Async plugin init on Beta

Async plugin init on Beta.ipynb

{"nbformat_minor": 0, "cells": [{"execution_count": 42, "cell_type": "code", "source": "import ujson as json\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport plotly.plotly as py\nimport re\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client, get_clients_history\nfrom moztelemetry.histogram import Histogram\n\n%pylab inline", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "sc.defaultParallelism", "outputs": [{"execution_count": 41, "output_type": "execute_result", "data": {"text/plain": "80"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 88, "cell_type": "code", "source": "pings = get_pings(sc,\n                  app=\"Firefox\",\n                  channel=\"beta\",\n                  version=\"40.0\",\n                  submission_date=\"20150803\",\n                  fraction=0.1,\n                  schema=\"v4\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 117, "cell_type": "code", "source": "name_version_regexp = re.compile(\"^([-a-zA-Z\\s\\d]*[a-zA-Z])([\\d.]*)$\")\n\ndef extract_name_version(raw):\n    m = re.match(name_version_regexp, raw)\n    if not m: # missing plugin name\n        return [None, None]\n    else:\n        return m.groups()\n    \ndef get_keyed_histograms(name, container, os, async):\n    histograms = container.get(name, None)\n    \n    if not histograms:\n        return []\n    \n    res = []\n    for k, v in histograms.iteritems():\n        plugin_name, version = extract_name_version(k)\n        res.append([name, os, async, plugin_name, version, Histogram(name, v).get_value()])\n        \n    return res\n            \ndef extract(ping):\n    output = []\n    os = ping[\"environment\"][\"system\"][\"os\"][\"name\"]\n    keyed = ping[\"payload\"][\"keyedHistograms\"]\n    async = ping[\"payload\"][\"info\"][\"asyncPluginInit\"]\n    \n    if keyed.get(\"BLOCKED_ON_PLUGIN_MODULE_INIT_MS\", None) is None:\n        return output\n \n    output += get_keyed_histograms(\"BLOCKED_ON_PLUGIN_MODULE_INIT_MS\", keyed, os, async)\n    output += get_keyed_histograms(\"BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS\", keyed, os, async)\n    output += get_keyed_histograms(\"BLOCKED_ON_PLUGIN_STREAM_INIT_MS\", keyed, os, async)\n    output += get_keyed_histograms(\"BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS\", keyed, os, async)\n    return output\n\ndata = pings.flatMap(extract)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 118, "cell_type": "code", "source": "collected_data = data.collect()\nlen(collected_data)", "outputs": [{"execution_count": 118, "output_type": "execute_result", "data": {"text/plain": "818874"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "frame = pd.DataFrame(data.collect(), columns=[\"histogram\", \"os\", \"async\", \"plugin\", \"version\", \"values\"])\ntop_plugins = frame[\"plugin\"].value_counts()[:5].index\ntop_frame = frame[frame[\"plugin\"].isin(top_plugins)]\ntop_frame.loc[:,\"ping\"] = 1", "outputs": [], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "grouped = top_frame.groupby([\"plugin\", \"os\", \"histogram\", \"async\"])\naggregates = grouped[[\"values\", \"ping\"]].apply(lambda x: pd.Series({\"values\": x[\"values\"].sum().values, \"pings\": x[\"ping\"].sum()}))", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "def plot(data):\n    for plugin in top_plugins: # Sorted by popularity\n        plot_os(plugin, data.ix[plugin])\n        \ndef plot_os(plugin, data):\n    for os in [\"Windows_NT\", \"Darwin\", \"Linux\"]:\n        plot_histograms(plugin, os, data.ix[os])\n\ndef plot_histograms(plugin, os, data):\n    print \"Operating System: \" + os # Useful for grepping\n    print \"Plugin: \" + plugin\n    \n    fig = plt.figure(figsize=(30, 5))\n    fig.subplots_adjust(hspace=0.3)\n    \n    for idx, histogram in enumerate(data.index.levels[0]):\n        ax = fig.add_subplot(1, 4, idx + 1)\n        plot_comparison(plugin, os, histogram, ax, data.ix[histogram])\n        \n    plt.show()\n    \ndef plot_comparison(plugin, os, histogram, ax, data):\n    hsync = Histogram(histogram, data.ix[False][\"values\"]) if False in data.index else None\n    hasync = Histogram(histogram, data.ix[True][\"values\"]) if True in data.index else None\n    \n    ax2 = ax.twinx()\n    width = 0.4\n    \n    if hasync is not None:\n        print \"async {} ({} submissions): Q1 {:.2f} - Q2 {:.2f} - Q3 {:.2f}\".format(histogram, data.ix[True][\"pings\"],\n            hasync.percentile(25), hasync.percentile(50), hasync.percentile(75))\n        \n        async = hasync.get_value()\n        async = async/async.sum()\n        