L3 Exercise 4 - Table Design.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 4: Optimizing Redshift Table Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sql extension is already loaded. To reload it, use:\n",
      "  %reload_ext sql\n"
     ]
    }
   ],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "import configparser\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = configparser.ConfigParser()\n",
    "config.read_file(open('dwh.cfg'))\n",
    "KEY=config.get('AWS','key')\n",
    "SECRET= config.get('AWS','secret')\n",
    "\n",
    "DWH_DB= config.get(\"DWH\",\"DWH_DB\")\n",
    "DWH_DB_USER= config.get(\"DWH\",\"DWH_DB_USER\")\n",
    "DWH_DB_PASSWORD= config.get(\"DWH\",\"DWH_DB_PASSWORD\")\n",
    "DWH_PORT = config.get(\"DWH\",\"DWH_PORT\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 1: Get the params of the created redshift cluster \n",
    "- We need:\n",
    "    - The redshift cluster <font color='red'>endpoint</font>\n",
    "    - The <font color='red'>IAM role ARN</font> that give access to Redshift to read from S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FILL IN THE REDSHIFT ENPOINT HERE\n",
    "# e.g. DWH_ENDPOINT=\"redshift-cluster-1.csmamz5zxmle.us-west-2.redshift.amazonaws.com\" \n",
    "DWH_ENDPOINT=\"dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com\" \n",
    "    \n",
    "#FILL IN THE IAM ROLE ARN you got in step 2.2 of the previous exercise\n",
    "#e.g DWH_ROLE_ARN=\"arn:aws:iam::988332130976:role/dwhRole\"\n",
    "DWH_ROLE_ARN=\"arn:aws:iam::712163294613:role/dwhRole\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 2: Connect to the Redshift Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "postgresql://dwhuser:Passw0rd@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Connected: dwhuser@dwh'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "conn_string=\"postgresql://{}:{}@{}:{}/{}\".format(DWH_DB_USER, DWH_DB_PASSWORD, DWH_ENDPOINT, DWH_PORT,DWH_DB)\n",
    "print(conn_string)\n",
    "%sql $conn_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# STEP 3: Create Tables\n",
    "- We are going to use a benchmarking data set common for benchmarking star schemas in data warehouses.\n",
    "- The data is pre-loaded in a public bucket on the `us-west-2` region\n",
    "- Our examples will be based on the Amazon Redshfit tutorial but in a scripted environment in our workspace.\n",
    "\n",
    "![afa](https://docs.aws.amazon.com/redshift/latest/dg/images/tutorial-optimize-tables-ssb-data-model.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Create tables (no distribution strategy) in the `nodist` schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "CREATE SCHEMA IF NOT EXISTS nodist;\n",
    "SET search_path TO nodist;\n",
    "\n",
    "DROP TABLE IF EXISTS part cascade;\n",
    "DROP TABLE IF EXISTS supplier;\n",
    "DROP TABLE IF EXISTS supplier;\n",
    "DROP TABLE IF EXISTS customer;\n",
    "DROP TABLE IF EXISTS dwdate;\n",
    "DROP TABLE IF EXISTS lineorder;\n",
    "\n",
    "CREATE TABLE part \n",
    "(\n",
    "  p_partkey     INTEGER NOT NULL,\n",
    "  p_name        VARCHAR(22) NOT NULL,\n",
    "  p_mfgr        VARCHAR(6) NOT NULL,\n",
    "  p_category    VARCHAR(7) NOT NULL,\n",
    "  p_brand1      VARCHAR(9) NOT NULL,\n",
    "  p_color       VARCHAR(11) NOT NULL,\n",
    "  p_type        VARCHAR(25) NOT NULL,\n",
    "  p_size        INTEGER NOT NULL,\n",
    "  p_container   VARCHAR(10) NOT NULL\n",
