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Bug 1276200 - Validate engagement measurements
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bug 1276200 - Validate engagement measurements"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hadoop/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unable to parse whitelist (/home/hadoop/anaconda2/lib/python2.7/site-packages/moztelemetry/histogram-whitelists.json). Assuming all histograms are acceptable.\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import ujson as json\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.plotly as py\n",
"import datetime as dt\n",
"from uuid import UUID\n",
"\n",
"from moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client, get_clients_history\n",
"\n",
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get two chunks of pings:\n",
" * broke_uri_pings: after the engagement measurement land (in bug 1271313) up to bug 1293222 which fixes the broken URI counts\n",
" * latest_pings: after bug 1293222 lands"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The 'schema' parameter is deprecated. Version 4 is now the only schema supported.\n",
"The 'schema' parameter is deprecated. Version 4 is now the only schema supported.\n"
]
}
],
"source": [
"def dedupe(pings):\n",
" return pings.map(lambda p: (p[\"meta/documentId\"], p))\\\n",
" .reduceByKey(lambda a, b: a)\\\n",
" .values()\n",
"\n",
"def filter(pings):\n",
" subset = get_pings_properties(pings, [\"meta/clientId\",\n",
" \"meta/documentId\",\n",
" \"meta/submissionDate\",\n",
" \"environment/profile/creationDate\",\n",
" \"environment/profile/resetDate\",\n",
" \"environment/build\",\n",
" \"environment/partner\",\n",
" \"environment/system\",\n",
" \"payload/info/reason\",\n",
" \"payload/info/sessionId\",\n",
" \"payload/info/subsessionLength\",\n",
" \"payload/info/sessionLength\",\n",
" \"payload/info/profileSubsessionCounter\",\n",
" \"payload/processes/parent/scalars\"])\n",
" return dedupe(subset)\n",
"\n",
"broken_uri_pings = filter(get_pings(sc,\n",
" app=\"Firefox\",\n",
" channel=\"nightly\",\n",
" doc_type=\"main\",\n",
" schema=\"v4\",\n",
" submission_date=(\"20160808\", \"20160815\"), # Only one week of submissions.\n",
" build_id=(\"20160722000000\", \"20160815000000\"), # Up to bug 1293222\n",
" fraction=1.0))\n",
"\n",
"latest_pings = filter(get_pings(sc,\n",
" app=\"Firefox\",\n",
" channel=\"nightly\",\n",
" doc_type=\"main\",\n",
" schema=\"v4\",\n",
" submission_date=(\"20160815\", \"20160822\"),\n",
" build_id=(\"20160815000000\", \"20160822000000\"), # Post bug 1293222\n",
" fraction=1.0))\n",
"\n",
"all_pings = broken_uri_pings + latest_pings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure each ping has a scalar section and the contained engagament measurements scalar have the right formats."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(int,\n",
" {'': 2817510,\n",
" 'browser.engagement.max_concurrent_tab_count not reported': 9272,\n",
" 'browser.engagement.max_concurrent_window_count not reported': 30,\n",
" 'scalars section is None': 50})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def engagement_measurements_check(p):\n",
" known_engagement_scalars = {\n",
" \"browser.engagement.max_concurrent_tab_count\": int,\n",
" \"browser.engagement.max_concurrent_window_count\": int,\n",
" \"browser.engagement.tab_open_event_count\": int,\n",
" \"browser.engagement.total_uri_count\": int,\n",
" \"browser.engagement.unique_domains_count\": int,\n",
" \"browser.engagement.window_open_event_count\": int,\n",
" }\n",
" \n",
" # We know these scalars must be there for the referenced timeframe.\n",
" expected_scalars = [\n",
" \"browser.engagement.max_concurrent_tab_count\",\n",
" \"browser.engagement.max_concurrent_window_count\"\n",
" ]\n",
" \n",
" scalars = p[\"payload/processes/parent/scalars\"]\n",
" \n",
" if scalars is None:\n",
" return (\"scalars section is None\", p)\n",
"\n",
" # We don't expect all the engagement measurements to be there but,\n",
" # if they are, make sure they have the correct format.\n",
" for k, v in known_engagement_scalars.iteritems():\n",
" if k in scalars:\n",
" if type(scalars[k]) != v:\n",
" return (\"wrong type: \" + k, p)\n",
" if scalars[k] < 0:\n",
" return (\"check failed: \" + k + \" < 1\", p)\n",
" \n",
" # We're not expecting other scalars from these builds.\n",
" for k in scalars:\n",
" if k not in known_engagement_scalars:\n",
" return (\"unexpected scalar: \" + k, p)\n",
" \n",
" for s in expected_scalars:\n",
