telemetry-analysis.ipynb

{"nbformat_minor": 0, "cells": [{"source": "### Telemetry Analysis", "cell_type": "markdown", "metadata": {}}, {"execution_count": 140, "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\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client\n\n%pylab inline", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "How many parallel workers do we have?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 141, "cell_type": "code", "source": "sc.defaultParallelism", "outputs": [{"execution_count": 141, "output_type": "execute_result", "data": {"text/plain": "16"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Fetch all the submissions for the nightly build made on 2015-07-15 to 2015-07-25:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 149, "cell_type": "code", "source": "pings = get_pings(sc, app=\"Firefox\", channel=\"nightly\", build_id=(\"20150715000000\", \"20150725999999\"), fraction=1.0)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Extract the device pixels per virtual pixel setting from each ping (one per user) and filter out the unspecified values:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 150, "cell_type": "code", "source": "subset = get_pings_properties(pings, [\"clientID\", \"environment/settings/userPrefs/layout.css.devPixelsPerPx\"])\nsubset = get_one_ping_per_client(subset)\nvalid_subset = subset.filter(lambda x: x['environment/settings/userPrefs/layout.css.devPixelsPerPx'] != None)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "What percentage of users have this setting enabled?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 153, "cell_type": "code", "source": "valid_subset.first()", "outputs": [{"execution_count": 153, "output_type": "execute_result", "data": {"text/plain": "{'clientID': u'facef8c1-418b-44a2-b8bb-e6383c508b61',\n 'environment/settings/userPrefs/layout.css.devPixelsPerPx': u'1'}"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 152, "cell_type": "code", "source": "valid_count, total_count = valid_subset.count(), subset.count()\n\"{}% ({} of {})\".format(100.0 * valid_count / total_count, valid_count, total_count)", "outputs": [{"execution_count": 152, "output_type": "execute_result", "data": {"text/plain": "'0.261086171757% (196 of 75071)'"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Caching is fundamental as it allows for an iterative, real-time development workflow:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 154, "cell_type": "code", "source": "cached = valid_subset.cache()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Aggregate the settings by their value:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 159, "cell_type": "code", "source": "settings = cached.map(lambda x: (x['environment/settings/userPrefs/layout.css.devPixelsPerPx'], 1)).reduceByKey(lambda a, b: a + b).collectAsMap()\nsettings", "outputs": [{"execution_count": 159, "output_type": "execute_result", "data": {"text/plain": "{u'-1': 11,\n u'-1.0': 1,\n u'0': 2,\n u'0.8': 1,\n u'0.85': 2,\n u'0.9': 1,\n u'0.92': 1,\n u'1': 55,\n u'1,8': 1,\n u'1.': 1,\n u'1.0': 28,\n u'1.00': 1,\n u'1.05': 10,\n u'1.1': 9,\n u'1.15': 2,\n u'1.2': 10,\n u'1.25': 10,\n u'1.3': 3,\n u'1.333333': 1,\n u'1.4': 6,\n u'1.5': 15,\n u'1.8': 5,\n u'1.88': 1,\n u'1.9': 1,\n u'2': 14,\n u'2.0': 4}"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "And finally plot the data:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 161, "cell_type": "code", "source": "plt.figure(figsize=(15, 7))\npairs = sorted(settings.items(), key=lambda x: x[0])\nwidth = 0.8\nplt.bar(range(len(pairs)), map(lambda x: x[1], pairs), width=width)\nax = plt.gca()\nax.set_xticks(np.arange(len(pairs)) + width/2)\nax.set_xticklabels(map(lambda x: x[0], pairs), rotation=90)\nplt.xlabel(\"layout.css.devPixelsPerPx Value\")\nplt.ylabel(\"Number of client IDs\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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EMQAAi7CTEPeLm2zbnVNaAgAArLidrhN3kyQ37+43VNVVkxzU3Z8ZtWGWGIAklhiYN+cT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\n2913TvK9SV680x1X1YHD839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"text/plain": "<matplotlib.figure.Figure at 0x7ffcb82e7450>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Some of these values are duplicated. To group settings by their numerical values, we parse the labels:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 164, "cell_type": "code", "source": "plt.figure(figsize=(15, 7))\nfrom collections import defaultdict\nnumerical_settings = defaultdict(int)\nfor setting, count in settings.items():\n    try: numerical_settings[float(setting)] += count\n    except ValueError: pass\npairs = sorted(numerical_settings.items(), key=lambda x: x[0])\nwidth = 0.8\nplt.bar(range(len(pairs)), map(lambda x: x[1], pairs), width=width)\nax = plt.gca()\nax.set_xticks(np.arange(len(pairs)) + width/2)\nax.set_xticklabels(map(lambda x: x[0], pairs), rotation=90)\nplt.xlabel(\"layout.css.devPixelsPerPx Value\")\nplt.ylabel(\"Number of client IDs\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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S1BBDnCRJkiQ1xBAnSZIkSQ3pLcQlOSrJ5iSfT/KyyexlbjKbXVFzE99DkpqW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\nHHBaVf1pj+VJmmGGOEmSpGV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