Missing child payloads

Missing child payloads.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing child payloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to parse whitelist (/home/hadoop/anaconda2/lib/python2.7/site-packages/moztelemetry/bucket-whitelist.json). Assuming all histograms are acceptable.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['Annotation', 'Figure']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\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",
    "from plotly.graph_objs import *\n",
    "\n",
    "from moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client, get_clients_history, get_records\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get pings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "submission_dates = (\"20160410\", \"20160420\")\n",
    "fraction = 0.1\n",
    "channel = \"aurora\"\n",
    "ss_pings = get_pings(sc,\n",
    "                     app=\"Firefox\",\n",
    "                     channel=channel,\n",
    "                     doc_type=\"saved_session\",\n",
    "                     schema=\"v4\",\n",
    "                     submission_date=submission_dates,\n",
    "                     fraction=fraction)\n",
    "main_pings = get_pings(sc,\n",
    "                     app=\"Firefox\",\n",
    "                     channel=channel,\n",
    "                     doc_type=\"main\",\n",
    "                     schema=\"v4\",\n",
    "                     submission_date=submission_dates,\n",
    "                     fraction=fraction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275014, 390481)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = [\"clientId\",\n",
    "              \"environment/settings/e10sEnabled\",\n",
    "              \"meta/reason\",\n",
    "              \"payload/childPayloads\",\n",
    "              \"payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/content\",\n",
    "              \"payload/info/sessionLength\"]\n",
    "ss_props = get_pings_properties(ss_pings, properties)\n",
    "main_props = get_pings_properties(main_pings, properties)\n",
    "\n",
    "(ss_props.count(), main_props.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only want to look at pings with e10s enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def e10s_enabled(ping):\n",
    "    return ping.get(\"environment/settings/e10sEnabled\", False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225365, 233906)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = ss_props.filter(e10s_enabled)\n",
    "main = main_props.filter(e10s_enabled)\\\n",
    "                 .filter(lambda p: p.get(\"meta/reason\") == \"shutdown\")\n",
    "\n",
    "counts = (ss.count(), main.count())\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now look for pings with missing child payloads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8471, 8955)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def missing_childPayloads(ping):\n",
    "    return None == ping.get(\"payload/childPayloads\", None)\n",
    "\n",
    "ss_missing = ss.filter(missing_childPayloads)\n",
    "main_missing = main.filter(missing_childPayloads)\n",
    "\n",
    "missing_counts = (ss_missing.count(), main_missing.count())\n",
    "missing_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0376, 0.0383)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def relative(sub, full):\n",
    "    return (round(float(sub[0]) / full[0], 4), round(float(sub[1]) / full[1], 4))\n",
    "relative(missing_counts, counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does it correlate with crashes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe missing child payloads correlate with content crashes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6578, 0.6365)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def has_content_crash(p):\n",
    "    return 0 < p.get(\"payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/content\", 0)\n",
    "\n",
    "ss_missing_crash = ss_missing.filter(has_content_crash)\n",
    "main_missing_crash = main_missing.filter(has_content_crash)\n",
    "\n",
    "missing_crash_counts = (ss_missing_crash.count(), main_missing_crash.count())\n",
    "relative(missing_crash_counts, missing_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, yes, the bulk of the missing child payloads can be blamed on content crashes.\n",
    "If the content process crashes, it won't send its Telemetry data to the parent process on shutdown.\n",
    "This still leaves >30% of the pings unexplained though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation to session lengths?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another reason for missing child payloads could simply be short session lengths.\n",
    "If a session is too short we might not have spawned child processes or not have finished the Telemetry initialization in the child process etc.\n",
    "Let's check that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mapper(p):\n",
    "    return p.get(\"payload/info/sessionLength\", -1)\n",
    "\n",
    "ss_other = ss_missing.filter(lambda p: not has_content_crash(p))\\\n",
    "                     .map(mapper)\n",
    "main_other = main_missing.filter(lambda p: not has_content_crash(p))\\\n",
    "                         .map(mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ss_series = pd.Series(ss_other.collect())\n",
    "main_series = pd.Series(main_other.collect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       2132.000000\n",
       "mean       13740.070826\n",
       "std        60583.189482\n",
       "min            0.000000\n",
       "25%           21.000000\n",
       "50%          113.000000\n",
       "75%         2510.000000\n",
       "95%        68086.450000\n",
       "99%       266094.550000\n",
       "max      1102165.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss_series.describe(percentiles=[.25, .5, .75, .95, .99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       2499.000000\n",
       "mean       15880.789916\n",
       "std        81374.056969\n",
       "min            0.000000\n",
       "25%           21.500000\n",
       "50%          140.000000\n",
       "75%         3058.500000\n",
       "95%        77599.000000\n",
       "99%       299766.860000\n",
       "max      2925789.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_series.describe(percentiles=[.25, .5, .75, .95, .99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, most of the remaining missing child payloads are correlating with short session lengths.\n",
    "We can investigate that in more detail for improved data quality, but this doesn't seem to be a big problem right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f74ec320810>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RcTVARDxbmeUnLOoCOIvu38kYmct6Km+0vQHtxWFm1ikiYkD/9cVA3AH9HJgaEWfUCtT9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\ngMzMrIj/AR98bXcl1oyHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74ec2f4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ss_series.plot(kind='hist', bins=10, title=\"Histogram of session length distributions in saved-session\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f74d9184890>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74d929fad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_series.plot(kind='hist', bins=10, title=\"Histogram of session length distributions in shutdown\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

