MDMS with Rheem and plotly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MDMS with Rheem and plotly\n",
    "\n",
    "This notebooks is used to test the MDMS together with Rheem to empower analytics and plotly to visualize results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "At first we need to prepare our notebooks at first, namely configuring where to find the data and load the \n",
    "MDMS dependencies into the kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[36mpathToData\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"/Users/basti/Work/Notebooks/mdms-on-jupyter/data\"\u001b[0m\n",
       "\u001b[36mmdmsVersion\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"0.0.3-SNAPSHOT\"\u001b[0m\n",
       "\u001b[36mrheemVersion\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"0.2.0\"\u001b[0m\n",
       "\u001b[36mlocalRepository\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"file:///Users/basti/.m2/repository\"\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "// Path to the data files\n",
    "val pathToData = new java.io.File(\"data\").getAbsolutePath\n",
    "\n",
    "// MDMS version to use.\n",
    "val mdmsVersion = \"0.0.3-SNAPSHOT\"\n",
    "val rheemVersion = \"0.2.0\"\n",
    "\n",
    "// Local Maven repository to load MDMS dependencies from.\n",
    "val localRepository = \"file:///Users/basti/.m2/repository\"\n",
    "classpath.addRepository(localRepository)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding 2 artifact(s)\n",
      "Using logger implementation from (file:/Users/basti/.coursier/cache/v1/https/repo1.maven.org/maven2/org/slf4j/slf4j-nop/1.7.2/slf4j-nop-1.7.2.jar <no signer certificates>).\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "// We do a little hacking to try to slf4j-nop as logging implementation.\n",
    "// Too much logging output severly slows down the notebook.\n",
    "classpath.add(\"org.slf4j\" % \"slf4j-nop\" % \"1.7.2\")\n",
    "locally {\n",
    "    val loggerSource = Class.forName(\"org.slf4j.impl.StaticLoggerBinder\").getProtectionDomain.getCodeSource\n",
    "    println(s\"Using logger implementation from $loggerSource.\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resolving dependencies from:\n",
      "* ivy2Local\n",
      "* https://repo1.maven.org/maven2\n",
      "* https://oss.sonatype.org/content/repositories/releases\n",
      "* file:///Users/basti/.m2/repository\n",
      "Adding 177 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 0 artifact(s)\n",
      "Adding 18 artifact(s)\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "println(\"Resolving dependencies from:\")\n",
    "classpath.repositories.foreach(repo => println(s\"* $repo\"))\n",
    "\n",
    "classpath.add(\"de.hpi.isg\" % \"mdms-analytics\" % mdmsVersion)\n",
    "classpath.add(\"de.hpi.isg\" % \"mdms-model\" % mdmsVersion)\n",
    "classpath.add(\"de.hpi.isg\" % \"mdms-dependencies\" % mdmsVersion)\n",
    "classpath.add(\"de.hpi.isg\" % \"mdms-simple\" % mdmsVersion)\n",
    "\n",
    "classpath.add(\"org.qcri.rheem\" % \"rheem-api\" % rheemVersion)\n",
    "classpath.add(\"org.qcri.rheem\" % \"rheem-basic\" % rheemVersion)\n",
    "classpath.add(\"org.qcri.rheem\" % \"rheem-java\" % rheemVersion)\n",
    "\n",
    "classpath.add(\"org.plotly-scala\" %% \"plotly-jupyter-scala\" % \"0.1.0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create & Populate a Metadata Store\n",
    "\n",
    "A metadata store is responsible of storing metadata and making it accessible via APIs. There are various storage backends for metadata store. In this notebook, we are using the simplest one, the `DefaultMetadataStore`, which keeps all of its information in memory and leverages Java's standard serialization.\n",
    "\n",
