analytix-hostmetrics-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://swan.web.cern.ch/sites/swan.web.cern.ch/files/pictures/logo_swan_letters.png\" alt=\"SWAN\" style=\"float: left; width: 20%; margin-right: 15%; margin-left: 15%; margin-bottom: 2.0em;\">\n",
    "<img src=\"https://spark.apache.org/images/spark-logo-trademark.png\" alt=\"EP-SFT\" style=\"float: left; width: 25%; margin-right: 15%; margin-bottom: 2.0em;\">\n",
    "<p style=\"clear: both;\">\n",
    "<div style=\"text-align:center\"><h1>Integration of SWAN with Spark clusters</h1></div>\n",
    "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The current setup allows to execute PySpark operations on CERN Hadoop and Spark clusters. \n",
    "\n",
    "This notebook illustrates the use of __Spark in SWAN to analyze the monitoring data available on HDFS (analytix)__ and plots a heatmap of loadAvg across machines in a particular service."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the cluster (analytix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To connect to a cluster, click on the star button on the top and follow the instructions\n",
    "* The star button only appears if you have selected a SPARK cluster in the configuration\n",
    "* The star button is active after the notebook kernel is ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary spark and python stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import from_unixtime, when, col\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### select the data\n",
    "*path_on_hdfs_to_your_data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = spark.read.json(\"/project/itmon/archive/lemon/hadoop_ng/2018-04/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- _corrupt_record: string (nullable = true)\n",
      " |-- aggregated: string (nullable = true)\n",
      " |-- body: string (nullable = true)\n",
      " |-- entity: string (nullable = true)\n",
      " |-- metric_id: string (nullable = true)\n",
      " |-- metric_name: string (nullable = true)\n",
      " |-- producer: string (nullable = true)\n",
      " |-- submitter_environment: string (nullable = true)\n",
      " |-- submitter_host: string (nullable = true)\n",
      " |-- submitter_hostgroup: string (nullable = true)\n",
      " |-- timestamp: string (nullable = true)\n",
      " |-- toplevel_hostgroup: string (nullable = true)\n",
      " |-- type: string (nullable = true)\n",
      " |-- version: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Filter for loadAvg\n",
    "_https://metricmgr.cern.ch/metric/20002/_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "body_schema = StructType([StructField(\"LoadAvg\",DoubleType())])\n",
    "df_loadAvg = df.where(col(\"metric_id\") == \"20002\").withColumn('body', from_json('body', body_schema))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create temporary table view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# body_schema = spark.read.json(df_loadAvg.rdd.map(lambda row: row.body)).schema\n",
    "df_loadAvg.createOrReplaceTempView(\"loadAvg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the heavylifting in spark and collect aggregated view to panda DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_loadAvg_pandas = spark.sql(\"SELECT substring(submitter_host,7,length(submitter_host)) as host, \\\n",
    "                                      avg(body.LoadAvg) as avg, \\\n",
    "                                      hour(from_unixtime(timestamp / 1000, 'yyyy-MM-dd HH:mm:ss')) as hr \\\n",
    "                               FROM loadAvg \\\n",
    "                               WHERE submitter_hostgroup like 'hadoop_ng/nxcals%' \\\n",
    "                               AND dayofmonth(from_unixtime(timestamp / 1000, 'yyyy-MM-dd HH:mm:ss')) = 22 \\\n",
    "                               GROUP BY hour(from_unixtime(timestamp / 1000, 'yyyy-MM-dd HH:mm:ss')), submitter_host\")\\\n",
    "                    .toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Heatmap of loadAvg for NXCals service on 22nd April 2018')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wYADNmjWrtc/CwkJ++9vf1nr99OnTWK1WgoOD9X3GxcVRUlJSZ9uvd167urri6OioP2/W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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1df88af610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of loadAvg\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = sns.heatmap(df_loadAvg_pandas.pivot(index='host', columns='hr', values='avg'), cmap=\"Blues\")\n",
    "ax.set_title(\"Heatmap of loadAvg for NXCals service on 22nd April 2018\")"
   ]
  }
 ],
 "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": "2.7.13"
  },
  "sparkconnect": {
   "bundled_options": [],
   "list_of_options": [
    {
     "name": "spark.dynamicAllocation.maxExecutors",
     "value": "600"
    },
    {
     "name": "spark.scheduler.maxRegisteredResourcesWaitingTime",
     "value": "10"
    },
    {
     "name": "spark.yarn.archive",
     "value": "hdfs:///project/swan/sparkjars/LCG_93/spark-libs.jar"
    }
   ]
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}