    ");\n",
    "\n",
    "CREATE TABLE supplier \n",
    "(\n",
    "  s_suppkey   INTEGER NOT NULL,\n",
    "  s_name      VARCHAR(25) NOT NULL,\n",
    "  s_address   VARCHAR(25) NOT NULL,\n",
    "  s_city      VARCHAR(10) NOT NULL,\n",
    "  s_nation    VARCHAR(15) NOT NULL,\n",
    "  s_region    VARCHAR(12) NOT NULL,\n",
    "  s_phone     VARCHAR(15) NOT NULL\n",
    ");\n",
    "\n",
    "CREATE TABLE customer \n",
    "(\n",
    "  c_custkey      INTEGER NOT NULL,\n",
    "  c_name         VARCHAR(25) NOT NULL,\n",
    "  c_address      VARCHAR(25) NOT NULL,\n",
    "  c_city         VARCHAR(10) NOT NULL,\n",
    "  c_nation       VARCHAR(15) NOT NULL,\n",
    "  c_region       VARCHAR(12) NOT NULL,\n",
    "  c_phone        VARCHAR(15) NOT NULL,\n",
    "  c_mktsegment   VARCHAR(10) NOT NULL\n",
    ");\n",
    "\n",
    "CREATE TABLE dwdate \n",
    "(\n",
    "  d_datekey            INTEGER NOT NULL,\n",
    "  d_date               VARCHAR(19) NOT NULL,\n",
    "  d_dayofweek          VARCHAR(10) NOT NULL,\n",
    "  d_month              VARCHAR(10) NOT NULL,\n",
    "  d_year               INTEGER NOT NULL,\n",
    "  d_yearmonthnum       INTEGER NOT NULL,\n",
    "  d_yearmonth          VARCHAR(8) NOT NULL,\n",
    "  d_daynuminweek       INTEGER NOT NULL,\n",
    "  d_daynuminmonth      INTEGER NOT NULL,\n",
    "  d_daynuminyear       INTEGER NOT NULL,\n",
    "  d_monthnuminyear     INTEGER NOT NULL,\n",
    "  d_weeknuminyear      INTEGER NOT NULL,\n",
    "  d_sellingseason      VARCHAR(13) NOT NULL,\n",
    "  d_lastdayinweekfl    VARCHAR(1) NOT NULL,\n",
    "  d_lastdayinmonthfl   VARCHAR(1) NOT NULL,\n",
    "  d_holidayfl          VARCHAR(1) NOT NULL,\n",
    "  d_weekdayfl          VARCHAR(1) NOT NULL\n",
    ");\n",
    "CREATE TABLE lineorder \n",
    "(\n",
    "  lo_orderkey          INTEGER NOT NULL,\n",
    "  lo_linenumber        INTEGER NOT NULL,\n",
    "  lo_custkey           INTEGER NOT NULL,\n",
    "  lo_partkey           INTEGER NOT NULL,\n",
    "  lo_suppkey           INTEGER NOT NULL,\n",
    "  lo_orderdate         INTEGER NOT NULL,\n",
    "  lo_orderpriority     VARCHAR(15) NOT NULL,\n",
    "  lo_shippriority      VARCHAR(1) NOT NULL,\n",
    "  lo_quantity          INTEGER NOT NULL,\n",
    "  lo_extendedprice     INTEGER NOT NULL,\n",
    "  lo_ordertotalprice   INTEGER NOT NULL,\n",
    "  lo_discount          INTEGER NOT NULL,\n",
    "  lo_revenue           INTEGER NOT NULL,\n",
    "  lo_supplycost        INTEGER NOT NULL,\n",
    "  lo_tax               INTEGER NOT NULL,\n",
    "  lo_commitdate        INTEGER NOT NULL,\n",
    "  lo_shipmode          VARCHAR(10) NOT NULL\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Create tables (with a distribution strategy) in the `dist` schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "CREATE SCHEMA IF NOT EXISTS dist;\n",
    "SET search_path TO dist;\n",
    "\n",
    "DROP TABLE IF EXISTS part cascade;\n",
    "DROP TABLE IF EXISTS supplier;\n",
    "DROP TABLE IF EXISTS supplier;\n",
    "DROP TABLE IF EXISTS customer;\n",
    "DROP TABLE IF EXISTS dwdate;\n",
    "DROP TABLE IF EXISTS lineorder;\n",
    "\n",
    "CREATE TABLE part (\n",
    "  p_partkey         integer         not null    sortkey distkey,\n",
    "  p_name            varchar(22)     not null,\n",
    "  p_mfgr            varchar(6)      not null,\n",
    "  p_category        varchar(7)      not null,\n",
