examples/basic_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spylon Kernel Test with Spark 2.1\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization of the spark context\n",
    "\n",
    "Note that we can set things like driver memory etc.\n",
    "\n",
    "If `launcher._spark_home` is not set it will default to looking at the `SPARK_HOME` environment variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%init_spark\n",
    "launcher.num_executors = 4\n",
    "launcher.executor_cores = 2\n",
    "launcher.driver_memory = '4g'\n",
    "launcher.conf.set(\"spark.sql.catalogImplementation\", \"hive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have additional jars you want available in the Spark runtime,use the `launcher.jars` and/or `launcher.packages` settings, the latter using Maven \"GAV\" coordinates:\n",
    "```scala\n",
    "launcher.jars = [\"/some/local/path/to/a/file.jar\"]\n",
    "launcher.packages = [\"com.acme:super:1.0.1\"]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets write some scala!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Intitializing scala interpreter....'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Scala interpreter initialized.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "res1: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@174541\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "res2: String = 2.1.0\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data: Seq[(String, Int)] = List((a,0), (b,1), (c,2), (d,3))\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val data = Seq(\"a\", \"b\", \"c\", \"d\") zip (0 to 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ds: org.apache.spark.sql.Dataset[(String, Int)] = [_1: string, _2: int]\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val ds = spark.createDataset(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can define functions and classes as we need them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defined class DataRow\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case class DataRow(name: String, value: Integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ds2: org.apache.spark.sql.Dataset[DataRow] = [name: string, value: int]\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val ds2 = ds.map{case (a, b) => DataRow(a,b)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "add: (x: Integer, y: Integer)Int\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add(x: Integer, y: Integer) = x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "res3: Int = 15\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add(7, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sharing scala spark datasets with python\n",
    "\n",
    "We can share a dataset by registering it as a view.  The spark session that exists in the python magic is the same one as on the scala side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds2.toDF.createOrReplaceTempView(\"foo2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "df = spark.sql(\"select name from foo2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[name: string]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%python\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting example\n",
    "\n",
    "Plotting with matplotlib can be a little tedious but still pretty simple to do.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%python\n",
    "import matplotlib\n",
    "matplotlib.use(\"agg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wfJ19XKK6fSePck3NzSWSg1QARUTEOVjTIeIj2DAJMpLB1RNaDoEHB4Cbh9npRHKUCqCI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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%python\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import Image\n",
    "import tempfile\n",
    "plt.clf()\n",
    "plt.plot(range(10), label=\"a\")\n",
    "plt.plot(range(4, 14), label=\"b\")\n",
    "plt.plot(range(12, 0, -1), label=\"c\")\n",
    "plt.legend()\n",
    "plt.title(\"Example Plot\")\n",
    "with tempfile.NamedTemporaryFile(suffix=\".png\") as fo:\n",
    "    plt.savefig(fo.name)\n",
    "    retval = Image(filename=fo.name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spylon-kernel",
   "language": "scala",
   "name": "spylon-kernel"
  },
  "language_info": {
   "codemirror_mode": "text/x-scala",
   "file_extension": ".scala",
   "help_links": [
    {
     "text": "MetaKernel Magics",
     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
    }
   ],
   "mimetype": "text/x-scala",
   "name": "scala",
   "pygments_lexer": "scala",
   "version": "1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}