IN alternatives.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.sql import text\n",
    "import pandas as pd\n",
    "import sqlparse\n",
    "import os\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(os.environ['DB_CONNECTION_STRING'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exec_sql(sql):\n",
    "    with engine.connect() as conn:\n",
    "        return conn.execute(text(sql))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_sql(sql):\n",
    "    print(sqlparse.format(sql, reindent=True, keyword_case='upper'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# contains 2015 - 2020\n",
    "TABLE = 'ingest_staging_table'\n",
    "\n",
    "# all queries get these fields from the DB\n",
    "FIELDS = ['requesttype', 'nc', 'createddate', 'srnumber', 'latitude', 'longitude']\n",
    "\n",
    "# date range for all queries\n",
    "START_DATE = '2019-01-01'\n",
    "END_DATE = '2020-01-01'\n",
    "\n",
    "# these are all the valid NC codes \n",
    "ALL_NCS = [\n",
    "    4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, \n",
    "    17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, \n",
    "    28, 29, 30, 32, 33, 34, 36, 37, 38, 39, 40, \n",
    "    41, 42, 43, 44, 46, 47, 48, 50, 52, 53, 54, \n",
    "    55, 58, 60, 61, 62, 63, 64, 66, 67, 68, 70, \n",
    "    71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 84, \n",
    "    86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, \n",
    "    99, 100, 101, 102, 104, 109, 110, 111, 112, \n",
    "    113, 114, 115, 118, 119, 120, 121, 123, 124, \n",
    "    125, 126, 127, 128\n",
    "]\n",
    "\n",
    "ALL_RTS = [\n",
    "    \"Bulky Items\",\"Graffiti Removal\",\"Homeless Encampment\",\n",
    "    \"Illegal Dumping Pickup\",\"Dead Animal Removal\",\"Electronic Waste\",\n",
    "    \"Single Streetlight Issue\",\"Multiple Streetlight Issue\",\n",
    "    \"Metal/Household Appliances\",\"Feedback\",\"Other\"\n",
    "]\n",
    "\n",
    "# run queries for each of these values\n",
    "NUMBER_OF_NCS = [1, 3, 6, 10, 15, 20, 30, 50, 70, 90, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timing helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_process(variable, values, process, runs=7):\n",
    "    times = []\n",
    "    for value in values:\n",
    "        timer = timeit.Timer(lambda: process(value))\n",
    "        run_times = timer.repeat(repeat=runs, number=1)\n",
    "        avg_time = round(sum(run_times) / len(run_times), 4)\n",
    "        print(f'{variable} = {value} ===> average {avg_time} seconds over {runs} runs')\n",
    "        times.append(avg_time)\n",
    "    return times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_processes(variable, values, processes):\n",
    "    times = {}\n",
    "    for name, process in processes.items():\n",
    "        print(name)\n",
    "        times[name] = time_process(variable, values, process)\n",
    "    return pd.DataFrame(times, index=pd.Index(values, name=variable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chart_times(times, title):\n",
    "    chart = times.plot.bar(figsize=(10, 6))\n",
    "    chart.set_ylabel('execution time (secs)', fontsize=16)\n",
    "    chart.set_title(title, fontsize=20)\n",
    "    chart.xaxis.label.set_size(16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IN query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT requesttype,\n",
      "       nc,\n",
      "       createddate,\n",
      "       srnumber,\n",
      "       latitude,\n",
      "       longitude\n",
      "FROM ingest_staging_table\n",
      "WHERE createddate > '2019-01-01'\n",
      "  AND createddate < '2020-01-01'\n",
      "  AND nc IN (4,\n",
      "             5,\n",
      "             6)\n"
     ]
    }
   ],
   "source": [
    "def in_query(ncList):\n",
    "    return f\"\"\"\n",
    "        SELECT {(', ').join(FIELDS)}\n",
    "        FROM {TABLE}\n",
    "        WHERE \n",
    "            createddate > '{START_DATE}' AND \n",
    "            createddate < '{END_DATE}' AND \n",
    "            nc IN ({(', ').join([str(nc) for nc in ncList])})\n",
    "    \"\"\"\n",
    "\n",
    "# example with ncList = [4, 5, 6]\n",
    "print_sql(in_query([4, 5, 6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IN query\n",
      "number of NCs = 1 ===> average 0.6142 seconds over 7 runs\n",
