5734.0           5760   \n",
       "45     kafka              41.0    28.987065          5960.0           5760   \n",
       "46     kafka              42.0    31.150216          5716.0           5760   \n",
       "47     kafka              43.0    30.153877          5707.0           5760   \n",
       "48     kafka              44.0          NaN          6069.0           5760   \n",
       "49     kafka              45.0    31.030521          5625.0           5760   \n",
       "50     kafka              46.0    31.151429          6205.0           5760   \n",
       "51     kafka              47.0          NaN          5725.0           5760   \n",
       "52     kafka              48.0    30.066934          5793.0           5760   \n",
       "53     kafka              49.0    30.669367          5209.0           5760   \n",
       "54     kafka              50.0    31.582060          5840.0           5760   \n",
       "55     kafka              51.0          NaN          5835.0           5760   \n",
       "56     kafka              52.0    30.953467          5759.0           5760   \n",
       "57     kafka              53.0    30.571929          5731.0           5760   \n",
       "58     kafka              54.0          NaN          5945.0           5760   \n",
       "59     kafka              55.0    30.220520          5672.0           5760   \n",
       "\n",
       "                  time   timeSec  percentile_50_second  percentile_99_second  \\\n",
       "0  2020-09-20 11:33:41  8.914867                2568.0                3201.0   \n",
       "1  2020-09-20 11:33:42  8.931533                2554.0                3160.0   \n",
       "2  2020-09-20 11:33:43  8.948200                2558.0                3131.0   \n",
       "3  2020-09-20 11:33:44  8.964867                2780.0                3003.0   \n",
       "4                  NaT       NaN                   NaN                   NaN   \n",
       "5                  NaT       NaN                   NaN                   NaN   \n",
       "6                  NaT       NaN                   NaN                   NaN   \n",
       "7                  NaT       NaN                   NaN                   NaN   \n",
       "8                  NaT       NaN                   NaN                   NaN   \n",
       "9                  NaT       NaN                   NaN                   NaN   \n",
       "10                 NaT       NaN                   NaN                   NaN   \n",
       "11                 NaT       NaN                   NaN                   NaN   \n",
       "12 2020-09-20 11:33:53  9.114867               11769.0               12339.0   \n",
       "13 2020-09-20 11:33:54  9.131533               11941.0               12720.0   \n",
       "14 2020-09-20 11:33:55  9.148200               10026.0               13281.0   \n",
       "15 2020-09-20 11:33:56  9.164867                6734.0               14212.0   \n",
       "16 2020-09-20 11:33:57  9.181533                5439.0               17261.0   \n",
       "17 2020-09-20 11:33:58  9.198200               10709.0               17683.0   \n",
       "18 2020-09-20 11:33:59  9.214867               10471.0               18241.0   \n",
       "19 2020-09-20 11:34:00  9.231533               10844.0               18865.0   \n",
       "20 2020-09-20 11:34:01  9.248200               12824.0               20329.0   \n",
       "21 2020-09-20 11:34:02  9.264867               13130.0               21395.0   \n",
       "22 2020-09-20 11:34:03  9.281533               10294.0               22291.0   \n",
       "23 2020-09-20 11:34:04  9.298200               11561.0               22562.0   \n",
       "24 2020-09-20 11:34:05  9.314867               10889.0               23652.0   \n",
       "25 2020-09-20 11:34:06  9.331533                8572.0               24083.0   \n",
       "26 2020-09-20 11:34:07  9.348200                5818.0               25030.0   \n",
       "27 2020-09-20 11:34:08  9.364867                6872.0               25700.0   \n",
       "28 2020-09-20 11:34:09  9.381533                4015.0               13331.0   \n",
       "29 2020-09-20 11:34:10  9.398200                3456.0               11419.0   \n",
       "30 2020-09-20 11:34:11  9.414867                2667.0                6592.0   \n",
       "31 2020-09-20 11:34:12  9.431533                2413.0                3357.0   \n",
       "32 2020-09-20 11:34:13  9.448200                2374.0                3274.0   \n",
       "33 2020-09-20 11:34:14  9.464867                2352.0                3232.0   \n",
       "34 2020-09-20 11:34:15  9.481533                2369.0                3229.0   \n",
       "35 2020-09-20 11:34:16  9.498200                2367.0                3187.0   \n",
       "36 2020-09-20 11:34:17  9.514867                2378.0                3230.0   \n",
       "37 2020-09-20 11:34:18  9.531533                2406.0                3201.0   \n",
       "38 2020-09-20 11:34:19  9.548200                2434.0                3242.0   \n",
       "39 2020-09-20 11:34:20  9.564867                2395.0                3268.0   \n",
       "40 2020-09-20 11:34:21  9.581533                2379.0                3155.0   \n",
       "41 2020-09-20 11:34:22  9.598200                2355.0                3161.0   \n",
       "42 2020-09-20 11:34:23  9.614867                2349.0                3147.0   \n",
       "43 2020-09-20 11:34:24  9.631533                2379.0                3183.0   \n",
       "44 2020-09-20 11:34:25  9.648200                2380.0                3241.0   \n",
       "45 2020-09-20 11:34:26  9.664867                2379.0                3166.0   \n",
       "46 2020-09-20 11:34:27  9.681533                2378.0                3159.0   \n",
       "47 2020-09-20 11:34:28  9.698200                2404.0                3203.0   \n",
       "48 2020-09-20 11:34:29  9.714867                2386.0                3259.0   \n",
       "49 2020-09-20 11:34:30  9.731533                2394.0                3238.0   \n",
       "50 2020-09-20 11:34:31  9.748200                2388.0                3213.0   \n",
       "51 2020-09-20 11:34:32  9.764867                2349.0                3222.0   \n",
       "52 2020-09-20 11:34:33  9.781533                2360.0                3254.0   \n",
       "53 2020-09-20 11:34:34  9.798200                2378.0                3301.0   \n",
       "54 2020-09-20 11:34:35  9.814867                2359.0                3237.0   \n",
       "55 2020-09-20 11:34:36  9.831533                2361.0                3188.0   \n",
       "56 2020-09-20 11:34:37  9.848200                2379.0                3174.0   \n",
       "57 2020-09-20 11:34:38  9.864867                2388.0                3221.0   \n",
       "58 2020-09-20 11:34:39  9.881533                2374.0                3244.0   \n",
       "59 2020-09-20 11:34:40  9.898200                2382.0                3222.0   \n",
       "\n",
       "    timeSinceFailure  \n",
       "0                0.0  \n",
       "1                1.0  \n",
       "2                2.0  \n",
       "3                3.0  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "10               NaN  \n",
       "11               NaN  \n",
       "12              12.0  \n",
       "13              13.0  \n",
       "14              14.0  \n",
       "15              15.0  \n",
       "16              16.0  \n",
       "17              17.0  \n",
       "18              18.0  \n",
       "19              19.0  \n",
       "20              20.0  \n",
       "21              21.0  \n",
       "22              22.0  \n",
       "23              23.0  \n",
       "24              24.0  \n",
       "25              25.0  \n",
       "26              26.0  \n",
       "27              27.0  \n",
       "28              28.0  \n",
       "29              29.0  \n",
       "30              30.0  \n",
       "31              31.0  \n",
       "32              32.0  \n",
       "33              33.0  \n",
       "34              34.0  \n",
       "35              35.0  \n",
       "36              36.0  \n",
       "37              37.0  \n",
       "38              38.0  \n",
       "39              39.0  \n",
       "40              40.0  \n",
       "41              41.0  \n",
       "42              42.0  \n",
       "43              43.0  \n",
       "44              44.0  \n",
       "45              45.0  \n",
       "46              46.0  \n",
       "47              47.0  \n",
