Investigation high ration of health ping clients

Investigation high ration of health ping clients.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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 pandas as pd\n",
    "\n",
    "from plotly.graph_objs import *\n",
    "from moztelemetry import get_pings_properties, get_one_ping_per_client\n",
    "from moztelemetry.dataset import Dataset\n",
    "from collections import Counter\n",
    "import operator\n",
    "from operator import itemgetter\n",
    "\n",
    "get_ipython().magic(u'matplotlib inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pings = Dataset.from_source(\"telemetry\") \\\n",
    "                .where(docType='health', appUpdateChannel=\"beta\") \\\n",
    "                .records(sc, sample=1)\n",
    "        \n",
    "cachedData = get_pings_properties(pings, [\"creationDate\", \"payload/pingDiscardedForSize\", \"payload/sendFailure\", \n",
    "                                             \"clientId\", \"meta/submissionDate\", \"payload/os\", \"payload/reason\", \n",
    "                                              \"meta/geoCountry\", \"application/version\"]).cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "def plotlistofTuples(listOfTuples, title=\"\", inColor='blue'):\n",
    "    keys = [t[0] for t in listOfTuples]\n",
    "    values = [t[1] for t in listOfTuples]\n",
    "\n",
    "    plt.figure(1)\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(15, 7)\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\n",
    "    plt.bar(range(len(listOfTuples)), values, align='center', color=inColor)\n",
    "    plt.xticks(range(len(listOfTuples)), keys, rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Correlation between sendFailure count and Health ping reason\n",
    "analyse only sendFailure, as discardedForSize bring small amount of problems "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getKey(dictionary, key):\n",
    "    return dictionary.get(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def distrReasonToFailureCount(data, key):\n",
    "    return data.map(lambda p: (p['payload/reason'], p['payload/sendFailure'])) \\\n",
    "                            .filter(lambda pair: pair[1] != None) \\\n",
    "                            .map(lambda pair: (pair[0], getKey(pair[1], key))) \\\n",
    "                            .filter(lambda pair: pair[1] != None) \\\n",
    "                            .groupByKey() \\\n",
    "                            .map(lambda pair: (pair[0], sum(pair[1]))) \\\n",
    "                            .collect()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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1giYnJ/lKa+0Z4/Pzquq+SdYn+cC8+wcAAACAWXbGGl33TPLBqbL3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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed1c37dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotlistofTuples(distrReasonToFailureCount(cachedData, 'eChannelOpen'), \\\n",
    "                 title='eChannelOpen Failures distribution per reason')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most pings with reason with sendFailure = \"eChannelOpen\" (Can't open channel error) have reason \"shutdown\", which could be because network is shouted down before getting signal of shutdown on client side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EXiNnoUuSpGmU5E3A14DvAm8Etht8wPww8OqqOqDVgJI6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed0fc16190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotlistofTuples(distrReasonToFailureCount(cachedData, 'eUnreachable'), \\\n",
    "                 title='eUnreacheable Failures distribution per reason')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AwACKOAAAAAAYQBEHAAAAAAMo4gAAAABgAEUcAAAAAAyg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed1c28d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotlistofTuples(distrReasonToFailureCount(cachedData, 'timeout'), \\\n",
    "                 title='timeout Failures distribution per reason')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OS distribution Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def osDistr(cachedData):    \n",
    "    distr = cachedData.map(lambda p: p['payload/os']).filter(lambda p: p != None).map(lambda p: p.get('name'))\n",
    "    result = Counter(distr.collect()).items()\n",
    "    plotlistofTuples(result)\n",
    "    print result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'WINNT', 28052038), (u'Android', 2848367), (u'Darwin', 118220), (u'Linux', 60411)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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R62Xq0FVVP57kyiQvS/K9ST6W5Kqq2rbC9mcneVeS9yU5L8lrk7yxqi5e28gA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\niAw/o5MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec75812150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "osDistr(cachedData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Most of the pings submitted from Windows. Probably if we have networking problems, it is somehow connected to os."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Client distribution by country distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unique_by_key(elements, key=None):\n",
    "    if key is None:\n",
    "        # no key: the whole element must be unique\n",
    "        key = lambda e: e\n",
    "    return {key(el): el for el in elements}.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "itemgetter expected 1 arguments, got 2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-84f1c41d702c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcachedData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"meta/geoCountry\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"clientId\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupByKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: itemgetter expected 1 arguments, got 2"
     ]
    }
   ],
   "source": [
    "date = cachedData.map(lambda p: (p[\"meta/geoCountry\"], p[\"clientId\"])).groupByKey()\n",
    "result = sorted(date.map(lambda p: (p[0], len(set(p[1])))).collect(), key=itemgetter(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pingsPerDaySeries = pd.Series([y for x, y in result])\n",
    "quantile05 = pingsPerDaySeries.quantile(0.75)\n",
    "plotlistofTuples([(x, y) for x, y in result if y > quantile05])\n",
    "plt.title(\"Health ping users per country distribution (Plot represents only conuntries with uusers more that 0.5 quantile)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "quantile75 = pingsPerDaySeries.quantile(0.75)\n",
    "plotlistofTuples([(x, y) for x, y in usersPerClient if y > quantile05])\n",
    "plt.title(\"Health ping users per country distribution (Plot represents only conuntries with uusers more that 0.75 quantile)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Most of the clients submitting health png from India (not US), \n",
    "but turns out that currenlty in Beta we have more users from India. https://sql.telemetry.mozilla.org/queries/27098/source\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pings.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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Investigation high ration of health ping clients.py