" if s not in scalars:\n",
" return (\"{} not reported\".format(s), p)\n",
" \n",
" return (\"\", p)\n",
"\n",
"checked_pings = all_pings.map(engagement_measurements_check)\n",
"result_counts = checked_pings.countByKey()\n",
"result_counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's dig into the pings with missing engagement measurements."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defaultdict(int,\n",
" {'': 555501,\n",
" 'browser.engagement.max_concurrent_tab_count not reported': 1728,\n",
" 'browser.engagement.max_concurrent_window_count not reported': 4,\n",
" 'scalars section is None': 1})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latest_pings.map(engagement_measurements_check).countByKey()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"missing_eng = latest_pings.filter(lambda p: (p[\"payload/processes/parent/scalars\"] != None) and\\\n",
" (len(p[\"payload/processes/parent/scalars\"].keys()) == 0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many clients are sending an empy scalars section?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"702"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(missing_eng.map(lambda p: p[\"meta/clientId\"]).distinct().collect())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check the distribution of the subsession lengths."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1720.000000\n",
"mean 2230.333140\n",
"std 11249.685988\n",
"min 0.000000\n",
"50% 26.000000\n",
"75% 290.250000\n",
"95% 6903.250000\n",
"99% 54194.280000\n",
"99.5% 80202.715000\n",
"max 257913.000000\n",
"dtype: float64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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z5yBJGswcPM+hGXYInWmGHkCnap/XNV+5as3WNM1Wep7DEvU+7EeSNk/fD/ux5zAX25p9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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2940bf0210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"missing_ssl = missing_eng.map(lambda p: p[\"payload/info/subsessionLength\"]).collect()\n",
"plot_series(pd.Series(missing_ssl), 30, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper functions for plotting and analysing."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_series(series, graph_bins=100, graph_min=0.1):\n",
" # Plot to an histogram.\n",
" fig, ax = plt.subplots()\n",
" series.hist(ax=ax, bins=graph_bins, bottom=graph_min)\n",
" ax.set_yscale('log')\n",
" # Return some descriptive statistics.\n",
" return series.describe(percentiles=[.5, .75, .95, .99, .995])\n",
"\n",
"def plot_histogram_scalar(pings, scalar_name):\n",
" scalar_values = pings.filter(lambda p: p[\"payload/processes/parent/scalars\"] and p[\"payload/processes/parent/scalars\"].get(scalar_name, False))\\\n",
" .map(lambda p: p[\"payload/processes/parent/scalars\"][scalar_name])\n",
" scalar_series = pd.Series(scalar_values.collect())\n",
" return plot_series(scalar_series)\n",
"\n",
"def values_per_day(pings, scalar):\n",
" # Each entry in the |daily_per_user| RDD is like:\n",
" # ((\"date\", \"clientId\"), [ ... scalar values for the client, date ... ])\n",
" daily_per_user = pings.filter(lambda p: p[\"payload/processes/parent/scalars\"] and \\\n",
" p[\"payload/processes/parent/scalars\"].get(scalar, False))\\\n",
" .map(lambda p: ((p[\"meta/submissionDate\"], p[\"meta/clientId\"]), [ p[\"payload/processes/parent/scalars\"].get(scalar) ]))\\\n",
" .reduceByKey(lambda a,b: a + b)\n",
" return daily_per_user\n",
"\n",
"def pct(a, b):\n",
" return round(float(a) / b, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maximum Concurrent Tab Count"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1.400329e+06\n",
"mean 1.792369e+01\n",
"std 7.510422e+01\n",
"min 1.000000e+00\n",
"50% 3.000000e+00\n",
"75% 8.000000e+00\n",
"95% 6.700000e+01\n",
"99% 2.970000e+02\n",
"99.5% 4.570000e+02\n",
"max 3.159000e+03\n",
"dtype: float64"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5ee130d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(all_pings, \"browser.engagement.max_concurrent_tab_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's the maximum number of concurrent tabs each user has, per day?"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 335413.000000\n",
"mean 20.150313\n",
"std 85.240617\n",
"min 1.000000\n",
"50% 5.000000\n",
"75% 12.000000\n",
"95% 62.000000\n",
"99% 317.000000\n",
"99.5% 512.000000\n",
"max 3159.000000\n",
"dtype: float64"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f817c5fea50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_max_tabs_per_user = values_per_day(all_pings, \"browser.engagement.max_concurrent_tab_count\")\\\n",