       "48              48.0  \n",
       "49              49.0  \n",
       "50              50.0  \n",
       "51              51.0  \n",
       "52              52.0  \n",
       "53              53.0  \n",
       "54              54.0  \n",
       "55              55.0  \n",
       "56              56.0  \n",
       "57              57.0  \n",
       "58              58.0  \n",
       "59              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPARK_ALO\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spark</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:21</td>\n",
       "      <td>9.427617</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spark</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2960.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:22</td>\n",
       "      <td>9.444283</td>\n",
       "      <td>973.0</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spark</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8351.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:23</td>\n",
       "      <td>9.460950</td>\n",
       "      <td>948.0</td>\n",
       "      <td>1633.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spark</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5969.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:24</td>\n",
       "      <td>9.477617</td>\n",
       "      <td>849.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>spark</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:25</td>\n",
       "      <td>9.494283</td>\n",
       "      <td>820.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spark</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:26</td>\n",
       "      <td>9.510950</td>\n",
       "      <td>793.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>spark</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.216940</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:27</td>\n",
       "      <td>9.527617</td>\n",
       "      <td>869.0</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spark</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.651234</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:28</td>\n",
       "      <td>9.544283</td>\n",
       "      <td>878.0</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spark</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:29</td>\n",
       "      <td>9.560950</td>\n",
       "      <td>828.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spark</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.824687</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:30</td>\n",
       "      <td>9.577617</td>\n",
       "      <td>903.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>spark</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.691322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spark</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>spark</td>\n",
       "      <td>6.0</td>\n",
       "      <td>43.613937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>spark</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>spark</td>\n",
       "      <td>8.0</td>\n",
       "      <td>81.481248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>spark</td>\n",
       "      <td>9.0</td>\n",
       "      <td>98.553473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spark</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.169788</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:37</td>\n",
       "      <td>9.694283</td>\n",
       "      <td>6336.0</td>\n",
       "      <td>7461.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spark</td>\n",
       "      <td>11.0</td>\n",
       "      <td>98.791021</td>\n",
       "      <td>1751.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:38</td>\n",
       "      <td>9.710950</td>\n",
       "      <td>7012.0</td>\n",
       "      <td>7594.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spark</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4788.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:39</td>\n",
       "      <td>9.727617</td>\n",
       "      <td>7162.0</td>\n",
       "      <td>7926.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spark</td>\n",
       "      <td>13.0</td>\n",
       "      <td>101.261210</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:40</td>\n",
       "      <td>9.744283</td>\n",
       "      <td>7206.0</td>\n",
       "      <td>7980.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>spark</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.640276</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:41</td>\n",
       "      <td>9.760950</td>\n",
       "      <td>7406.0</td>\n",
       "      <td>8289.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>spark</td>\n",
       "      <td>15.0</td>\n",
       "      <td>99.698591</td>\n",
       "      <td>5285.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:42</td>\n",
       "      <td>9.777617</td>\n",
       "      <td>7675.0</td>\n",
       "      <td>8496.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>spark</td>\n",
       "      <td>16.0</td>\n",
       "      <td>102.818692</td>\n",
       "      <td>6235.0</td>\n",
       "      <td>5664</td>\n",
       "      <td>2020-09-18 21:07:43</td>\n",
       "      <td>9.794283</td>\n",
       "      <td>7736.0</td>\n",
       "      <td>8513.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>spark</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10783.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:44</td>\n",
       "      <td>9.810950</td>\n",
       "      <td>7179.0</td>\n",
       "      <td>8099.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>spark</td>\n",
       "      <td>18.0</td>\n",
       "      <td>97.593242</td>\n",
       "      <td>7951.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:45</td>\n",
       "      <td>9.827617</td>\n",
       "      <td>6415.0</td>\n",
       "      <td>7536.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>spark</td>\n",
       "      <td>19.0</td>\n",
       "      <td>100.854576</td>\n",
       "      <td>10066.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:46</td>\n",
       "      <td>9.844283</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>7002.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>spark</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10910.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:47</td>\n",
       "      <td>9.860950</td>\n",
       "      <td>5083.0</td>\n",
       "      <td>5917.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>spark</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.516395</td>\n",
       "      <td>10547.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:48</td>\n",
       "      <td>9.877617</td>\n",
       "      <td>4263.0</td>\n",
       "      <td>5535.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>spark</td>\n",
       "      <td>22.0</td>\n",
       "      <td>101.267123</td>\n",
       "      <td>13007.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:49</td>\n",
       "      <td>9.894283</td>\n",
       "      <td>3130.0</td>\n",
       "      <td>4563.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>spark</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16229.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:50</td>\n",
       "      <td>9.910950</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>2909.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>spark</td>\n",
       "      <td>24.0</td>\n",
       "      <td>61.610800</td>\n",
       "      <td>6811.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:51</td>\n",
       "      <td>9.927617</td>\n",
       "      <td>838.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>spark</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4739.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:52</td>\n",
       "      <td>9.944283</td>\n",
       "      <td>909.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>spark</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43.022254</td>\n",
       "      <td>5989.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:53</td>\n",
       "      <td>9.960950</td>\n",
       "      <td>962.0</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>spark</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6552.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:54</td>\n",
