Responsiveness metrics over the jank threshold (100ms) comparing E10s to non-E10s on the second Beta 45 e10s experiment

beta45ex2_responsiveness_100.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E10S Experiment 2 Beta 45 (without addons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
    "\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": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.defaultParallelism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def chi2_distance(xs, ys, eps = 1e-10, normalize = True):\n",
    "    histA = xs.sum(axis=0)\n",
    "    histB = ys.sum(axis=0)\n",
    "    \n",
    "    if normalize:\n",
    "        histA = histA/histA.sum()\n",
    "        histB = histB/histB.sum()\n",
    "    \n",
    "    d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)\n",
    "        for (a, b) in zip(histA, histB)])\n",
    "\n",
    "    return d\n",
    "\n",
    "def median_diff(xs, ys):\n",
    "    return np.median(xs) - np.median(ys)\n",
    "\n",
    "def compare_histogram(histogram, e10s, none10s):\n",
    "    # Normalize individual histograms\n",
    "    e10s = e10s.map(lambda x: x/x.sum())\n",
    "    e10s = e10s.map(lambda x: x[x.index > 100])\n",
    "    none10s = none10s.map(lambda x: x/x.sum())\n",
    "    none10s = none10s.map(lambda x: x[x.index > 100])\n",
    "    \n",
    "    pvalue = grouped_permutation_test(chi2_distance, [e10s, none10s], num_samples=100)\n",
    "    \n",
    "    eTotal = e10s.sum()\n",
    "    nTotal = none10s.sum()\n",
    "        \n",
    "    eTotal = 100*eTotal/eTotal.sum()\n",
    "    nTotal = 100*nTotal/nTotal.sum()\n",
    "        \n",
    "    fig = plt.figure()\n",
    "    fig.subplots_adjust(hspace=0.3)\n",
    "        \n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax2 = ax.twinx()\n",
    "    width = 0.4\n",
    "    ylim = max(eTotal.max(), nTotal.max())\n",
    "        \n",
    "    eTotal.plot(kind=\"bar\", alpha=0.5, color=\"green\", label=\"e10s\", ax=ax, width=width, position=0, ylim=(0, ylim + 1))\n",
    "    nTotal.plot(kind=\"bar\", alpha=0.5, color=\"blue\", label=\"non e10s\", ax=ax2, width=width, position=1, grid=False, ylim=ax.get_ylim())\n",
    "        \n",
    "    ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0],\n",
    "              [\"e10s ({} samples\".format(len(e10s)), \"non e10s ({} samples)\".format(len(none10s))])\n",
    "\n",
    "    plt.title(histogram)\n",
    "    plt.xlabel(histogram)\n",
    "    plt.ylabel(\"Frequency %\")\n",
    "    plt.show()\n",
    "        \n",
    "    print \"The probability that the distributions for {} are differing by chance is {:.2f}.\".format(histogram, pvalue)\n",
    "\n",
    "def normalize_uptime_hour(frame):\n",
    "    frame = frame[frame[\"payload/simpleMeasurements/uptime\"] > 0]\n",
    "    frame = 60 * frame.apply(lambda x: x/frame[\"payload/simpleMeasurements/uptime\"]) # Metric per hour\n",
    "    frame.drop('payload/simpleMeasurements/uptime', axis=1, inplace=True)\n",
    "    return frame\n",
    "    \n",
    "def compare_count_histograms(pings, *histograms_names):\n",
    "    properties = histograms_names + (\"payload/simpleMeasurements/uptime\", \"e10s\")\n",
    "\n",
    "    frame = pd.DataFrame(get_pings_properties(pings, properties).collect())\n",
    "\n",
    "    e10s = frame[frame[\"e10s\"] == True]\n",
    "    e10s = normalize_uptime_hour(e10s)\n",
    "    \n",
    "    none10s = frame[frame[\"e10s\"] == False]\n",
    "    none10s = normalize_uptime_hour(none10s)\n",
    "    \n",
    "    for histogram in e10s.columns:\n",
    "        if histogram == \"e10s\" or histogram.endswith(\"_parent\") or histogram.endswith(\"_children\"):\n",
    "            continue\n",
    "            \n",
    "        compare_scalars(histogram + \" per hour\", e10s[histogram].dropna(), none10s[histogram].dropna())\n",
    "\n",
    "        \n",
    "def compare_histograms(pings, *histogram_names):\n",
    "    frame = pd.DataFrame(get_pings_properties(pings, histogram_names + (\"e10s\",) , with_processes=True).collect())\n",
    "    e10s = frame[frame[\"e10s\"] == True]\n",
    "    none10s = frame[frame[\"e10s\"] == False]\n",
    "    for histogram in none10s.columns:\n",
    "        if histogram == \"e10s\" or histogram.endswith(\"_parent\") or histogram.endswith(\"_children\"):\n",
    "            continue\n",
    "            \n",
    "        has_children = np.sum(e10s[histogram + \"_children\"].notnull()) > 0\n",
    "        has_parent = np.sum(e10s[histogram + \"_parent\"].notnull()) > 0\n",
    "        \n",
    "        if has_children and has_parent:\n",
    "            compare_histogram(histogram + \" (parent + children)\", e10s[histogram].dropna(), none10s[histogram].dropna())\n",
    "            \n",
    "        if has_parent:\n",
    "            compare_histogram(histogram + \" (parent)\", e10s[histogram + \"_parent\"].dropna(), none10s[histogram].dropna())\n",
    "            \n",
    "        if has_children:\n",
    "            compare_histogram(histogram + \" (children)\", e10s[histogram + \"_children\"].dropna(), none10s[histogram].dropna())\n",
