WebIDE Runtime Type.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": 16, "cell_type": "code", "source": "def plot_keyed_property(key_name):\n    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)\n\n    properties = get_keyed_properties(pings, \"DEVTOOLS_WEBIDE_\" + key_name)\n\n    subset = get_pings_properties(pings, properties)\n\n    histograms = subset.flatMap(lambda p: list(p.iteritems()))\n\n    byKey = histograms.reduceByKey(lambda x, y: x + y).collect()\n\n    votes = map(lambda x: (x[0], x[1][1]), byKey)\n    votes = filter(lambda x: not x[0].endswith(\"_parent\"), votes)\n    votes = map(lambda x: (x[0].split(\"/\")[1], x[1]), votes)\n\n    aggregates = pd.Series(dict(votes))\n\n    frame = pd.DataFrame(aggregates)\n\n    frame.plot(kind = \"bar\")\n    plt.title(key_name)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "plot_keyed_property(\"CONNECTED_RUNTIME_TYPE\")", "outputs": [{"output_type": "display_data", "data": {"image/png": 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