" .map(lambda x: np.max(x[1]))\n",
"plot_series(pd.Series(daily_max_tabs_per_user.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Maximum Concurrent Window Count"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1.400315e+06\n",
"mean 1.366290e+00\n",
"std 1.286716e+00\n",
"min 1.000000e+00\n",
"50% 1.000000e+00\n",
"75% 1.000000e+00\n",
"95% 3.000000e+00\n",
"99% 5.000000e+00\n",
"99.5% 8.000000e+00\n",
"max 1.450000e+02\n",
"dtype: float64"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f817c641410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(all_pings, \"browser.engagement.max_concurrent_window_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's the maximum number of concurrent windows each user has, per day?"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 335413.000000\n",
"mean 1.796054\n",
"std 1.826559\n",
"min 1.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"95% 4.000000\n",
"99% 8.000000\n",
"99.5% 11.000000\n",
"max 145.000000\n",
"dtype: float64"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5eddad90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_max_wins_per_user = values_per_day(all_pings, \"browser.engagement.max_concurrent_window_count\")\\\n",
" .map(lambda x: np.max(x[1]))\n",
"plot_series(pd.Series(daily_max_wins_per_user.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tab Open Event Count"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 845874.000000\n",
"mean 14.339854\n",
"std 46.307356\n",
"min 1.000000\n",
"50% 4.000000\n",
"75% 12.000000\n",
"95% 57.000000\n",
"99% 150.000000\n",
"99.5% 216.000000\n",
"max 3429.000000\n",
"dtype: float64"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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myh+W8jdHRDOQZTS3qD4gIpXTMFE0bVefJ4afrYjUp6HDRG3SvYR1jPrPbu4+AymGmc+x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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f815c4d2e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(all_pings, \"browser.engagement.tab_open_event_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many tabs are being opened by each user, per day?"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 282880.000000\n",
"mean 42.879348\n",
"std 128.901057\n",
"min 1.000000\n",
"50% 15.000000\n",
"75% 44.000000\n",
"95% 162.000000\n",
"99% 368.000000\n",
"99.5% 523.605000\n",
"max 31978.000000\n",
"dtype: float64"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5e469c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_tab_opens_per_user = values_per_day(all_pings, \"browser.engagement.tab_open_event_count\")\\\n",
" .map(lambda x: np.sum(x[1]))\n",
"plot_series(pd.Series(daily_tab_opens_per_user.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare tab open event and the maximum tab count, over a subsession:\n",
"* Get the maximum among all the fragments for the concurrent tabs\n",
"* Sum the open events for each fragment"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def map_to_tab_measurements(p):\n",
" scalars = p[\"payload/processes/parent/scalars\"]\n",
" max_cnt = scalars.get(\"browser.engagement.max_concurrent_tab_count\", 0)\n",
" open_cnt = scalars.get(\"browser.engagement.tab_open_event_count\", 0)\n",
" return ((p[\"meta/clientId\"], p[\"payload/info/sessionId\"]), (open_cnt, max_cnt))\n",
"\n",
"per_session_tab = latest_pings.filter(lambda p: p[\"payload/processes/parent/scalars\"])\\\n",
" .map(map_to_tab_measurements)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"combined_per_session_tab = per_session_tab.combineByKey(lambda x: x,\n",
" lambda acc, x: (acc[0] + x[0], max(acc[1], x[1])),\n",
" lambda x, y: (x[0] + y[0], max(x[1], y[1])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot and describe the number of tab open events per client session."
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 402659.000000\n",
"mean 12.403341\n",
"std 48.905876\n",
"min 0.000000\n",
"50% 2.000000\n",
"75% 9.000000\n",
"95% 54.000000\n",
"99% 162.000000\n",
"99.5% 237.000000\n",
"max 8793.000000\n",
"dtype: float64"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5b637310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"per_session_tab_open_events = combined_per_session_tab.map(lambda x: x[1][0])\n",
"plot_series(pd.Series(per_session_tab_open_events.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot and describe the maximum number of concurrent tabs per client session."
