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saptarshiguha/win10error.ipnyb

Created August 21, 2015 21:20
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win10error.ipnyb
{"nbformat_minor": 0, "cells": [{"source": "### switching defaults in Windows10", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "import ujson as json\nimport boto\nimport binascii\nimport ujson as json\n\nfrom moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client, get_clients_history\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": false}}, {"source": "### Basics", "cell_type": "markdown", "metadata": {}}, {"source": " Let's see how many parallel workers we have at our disposal:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "sc.defaultParallelism", "outputs": [{"execution_count": 2, "output_type": "execute_result", "data": {"text/plain": "240"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "We will need all the pings because we need to stitch", "cell_type": "markdown", "metadata": {}}, {"execution_count": 3, "cell_type": "code", "source": "pings = get_pings(sc, app=\"Firefox\", channel=\"beta\", schema=\"v4\",submission_date=(\"20150515\",\"20150820\"), fraction=1)", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 4, "cell_type": "code", "source": "json.dumps(pings.take(1)[0])", "outputs": [{"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": 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"metadata": {}}], "metadata": {"collapsed": true, "trusted": false}}, {"source": "Since we will join the pings by client id (a resource hungry job) we ought to only take what we need.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "cell_type": "code", "source": "subset = get_pings_properties(pings, [\"clientId\",\n \"environment/profile/creationDate\",\n \"environment/settings/isDefaultBrowser\",\n \"environment/system/os/name\",\n \"environment/system/os/version\",\n \"environment/system/os/installYear\",\n \"application/version\",\n \"payload/info/sessionStartDate\",\n \"payload/info/subsessionStartDate\",\n \"payload/info/previousSessionId\",\n \"payload/info/previousSubsessionId\",\n \"payload/info/sessionId\",\n \"payload/info/subsessionId\",\n \"payload/simpleMeasurements/activeTicks\",\n \"payload/simpleMeasurements/totalTime\",\n \"meta/sampleId\"\n ])", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"source": "We want only Windows os.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 6, "cell_type": "code", "source": "subset1p = subset.filter(lambda p: p.get(\"environment/system/os/name\", \"missing\") == \"Windows_NT\" and \"meta/sampleId\"==5)", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"source": "A quick example of what we've got so far ...", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "source": "subset1p.take(1)", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": null, "cell_type": "code", "source": "1+1", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"execution_count": null, "cell_type": "code", "source": "subset1p = subset1p.map(lambda x: (x.get(\"clientId\",\"missing\"),[x])).groupByKey().mapValues(list)", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "### To Create a Random Sample in the Absence of meta/clientId", "cell_type": "markdown", "metadata": {}}, {"source": "Let us try this function that aggregates the pings using the following function: *get_one_ping_per_client*. But before that we will have a function that gets sample of client ids. The following function returns a 5% sample of client ids.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 7, "cell_type": "code", "source": "def getClientIDSample(aping):\n if aping is None:\n x = \"missing\"\n else:\n x = aping.get('clientId',\"missing\")\n if x is None:\n x = \"missing\"\n return binascii.crc32(x) % 100 < 5", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 51, "cell_type": "code", "source": "getClientIDSample(None)", "outputs": [{"execution_count": 51, "output_type": "execute_result", "data": {"text/plain": "False"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Okay, so now we will create subset2 that is the percent sample and then group by client ID and aggregate their pings into a big list. Ultimately we ought to have a count that corresponds to 5% of the keys. In fact let's confirm this methodology works.", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "source": "checkClientCounts = subset.map(lambda x: (x['clientId'],1)).reduceByKey(add).count()", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 34, "cell_type": "code", "source": "checkClientCounts", "outputs": [{"execution_count": 34, "output_type": "execute_result", "data": {"text/plain": "5203104"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": null, "cell_type": "code", "source": "sampleCount = subset.filter(getClientIDSample).map(lambda x: (x['clientId'],1)).reduceByKey(add).count()", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": null, "cell_type": "code", "source": "sampleCount", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}
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