chutten/beta45_thread_activity.ipynb Secret

## beta45_thread_activity.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E10S Experiment Beta, thread activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.defaultParallelism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get e10s and non-e10s partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = sqlContext.read.load(\"s3://telemetry-parquet/e10s-experiment/e10s-beta45-withaddons@experiments.mozilla.org/generationDate=20160201\", \"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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = dataset.filter(dataset.buildId >= '20151228134903')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample by clientId:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampled = dataset.filter(dataset.sampleId <= 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38967"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled.filter(dataset[\"experimentBranch\"] == \"experiment\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41222"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### Thread activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5634013e90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FAACgkxReAAAAOknhBQAAoJMUXgAAADpJ4QUAAKCTFF4AAAA6SeEFAACgkxRe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\nwgsAAEAnKbwAAAB00v8Pq4OGNqcTQycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5649c93390>"
      ]
     },
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     38958.000000\n",
       "mean        174.386647\n",
       "std        2011.598749\n",
       "min           0.000000\n",
       "25%          15.000000\n",
       "50%          33.000000\n",
       "75%          95.000000\n",
       "90%         284.000000\n",
       "95%         570.150000\n",
       "max      343402.000000\n",
       "Name: payload/histograms/GECKO_THREAD_ACTIVITY_MS, dtype: float64"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     41211.000000\n",
       "mean        865.592948\n",
       "std        2951.057977\n",
       "min           0.000000\n",
       "25%          34.000000\n",
       "50%         141.000000\n",
       "75%         597.000000\n",
       "90%        1942.000000\n",
       "95%        3796.500000\n",
       "max      176216.000000\n",
       "Name: payload/histograms/GECKO_THREAD_ACTIVITY_MS, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_sum.describe(percentiles=[0.25, 0.5, 0.75, 0.90, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, without addons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5649d22590>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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otYEV16o6qKr+taq+XVXXVdVbp/ePVtXNVXX19Nfxg8oAAADA/Ld4gM99b5I/bK2trqq9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\ngF5TXAEAAOg1xRUAAIBeU1wBAADotf8fyif4MFKM1Y0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5649d32910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = subset.filter(lambda p: len(p[\"environment\"][\"addons\"][\"activeAddons\"]) == 0)\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]\n",
    "\n",
    "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\"])"
   ]
  }
 ],
 "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
}