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 402659.000000\n",
"mean 10.811237\n",
"std 56.562810\n",
"min 0.000000\n",
"50% 3.000000\n",
"75% 6.000000\n",
"95% 28.000000\n",
"99% 176.000000\n",
"99.5% 306.000000\n",
"max 3159.000000\n",
"dtype: float64"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5b6e2890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"per_session_max_tabs = combined_per_session_tab.map(lambda x: x[1][1])\n",
"plot_series(pd.Series(per_session_max_tabs.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Window Open Event Count"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 209299.000000\n",
"mean 3.701327\n",
"std 9.056931\n",
"min 1.000000\n",
"50% 2.000000\n",
"75% 3.000000\n",
"95% 12.000000\n",
"99% 34.000000\n",
"99.5% 50.000000\n",
"max 647.000000\n",
"dtype: float64"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2OX9azp+3sQ8AEfGpiHjbuPdjZmbDGboHIOkocA+wEhF396zfCxyhM6gcjYhH+t737oj4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\nM8oDgJnZjPIAYGY2ozwAmJnNqH8DsEgDXPRGWM0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5ba5a8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(all_pings, \"browser.engagement.window_open_event_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many windows are being opened by the clients, per day?"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 130093.000000\n",
"mean 5.954848\n",
"std 14.998016\n",
"min 1.000000\n",
"50% 2.000000\n",
"75% 5.000000\n",
"95% 21.000000\n",
"99% 56.000000\n",
"99.5% 85.000000\n",
"max 952.000000\n",
"dtype: float64"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5f9c9990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_win_opens_per_user = values_per_day(all_pings, \"browser.engagement.window_open_event_count\")\\\n",
" .map(lambda x: np.sum(x[1]))\n",
"plot_series(pd.Series(daily_win_opens_per_user.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare window open event and the maximum window count, over a subsession:\n",
"* Get the maximum among all the fragments for the concurrent windows\n",
"* Sum the open events for each fragment"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def map_to_win_measurements(p):\n",
" scalars = p[\"payload/processes/parent/scalars\"]\n",
" max_cnt = scalars.get(\"browser.engagement.max_concurrent_window_count\", 0)\n",
" open_cnt = scalars.get(\"browser.engagement.window_open_event_count\", 0)\n",
" return ((p[\"meta/clientId\"], p[\"payload/info/sessionId\"]), (open_cnt, max_cnt))\n",
"\n",
"per_session_win = latest_pings.filter(lambda p: p[\"payload/processes/parent/scalars\"])\\\n",
" .map(map_to_win_measurements)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"combined_per_session_win = per_session_win.combineByKey(lambda x: x,\n",
" lambda acc, x: (acc[0] + x[0], max(acc[1], x[1])),\n",
" lambda x, y: (x[0] + y[0], max(x[1], y[1])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot and describe the number of window open events per client session."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 402659.000000\n",
"mean 0.797240\n",
"std 5.183276\n",
"min 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"95% 4.000000\n",
"99% 14.000000\n",
"99.5% 22.000000\n",
"max 953.000000\n",
"dtype: float64"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f822041c690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"per_session_win_open_events = combined_per_session_win.map(lambda x: x[1][0])\n",
"plot_series(pd.Series(per_session_win_open_events.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot and describe the number of maximum concurrent windows per client session."
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 402659.000000\n",
"mean 1.350182\n",
"std 1.104613\n",
"min 0.000000\n",
"50% 1.000000\n",
"75% 1.000000\n",
"95% 3.000000\n",
"99% 5.000000\n",
"99.5% 6.000000\n",
"max 145.000000\n",
"dtype: float64"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5b54f190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"per_session_max_windows = combined_per_session_win.map(lambda x: x[1][1])\n",
"plot_series(pd.Series(per_session_max_windows.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many subsessions don't include a window open event? The statistics below point out that most of the subsessions have 0 window open events."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1.403181e+06\n",
"mean 5.520913e-01\n",
"std 3.738174e+00\n",
"min 0.000000e+00\n",
"50% 0.000000e+00\n",
"75% 0.000000e+00\n",
"95% 2.000000e+00\n",
"99% 1.000000e+01\n",
"99.5% 1.600000e+01\n",
"max 6.470000e+02\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe42c6a2e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"subsessions_with_window_open = all_pings.filter(lambda p: p[\"payload/processes/parent/scalars\"])\\\n",
" .map(lambda p: p[\"payload/processes/parent/scalars\"]\\\n",
" .get(\"browser.engagement.window_open_event_count\", 0))\n",
"\n",
"scalar_series = pd.Series(subsessions_with_window_open.collect())\n",
"plot_series(scalar_series)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Total count of URIs"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 669517.000000\n",
"mean 58.239405\n",
"std 296.168179\n",
"min 1.000000\n",
"50% 13.000000\n",
"75% 46.000000\n",
"95% 247.000000\n",
"99% 641.000000\n",
"99.5% 878.000000\n",
"max 70708.000000\n",
"dtype: float64"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5e4bc250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(broken_uri_pings, \"browser.engagement.total_uri_count\")"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 461124.000000\n",
"mean 52.225380\n",
"std 203.121462\n",
"min 1.000000\n",
"50% 13.000000\n",
"75% 44.000000\n",
"95% 222.000000\n",
"99% 562.000000\n",
"99.5% 767.000000\n",
"max 47162.000000\n",
"dtype: float64"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f815c52fc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(latest_pings, \"browser.engagement.total_uri_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many URIs are the clients opening, per day?"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 122796.000000\n",
"mean 196.116942\n",
"std 515.217601\n",
"min 1.000000\n",
"50% 87.000000\n",
"75% 232.000000\n",
"95% 711.000000\n",
"99% 1436.050000\n",
"99.5% 1866.025000\n",
"max 65946.000000\n",
"dtype: float64"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f815c558890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"daily_uris_per_user = values_per_day(latest_pings, \"browser.engagement.total_uri_count\")\\\n",
" .map(lambda x: np.sum(x[1]))\n",
"plot_series(pd.Series(daily_uris_per_user.collect()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a look at the clients opening more than > 10k URIs per subsession."