       "      <td>9.977617</td>\n",
       "      <td>844.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>spark</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.012750</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:55</td>\n",
       "      <td>9.994283</td>\n",
       "      <td>797.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>spark</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.376505</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:56</td>\n",
       "      <td>10.010950</td>\n",
       "      <td>780.0</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>spark</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.014505</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:57</td>\n",
       "      <td>10.027617</td>\n",
       "      <td>803.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>spark</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.049859</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:58</td>\n",
       "      <td>10.044283</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>spark</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:07:59</td>\n",
       "      <td>10.060950</td>\n",
       "      <td>829.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>spark</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.694364</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:00</td>\n",
       "      <td>10.077617</td>\n",
       "      <td>755.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>spark</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.934890</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:01</td>\n",
       "      <td>10.094283</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1361.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>spark</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.651445</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:02</td>\n",
       "      <td>10.110950</td>\n",
       "      <td>794.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>spark</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:03</td>\n",
       "      <td>10.127617</td>\n",
       "      <td>784.0</td>\n",
       "      <td>1336.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>spark</td>\n",
       "      <td>37.0</td>\n",
       "      <td>49.809688</td>\n",
       "      <td>5177.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:04</td>\n",
       "      <td>10.144283</td>\n",
       "      <td>857.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>spark</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6343.0</td>\n",
       "      <td>5621</td>\n",
       "      <td>2020-09-18 21:08:05</td>\n",
       "      <td>10.160950</td>\n",
       "      <td>835.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>spark</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.899368</td>\n",
       "      <td>5621.0</td>\n",
       "      <td>5754</td>\n",
       "      <td>2020-09-18 21:08:06</td>\n",
       "      <td>10.177617</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>spark</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.812065</td>\n",
       "      <td>5754.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:07</td>\n",
       "      <td>10.194283</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>spark</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:08</td>\n",
       "      <td>10.210950</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>spark</td>\n",
       "      <td>42.0</td>\n",
       "      <td>34.556851</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:09</td>\n",
       "      <td>10.227617</td>\n",
       "      <td>766.0</td>\n",
       "      <td>1334.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>spark</td>\n",
       "      <td>43.0</td>\n",
       "      <td>23.417142</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:10</td>\n",
       "      <td>10.244283</td>\n",
       "      <td>764.0</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>spark</td>\n",
       "      <td>44.0</td>\n",
       "      <td>16.546367</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:11</td>\n",
       "      <td>10.260950</td>\n",
       "      <td>781.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>spark</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:12</td>\n",
       "      <td>10.277617</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>spark</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28.043365</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:13</td>\n",
       "      <td>10.294283</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>spark</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:14</td>\n",
       "      <td>10.310950</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>spark</td>\n",
       "      <td>48.0</td>\n",
       "      <td>26.013839</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:15</td>\n",
       "      <td>10.327617</td>\n",
       "      <td>762.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>spark</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23.700927</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:16</td>\n",
       "      <td>10.344283</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>spark</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:17</td>\n",
       "      <td>10.360950</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>spark</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.424774</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:18</td>\n",
       "      <td>10.377617</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>spark</td>\n",
       "      <td>52.0</td>\n",
       "      <td>30.283093</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:19</td>\n",
       "      <td>10.394283</td>\n",
       "      <td>775.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>spark</td>\n",
       "      <td>53.0</td>\n",
       "      <td>12.458681</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:08:20</td>\n",
       "      <td>10.410950</td>\n",
       "      <td>759.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   framework  timeSinceRestart  cpuUsagePct  outputMsgCount  inputMsgCount  \\\n",
       "0      spark              -6.0          NaN          5760.0           5760   \n",
       "1      spark              -5.0          NaN          2960.0           5760   \n",
       "2      spark              -4.0          NaN          8351.0           5760   \n",
       "3      spark              -3.0          NaN          5969.0           5760   \n",
       "4      spark              -2.0          NaN          5760.0           5760   \n",
       "5      spark              -1.0          NaN          5760.0           5760   \n",
       "6      spark               0.0    44.216940          5760.0           5760   \n",
       "7      spark               1.0    57.651234          5760.0           5760   \n",
       "8      spark               2.0          NaN          5760.0           5760   \n",
       "9      spark               3.0    65.824687          5760.0           5760   \n",
       "10     spark               4.0    40.691322             NaN           5760   \n",
       "11     spark               5.0          NaN             NaN           5760   \n",
       "12     spark               6.0    43.613937             NaN           5760   \n",
       "13     spark               7.0          NaN             NaN           5760   \n",
       "14     spark               8.0    81.481248             NaN           5760   \n",
       "15     spark               9.0    98.553473             NaN           5760   \n",
       "16     spark              10.0   102.169788          4981.0           5760   \n",
       "17     spark              11.0    98.791021          1751.0           5760   \n",
       "18     spark              12.0          NaN          4788.0           5760   \n",
       "19     spark              13.0   101.261210          5760.0           5760   \n",
       "20     spark              14.0    97.640276          5760.0           5760   \n",
       "21     spark              15.0    99.698591          5285.0           5760   \n",
       "22     spark              16.0   102.818692          6235.0           5664   \n",
       "23     spark              17.0          NaN         10783.0           5760   \n",
       "24     spark              18.0    97.593242          7951.0           5760   \n",