    "The first kind of metadata we need to populate our metadate store with is a (relational) schema with its tables and their columns. All dependencies/integrity constraints/metadata, that we add later on, reference schema elements. This is crucial to integrate and jointly query different metadata types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLF4J: Class path contains multiple SLF4J bindings.\n",
      "SLF4J: Found binding in [jar:file:/Users/basti/.coursier/cache/v1/https/repo1.maven.org/maven2/org/slf4j/slf4j-nop/1.7.2/slf4j-nop-1.7.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]\n",
      "SLF4J: Found binding in [jar:file:/Users/basti/.coursier/cache/v1/https/repo1.maven.org/maven2/org/slf4j/slf4j-simple/1.7.13/slf4j-simple-1.7.13.jar!/org/slf4j/impl/StaticLoggerBinder.class]\n",
      "SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.\n",
      "SLF4J: Actual binding is of type [org.slf4j.helpers.NOPLoggerFactory]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.model.DefaultMetadataStore\u001b[0m\n",
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.analytics.visualization.{HTMLTable, Chart, RenderingConfig}\u001b[0m\n",
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.tools.apps.{CreateSchemaForCsvFilesApp, MetanomeDependencyImportApp, MetanomeStatisticsImportApp}\u001b[0m\n",
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.domain.constraints._\u001b[0m\n",
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.analytics.ConstraintImplicits._\u001b[0m\n",
       "\u001b[36mstore\u001b[0m: \u001b[32mde\u001b[0m.\u001b[32mhpi\u001b[0m.\u001b[32misg\u001b[0m.\u001b[32mmdms\u001b[0m.\u001b[32mmodel\u001b[0m.\u001b[32mDefaultMetadataStore\u001b[0m = MetadataStore[1 schemas, 0 constraint collections]\n",
       "\u001b[36mschemaName\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"musicbrainz\"\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import de.hpi.isg.mdms.model.DefaultMetadataStore\n",
    "import de.hpi.isg.mdms.analytics.visualization.{HTMLTable, Chart, RenderingConfig}\n",
    "import de.hpi.isg.mdms.tools.apps.{CreateSchemaForCsvFilesApp, MetanomeDependencyImportApp, MetanomeStatisticsImportApp}\n",
    "import de.hpi.isg.mdms.domain.constraints._\n",
    "\n",
    "import de.hpi.isg.mdms.analytics.ConstraintImplicits._\n",
    "\n",
    "\n",
    "// Create an in-memory Metadatastore\n",
    "val store = new DefaultMetadataStore\n",
    "val schemaName = \"musicbrainz\"\n",
    "\n",
    "// Import Schema\n",
    "CreateSchemaForCsvFilesApp.fromParameters(store,\n",
    "    s\"$pathToData/data-sample\",\n",
    "    schemaName,\n",
    "    \"tab\",\n",
    "    \"none\",\n",
    "    \"false\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we import FDs, INDs etc. In general, these can be obtained using [Metanome](https://www.metanome.de)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "// Import Multi-Column Dependencies\n",
    "MetanomeDependencyImportApp.fromParameters(store,\n",
    "    s\"$pathToData/mc-results/FDs.txt\",\n",
    "    schemaName,\n",
    "    \"FD\")\n",
    "\n",
    "MetanomeDependencyImportApp.fromParameters(store,\n",
    "    s\"$pathToData/mc-results/INDs.txt\",\n",
    "    schemaName,\n",
    "    \"IND\")\n",
    "\n",
    "MetanomeDependencyImportApp.fromParameters(store,\n",
    "    s\"$pathToData/mc-results/UCCs.txt\",\n",
    "    schemaName,\n",
    "    \"UCC\")\n",
    "\n",
    "// Import Single-Column Statistics\n",
    "MetanomeStatisticsImportApp.fromParameters(store,\n",
    "    s\"$pathToData/sc-results\",\n",
    "    schemaName)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analytics\n",
    "\n",
    "Having initialized the metadata store with a schema and corresponding metadata, we can finally do analytics to serve various use cases, e.g., data exploration.\n",
    "\n",
    "In a very first effort, we just count "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <script type=\"text/javascript\">\n",
       "        require.config({\n",
       "  paths: {\n",