async.plot(kind=\"bar\", color=\"green\", ax=ax, width=width, position=0, label=\"async\")\n        \n    if hsync is not None:\n        print \"sync {} ({} submissions): Q1 {:.2f} - Q2 {:.2f} - Q3 {:.2f}\\n\".format(histogram, data.ix[False][\"pings\"],\n            hsync.percentile(25), hsync.percentile(50), hsync.percentile(75))\n        \n        sync = hsync.get_value()\n        sync = sync/sync.sum()\n        sync.plot(kind=\"bar\", color=\"blue\", ax=ax2, width=width, position=1, grid=False, label=\"sync\", ylim=ax.get_ylim())\n    \n    plt.title(\"{} - {} - {}\".format(os, plugin, histogram))\n    plt.ylabel(\"Normalized count\")\n    \n    if hasync and hsync:\n        ax.legend(ax.get_legend_handles_labels()[0] + ax2.get_legend_handles_labels()[0], [\"async\", \"sync\"])\n    elif hasync:\n        ax.legend(ax.get_legend_handles_labels()[0], [\"async\"])\n    else:\n        ax.legend(ax2.get_legend_handles_labels()[0], [\"sync\"])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 124, "cell_type": "code", "source": "plot(aggregates)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Operating System: Windows_NT\nPlugin: Shockwave Flash\nasync BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS (184590 submissions): Q1 31.72 - Q2 52.93 - Q3 89.33\nasync BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS (185212 submissions): Q1 0.25 - Q2 0.50 - Q3 0.76\nasync BLOCKED_ON_PLUGIN_MODULE_INIT_MS (185235 submissions): Q1 0.43 - Q2 0.86 - Q3 2.54\nasync BLOCKED_ON_PLUGIN_STREAM_INIT_MS (176909 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7faefc5f16d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Darwin\nPlugin: Shockwave Flash\nasync BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS (3274 submissions): Q1 42.58 - Q2 57.48 - Q3 97.68\nasync BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS (3274 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\nasync BLOCKED_ON_PLUGIN_MODULE_INIT_MS (3274 submissions): Q1 0.58 - Q2 1.48 - Q3 21.70\nasync BLOCKED_ON_PLUGIN_STREAM_INIT_MS (3146 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa3432370d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Windows_NT\nPlugin: Plugins PDK\nasync BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS (3769 submissions): Q1 0.92 - Q2 1.82 - Q3 4.81\nasync BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS (3810 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\nasync BLOCKED_ON_PLUGIN_MODULE_INIT_MS (3810 submissions): Q1 7.00 - Q2 96.37 - Q3 247.59\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa34050f390>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Darwin\nPlugin: Plugins PDK\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa33df3f9d0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Linux\nPlugin: Plugins PDK\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa3403b8910>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Windows_NT\nPlugin: EagleGet\nasync BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS (2191 submissions): Q1 0.28 - Q2 0.57 - Q3 0.85\nasync BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS (2200 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\nasync BLOCKED_ON_PLUGIN_MODULE_INIT_MS (2200 submissions): Q1 2.96 - Q2 4.76 - Q3 13.87\nasync BLOCKED_ON_PLUGIN_STREAM_INIT_MS (186 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa33d673d90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Darwin\nPlugin: EagleGet\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa33ca35e90>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Windows_NT\nPlugin: Content Blocker KAV\nasync BLOCKED_ON_PLUGIN_INSTANCE_DESTROY_MS (2109 submissions): Q1 0.73 - Q2 1.59 - Q3 4.22\nasync BLOCKED_ON_PLUGIN_INSTANCE_INIT_MS (2121 submissions): Q1 0.25 - Q2 0.50 - Q3 0.75\nasync BLOCKED_ON_PLUGIN_MODULE_INIT_MS (2121 submissions): Q1 4.63 - Q2 23.70 - Q3 161.51\n"}, {"output_type": "display_data", "data": {"image/png": 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9jrTNvKSpEw9rZXSmp1MTz2y91qmJn23Go3NAfz1pn/Qe0vz/MOlq3T1IV7m2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\nWbXllMxqNadkVm05JbNqyymsqZrW8vunRrVtoxr3f8tq228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VW47WVuP7RxvzM7bcatz/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"text/plain": "<matplotlib.figure.Figure at 0x7fa33c752d50>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Darwin\nPlugin: Content Blocker KAV\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fa33b74fcd0>"}, "metadata": {}}, {"output_type": "stream", "name": "stdout", "text": "Operating System: Darwin\nPlugin: Virtual Keyboard KAV\n"}, {"output_type": "display_data", "data": {"image/png": 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