       "25     spark              19.0   100.854576         10066.0           5760   \n",
       "26     spark              20.0          NaN         10910.0           5760   \n",
       "27     spark              21.0    96.516395         10547.0           5760   \n",
       "28     spark              22.0   101.267123         13007.0           5760   \n",
       "29     spark              23.0          NaN         16229.0           5760   \n",
       "30     spark              24.0    61.610800          6811.0           5760   \n",
       "31     spark              25.0          NaN          4739.0           5760   \n",
       "32     spark              26.0    43.022254          5989.0           5760   \n",
       "33     spark              27.0          NaN          6552.0           5760   \n",
       "34     spark              28.0    40.012750          5760.0           5760   \n",
       "35     spark              29.0    37.376505          5760.0           5760   \n",
       "36     spark              30.0    22.014505          5760.0           5760   \n",
       "37     spark              31.0    21.049859          5760.0           5760   \n",
       "38     spark              32.0          NaN          5760.0           5760   \n",
       "39     spark              33.0    38.694364          5760.0           5760   \n",
       "40     spark              34.0    34.934890          5760.0           5760   \n",
       "41     spark              35.0    35.651445          5760.0           5760   \n",
       "42     spark              36.0          NaN          5760.0           5760   \n",
       "43     spark              37.0    49.809688          5177.0           5760   \n",
       "44     spark              38.0          NaN          6343.0           5621   \n",
       "45     spark              39.0    51.899368          5621.0           5754   \n",
       "46     spark              40.0    21.812065          5754.0           5760   \n",
       "47     spark              41.0          NaN          5760.0           5760   \n",
       "48     spark              42.0    34.556851          5760.0           5760   \n",
       "49     spark              43.0    23.417142          5760.0           5760   \n",
       "50     spark              44.0    16.546367          5760.0           5760   \n",
       "51     spark              45.0          NaN          5760.0           5760   \n",
       "52     spark              46.0    28.043365          5760.0           5760   \n",
       "53     spark              47.0          NaN          5760.0           5760   \n",
       "54     spark              48.0    26.013839          5760.0           5760   \n",
       "55     spark              49.0    23.700927          5760.0           5760   \n",
       "56     spark              50.0          NaN          5760.0           5760   \n",
       "57     spark              51.0    19.424774          5760.0           5760   \n",
       "58     spark              52.0    30.283093          5760.0           5760   \n",
       "59     spark              53.0    12.458681          5760.0           5760   \n",
       "\n",
       "                  time    timeSec  percentile_50_second  percentile_99_second  \\\n",
       "0  2020-09-18 21:07:21   9.427617                 769.0                1345.0   \n",
       "1  2020-09-18 21:07:22   9.444283                 973.0                1488.0   \n",
       "2  2020-09-18 21:07:23   9.460950                 948.0                1633.0   \n",
       "3  2020-09-18 21:07:24   9.477617                 849.0                1503.0   \n",
       "4  2020-09-18 21:07:25   9.494283                 820.0                1375.0   \n",
       "5  2020-09-18 21:07:26   9.510950                 793.0                1363.0   \n",
       "6  2020-09-18 21:07:27   9.527617                 869.0                1478.0   \n",
       "7  2020-09-18 21:07:28   9.544283                 878.0                1488.0   \n",
       "8  2020-09-18 21:07:29   9.560950                 828.0                1383.0   \n",
       "9  2020-09-18 21:07:30   9.577617                 903.0                1492.0   \n",
       "10                 NaT        NaN                   NaN                   NaN   \n",
       "11                 NaT        NaN                   NaN                   NaN   \n",
       "12                 NaT        NaN                   NaN                   NaN   \n",
       "13                 NaT        NaN                   NaN                   NaN   \n",
       "14                 NaT        NaN                   NaN                   NaN   \n",
       "15                 NaT        NaN                   NaN                   NaN   \n",
       "16 2020-09-18 21:07:37   9.694283                6336.0                7461.0   \n",
       "17 2020-09-18 21:07:38   9.710950                7012.0                7594.0   \n",
       "18 2020-09-18 21:07:39   9.727617                7162.0                7926.0   \n",
       "19 2020-09-18 21:07:40   9.744283                7206.0                7980.0   \n",
       "20 2020-09-18 21:07:41   9.760950                7406.0                8289.0   \n",
       "21 2020-09-18 21:07:42   9.777617                7675.0                8496.0   \n",
       "22 2020-09-18 21:07:43   9.794283                7736.0                8513.0   \n",
       "23 2020-09-18 21:07:44   9.810950                7179.0                8099.0   \n",
       "24 2020-09-18 21:07:45   9.827617                6415.0                7536.0   \n",
       "25 2020-09-18 21:07:46   9.844283                5998.0                7002.0   \n",
       "26 2020-09-18 21:07:47   9.860950                5083.0                5917.0   \n",
       "27 2020-09-18 21:07:48   9.877617                4263.0                5535.0   \n",
       "28 2020-09-18 21:07:49   9.894283                3130.0                4563.0   \n",
       "29 2020-09-18 21:07:50   9.910950                1733.0                2909.0   \n",
       "30 2020-09-18 21:07:51   9.927617                 838.0                1517.0   \n",
       "31 2020-09-18 21:07:52   9.944283                 909.0                1496.0   \n",
       "32 2020-09-18 21:07:53   9.960950                 962.0                1561.0   \n",
       "33 2020-09-18 21:07:54   9.977617                 844.0                1541.0   \n",
       "34 2020-09-18 21:07:55   9.994283                 797.0                1381.0   \n",
       "35 2020-09-18 21:07:56  10.010950                 780.0                1342.0   \n",
       "36 2020-09-18 21:07:57  10.027617                 803.0                1366.0   \n",
       "37 2020-09-18 21:07:58  10.044283                 785.0                1371.0   \n",
       "38 2020-09-18 21:07:59  10.060950                 829.0                1436.0   \n",
       "39 2020-09-18 21:08:00  10.077617                 755.0                1296.0   \n",
       "40 2020-09-18 21:08:01  10.094283                 785.0                1361.0   \n",
       "41 2020-09-18 21:08:02  10.110950                 794.0                1422.0   \n",
       "42 2020-09-18 21:08:03  10.127617                 784.0                1336.0   \n",
       "43 2020-09-18 21:08:04  10.144283                 857.0                1483.0   \n",
       "44 2020-09-18 21:08:05  10.160950                 835.0                1525.0   \n",
       "45 2020-09-18 21:08:06  10.177617                 787.0                1388.0   \n",
       "46 2020-09-18 21:08:07  10.194283                 768.0                1349.0   \n",
       "47 2020-09-18 21:08:08  10.210950                 781.0                1373.0   \n",
       "48 2020-09-18 21:08:09  10.227617                 766.0                1334.0   \n",
       "49 2020-09-18 21:08:10  10.244283                 764.0                1338.0   \n",
       "50 2020-09-18 21:08:11  10.260950                 781.0                1347.0   \n",
       "51 2020-09-18 21:08:12  10.277617                 790.0                1383.0   \n",
       "52 2020-09-18 21:08:13  10.294283                 782.0                1362.0   \n",
       "53 2020-09-18 21:08:14  10.310950                 790.0                1359.0   \n",
       "54 2020-09-18 21:08:15  10.327617                 762.0                1329.0   \n",
       "55 2020-09-18 21:08:16  10.344283                 785.0                1354.0   \n",
       "56 2020-09-18 21:08:17  10.360950                 877.0                1455.0   \n",