# coding: utf-8

# In[1]:

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

from plotly.graph_objs import *
from moztelemetry import get_pings_properties, get_one_ping_per_client
from moztelemetry.dataset import Dataset
from collections import Counter
import operator
from operator import itemgetter

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


# In[ ]:

pings = Dataset.from_source("telemetry")                 .where(docType='health', appUpdateChannel="beta")                 .records(sc, sample=1)
        
cachedData = get_pings_properties(pings, ["creationDate", "payload/pingDiscardedForSize", "payload/sendFailure", 
                                             "clientId", "meta/submissionDate", "payload/os", "payload/reason", 
                                              "meta/geoCountry", "application/version"]).cache()


# In[ ]:

import matplotlib.dates as mdates
def plotlistofTuples(listOfTuples, title="", inColor='blue'):
    keys = [t[0] for t in listOfTuples]
    values = [t[1] for t in listOfTuples]

    plt.figure(1)
    fig = plt.gcf()
    fig.set_size_inches(15, 7)

    plt.title(title)
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
    plt.bar(range(len(listOfTuples)), values, align='center', color=inColor)
    plt.xticks(range(len(listOfTuples)), keys, rotation=90)


# # Correlation between sendFailure count and Health ping reason
# analyse only sendFailure, as discardedForSize bring small amount of problems 

# In[11]:

def getKey(dictionary, key):
    return dictionary.get(key)


# In[12]:

def distrReasonToFailureCount(data, key):
    return data.map(lambda p: (p['payload/reason'], p['payload/sendFailure']))                             .filter(lambda pair: pair[1] != None)                             .map(lambda pair: (pair[0], getKey(pair[1], key)))                             .filter(lambda pair: pair[1] != None)                             .groupByKey()                             .map(lambda pair: (pair[0], sum(pair[1])))                             .collect()
        


# In[17]:

plotlistofTuples(distrReasonToFailureCount(cachedData, 'eChannelOpen'),                  title='eChannelOpen Failures distribution per reason')
plt.show()


# Most pings with reason with sendFailure = "eChannelOpen" (Can't open channel error) have reason "shutdown", which could be because network is shouted down before getting signal of shutdown on client side.

# In[18]:

plotlistofTuples(distrReasonToFailureCount(cachedData, 'eUnreachable'),                  title='eUnreacheable Failures distribution per reason')
plt.show()


# In[20]:

plotlistofTuples(distrReasonToFailureCount(cachedData, 'timeout'),                  title='timeout Failures distribution per reason')
plt.show()


# # OS distribution Beta

# In[34]:

def osDistr(cachedData):    
    distr = cachedData.map(lambda p: p['payload/os']).filter(lambda p: p != None).map(lambda p: p.get('name'))
    result = Counter(distr.collect()).items()
    plotlistofTuples(result)
    print result


# In[35]:

osDistr(cachedData)


# Most of the pings submitted from Windows. Probably if we have networking problems, it is somehow connected to os.

# # Client distribution by country distribution

# In[4]:

def unique_by_key(elements, key=None):
    if key is None:
        # no key: the whole element must be unique
        key = lambda e: e
    return {key(el): el for el in elements}.values()


# In[11]:

date = cachedData.map(lambda p: (p["meta/geoCountry"], p["clientId"])).groupByKey()
result = sorted(date.map(lambda p: (p[0], len(set(p[1])))).collect(), key=itemgetter(1))


# In[ ]:

pingsPerDaySeries = pd.Series([y for x, y in result])
quantile05 = pingsPerDaySeries.quantile(0.75)
plotlistofTuples([(x, y) for x, y in result if y > quantile05])
plt.title("Health ping users per country distribution (Plot represents only conuntries with uusers more that 0.5 quantile)")


# In[ ]:

quantile75 = pingsPerDaySeries.quantile(0.75)
plotlistofTuples([(x, y) for x, y in usersPerClient if y > quantile05])
plt.title("Health ping users per country distribution (Plot represents only conuntries with uusers more that 0.75 quantile)")


# In[ ]:

Most of the clients submitting health png from India (not US), 
but turns out that currenlty in Beta we have more users from India. https://sql.telemetry.mozilla.org/queries/27098/source


# In[ ]:

pings.first()


# In[ ]: