spark-geomesa-fs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apache Spark with GeoMesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pyspark\n",
    "from pyspark import SparkConf\n",
    "from pyspark import SparkContext\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "import geomesa_pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geomesa_fs_jar_filepath = os.path.join(\n",
    "    os.environ['GEOMESA_FS_HOME'], \n",
    "    \"dist/spark/geomesa-fs-spark-runtime_2.11-3.0.0.jar\")\n",
    "geomesa_hbase_jar_filepath = os.path.join(\n",
    "    os.environ['GEOMESA_HBASE_HOME'],\n",
    "    \"dist/spark/geomesa-hbase-spark-runtime-hbase2_2.11-3.0.0.jar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = geomesa_pyspark.configure(\n",
    "    jars=[geomesa_fs_jar_filepath],\n",
    "    packages=['geomesa_pyspark','pytz'],\n",
    "    spark_home=os.environ['SPARK_HOME']).\\\n",
    "    setAppName('MyTestApp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 316 ms, sys: 133 ms, total: 449 ms\n",
      "Wall time: 6min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .config(conf=conf) \\\n",
    "    .enableHiveSupport() \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 51 ms, sys: 21.2 ms, total: 72.1 ms\n",
      "Wall time: 4min 57s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "86770"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "hdfs_filepath = \"hdfs://node-master:54310/tmp/geomesa/gdelt_custom_20200101\"\n",
    "\n",
    "df_fs_sample = spark.read.format(\"geomesa\") \\\n",
    "                    .option(\"fs.path\", hdfs_filepath) \\\n",
    "                    .option(\"geomesa.feature\", \"gdelt_custom\") \\\n",
    "                    .load()\n",
    "df_fs_sample.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.31 ms, sys: 885 µs, total: 4.2 ms\n",
      "Wall time: 3.44 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "176"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "hdfs_filepath = \"hdfs://node-master:54310/tmp/geomesa/ne/countries\"\n",
    "\n",
    "df_ne_fs = spark.read.format(\"geomesa\") \\\n",
    "                .option(\"fs.path\", hdfs_filepath) \\\n",
    "                .option(\"geomesa.feature\", \"ne_countries\") \\\n",
    "                .load()\n",
    "df_ne_fs.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------------------+-----------+\n",
      "|database|           tableName|isTemporary|\n",
      "+--------+--------------------+-----------+\n",
      "| default|      gdelt_csv_2019|      false|\n",
      "| default|      gdelt_csv_2020|      false|\n",
      "| default|  gdelt_parquet_2020|      false|\n",
      "| default|gdelt_parquet_ins...|      false|\n",
      "| default|gdelt_parquet_ins...|      false|\n",
      "| default|ne_10_states_prov...|      false|\n",
      "| default|ne_110_countries_...|      false|\n",
      "|        |gdelt_custom_2020...|       true|\n",
      "|        |        ne_countries|       true|\n",
      "+--------+--------------------+-----------+\n",
      "\n",
      "CPU times: user 6.77 ms, sys: 1.2 ms, total: 7.98 ms\n",
      "Wall time: 10.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_fs_sample.createOrReplaceTempView(\"gdelt_custom_20200101\")\n",
    "df_ne_fs.createOrReplaceTempView(\"ne_countries\")\n",
    "spark.sql(\"SHOW TABLES\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geospatial Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "EXTENT = [9.5307, 46.3723, 17.1608, 49.0205]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.1 ms, sys: 12.9 ms, total: 40 ms\n",
      "Wall time: 8.33 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_root_code</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event_root_code  cnt\n",
       "0               12    3\n",
       "1                1   39\n",
       "2               13    1\n",
       "3               16    1\n",
       "4                6    5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df = spark.sql(\"\"\"\n",
    "  SELECT\n",
    "    event_root_code,\n",
    "    COUNT(event_id) AS cnt\n",
    "  FROM\n",
    "    gdelt_custom_20200101\n",
    "  WHERE \n",
    "    ST_Within(geom, st_makeBBOX({}, {}, {}, {}))\n",
    "  GROUP BY event_root_code\n",
    "\"\"\".format(*EXTENT)).toPandas()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 117 ms, sys: 15.7 ms, total: 133 ms\n",
      "Wall time: 7min 28s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_a2</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DZ</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MM</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LT</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CI</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_a2  cnt\n",
       "0     DZ   56\n",
       "1     MM   65\n",
       "2     LT   30\n",
       "3     CI   47\n",
       "4     AZ   48"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df = spark.sql(\"\"\"\n",
    "  SELECT\n",
    "    c.iso_a2,\n",
    "    COUNT(g.event_id) AS cnt\n",
    "  FROM\n",
    "    gdelt_custom_20200101 AS g,\n",
    "    ne_countries AS c\n",
    "  WHERE ST_Within(g.geom, c.polygons)\n",
    "  GROUP BY c.iso_a2\n",
    "\"\"\").toPandas()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = df.set_index('iso_a2')['cnt'].sort_values(ascending=False)[:30][::-1]\n",
    "df_plot.plot(kind='barh', figsize=(12, 10), grid=True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spark",
   "language": "python",
   "name": "spark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}