       "57 2020-09-18 21:08:18  10.377617                 756.0                1311.0   \n",
       "58 2020-09-18 21:08:19  10.394283                 775.0                1355.0   \n",
       "59 2020-09-18 21:08:20  10.410950                 759.0                1299.0   \n",
       "\n",
       "    timeSinceFailure  \n",
       "0                0.0  \n",
       "1                1.0  \n",
       "2                2.0  \n",
       "3                3.0  \n",
       "4                4.0  \n",
       "5                5.0  \n",
       "6                6.0  \n",
       "7                7.0  \n",
       "8                8.0  \n",
       "9                9.0  \n",
       "10               NaN  \n",
       "11               NaN  \n",
       "12               NaN  \n",
       "13               NaN  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16              16.0  \n",
       "17              17.0  \n",
       "18              18.0  \n",
       "19              19.0  \n",
       "20              20.0  \n",
       "21              21.0  \n",
       "22              22.0  \n",
       "23              23.0  \n",
       "24              24.0  \n",
       "25              25.0  \n",
       "26              26.0  \n",
       "27              27.0  \n",
       "28              28.0  \n",
       "29              29.0  \n",
       "30              30.0  \n",
       "31              31.0  \n",
       "32              32.0  \n",
       "33              33.0  \n",
       "34              34.0  \n",
       "35              35.0  \n",
       "36              36.0  \n",
       "37              37.0  \n",
       "38              38.0  \n",
       "39              39.0  \n",
       "40              40.0  \n",
       "41              41.0  \n",
       "42              42.0  \n",
       "43              43.0  \n",
       "44              44.0  \n",
       "45              45.0  \n",
       "46              46.0  \n",
       "47              47.0  \n",
       "48              48.0  \n",
       "49              49.0  \n",
       "50              50.0  \n",
       "51              51.0  \n",
       "52              52.0  \n",
       "53              53.0  \n",
       "54              54.0  \n",
       "55              55.0  \n",
       "56              56.0  \n",
       "57              57.0  \n",
       "58              58.0  \n",
       "59              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STRUCTUREDSTREAMING_ALO\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>framework</th>\n",
       "      <th>timeSinceRestart</th>\n",
       "      <th>cpuUsagePct</th>\n",
       "      <th>outputMsgCount</th>\n",
       "      <th>inputMsgCount</th>\n",
       "      <th>time</th>\n",
       "      <th>timeSec</th>\n",
       "      <th>percentile_50_second</th>\n",
       "      <th>percentile_99_second</th>\n",
       "      <th>timeSinceFailure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:00</td>\n",
       "      <td>9.458400</td>\n",
       "      <td>4430.0</td>\n",
       "      <td>4996.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5312.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:01</td>\n",
       "      <td>9.475067</td>\n",
       "      <td>4669.0</td>\n",
       "      <td>5372.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:02</td>\n",
       "      <td>9.491733</td>\n",
       "      <td>4719.0</td>\n",
       "      <td>5261.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.975392</td>\n",
       "      <td>9244.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:03</td>\n",
       "      <td>9.508400</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>5932.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.138944</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:04</td>\n",
       "      <td>9.525067</td>\n",
       "      <td>5309.0</td>\n",
       "      <td>6302.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.873252</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:05</td>\n",
       "      <td>9.541733</td>\n",
       "      <td>4912.0</td>\n",
       "      <td>5465.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.106995</td>\n",
       "      <td>16453.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:06</td>\n",
       "      <td>9.558400</td>\n",
       "      <td>3854.0</td>\n",
       "      <td>5157.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>827.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:07</td>\n",
       "      <td>9.575067</td>\n",
       "      <td>3599.0</td>\n",
       "      <td>4002.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.248234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>7.0</td>\n",
       "      <td>82.756722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>8.0</td>\n",
       "      <td>79.014158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:12</td>\n",
       "      <td>9.658400</td>\n",
       "      <td>8449.0</td>\n",
       "      <td>8970.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>10.0</td>\n",
       "      <td>71.404117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4621.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:14</td>\n",
       "      <td>9.691733</td>\n",
       "      <td>9301.0</td>\n",
       "      <td>9847.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>12.0</td>\n",
       "      <td>54.346442</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:15</td>\n",
       "      <td>9.708400</td>\n",
       "      <td>9785.0</td>\n",
       "      <td>10302.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>13.0</td>\n",
       "      <td>61.762291</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:16</td>\n",
       "      <td>9.725067</td>\n",
       "      <td>7920.0</td>\n",
       "      <td>10423.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17280.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:17</td>\n",
       "      <td>9.741733</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>6939.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.671971</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:18</td>\n",
       "      <td>9.758400</td>\n",
       "      <td>4247.0</td>\n",
       "      <td>4773.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.721096</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:19</td>\n",
       "      <td>9.775067</td>\n",
       "      <td>3987.0</td>\n",
       "      <td>4516.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.711234</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:20</td>\n",
       "      <td>9.791733</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>4313.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:21</td>\n",
       "      <td>9.808400</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>4271.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.662217</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:22</td>\n",
       "      <td>9.825067</td>\n",
       "      <td>3478.0</td>\n",
       "      <td>4120.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.213930</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:23</td>\n",
       "      <td>9.841733</td>\n",
       "      <td>3267.0</td>\n",
       "      <td>3821.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.898511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:25</td>\n",
       "      <td>9.875067</td>\n",
       "      <td>3552.0</td>\n",
       "      <td>4134.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>23.0</td>\n",
       "      <td>12.046098</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:26</td>\n",
       "      <td>9.891733</td>\n",
       "      <td>3321.0</td>\n",
       "      <td>3903.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:27</td>\n",
       "      <td>9.908400</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>3914.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.916753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19.611052</td>\n",
       "      <td>11481.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:29</td>\n",
       "      <td>9.941733</td>\n",
       "      <td>3558.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5799.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:30</td>\n",
       "      <td>9.958400</td>\n",
       "      <td>3280.0</td>\n",
       "      <td>3998.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.963199</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:31</td>\n",
       "      <td>9.975067</td>\n",
       "      <td>3076.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.261309</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:33</td>\n",
       "      <td>10.008400</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>4370.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.189360</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:34</td>\n",
       "      <td>10.025067</td>\n",
       "      <td>3689.0</td>\n",
       "      <td>4264.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.240812</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:35</td>\n",
       "      <td>10.041733</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:36</td>\n",
       "      <td>10.058400</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.729717</td>\n",
       "      <td>427.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:37</td>\n",
       "      <td>10.075067</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>3998.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5333.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:38</td>\n",
       "      <td>10.091733</td>\n",
       "      <td>3605.0</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.481124</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:39</td>\n",
       "      <td>10.108400</td>\n",
       "      <td>3972.0</td>\n",
       "      <td>4494.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:40</td>\n",
       "      <td>10.125067</td>\n",
       "      <td>3537.0</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>38.0</td>\n",
       "      <td>15.614313</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:41</td>\n",
       "      <td>10.141733</td>\n",
       "      <td>3088.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5749.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:42</td>\n",
       "      <td>10.158400</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>3994.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.899643</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:43</td>\n",
       "      <td>10.175067</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.642462</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:44</td>\n",
       "      <td>10.191733</td>\n",
       "      <td>3514.0</td>\n",
       "      <td>4168.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:45</td>\n",
       "      <td>10.208400</td>\n",
       "      <td>3181.0</td>\n",
       "      <td>3710.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.479453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:47</td>\n",
       "      <td>10.241733</td>\n",
       "      <td>3809.0</td>\n",
       "      <td>4368.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.530949</td>\n",
       "      <td>7444.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:48</td>\n",
       "      <td>10.258400</td>\n",
       "      <td>3575.0</td>\n",
       "      <td>4151.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9836.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:49</td>\n",
       "      <td>10.275067</td>\n",
       "      <td>3389.0</td>\n",
       "      <td>4054.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.175695</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:50</td>\n",
       "      <td>10.291733</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>3533.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.784058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:52</td>\n",
       "      <td>10.325067</td>\n",
       "      <td>3828.0</td>\n",
       "      <td>4396.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>50.0</td>\n",
       "      <td>17.329568</td>\n",
       "      <td>10869.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:53</td>\n",
       "      <td>10.341733</td>\n",
       "      <td>3515.0</td>\n",
       "      <td>4099.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5860.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:54</td>\n",
       "      <td>10.358400</td>\n",
       "      <td>3155.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.066197</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:55</td>\n",
       "      <td>10.375067</td>\n",
       "      <td>2876.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>53.0</td>\n",
       "      <td>9.484126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:57</td>\n",
       "      <td>10.408400</td>\n",
       "      <td>3993.0</td>\n",
       "      <td>4546.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>structuredstreaming</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.673475</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>5760</td>\n",
       "      <td>2020-09-18 21:38:58</td>\n",
       "      <td>10.425067</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>4335.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              framework  timeSinceRestart  cpuUsagePct  outputMsgCount  \\\n",
       "0   structuredstreaming              -4.0          NaN             NaN   \n",
       "1   structuredstreaming              -3.0          NaN           448.0   \n",
       "2   structuredstreaming              -2.0          NaN          5312.0   \n",
       "3   structuredstreaming              -1.0          NaN          5760.0   \n",
       "4   structuredstreaming               0.0    43.975392          9244.0   \n",
       "5   structuredstreaming               1.0    57.138944          2276.0   \n",
       "6   structuredstreaming               2.0    66.873252          5760.0   \n",
       "7   structuredstreaming               3.0    48.106995         16453.0   \n",
       "8   structuredstreaming               4.0          NaN           827.0   \n",
       "9   structuredstreaming               5.0    33.248234             NaN   \n",
       "10  structuredstreaming               6.0          NaN             NaN   \n",
       "11  structuredstreaming               7.0    82.756722             NaN   \n",
       "12  structuredstreaming               8.0    79.014158             NaN   \n",
       "13  structuredstreaming               9.0          NaN          5760.0   \n",
       "14  structuredstreaming              10.0    71.404117             NaN   \n",
       "15  structuredstreaming              11.0          NaN          4621.0   \n",
       "16  structuredstreaming              12.0    54.346442          1139.0   \n",
       "17  structuredstreaming              13.0    61.762291         28800.0   \n",
       "18  structuredstreaming              14.0          NaN         17280.0   \n",
       "19  structuredstreaming              15.0    19.671971          5760.0   \n",
       "20  structuredstreaming              16.0    20.721096          5760.0   \n",
       "21  structuredstreaming              17.0    19.711234          5760.0   \n",
       "22  structuredstreaming              18.0          NaN          5760.0   \n",
       "23  structuredstreaming              19.0    28.662217         11520.0   \n",
       "24  structuredstreaming              20.0    22.213930          5760.0   \n",
       "25  structuredstreaming              21.0     7.898511             NaN   \n",
       "26  structuredstreaming              22.0          NaN         11520.0   \n",
       "27  structuredstreaming              23.0    12.046098          5760.0   \n",
       "28  structuredstreaming              24.0          NaN          5760.0   \n",
       "29  structuredstreaming              25.0    19.916753             NaN   \n",
       "30  structuredstreaming              26.0    19.611052         11481.0   \n",
       "31  structuredstreaming              27.0          NaN          5799.0   \n",
       "32  structuredstreaming              28.0     6.963199          5760.0   \n",
       "33  structuredstreaming              29.0          NaN             NaN   \n",
       "34  structuredstreaming              30.0    12.261309          5760.0   \n",
       "35  structuredstreaming              31.0     7.189360          5760.0   \n",
       "36  structuredstreaming              32.0     9.240812         11520.0   \n",
       "37  structuredstreaming              33.0          NaN          5760.0   \n",
       "38  structuredstreaming              34.0     6.729717           427.0   \n",
       "39  structuredstreaming              35.0          NaN          5333.0   \n",
       "40  structuredstreaming              36.0     4.481124          5760.0   \n",