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"URI_THRESHOLD = 10000 # 10k uris\n",
"pings_many_uris = latest_pings.filter(lambda p: p[\"payload/processes/parent/scalars\"])\\\n",
" .filter(lambda p: p[\"payload/processes/parent/scalars\"].get(\"browser.engagement.total_uri_count\", 0) > URI_THRESHOLD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's the distribution of their subsession lengths?"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 16.000000\n",
"mean 42023.562500\n",
"std 30590.723206\n",
"min 6330.000000\n",
"50% 35236.500000\n",
"75% 61303.500000\n",
"95% 87760.750000\n",
"99% 91021.750000\n",
"99.5% 91429.375000\n",
"max 91837.000000\n",
"dtype: float64"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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g6YwQwmMDLKWrSyS9zcwekvR9SW8ysxslHZ3CLI5IOhxC2N8+/7GaYjGNfy8uk/RQCOGx\n9jvbWyW9TtOZxZIUax/ra26O4rCWfz7iW2rOA788cu02SXvax1dJ+tnI9d1mtsHMzpO0XdIf263l\nE2Z2sTX/pdUHl73mqvbxu9TcHVWcEMJnQwhbQgivUPPn+7sQwgfUvM/Wnva3TUsWi5IOm9n57aVL\n1dyxNHV/L9QcJ73WzF7UruFSSX/TdGWx/OfFUqz915LebGZnmtlLJb25vbayTE2ZK9TczTMv6dO5\nm0Q9rekSNW9PfkDNXQb3tOs8W9Jv2/XeLumskdd8Rs1dCMtvVXuNmtth5yV9eeT6Rkm3tNfvlrQt\n97pXkcsb5A3pqcxC0qvVfFN0QNJP1Nw1Mq1Z7GvXdVDSd9TcsTgVWUi6SdI/Jf1PTaHcq6Y5P/ja\n1RSgeTW3s67qVtZs/4c0AKBcNKQBABGKAwAgQnEAAEQoDgCACMUBABChOAAAIhQHAEDk/0DM4+6E\nnZSFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f815c40ae90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pings_many_uris_ssl = pings_many_uris.map(lambda p: p.get(\"payload/info/subsessionLength\"))\n",
"plot_series(pd.Series(pings_many_uris_ssl.collect()), 10, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And what about their session lengths?"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 8.000000\n",
"mean 55586.875000\n",
"std 40353.429493\n",
"min 9927.000000\n",
"50% 51178.500000\n",
"75% 66723.500000\n",
"95% 118297.200000\n",
"99% 127623.440000\n",
"99.5% 128789.220000\n",
"max 129955.000000\n",
"dtype: float64"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+Dd9xnyvpkymlcyX9XcN3THP/+TezUzQ8I8B2DT9FPNjMXqkA7WsQrtnM3ifpSErpS9O8\n2SneVnnjZg+U9F5JC7O6izHv7DEc5uq/jzCzzRoGw+6U0rfy5cNmdnp++1ZJf8jX75L0yIl3X24/\n3vUV72NmJ2hYmN0zpfxnSLrQzA5K+pKk55jZbkl3B+m/U8N3Tb/Mr1+tYVhE+Pw/V9LBlNI9+bu0\nb0h6epD21Vo0z+zfvZntkvR8SZP/e4AI7Y/WsE/4tZndmm93j5mddj/32a5/GmeY6zxzO0FHF9Jb\nNCykH9u6Y6Ln85I+suraB5XP+3TsBd0WDUcik0ui6zQ835Rp+NF2Z77+Jh1dEl2kGSyk820/W0d3\nDh+K0i/px5LOyi8v5M/93H/+833dIOkB+T6vkPTmIO07JN3Q8u+7Vi5Fl18+ZQrtOzU8yu3hq37f\n3LUfq3/V226V9LB56Z/6F6k1foJ2anhk0AFJ7/ZoyB3P0PCU4nvzH8Se3HaqpB/mxmsnP5EaHl72\nW5UPL3uyhi8WByR9fOL6SZK+kq9fJ2nHjD6WyeEQpl/SkzT8B5V7JX09/wUO0a9hmN2s4WGFn9Pw\n6Lu5bpd0paTfS/qnhn3JxfkLxsybJe3K129R3UNZj9V+QMMDA/bkX5+ax/bj9a96+0GVD2V161/z\nE+8BADYOFtIAgALDAQBQYDgAAAoMBwBAgeEAACgwHAAABYYDAKDwH3ZvkZMkE2meAAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5b5e4cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pings_many_uris_sl = pings_many_uris.filter(lambda p: p.get(\"payload/info/reason\") == \"shutdown\")\\\n",
" .map(lambda p: p.get(\"payload/info/sessionLength\"))\n",
"plot_series(pd.Series(pings_many_uris_sl.collect()), 10, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many clients are acting like that?"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"heavy_uri_loaders_clients = pings_many_uris.map(lambda p: p[\"meta/clientId\"]).distinct().collect()\n",
"heavy_uri_loaders = len(heavy_uri_loaders_clients)\n",
"total_clients = latest_pings.map(lambda p: p[\"meta/clientId\"]).distinct().count()"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11 clients (0.0) are opening more than 10k URIs\n"
]
}
],
"source": [
"print \"{} clients ({}) are opening more than 10k URIs\"\\\n",
" .format(heavy_uri_loaders, pct(heavy_uri_loaders, total_clients))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do these clients always behave the same?"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"uri_behaviour = latest_pings.filter(lambda p: p[\"meta/clientId\"] in heavy_uri_loaders_clients)\\\n",
" .filter(lambda p: p[\"payload/processes/parent/scalars\"] and \\\n",
" p[\"payload/processes/parent/scalars\"].get(\"browser.engagement.total_uri_count\", False))\\\n",
" .map(lambda p: (p[\"meta/clientId\"], [ p[\"payload/processes/parent/scalars\"].get(\"browser.engagement.total_uri_count\", 0)]))\\\n",
" .reduceByKey(lambda x,y: x + y)"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th># URIs min</th>\n",
" <th># URIs max</th>\n",
" <th>p75</th>\n",
" <th>p95</th>\n",
" <th>Samples</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>44102</td>\n",
" <td>30251.50</td>\n",
" <td>42022.65</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>16630</td>\n",
" <td>421.50</td>\n",
" <td>8779.50</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>11920</td>\n",
" <td>44.75</td>\n",
" <td>6614.50</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>11269</td>\n",
" <td>2836.75</td>\n",
" <td>9582.55</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4280</td>\n",
" <td>16728</td>\n",
" <td>15661.50</td>\n",
" <td>16514.70</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>47162</td>\n",
" <td>117.50</td>\n",
" <td>16462.80</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>17137</td>\n",
" <td>98.00</td>\n",
" <td>4908.20</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>11416</td>\n",
" <td>210.00</td>\n",
" <td>2186.70</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20</td>\n",
" <td>29215</td>\n",
" <td>353.00</td>\n",
" <td>20603.20</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18</td>\n",
" <td>20951</td>\n",
" <td>4624.00</td>\n",
" <td>17685.60</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2</td>\n",
" <td>12840</td>\n",
" <td>3039.50</td>\n",
" <td>7322.80</td>\n",
" <td>75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" # URIs min # URIs max p75 p95 Samples\n",
"0 2 44102 30251.50 42022.65 8\n",
"1 3 16630 421.50 8779.50 11\n",
"2 1 11920 44.75 6614.50 10\n",
"3 1 11269 2836.75 9582.55 4\n",
"4 4280 16728 15661.50 16514.70 4\n",
"5 1 47162 117.50 16462.80 15\n",
"6 2 17137 98.00 4908.20 65\n",
"7 1 11416 210.00 2186.70 18\n",
"8 20 29215 353.00 20603.20 7\n",
"9 18 20951 4624.00 17685.60 5\n",
"10 2 12840 3039.50 7322.80 75"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uri_behaviour_rdd = uri_behaviour.map(lambda x: (np.min(x[1]),\\\n",
" np.max(x[1]),\\\n",
" np.percentile(x[1], 75),\\\n",
" np.percentile(x[1], 95),\\\n",
" len(x[1])))\n",
"uri_behaviour_df = pd.DataFrame(uri_rdd.collect())\n",
"uri_behaviour_df.columns = [\"# URIs min\", \"# URIs max\", \"p75\", \"p95\", \"Samples\"]\n",
"uri_behaviour_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect other field to try to figure out if super high URI counts come from some automated instance of Firefox:\n",
"\n",
"* either a new or constantly resetting profile\n",
"* low session counts (1 or \"few\", profileSubsessionCounter as a proxy?)\n",
"* lower session lengths\n",
"* submit high counts with each session.\n",
"* Maybe a proxy is \"for pathological clients, the uri counts p25 is pretty close to p90\"?"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"UNIX_EPOCH_DAY = datetime.datetime.utcfromtimestamp(0)\n",
"\n",
"def get_session_info(p):\n",
" return {\n",
