WebIDE Runtime Version.ipynb

{"nbformat_minor": 0, "cells": [{"execution_count": 3, "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 datetime as dt\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}}, {"execution_count": 2, "cell_type": "code", "source": "sc.defaultParallelism", "outputs": [{"execution_count": 2, "output_type": "execute_result", "data": {"text/plain": "16"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "now = dt.datetime.now()\nstart = now - dt.timedelta(7)\nend = now - dt.timedelta(1)\n\npings = get_pings(sc, \n                  app=\"Firefox\", \n                  channel=\"aurora\", \n                  submission_date=(start.strftime(\"%Y%m%d\"), end.strftime(\"%Y%m%d\")), \n                  fraction=1)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "def get_keyed_properties(pings, histogram_name):\n    pings = pings.map(lambda p: json.loads(p))\n    \n    keys = pings.flatMap(lambda p: p.get(\"keyedHistograms\", {}).get(histogram_name, {}).keys()).distinct().collect()\n    keys = filter(lambda x: \"/\" not in x, keys)\n    return map(lambda k: \"keyedHistograms/{}/{}\".format(histogram_name, k), keys) + [\"clientID\"]\n    \nproperties = get_keyed_properties(pings, \"DEVTOOLS_WEBIDE_CONNECTED_RUNTIME_VERSION\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "def remove_clientid(ping):\n    ping.pop(\"clientID\")\n    return ping\n\nsubset = get_pings_properties(pings, properties).map(remove_clientid)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "histograms = subset.flatMap(lambda p: list(p.iteritems()))", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "byKey = histograms.reduceByKey(lambda x, y: x + y).collect()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "votes = map(lambda x: (x[0], x[1][1]), byKey)\nvotes = filter(lambda x: not x[0].endswith(\"_parent\"), votes)\nvotes = map(lambda x: (x[0].split(\"/\")[1], x[1]), votes)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "aggregates = pd.Series(dict(votes))", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 12, "cell_type": "code", "source": "aggregates", "outputs": [{"execution_count": 12, "output_type": "execute_result", "data": {"text/plain": "1.3.0.0-prerelease      43\n1.4.0.0-prerelease      19\n2.0.0.0                  5\n2.0.0.0-prerelease     381\n2.0.0.1                 35\n2.1.0.0-prerelease      11\n2.2.0.0-prerelease    1405\n3.0.0.0-prerelease     431\n38.0.1                  53\n38.0.5                  34\n39.0                    22\n40.0a2                   1\n41.0a1                   7\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "frame = pd.DataFrame(aggregates)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "frame.plot(kind = \"bar\")\nplt.title(\"CONNECTED_RUNTIME_VERSION\")", "outputs": [{"execution_count": 14, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.text.Text at 0x7fa240283c90>"}, "metadata": {}}, {"output_type": 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