       "41  structuredstreaming              37.0          NaN         11520.0   \n",
       "42  structuredstreaming              38.0    15.614313          5760.0   \n",
       "43  structuredstreaming              39.0          NaN          5749.0   \n",
       "44  structuredstreaming              40.0     7.899643            31.0   \n",
       "45  structuredstreaming              41.0    14.642462         11500.0   \n",
       "46  structuredstreaming              42.0          NaN          5760.0   \n",
       "47  structuredstreaming              43.0     5.479453             NaN   \n",
       "48  structuredstreaming              44.0          NaN          5760.0   \n",
       "49  structuredstreaming              45.0    10.530949          7444.0   \n",
       "50  structuredstreaming              46.0          NaN          9836.0   \n",
       "51  structuredstreaming              47.0     6.175695          5760.0   \n",
       "52  structuredstreaming              48.0     3.784058             NaN   \n",
       "53  structuredstreaming              49.0          NaN          6311.0   \n",
       "54  structuredstreaming              50.0    17.329568         10869.0   \n",
       "55  structuredstreaming              51.0          NaN          5860.0   \n",
       "56  structuredstreaming              52.0    12.066197          5760.0   \n",
       "57  structuredstreaming              53.0     9.484126             NaN   \n",
       "58  structuredstreaming              54.0          NaN          5760.0   \n",
       "59  structuredstreaming              55.0     9.673475          5760.0   \n",
       "\n",
       "    inputMsgCount                time    timeSec  percentile_50_second  \\\n",
       "0            5760                 NaT        NaN                   NaN   \n",
       "1            5760 2020-09-18 21:38:00   9.458400                4430.0   \n",
       "2            5760 2020-09-18 21:38:01   9.475067                4669.0   \n",
       "3            5760 2020-09-18 21:38:02   9.491733                4719.0   \n",
       "4            5760 2020-09-18 21:38:03   9.508400                4900.0   \n",
       "5            5760 2020-09-18 21:38:04   9.525067                5309.0   \n",
       "6            5760 2020-09-18 21:38:05   9.541733                4912.0   \n",
       "7            5760 2020-09-18 21:38:06   9.558400                3854.0   \n",
       "8            5760 2020-09-18 21:38:07   9.575067                3599.0   \n",
       "9            5760                 NaT        NaN                   NaN   \n",
       "10           5760                 NaT        NaN                   NaN   \n",
       "11           5760                 NaT        NaN                   NaN   \n",
       "12           5760                 NaT        NaN                   NaN   \n",
       "13           5760 2020-09-18 21:38:12   9.658400                8449.0   \n",
       "14           5760                 NaT        NaN                   NaN   \n",
       "15           5760 2020-09-18 21:38:14   9.691733                9301.0   \n",
       "16           5760 2020-09-18 21:38:15   9.708400                9785.0   \n",
       "17           5760 2020-09-18 21:38:16   9.725067                7920.0   \n",
       "18           5760 2020-09-18 21:38:17   9.741733                5132.0   \n",
       "19           5760 2020-09-18 21:38:18   9.758400                4247.0   \n",
       "20           5760 2020-09-18 21:38:19   9.775067                3987.0   \n",
       "21           5760 2020-09-18 21:38:20   9.791733                3724.0   \n",
       "22           5760 2020-09-18 21:38:21   9.808400                3737.0   \n",
       "23           5760 2020-09-18 21:38:22   9.825067                3478.0   \n",
       "24           5760 2020-09-18 21:38:23   9.841733                3267.0   \n",
       "25           5760                 NaT        NaN                   NaN   \n",
       "26           5760 2020-09-18 21:38:25   9.875067                3552.0   \n",
       "27           5760 2020-09-18 21:38:26   9.891733                3321.0   \n",
       "28           5760 2020-09-18 21:38:27   9.908400                3364.0   \n",
       "29           5760                 NaT        NaN                   NaN   \n",
       "30           5760 2020-09-18 21:38:29   9.941733                3558.0   \n",
       "31           5760 2020-09-18 21:38:30   9.958400                3280.0   \n",
       "32           5760 2020-09-18 21:38:31   9.975067                3076.0   \n",
       "33           5760                 NaT        NaN                   NaN   \n",
       "34           5760 2020-09-18 21:38:33  10.008400                3830.0   \n",
       "35           5760 2020-09-18 21:38:34  10.025067                3689.0   \n",
       "36           5760 2020-09-18 21:38:35  10.041733                3440.0   \n",
       "37           5760 2020-09-18 21:38:36  10.058400                3090.0   \n",
       "38           5760 2020-09-18 21:38:37  10.075067                3401.0   \n",
       "39           5760 2020-09-18 21:38:38  10.091733                3605.0   \n",
       "40           5760 2020-09-18 21:38:39  10.108400                3972.0   \n",
       "41           5760 2020-09-18 21:38:40  10.125067                3537.0   \n",
       "42           5760 2020-09-18 21:38:41  10.141733                3088.0   \n",
       "43           5760 2020-09-18 21:38:42  10.158400                3466.0   \n",
       "44           5760 2020-09-18 21:38:43  10.175067                3448.0   \n",
       "45           5760 2020-09-18 21:38:44  10.191733                3514.0   \n",
       "46           5760 2020-09-18 21:38:45  10.208400                3181.0   \n",
       "47           5760                 NaT        NaN                   NaN   \n",
       "48           5760 2020-09-18 21:38:47  10.241733                3809.0   \n",
       "49           5760 2020-09-18 21:38:48  10.258400                3575.0   \n",
       "50           5760 2020-09-18 21:38:49  10.275067                3389.0   \n",
       "51           5760 2020-09-18 21:38:50  10.291733                2976.0   \n",
       "52           5760                 NaT        NaN                   NaN   \n",
       "53           5760 2020-09-18 21:38:52  10.325067                3828.0   \n",
       "54           5760 2020-09-18 21:38:53  10.341733                3515.0   \n",
       "55           5760 2020-09-18 21:38:54  10.358400                3155.0   \n",
       "56           5760 2020-09-18 21:38:55  10.375067                2876.0   \n",
       "57           5760                 NaT        NaN                   NaN   \n",
       "58           5760 2020-09-18 21:38:57  10.408400                3993.0   \n",
       "59           5760 2020-09-18 21:38:58  10.425067                3813.0   \n",
       "\n",
       "    percentile_99_second  timeSinceFailure  \n",
       "0                    NaN               NaN  \n",
       "1                 4996.0               1.0  \n",
       "2                 5372.0               2.0  \n",
       "3                 5261.0               3.0  \n",
       "4                 5932.0               4.0  \n",
       "5                 6302.0               5.0  \n",
       "6                 5465.0               6.0  \n",
       "7                 5157.0               7.0  \n",
       "8                 4002.0               8.0  \n",
       "9                    NaN               NaN  \n",
       "10                   NaN               NaN  \n",
       "11                   NaN               NaN  \n",
       "12                   NaN               NaN  \n",
       "13                8970.0              13.0  \n",
       "14                   NaN               NaN  \n",
       "15                9847.0              15.0  \n",
       "16               10302.0              16.0  \n",
       "17               10423.0              17.0  \n",
       "18                6939.0              18.0  \n",
       "19                4773.0              19.0  \n",
       "20                4516.0              20.0  \n",
       "21                4313.0              21.0  \n",
       "22                4271.0              22.0  \n",
       "23                4120.0              23.0  \n",
       "24                3821.0              24.0  \n",
       "25                   NaN               NaN  \n",
       "26                4134.0              26.0  \n",
       "27                3903.0              27.0  \n",