       "    d3: 'https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.17/d3.min',\n",
       "    plotly: 'https://cdn.plot.ly/plotly-1.12.0.min'\n",
       "  },\n",
       "\n",
       "  shim: {\n",
       "    plotly: {\n",
       "      deps: ['d3', 'jquery'],\n",
       "      exports: 'plotly'\n",
       "    }\n",
       "  }\n",
       "});\n",
       "        \n",
       "\n",
       "        require(['plotly'], function(Plotly) {\n",
       "          window.Plotly = Plotly;\n",
       "        });\n",
       "      </script>\n",
       "    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[32mimport \u001b[36mplotly._\u001b[0m\n",
       "\u001b[32mimport \u001b[36mplotly.element._\u001b[0m\n",
       "\u001b[32mimport \u001b[36mplotly.layout._\u001b[0m\n",
       "\u001b[32mimport \u001b[36mplotly.JupyterScala._\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly._\n",
    "import plotly.element._\n",
    "import plotly.layout._\n",
    "import plotly.JupyterScala._\n",
    "\n",
    "plotly.JupyterScala.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32mimport \u001b[36mde.hpi.isg.mdms.analytics._\u001b[0m\n",
       "\u001b[32mimport \u001b[36morg.qcri.rheem.core.api._\u001b[0m\n",
       "\u001b[32mimport \u001b[36morg.qcri.rheem.api._\u001b[0m\n",
       "\u001b[32mimport \u001b[36morg.qcri.rheem.java.Java\u001b[0m\n",
       "\u001b[36mrheemConfig\u001b[0m: \u001b[32morg\u001b[0m.\u001b[32mqcri\u001b[0m.\u001b[32mrheem\u001b[0m.\u001b[32mcore\u001b[0m.\u001b[32mapi\u001b[0m.\u001b[32mConfiguration\u001b[0m = Configuration[(no name)]\n",
       "\u001b[36mrheemCtx\u001b[0m: \u001b[32morg\u001b[0m.\u001b[32mqcri\u001b[0m.\u001b[32mrheem\u001b[0m.\u001b[32mcore\u001b[0m.\u001b[32mapi\u001b[0m.\u001b[32mRheemContext\u001b[0m = org.qcri.rheem.core.api.RheemContext@5addf079"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import de.hpi.isg.mdms.analytics._\n",
    "import org.qcri.rheem.core.api._\n",
    "import org.qcri.rheem.api._\n",
    "import org.qcri.rheem.java.Java\n",
    "\n",
    "val rheemConfig = new Configuration\n",
    "val rheemCtx = new RheemContext(rheemConfig).withPlugin(Java.basicPlugin).withPlugin(Java.graphPlugin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID   2096544347: FD import (Thu Sep 22 11:20:46 CEST 2016)\n",
      "ID   1913119238: IND import (Thu Sep 22 11:20:47 CEST 2016)\n",
      "ID    926104209: UCC import (Thu Sep 22 11:20:49 CEST 2016)\n",
      "ID   1653643669: Single column statistics import\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001b[32mimport \u001b[36mscala.collection.JavaConversions._\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scala.collection.JavaConversions._\n",
    "store.getConstraintCollections.foreach(cc => println(f\"ID ${cc.getId}%12d: ${cc.getDescription}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[36mfdCcId\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m2096544347\u001b[0m\n",
       "\u001b[36mindCcId\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m1913119238\u001b[0m\n",
       "\u001b[36muccCcId\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m926104209\u001b[0m\n",
       "\u001b[36mstatisticsCcId\u001b[0m: \u001b[32mInt\u001b[0m = \u001b[32m1653643669\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val fdCcId = 2096544347\n",
    "val indCcId = 1913119238\n",
    "val uccCcId = 926104209\n",
    "val statisticsCcId = 1653643669"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we visualize above results in a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"chart\" id=\"plot-2114779130\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "requirejs([\"plotly\"], function(Plotly) {\n",
       "  (function () {\n",
       "  var data0 = {\"type\":\"bar\",\"x\":[\"FDs\",\"INDs\",\"UCCs\",\"statistics\"],\"y\":[4193.0,318562.0,510.0,1522.0]};\n",
       "\n",
       "  var data = [data0];\n",