Missing child payloads.py

# coding: utf-8

# ### Missing child payloads

# In[2]:

import ujson as json
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.plotly as py
from plotly.graph_objs import *

from moztelemetry import get_pings, get_pings_properties, get_one_ping_per_client, get_clients_history, get_records

get_ipython().magic(u'pylab inline')


# ### Get pings

# In[44]:

submission_dates = ("20160410", "20160420")
fraction = 0.1
channel = "aurora"
ss_pings = get_pings(sc,
                     app="Firefox",
                     channel=channel,
                     doc_type="saved_session",
                     schema="v4",
                     submission_date=submission_dates,
                     fraction=fraction)
main_pings = get_pings(sc,
                     app="Firefox",
                     channel=channel,
                     doc_type="main",
                     schema="v4",
                     submission_date=submission_dates,
                     fraction=fraction)


# In[45]:

properties = ["clientId",
              "environment/settings/e10sEnabled",
              "meta/reason",
              "payload/childPayloads",
              "payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/content",
              "payload/info/sessionLength"]
ss_props = get_pings_properties(ss_pings, properties)
main_props = get_pings_properties(main_pings, properties)

(ss_props.count(), main_props.count())


# We only want to look at pings with e10s enabled.

# In[46]:

def e10s_enabled(ping):
    return ping.get("environment/settings/e10sEnabled", False)


# In[47]:

ss = ss_props.filter(e10s_enabled)
main = main_props.filter(e10s_enabled)                 .filter(lambda p: p.get("meta/reason") == "shutdown")

counts = (ss.count(), main.count())
counts


# Now look for pings with missing child payloads.

# In[48]:

def missing_childPayloads(ping):
    return None == ping.get("payload/childPayloads", None)

ss_missing = ss.filter(missing_childPayloads)
main_missing = main.filter(missing_childPayloads)

missing_counts = (ss_missing.count(), main_missing.count())
missing_counts


# In[49]:

def relative(sub, full):
    return (round(float(sub[0]) / full[0], 4), round(float(sub[1]) / full[1], 4))
relative(missing_counts, counts)


# ### Does it correlate with crashes?

# Maybe missing child payloads correlate with content crashes?

# In[50]:

def has_content_crash(p):
    return 0 < p.get("payload/keyedHistograms/SUBPROCESS_ABNORMAL_ABORT/content", 0)

ss_missing_crash = ss_missing.filter(has_content_crash)
main_missing_crash = main_missing.filter(has_content_crash)

missing_crash_counts = (ss_missing_crash.count(), main_missing_crash.count())
relative(missing_crash_counts, missing_counts)


# So, yes, the bulk of the missing child payloads can be blamed on content crashes.
# If the content process crashes, it won't send its Telemetry data to the parent process on shutdown.
# This still leaves >30% of the pings unexplained though.

# ### Correlation to session lengths?

# Another reason for missing child payloads could simply be short session lengths.
# If a session is too short we might not have spawned child processes or not have finished the Telemetry initialization in the child process etc.
# Let's check that.

# In[51]:

def mapper(p):
    return p.get("payload/info/sessionLength", -1)

ss_other = ss_missing.filter(lambda p: not has_content_crash(p))                     .map(mapper)
main_other = main_missing.filter(lambda p: not has_content_crash(p))                         .map(mapper)


# In[52]:

ss_series = pd.Series(ss_other.collect())
main_series = pd.Series(main_other.collect())


# In[53]:

ss_series.describe(percentiles=[.25, .5, .75, .95, .99])


# In[54]:

main_series.describe(percentiles=[.25, .5, .75, .95, .99])


# So, most of the remaining missing child payloads are correlating with short session lengths.
# We can investigate that in more detail for improved data quality, but this doesn't seem to be a big problem right now.

# In[55]:

ss_series.plot(kind='hist', bins=10, title="Histogram of session length distributions in saved-session")


# In[56]:

main_series.plot(kind='hist', bins=10, title="Histogram of session length distributions in shutdown")