Gecko thread activity, number of hangs, no extensions

Gecko thread activity, number of hangs, no extensions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E10S Experiment Beta, thread activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import ujson as json\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "import IPython\n",
    "import functools\n",
    "\n",
    "from __future__ import division\n",
    "from moztelemetry.spark import get_pings, get_one_ping_per_client, get_pings_properties\n",
    "from montecarlino import grouped_permutation_test\n",
    "\n",
    "%pylab inline\n",
    "IPython.core.pylabtools.figsize(16, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.defaultParallelism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get e10s and non-e10s partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = sqlContext.read.load(\"s3://telemetry-parquet/e10s-experiment/e10s-enabled-beta-20151214@experiments.mozilla.org/generationDate=20160106\", \"parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only consider builds with [bug 1234618](https://bugzilla.mozilla.org/show_bug.cgi?id=1234618) BHR fix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = dataset.filter(dataset.buildId >= '20151228134903')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample by clientId:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampled = dataset.filter(dataset.sampleId <= 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18640"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled.filter(dataset[\"experimentBranch\"] == \"experiment\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19208"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled.filter(dataset[\"experimentBranch\"] == \"control\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform Dataframe to RDD of pings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def row_2_ping(row):\n",
    "    ping = {\"clientId\": row.clientId,\n",
    "            \"payload\": {\"threadHangStats\": json.loads(row.threadHangStats)},\n",
    "            \"environment\": {\"addons\": json.loads(row.addons)},\n",
    "            \"e10s\": True if row.experimentBranch == \"experiment\" else False}\n",
    "    return ping\n",
    "\n",
    "subset = sampled.rdd.map(row_2_ping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = subset.filter(lambda p: len(p[\"environment\"][\"addons\"][\"activeAddons\"]) == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thread activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_activity(ping):\n",
    "    for thread in ping[\"payload\"][\"threadHangStats\"]:\n",
    "        if thread[\"name\"] != \"Gecko\":\n",
    "            continue\n",
    "\n",
    "        values = {str(int(k) + 1): v for k, v in thread[\"activity\"][\"values\"].iteritems()}                                       \n",
    "        yield {\"e10s\": ping[\"e10s\"],\n",
    "               \"clientId\": ping[\"clientId\"],\n",
    "               \"payload\": {\"histograms\": {\"GECKO_THREAD_ACTIVITY_MS\": {\"values\": values}}}}\n",
    "        \n",
    "\n",
    "\n",
    "histograms = json.loads(\n",
    "    \"\"\"\n",
    "    {\n",
    "      \"GECKO_THREAD_ACTIVITY_MS\": {\n",
    "        \"expires_in_version\": \"default\",\n",
    "        \"kind\": \"exponential\",\n",
    "        \"high\": \"2**24\",\n",
    "        \"n_buckets\": 26,\n",
    "        \"description\": \"\"\n",
    "      }\n",
    "    }\n",
    "    \"\"\")\n",
    "                \n",
    "histogram = \"payload/histograms/GECKO_THREAD_ACTIVITY_MS\"\n",
    "props = get_pings_properties(subset.flatMap(get_activity), [\"clientId\", \"e10s\", histogram], additional_histograms=histograms)\n",
    "frame = pd.DataFrame(props.collect())\n",
    "    \n",
    "e10s = frame[frame[\"e10s\"] == True]\n",
    "none10s = frame[frame[\"e10s\"] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42c157a190>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAADoJcuPAQAA6C2lFgAAgN5SagEAAOgtpRYAAIDeUmoBAADoLVc/BqB3Vqz4ThYvHus6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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42c2dba890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_sum = pd.Series(e10s[\"payload/histograms/GECKO_THREAD_ACTIVITY_MS\"].map(lambda h: h[h.index >= 128].sum()))\n",
    "n_sum = pd.Series(none10s[\"payload/histograms/GECKO_THREAD_ACTIVITY_MS\"].map(lambda h: h[h.index >= 128].sum()))\n",
    "pd.DataFrame({\"e10s\": np.log2(e_sum[e_sum > 1]), \"none10s\": np.log2(n_sum[n_sum > 1])}).plot(kind=\"hist\", alpha=0.5, bins=20, color=[\"green\", \"blue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4909.000000\n",
       "mean       3361.968833\n",
       "std       10815.265192\n",
       "min           0.000000\n",
       "25%          35.000000\n",
       "50%         247.000000\n",
       "75%        2151.000000\n",
       "90%        8410.600000\n",
       "95%       16051.000000\n",
       "max      266603.000000\n",
       "Name: payload/histograms/GECKO_THREAD_ACTIVITY_MS, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_sum.describe(percentiles=[0.25, 0.5, 0.75, 0.90, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4965.000000\n",
       "mean       1768.283787\n",
       "std        5737.127090\n",
       "min           0.000000\n",
       "25%          49.000000\n",
       "50%         233.000000\n",
       "75%        1115.000000\n",
       "90%        3904.600000\n",
       "95%        8100.800000\n",
       "max      138976.000000\n",
       "Name: payload/histograms/GECKO_THREAD_ACTIVITY_MS, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_sum.describe(percentiles=[0.25, 0.5, 0.75, 0.90, 0.95])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}