      "number of NCs = 3 ===> average 0.8814 seconds over 7 runs\n",
      "number of NCs = 6 ===> average 0.7203 seconds over 7 runs\n",
      "number of NCs = 10 ===> average 0.8771 seconds over 7 runs\n",
      "number of NCs = 15 ===> average 1.0266 seconds over 7 runs\n",
      "number of NCs = 20 ===> average 1.0633 seconds over 7 runs\n",
      "number of NCs = 30 ===> average 12.3063 seconds over 7 runs\n",
      "number of NCs = 50 ===> average 11.7888 seconds over 7 runs\n",
      "number of NCs = 70 ===> average 11.874 seconds over 7 runs\n",
      "number of NCs = 90 ===> average 12.0155 seconds over 7 runs\n",
      "number of NCs = 100 ===> average 10.8955 seconds over 7 runs\n"
     ]
    }
   ],
   "source": [
    "in_times = time_processes(\n",
    "    variable='number of NCs',\n",
    "    values=NUMBER_OF_NCS,\n",
    "    processes={\n",
    "        'IN query': lambda num_ncs: exec_sql(in_query(ALL_NCS[:num_ncs]))\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_times(in_times, \"IN query execution times (one year period)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JOIN query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM\n",
      "  (SELECT requesttype,\n",
      "          nc,\n",
      "          createddate,\n",
      "          srnumber,\n",
      "          latitude,\n",
      "          longitude\n",
      "   FROM ingest_staging_table\n",
      "   WHERE createddate > '2019-01-01'\n",
      "     AND createddate < '2020-01-01' ) AS sub\n",
      "INNER JOIN (\n",
      "            VALUES (4), (5), (6)) vals(v) ON (nc = v)\n"
     ]
    }
   ],
   "source": [
    "def join_query(ncList):\n",
    "    return f\"\"\"\n",
    "        SELECT * \n",
    "        FROM (\n",
    "            SELECT {(', ').join(FIELDS)}\n",
    "            FROM {TABLE}\n",
    "            WHERE \n",
    "                createddate > '{START_DATE}' AND \n",
    "                createddate < '{END_DATE}'\n",
    "        ) as sub\n",
    "        INNER JOIN ( \n",
    "            VALUES {(', ').join(['({})'.format(str(nc)) for nc in ncList])} ) vals(v)\n",
    "        ON (nc = v)\n",
    "    \"\"\"\n",
    "\n",
    "# example with ncList = [4, 5, 6]\n",
    "print_sql(join_query([4, 5, 6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JOIN query\n",
      "number of NCs = 1 ===> average 0.5477 seconds over 7 runs\n",
      "number of NCs = 3 ===> average 0.5949 seconds over 7 runs\n",
      "number of NCs = 6 ===> average 1.3586 seconds over 7 runs\n",
      "number of NCs = 10 ===> average 9.8942 seconds over 7 runs\n",
      "number of NCs = 15 ===> average 10.0761 seconds over 7 runs\n",
      "number of NCs = 20 ===> average 9.8363 seconds over 7 runs\n",
      "number of NCs = 30 ===> average 10.036 seconds over 7 runs\n",
      "number of NCs = 50 ===> average 8.9542 seconds over 7 runs\n",
      "number of NCs = 70 ===> average 9.2785 seconds over 7 runs\n",
      "number of NCs = 90 ===> average 10.2356 seconds over 7 runs\n",
      "number of NCs = 100 ===> average 9.5956 seconds over 7 runs\n"
     ]
    }
   ],
   "source": [
    "join_times = time_processes(\n",
    "    variable='number of NCs',\n",
    "    values=NUMBER_OF_NCS,\n",
    "    processes={\n",
    "        'JOIN query': lambda num_ncs: exec_sql(join_query(ALL_NCS[:num_ncs]))\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_times(join_times, 'JOIN query execution times (one year of data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IN vs. JOIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_vs_join = pd.concat([in_times, join_times], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_times(in_vs_join, 'IN vs. JOIN (one year period)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anti-Join query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT *\n",
      "FROM\n",
      "  (SELECT requesttype,\n",
      "          nc,\n",
      "          createddate,\n",
      "          srnumber,\n",
      "          latitude,\n",
      "          longitude\n",
      "   FROM ingest_staging_table\n",
      "   WHERE createddate > '2019-01-01'\n",
      "     AND createddate < '2020-01-01' ) AS sub\n",
      "LEFT OUTER JOIN (\n",
      "                 VALUES (4), (5), (6)) vals(v) ON (nc = v)\n",
      "WHERE v IS NULL\n"
     ]
    }