" \"submissionDate\": p.get(\"meta/submissionDate\"),\n",
" \"profileCreationDate\": UNIX_EPOCH_DAY + datetime.timedelta(days=p.get(\"environment/profile/creationDate\")),\n",
" \"reason\": p.get(\"payload/info/reason\"),\n",
" \"profileSubsessionCounter\": p.get(\"payload/info/profileSubsessionCounter\"),\n",
" \"sessionLength\": p.get(\"payload/info/sessionLength\"),\n",
" \"subsessionLength\": p.get(\"payload/info/subsessionLength\")\n",
" }\n",
"\n",
"many_uris_session = pings_many_uris.map(get_session_info)\n",
"many_uris_session.count()"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>profileCreationDate</th>\n",
" <th>profileSubsessionCounter</th>\n",
" <th>reason</th>\n",
" <th>sessionLength</th>\n",
" <th>submissionDate</th>\n",
" <th>subsessionLength</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-09-19</td>\n",
" <td>197</td>\n",
" <td>shutdown</td>\n",
" <td>52282</td>\n",
" <td>20160817</td>\n",
" <td>42494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-05-04</td>\n",
" <td>2464</td>\n",
" <td>environment-change</td>\n",
" <td>209783</td>\n",
" <td>20160822</td>\n",
" <td>14226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-10-09</td>\n",
" <td>526</td>\n",
" <td>shutdown</td>\n",
" <td>16602</td>\n",
" <td>20160822</td>\n",
" <td>16599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-08-18</td>\n",
" <td>616</td>\n",
" <td>daily</td>\n",
" <td>112926</td>\n",
" <td>20160818</td>\n",
" <td>86402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-05-26</td>\n",
" <td>310</td>\n",
" <td>shutdown</td>\n",
" <td>50075</td>\n",
" <td>20160822</td>\n",
" <td>50034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2013-12-03</td>\n",
" <td>1930</td>\n",
" <td>shutdown</td>\n",
" <td>9927</td>\n",
" <td>20160821</td>\n",
" <td>9926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2015-08-18</td>\n",
" <td>621</td>\n",
" <td>daily</td>\n",
" <td>52946</td>\n",
" <td>20160822</td>\n",
" <td>52941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2016-08-17</td>\n",
" <td>40</td>\n",
" <td>aborted-session</td>\n",
" <td>11301</td>\n",
" <td>20160822</td>\n",
" <td>11294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2015-08-18</td>\n",
" <td>617</td>\n",
" <td>daily</td>\n",
" <td>199328</td>\n",
" <td>20160819</td>\n",
" <td>86402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014-12-29</td>\n",
" <td>732</td>\n",
" <td>shutdown</td>\n",
" <td>96647</td>\n",
" <td>20160822</td>\n",
" <td>24926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2015-09-09</td>\n",
" <td>436</td>\n",
" <td>daily</td>\n",
" <td>95855</td>\n",
" <td>20160820</td>\n",
" <td>86391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2015-05-26</td>\n",
" <td>309</td>\n",
" <td>shutdown</td>\n",
" <td>129955</td>\n",
" <td>20160821</td>\n",
" <td>38024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2016-08-04</td>\n",
" <td>40</td>\n",
" <td>daily</td>\n",
" <td>22103</td>\n",
" <td>20160816</td>\n",
" <td>22102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2013-12-03</td>\n",
" <td>1937</td>\n",
" <td>shutdown</td>\n",
" <td>56749</td>\n",
" <td>20160821</td>\n",
" <td>6330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2015-05-26</td>\n",
" <td>308</td>\n",
" <td>daily</td>\n",
" <td>91930</td>\n",
" <td>20160820</td>\n",
" <td>91837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014-11-02</td>\n",
" <td>948</td>\n",
" <td>shutdown</td>\n",
" <td>32458</td>\n",
" <td>20160821</td>\n",
" <td>32449</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" profileCreationDate profileSubsessionCounter reason \\\n",
"0 2015-09-19 197 shutdown \n",