       "  var layout = {};\n",
       "\n",
       "  Plotly.plot('plot-2114779130', data, layout);\n",
       "})();\n",
       "});\n",
       "      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[36mres13\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"plot-2114779130\"\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locally {\n",
    "    val planBuilder = new PlanBuilder(rheemCtx)\n",
    "\n",
    "    val (constraintNames, constraintCounts) = Seq(\n",
    "        \"FDs\" -> store.loadFDs(fdCcId)(planBuilder).count.collect().head.toInt,\n",
    "        \"INDs\" -> store.loadINDs(indCcId)(planBuilder).count.collect().head.toInt,\n",
    "        \"UCCs\" -> store.loadUCCs(uccCcId)(planBuilder).count.collect().head.toInt,\n",
    "        \"statistics\" -> store.loadStatistics(statisticsCcId)(planBuilder).count.collect().head.toInt\n",
    "        ).unzip\n",
    "\n",
    "    Bar(constraintNames, constraintCounts).plot()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We proceed with a simple aggregation of constraints. The next query creates a histogram of columns according to their number of distinct values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"chart\" id=\"plot-714592476\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "requirejs([\"plotly\"], function(Plotly) {\n",
       "  (function () {\n",
       "  var data0 = {\"type\":\"bar\",\"x\":[\">= 0\",\">= 1\",\">= 2\",\">= 4\",\">= 8\",\">= 16\",\">= 32\",\">= 64\",\">= 128\",\">= 256\",\">= 512\",\">= 1024\",\">= 2048\",\">= 4096\",\">= 8192\",\">= 16384\",\">= 32768\",\">= 65536\",\">= 131072\",\">= 262144\",\">= 524288\",\">= 1048576\",\">= 2097152\",\">= 4194304\",\">= 8388608\",\">= 16777216\"],\"y\":[90.0,423.0,61.0,70.0,83.0,46.0,26.0,29.0,64.0,62.0,52.0,42.0,53.0,59.0,53.0,38.0,38.0,60.0,20.0,42.0,44.0,37.0,9.0,6.0,10.0,5.0]};\n",
       "\n",
       "  var data = [data0];\n",
       "  var layout = {};\n",
       "\n",
       "  Plotly.plot('plot-714592476', data, layout);\n",
       "})();\n",
       "});\n",
       "      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[36mres14\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"plot-714592476\"\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locally {\n",
    "    implicit val planBuilder = new PlanBuilder(rheemCtx)\n",
    "    \n",
    "    def calcHistogramKey(statistics: ColumnStatistics): Int = {\n",
    "        val base = 2\n",
    "        val numDistinctValues = statistics.getNumDistinctValues\n",
    "        if (numDistinctValues == 0L) 0 \n",
    "        else {\n",
    "                var lower = 1\n",
    "                while (lower * 2 < numDistinctValues) lower *= 2\n",
    "                lower\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    val distinctValueHistogram = store.loadStatistics(statisticsCcId)\n",
    "        .map(stats => (calcHistogramKey(stats), 1))\n",
    "        .reduceByKey(_._1, (t1, t2) => (t1._1, t1._2 + t2._2))\n",
    "        .collect()\n",
    "        .toSeq.sortBy(_._1)\n",
    "        .map { case (key, count) => (s\">= $key\", count) }\n",
    "    \n",
    "    val (x, y) = distinctValueHistogram.unzip\n",
    "    Bar(x, y).plot()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last but not least, we perform a little more intricate analysis. For each (unary) IND, we collect the numbers of distinct values of both the dependent and referenced column, such that we can map each IND into a coordinate system. The resulting scatter chart reveals different types of INDs found. The key point here is that the MDMS design allows to easily integrate different metadata types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"chart\" id=\"plot-1115052257\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "requirejs([\"plotly\"], function(Plotly) {\n",
       "  (function () {\n",