    "                    \n",
    "\n",
    "                \n",
    "def compare_scalars(metric, *groups):\n",
    "    print \"Median difference in {} is {:.2f}, ({:.2f}, {:.2f}).\".format(metric,\n",
    "                                                                        median_diff(*groups), \n",
    "                                                                        np.median(groups[0]),\n",
    "                                                                        np.median(groups[1]))\n",
    "    print \"The probablity of this effect being purely by chance is {:.2f}.\". \\\n",
    "        format(grouped_permutation_test(median_diff, groups, num_samples=10000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get e10s and non-e10s partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = sqlContext.read.load(\"s3://telemetry-parquet/e10s-experiment/e10s-beta45-withoutaddons@experiments.mozilla.org/generationDate=20160217\", \"parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample by clientId:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampled = dataset.filter(dataset.sampleId <= 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform Dataframe to RDD of pings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def row_2_ping(row):\n",
    "    ping = {\"payload\": {\"simpleMeasurements\": json.loads(row.simpleMeasurements),\n",
    "                        \"histograms\": json.loads(row.histograms),\n",
    "                        \"keyedHistograms\": json.loads(row.keyedHistograms),\n",
    "                        \"childPayloads\": json.loads(row.childPayloads),\n",
    "                        \"threadHangStats\": json.loads(row.threadHangStats)},\n",
    "           \"e10s\": True if row.experimentBranch == \"experiment-no-addons\" else False}\n",
    "    return ping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = sampled.rdd.filter(lambda r: r.experimentBranch in [\"experiment-no-addons\", \"control-no-addons\"]).map(row_2_ping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Responsivness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4ds/MVL16daWlpen777+XJL344ou66aab1KBBA9WvX18vv/yyTjvtNEleSL3t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\n492wYYOWL1+u7t3LJntWVH6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ABQAAAAAEAgEXAAAAABAIBFwAAAAAQCAQcAEAAAAAgUDA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\nB3SgSAd0Jt7+dpekMfWPdIZtO6BviH/3OrcrmTd3/LwOcCF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbe3e64a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/EVENTLOOP_UI_ACTIVITY_EXP_MS (parent + children) are differing by chance is 0.00.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAAoOwp7Yx4vDO4TlKyc+6n4hgMAAAAAKBMK9WMWBwn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbdee1efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/EVENTLOOP_UI_ACTIVITY_EXP_MS (parent) are differing by chance is 0.00.\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tp05cfvnlbLHFFlRXV3PCCScs9zgpJc4//3wGDBhA7969Ofjgg5k7d25p/3HH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\n9QQXuGZmZmZmZtYTXOCamZmZmZlZT/j/LqYbvJ7PMd8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbdef9b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/EVENTLOOP_UI_ACTIVITY_EXP_MS (children) are differing by chance is 0.69.\n"
     ]
    },
    {
     "data": {
      "image/png": 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IOH4EMZ4dEVd193eIiKdnGRa7FvW+MH4SuKQr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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbd76cd450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/INPUT_EVENT_RESPONSE_MS (parent + children) are differing by chance is 0.82.\n"
     ]
    },
    {
     "data": {
      "image/png": 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uAQAAAF8kIoVC2TXWfzhcc33XhHbt2ql37966/fbbSwSoKVOm6IEHHtD777+v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\nqGcorjbhSeEaqQUAAAAAkBy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AAAAAAo9wCwAAAAAIPMItAAAAACDwCLcAAAAAgMAj3AIA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\nNPLs3EqSJEmSRt7/B2Yg4zmpOUidAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbe0fff6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/INPUT_EVENT_RESPONSE_MS (parent) are differing by chance is 0.29.\n"
     ]
    },
    {
     "data": {
      "image/png": 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RbgEAAAAAgUe4BQAAAAAEXr2qNjSz6yRdKslJ+lLSxc65LdVV2K9J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\nim8Pur01FRFbZ+b9FdvbjjJX6cnu33dRiindDLy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbe26e61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that the distributions for payload/histograms/INPUT_EVENT_RESPONSE_MS (children) are differing by chance is 0.00.\n"
     ]
    }
   ],
   "source": [
    "compare_histograms(subset, \"payload/histograms/EVENTLOOP_UI_ACTIVITY_EXP_MS\",\n",
    "                           \"payload/histograms/INPUT_EVENT_RESPONSE_MS\")"
   ]
  }
 ],
 "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
}