   ],
   "source": [
    "def anti_join_query(ncList):\n",
    "    return f\"\"\"\n",
    "        SELECT *\n",
    "        FROM (\n",
    "            SELECT {(', ').join(FIELDS)}\n",
    "            FROM {TABLE}\n",
    "            WHERE \n",
    "                createddate > '{START_DATE}' AND \n",
    "                createddate < '{END_DATE}'\n",
    "        ) as sub\n",
    "        LEFT OUTER JOIN \n",
    "            ( VALUES {(', ').join(['({})'.format(str(nc)) for nc in ncList])} ) vals(v)\n",
    "        ON (nc = v)\n",
    "        WHERE v IS NULL\n",
    "    \"\"\"\n",
    "\n",
    "# example with ncList = [4, 5, 6]\n",
    "print_sql(anti_join_query([4, 5, 6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anti-Join query\n",
      "number of NCs = 1 ===> average 10.344 seconds over 7 runs\n",
      "number of NCs = 3 ===> average 9.9989 seconds over 7 runs\n",
      "number of NCs = 6 ===> average 10.2521 seconds over 7 runs\n",
      "number of NCs = 10 ===> average 10.0673 seconds over 7 runs\n",
      "number of NCs = 15 ===> average 9.0483 seconds over 7 runs\n",
      "number of NCs = 20 ===> average 9.1414 seconds over 7 runs\n",
      "number of NCs = 30 ===> average 8.9979 seconds over 7 runs\n",
      "number of NCs = 50 ===> average 8.7927 seconds over 7 runs\n",
      "number of NCs = 70 ===> average 10.1924 seconds over 7 runs\n",
      "number of NCs = 90 ===> average 10.4313 seconds over 7 runs\n",
      "number of NCs = 100 ===> average 10.2128 seconds over 7 runs\n"
     ]
    }
   ],
   "source": [
    "anti_join_times = time_processes(\n",
    "    variable='number of NCs',\n",
    "    values=NUMBER_OF_NCS,\n",
    "    processes={\n",
    "        'Anti-Join query': lambda num_ncs: exec_sql(anti_join_query(ALL_NCS[:num_ncs]))\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart_times(anti_join_times, \"Anti-Join execution times (one year period)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOT IN query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT requesttype,\n",
      "       nc,\n",
      "       createddate,\n",
      "       srnumber,\n",
      "       latitude,\n",
      "       longitude\n",
      "FROM ingest_staging_table\n",
      "WHERE createddate > '2019-01-01'\n",
      "  AND createddate < '2020-01-01'\n",
      "  AND nc NOT IN (4,\n",
      "                 5,\n",
      "                 6)\n"
     ]
    }
   ],
   "source": [
    "def not_in_query(ncList):\n",
    "    return f\"\"\"\n",
    "        SELECT {(', ').join(FIELDS)}\n",
    "        FROM {TABLE}\n",
    "        WHERE \n",
    "            createddate > '{START_DATE}' AND \n",
    "            createddate < '{END_DATE}' AND \n",
    "            nc NOT IN ({(', ').join([str(nc) for nc in ncList])})\n",
    "    \"\"\"\n",
    "\n",
    "# example with ncList = [4, 5, 6]\n",
    "print_sql(not_in_query([4, 5, 6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT IN query\n",
      "number of NCs = 1 ===> average 10.4433 seconds over 7 runs\n",
      "number of NCs = 3 ===> average 10.1845 seconds over 7 runs\n",
      "number of NCs = 6 ===> average 9.8309 seconds over 7 runs\n",
      "number of NCs = 10 ===> average 10.2907 seconds over 7 runs\n",
      "number of NCs = 15 ===> average 10.372 seconds over 7 runs\n",
      "number of NCs = 20 ===> average 10.6519 seconds over 7 runs\n",
      "number of NCs = 30 ===> average 10.2284 seconds over 7 runs\n",
      "number of NCs = 50 ===> average 10.0715 seconds over 7 runs\n",
      "number of NCs = 70 ===> average 10.2147 seconds over 7 runs\n",
      "number of NCs = 90 ===> average 9.7998 seconds over 7 runs\n"
     ]
    }
   ],
   "source": [
    "not_in_times = time_processes(\n",
    "    variable='number of NCs',\n",
    "    values=NUMBER_OF_NCS,\n",
    "    processes={\n",
    "        'NOT IN query': lambda num_ncs: exec_sql(not_in_query(ALL_NCS[:num_ncs]))\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart_times(not_in_times, \"NOT IN query execution times (one year period)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "all_times = pd.concat([in_times, join_times, anti_join_times, not_in_times], axis=1)\n",
    "with open('IN_alternatives.pkl', 'wb') as f:\n",
    "    pickle.dump(all_times, f)"
   ]
  }
 ],
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