       "  var data0 = {\"type\":\"scatter\",\"x\":[0.0,1.0,2.5796923E7],\"y\":[0.0,1.0,2.5796923E7],\"mode\":\"lines\",\"name\":\"diagonal\"};\n",
       "  var data1 = 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[#]\"};\n",
       "\n",
       "  var data = [data0, data1];\n",
       "  var layout = {\"title\":\"Distinct values of LHS and RHS of all unary INDs\",\"xaxis\":{\"title\":\"distinct values in dependent column\",\"type\":\"log\"},\"yaxis\":{\"title\":\"distinct values in referenced column\",\"type\":\"log\"}};\n",
       "\n",
       "  Plotly.plot('plot-1115052257', data, layout);\n",
       "})();\n",
       "});\n",
       "      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[36mres15\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"plot-1115052257\"\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locally {\n",
    "    implicit val planBuilder = new PlanBuilder(rheemCtx)\n",
    "    import org.qcri.rheem.basic.data.{Tuple2 => T2}\n",
    "    \n",
    "    def toExpBase(value: Long, base: Int): Long = {\n",
    "        if (value == 0L) 0 \n",
    "        else {\n",
    "                var lower = 1\n",
    "                while (lower * 2 < value) lower *= base\n",
    "                lower\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    def toIntervalCenter(value: Long, interval: Long) = value - (value % interval) + (interval / 2)\n",
    "    \n",
    "    def roundDistinctValues(numDistinctValues: Long): Long = numDistinctValues //toIntervalCenter(numDistinctValues, interval = 200)\n",
    "    \n",
    "    val distinctValues = store.loadStatistics(statisticsCcId)\n",
    "        .map(stats => (stats.getTargetReference.getTargetId, stats.getNumDistinctValues))\n",
    "    \n",
    "    val indDvCounts = store.loadINDs(indCcId)\n",
    "        .filter(_.getArity == 1)\n",
    "        .map {\n",
    "            unaryInd => \n",
    "                val targetRef = unaryInd.getTargetReference\n",
    "                (targetRef.getDependentColumns()(0), targetRef.getReferencedColumns()(0))\n",
    "        }\n",
    "        .join[(Int, Long), Int](_._1, distinctValues, _._1)\n",
    "        .map(joinLine => (joinLine.field1._2, joinLine.field0._2))\n",
    "        .join[(Int, Long), Int](_._2, distinctValues, _._1)\n",
    "        .map(joinLine => (joinLine.field0._1, joinLine.field1._2))\n",
    "        .filter { case (depDvc, refDvc) => depDvc <= refDvc}\n",
    "        .map(indDvs => ((roundDistinctValues(indDvs._1), roundDistinctValues(indDvs._2)), 1))\n",
    "        .reduceByKey(_._1, (indDvs1, indDvs2) => (indDvs1._1, indDvs1._2 + indDvs2._2))\n",
    "        .collect()\n",
    "        .toSeq\n",
    "    \n",
    "    val (indDvs, counts) = indDvCounts.unzip\n",
    "    val (depDvs, refDvs) = indDvs.unzip\n",
    "    \n",
    "    val layout = Layout(\n",
    "        title = \"Distinct values of LHS and RHS of all unary INDs\",\n",
    "        xaxis = Axis(`type` = AxisType.Log, title = \"distinct values in dependent column\"),\n",
    "        yaxis = Axis(`type` = AxisType.Log, title = \"distinct values in referenced column\")\n",
    "    )\n",
    "    \n",
    "    val maxDv = (1.1 * refDvs.max).toInt\n",
    "    val diagonal = Scatter(Seq(0, 1, maxDv), Seq(0, 1, maxDv), mode = ScatterMode(ScatterMode.Lines), name = \"diagonal\")\n",
    "    \n",
    "    val scatter = Scatter(\n",
    "        depDvs.map(_.toInt).toSeq,\n",
    "        refDvs.map(_.toInt).toSeq,\n",
    "        mode = ScatterMode(ScatterMode.Markers),\n",
    "        marker = Marker(size = counts.map(count => (0.1 * math.sqrt(count)).toInt + 3).toSeq),\n",
    "        name = \"INDs [#]\",\n",
    "        text = counts.map(count => f\"$count%,d INDs\")\n",
    "    )\n",
    "    \n",
    "    JupyterScala.plot(Seq(diagonal, scatter), layout)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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