"1 2012-05-04 2464 environment-change \n",
"2 2015-10-09 526 shutdown \n",
"3 2015-08-18 616 daily \n",
"4 2015-05-26 310 shutdown \n",
"5 2013-12-03 1930 shutdown \n",
"6 2015-08-18 621 daily \n",
"7 2016-08-17 40 aborted-session \n",
"8 2015-08-18 617 daily \n",
"9 2014-12-29 732 shutdown \n",
"10 2015-09-09 436 daily \n",
"11 2015-05-26 309 shutdown \n",
"12 2016-08-04 40 daily \n",
"13 2013-12-03 1937 shutdown \n",
"14 2015-05-26 308 daily \n",
"15 2014-11-02 948 shutdown \n",
"\n",
" sessionLength submissionDate subsessionLength \n",
"0 52282 20160817 42494 \n",
"1 209783 20160822 14226 \n",
"2 16602 20160822 16599 \n",
"3 112926 20160818 86402 \n",
"4 50075 20160822 50034 \n",
"5 9927 20160821 9926 \n",
"6 52946 20160822 52941 \n",
"7 11301 20160822 11294 \n",
"8 199328 20160819 86402 \n",
"9 96647 20160822 24926 \n",
"10 95855 20160820 86391 \n",
"11 129955 20160821 38024 \n",
"12 22103 20160816 22102 \n",
"13 56749 20160821 6330 \n",
"14 91930 20160820 91837 \n",
"15 32458 20160821 32449 "
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(many_uris_session.collect())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Total unique domains count"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 660137.000000\n",
"mean 6.646667\n",
"std 11.247891\n",
"min 1.000000\n",
"50% 3.000000\n",
"75% 7.000000\n",
"95% 25.000000\n",
"99% 61.000000\n",
"99.5% 83.000000\n",
"max 100.000000\n",
"dtype: float64"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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EhDFff9PULNXUHEB9xt4AuPs33P0x4HHgwXG/n4gMY/VGeP0MFWl9//QbuAEwsyNmtmRm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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5b91df10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(broken_uri_pings, \"browser.engagement.unique_domains_count\")"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 454187.000000\n",
"mean 5.685044\n",
"std 8.618674\n",
"min 1.000000\n",
"50% 3.000000\n",
"75% 6.000000\n",
"95% 21.000000\n",
"99% 43.000000\n",
"99.5% 56.000000\n",
"max 100.000000\n",
"dtype: float64"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zwLW+w7uBC+5+0d2vA8eBfQBm9iTwEeBnzeyJMePtgBA7gISE2AE0aPC+RcNMMGvcu6Rc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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7e5fa4c750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_histogram_scalar(latest_pings, \"browser.engagement.unique_domains_count\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"What's the percentage of session fragments which exactly recorded >= 100 unique domains?"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"571389"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_latest_pings = latest_pings.count()\n",
"num_latest_pings"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def hit_upper_domain_bound(p):\n",
" if not p[\"payload/processes/parent/scalars\"]:\n",
" return False\n",
" \n",
" domain_count = \\\n",
" p[\"payload/processes/parent/scalars\"].get(\"browser.engagement.unique_domains_count\", False)\n",
" if domain_count is False:\n",
" return False\n",
" \n",
" return domain_count >= 100 # 100 is the upper bound, we should never go beyond that.\n",
"\n",
"pings_hitting_domain_bounds = latest_pings.filter(hit_upper_domain_bound)\n",
"domain_count_upper_bounds = pings_hitting_domain_bounds.count()"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subsessions with >= unique domains: 465 - 0.001\n"
]
}
],
"source": [
"print \"Subsessions with >= unique domains: {} - {}\".format(domain_count_upper_bounds,\n",
" pct(domain_count_upper_bounds, num_latest_pings))"
]
},
{
"cell_type": "markdown",