    "  p_brand1          varchar(9)      not null,\n",
    "  p_color           varchar(11)     not null,\n",
    "  p_type            varchar(25)     not null,\n",
    "  p_size            integer         not null,\n",
    "  p_container       varchar(10)     not null\n",
    ");\n",
    "\n",
    "CREATE TABLE supplier (\n",
    "  s_suppkey     \tinteger        not null sortkey,\n",
    "  s_name        \tvarchar(25)    not null,\n",
    "  s_address     \tvarchar(25)    not null,\n",
    "  s_city        \tvarchar(10)    not null,\n",
    "  s_nation      \tvarchar(15)    not null,\n",
    "  s_region      \tvarchar(12)    not null,\n",
    "  s_phone       \tvarchar(15)    not null)\n",
    "diststyle all;\n",
    "\n",
    "CREATE TABLE customer (\n",
    "  c_custkey     \tinteger        not null sortkey,\n",
    "  c_name        \tvarchar(25)    not null,\n",
    "  c_address     \tvarchar(25)    not null,\n",
    "  c_city        \tvarchar(10)    not null,\n",
    "  c_nation      \tvarchar(15)    not null,\n",
    "  c_region      \tvarchar(12)    not null,\n",
    "  c_phone       \tvarchar(15)    not null,\n",
    "  c_mktsegment      varchar(10)    not null)\n",
    "diststyle all;\n",
    "\n",
    "CREATE TABLE dwdate (\n",
    "  d_datekey            integer       not null sortkey,\n",
    "  d_date               varchar(19)   not null,\n",
    "  d_dayofweek\t      varchar(10)   not null,\n",
    "  d_month      \t      varchar(10)   not null,\n",
    "  d_year               integer       not null,\n",
    "  d_yearmonthnum       integer  \t not null,\n",
    "  d_yearmonth          varchar(8)\tnot null,\n",
    "  d_daynuminweek       integer       not null,\n",
    "  d_daynuminmonth      integer       not null,\n",
    "  d_daynuminyear       integer       not null,\n",
    "  d_monthnuminyear     integer       not null,\n",
    "  d_weeknuminyear      integer       not null,\n",
    "  d_sellingseason      varchar(13)    not null,\n",
    "  d_lastdayinweekfl    varchar(1)    not null,\n",
    "  d_lastdayinmonthfl   varchar(1)    not null,\n",
    "  d_holidayfl          varchar(1)    not null,\n",
    "  d_weekdayfl          varchar(1)    not null)\n",
    "diststyle all;\n",
    "\n",
    "CREATE TABLE lineorder (\n",
    "  lo_orderkey      \t    integer     \tnot null,\n",
    "  lo_linenumber        \tinteger     \tnot null,\n",
    "  lo_custkey           \tinteger     \tnot null,\n",
    "  lo_partkey           \tinteger     \tnot null distkey,\n",
    "  lo_suppkey           \tinteger     \tnot null,\n",
    "  lo_orderdate         \tinteger     \tnot null sortkey,\n",
    "  lo_orderpriority     \tvarchar(15)     not null,\n",
    "  lo_shippriority      \tvarchar(1)      not null,\n",
    "  lo_quantity          \tinteger     \tnot null,\n",
    "  lo_extendedprice     \tinteger     \tnot null,\n",
    "  lo_ordertotalprice   \tinteger     \tnot null,\n",
    "  lo_discount          \tinteger     \tnot null,\n",
    "  lo_revenue           \tinteger     \tnot null,\n",
    "  lo_supplycost        \tinteger     \tnot null,\n",
    "  lo_tax               \tinteger     \tnot null,\n",
    "  lo_commitdate         integer         not null,\n",
    "  lo_shipmode          \tvarchar(10)     not null\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# STEP 4: Copying tables \n",
    "\n",
    "Our intent here is to run 5 COPY operations for the 5 tables respectively as show below.\n",
    "\n",