       "28                3914.0              28.0  \n",
       "29                   NaN               NaN  \n",
       "30                4157.0              30.0  \n",
       "31                3998.0              31.0  \n",
       "32                3610.0              32.0  \n",
       "33                   NaN               NaN  \n",
       "34                4370.0              34.0  \n",
       "35                4264.0              35.0  \n",
       "36                4138.0              36.0  \n",
       "37                3627.0              37.0  \n",
       "38                3998.0              38.0  \n",
       "39                4137.0              39.0  \n",
       "40                4494.0              40.0  \n",
       "41                4220.0              41.0  \n",
       "42                3615.0              42.0  \n",
       "43                3994.0              43.0  \n",
       "44                3699.0              44.0  \n",
       "45                4168.0              45.0  \n",
       "46                3710.0              46.0  \n",
       "47                   NaN               NaN  \n",
       "48                4368.0              48.0  \n",
       "49                4151.0              49.0  \n",
       "50                4054.0              50.0  \n",
       "51                3533.0              51.0  \n",
       "52                   NaN               NaN  \n",
       "53                4396.0              53.0  \n",
       "54                4099.0              54.0  \n",
       "55                4000.0              55.0  \n",
       "56                3400.0              56.0  \n",
       "57                   NaN               NaN  \n",
       "58                4546.0              58.0  \n",
       "59                4335.0              59.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for framework in frameworks:\n",
    "#     cpuTimeseriesDataWithStartup[framework].show()\n",
    "    print(framework)\n",
    "    time_of_restart = cpuTimeseriesDataWithStartup[framework]\\\n",
    "        .filter(col(\"containerName\").contains(\"-1\"))\\\n",
    "        .groupBy(\"containerTaskId\")\\\n",
    "        .agg(F.min(\"timeSec\").alias(\"timeSec\"))\\\n",
    "        .toPandas()[\"timeSec\"].max()\n",
    "    time_of_failure = cpuTimeseriesDataWithStartup[framework]\\\n",
    "        .filter(col(\"containerName\").contains(\"-1\"))\\\n",
    "        .groupBy(\"containerTaskId\")\\\n",
    "        .agg(F.max(\"timeSec\").alias(\"timeSec\"))\\\n",
    "        .toPandas()[\"timeSec\"].min()\n",
    "\n",
    "    latencyAfterFailure = latencyTimeseriesDataWithoutStartup[framework]\\\n",
    "        .filter((col(\"timeSec\") > time_of_failure))\\\n",
    "        .withColumn(\"timeSinceRestart\", F.round((col(\"timeSec\")-time_of_restart)*60))\\\n",
    "        .orderBy(\"timeSec\")\\\n",
    "        .select(\"framework\", \"time\", \"timeSec\", \"timeSinceRestart\", \"percentile_50_second\",\"percentile_99_second\")\n",
    "\n",
    "    tpAFterFailure = throughputTimeseriesDataWithStartup[framework]\\\n",
    "        .filter((col(\"timeSec\") > time_of_failure))\\\n",
    "        .withColumn(\"timeSinceRestart\", F.round((col(\"timeSec\")-time_of_restart)*60))\\\n",
    "        .orderBy(\"timeSec\")\\\n",
    "        .select(\"framework\", \"timeSinceRestart\", \"outputMsgCount\")\n",
    "        \n",
    "    inputTpAFterFailure = inputThroughputTimeseriesDataWithStartup[framework]\\\n",
    "        .filter((col(\"timeSec\") > time_of_failure))\\\n",
    "        .withColumn(\"timeSinceRestart\", F.round((col(\"timeSec\")-time_of_restart)*60))\\\n",
    "        .orderBy(\"timeSec\")\\\n",
    "        .select(\"framework\", \"timeSinceRestart\", \"inputMsgCount\")\n",
    "\n",
    "    cpuAfterFailure = cpuTimeseriesDataWithStartup[framework]\\\n",
    "        .filter(col(\"containerName\").contains(\"-1\"))\\\n",
    "        .filter((col(\"timeSec\") > time_of_failure))\\\n",
    "        .withColumn(\"timeSinceRestart\", F.round((col(\"timeSec\")-time_of_restart)*60))\\\n",
    "        .orderBy(\"timeSec\")\\\n",
    "        .select(col(\"framework\"), col(\"timeSinceRestart\"), col(\"cpuUsagePct\"))\n",
    "        \n",
    "\n",
    "    display(cpuAfterFailure.join(tpAFterFailure, [\"framework\", \"timeSinceRestart\"], \"outer\")\\\n",
    "        .join(inputTpAFterFailure, [\"framework\", \"timeSinceRestart\"], \"outer\")\\\n",
    "        .join(latencyAfterFailure, [\"framework\", \"timeSinceRestart\"], \"outer\")\\\n",
    "        .withColumn(\"timeSinceFailure\", F.round((col(\"timeSec\")-time_of_failure)*60))\\\n",
    "        .orderBy(\"timeSinceRestart\").limit(60).toPandas())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FLINK_ALO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[0.18586693331599236, 7.26107582449913, 10.56477427482605]\n",
      "CPU AFTER\n",
      "[0.14493587659671903, 7.02231377363205, 8.575864881277084]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[752.0, 797.0, 851.0]\n",
      "Latency AFTER\n",
      "[762.0, 799.0, 825.0]\n",
      "\n",
      "\n",
      "FLINK_EO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[0.1970410579815507, 7.398034632205963, 11.98154091835022]\n",
      "CPU AFTER\n",
      "[0.19419238669797778, 7.120272517204285, 9.267275780439377]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[773.0, 827.0, 861.0]\n",
      "Latency AFTER\n",
      "[758.0, 811.0, 838.0]\n",
      "\n",
      "\n",
      "KAFKASTREAMS_ALO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[19.95037943124771, 21.517854928970337, 25.54289996623993]\n",
      "CPU AFTER\n",
      "[19.222882390022278, 20.53268998861313, 23.35578054189682]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[686.0, 717.0, 782.0]\n",
      "Latency AFTER\n",
      "[710.0, 741.0, 1168.0]\n",
      "\n",
      "\n",
      "KAFKASTREAMS_EO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[29.903995990753174, 33.71347188949585, 38.436391949653625]\n",
      "CPU AFTER\n",
      "[27.025070786476135, 30.875319242477417, 46.85787260532379]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[1301.0, 2522.0, 2605.0]\n",
      "Latency AFTER\n",
      "[2276.0, 2370.0, 3043.0]\n",
      "\n",
      "\n",
      "SPARK_ALO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[0.008572253136662766, 13.41245025396347, 25.691628456115723]\n",
      "CPU AFTER\n",
      "[0.008601632725913078, 11.286494135856628, 21.023058891296387]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[720.0, 775.0, 3055.0]\n",
      "Latency AFTER\n",
      "[735.0, 770.0, 1093.0]\n",
      "\n",
      "\n",
      "STRUCTUREDSTREAMING_ALO\n",
      "-----------------------------\n",
      "CPU BEFORE\n",
      "[0.01104241018765606, 16.662803292274475, 28.078413009643555]\n",
      "CPU AFTER\n",
      "[0.011896565411007032, 10.48850491642952, 24.35939759016037]\n",
      "...................\n",
      "Latency BEFORE\n",
      "[2918.0, 3464.0, 10670.0]\n",
      "Latency AFTER\n",
      "[2863.0, 3427.0, 4447.0]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for framework in frameworks:\n",
    "    print(framework)\n",
    "    print(\"-----------------------------\")\n",
    "    print(\"CPU BEFORE\")\n",
    "    print(cpuTimeseriesDataWithStartup[framework]\\\n",
    "        .filter(((col(\"timeSec\") < time_of_restart-2)))\\\n",
    "        .stat.approxQuantile(\"cpuUsagePct\", [0.1,0.5,0.9], 0))\n",
    "        \n",
    "    print(\"CPU AFTER\")\n",
    "    print(cpuTimeseriesDataWithStartup[framework]\\\n",
    "        .filter((col(\"timeSec\") > time_of_restart+2))\\\n",
    "        .stat.approxQuantile(\"cpuUsagePct\", [0.1,0.5,0.9], 0))\n",
    "    \n",
    "        \n",
    "    print(\"...................\")\n",
    "    print(\"Latency BEFORE\")\n",
    "    print(latencyTimeseriesDataWithoutStartup[framework]\\\n",
    "        .filter(((col(\"timeSec\") < time_of_restart-2)))\\\n",
    "        .select(col(\"percentile_50_second\").cast(\"double\"))\\\n",
    "        .stat.approxQuantile(\"percentile_50_second\", [0.0,0.5,1.0], 0))\n",
    "        \n",
    "    print(\"Latency AFTER\")\n",
    "    print(latencyTimeseriesDataWithoutStartup[framework]\\\n",
    "        .filter((col(\"timeSec\") > time_of_restart+2))\\\n",
    "        .stat.approxQuantile(\"percentile_50_second\", [0.0,0.5,1.0], 0))\n",
    "    print(\"\\n\")"
   ]
  }
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