    "However, we want to do accomplish the following:\n",
    "- Make sure that the `DWH_ROLE_ARN` is substituted with the correct value in each query\n",
    "- Perform the data loading twice once for each schema (dist and nodist)\n",
    "- Collect timing statistics to compare the insertion times\n",
    "Thus, we have scripted the insertion as found below in the function `loadTables` which\n",
    "returns a pandas dataframe containing timing statistics for the copy operations\n",
    "\n",
    "```sql\n",
    "copy customer from 's3://awssampledbuswest2/ssbgz/customer' \n",
    "credentials 'aws_iam_role=<DWH_ROLE_ARN>'\n",
    "gzip region 'us-west-2';\n",
    "\n",
    "copy dwdate from 's3://awssampledbuswest2/ssbgz/dwdate' \n",
    "credentials 'aws_iam_role=<DWH_ROLE_ARN>'\n",
    "gzip region 'us-west-2';\n",
    "\n",
    "copy lineorder from 's3://awssampledbuswest2/ssbgz/lineorder' \n",
    "credentials 'aws_iam_role=<DWH_ROLE_ARN>'\n",
    "gzip region 'us-west-2';\n",
    "\n",
    "copy part from 's3://awssampledbuswest2/ssbgz/part' \n",
    "credentials 'aws_iam_role=<DWH_ROLE_ARN>'\n",
    "gzip region 'us-west-2';\n",
    "\n",
    "copy supplier from 's3://awssampledbuswest2/ssbgz/supplier' \n",
    "credentials 'aws_iam_role=<DWH_ROLE_ARN>'\n",
    "gzip region 'us-west-2';\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Automate  the copying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadTables(schema, tables):\n",
    "    loadTimes = []\n",
    "    SQL_SET_SCEMA = \"SET search_path TO {};\".format(schema)\n",
    "    %sql $SQL_SET_SCEMA\n",
    "    \n",
    "    for table in tables:\n",
    "        SQL_COPY = \"\"\"\n",
    "copy {} from 's3://awssampledbuswest2/ssbgz/{}' \n",
    "credentials 'aws_iam_role={}'\n",
    "gzip region 'us-west-2';\n",
    "        \"\"\".format(table,table, DWH_ROLE_ARN)\n",
    "\n",
    "        print(\"======= LOADING TABLE: ** {} ** IN SCHEMA ==> {} =======\".format(table, schema))\n",
    "        print(SQL_COPY)\n",
    "\n",
    "        t0 = time()\n",
    "        %sql $SQL_COPY\n",
    "        loadTime = time()-t0\n",
    "        loadTimes.append(loadTime)\n",
    "\n",
    "        print(\"=== DONE IN: {0:.2f} sec\\n\".format(loadTime))\n",
    "    return pd.DataFrame({\"table\":tables, \"loadtime_\"+schema:loadTimes}).set_index('table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "======= LOADING TABLE: ** customer ** IN SCHEMA ==> nodist =======\n",
      "\n",
      "copy customer from 's3://awssampledbuswest2/ssbgz/customer' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 10.84 sec\n",
      "\n",
      "======= LOADING TABLE: ** dwdate ** IN SCHEMA ==> nodist =======\n",
      "\n",
      "copy dwdate from 's3://awssampledbuswest2/ssbgz/dwdate' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 1.25 sec\n",
      "\n",
      "======= LOADING TABLE: ** supplier ** IN SCHEMA ==> nodist =======\n",
      "\n",
      "copy supplier from 's3://awssampledbuswest2/ssbgz/supplier' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 5.83 sec\n",
      "\n",
      "======= LOADING TABLE: ** part ** IN SCHEMA ==> nodist =======\n",
      "\n",
      "copy part from 's3://awssampledbuswest2/ssbgz/part' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 4.12 sec\n",
      "\n",
      "======= LOADING TABLE: ** lineorder ** IN SCHEMA ==> nodist =======\n",
      "\n",
      "copy lineorder from 's3://awssampledbuswest2/ssbgz/lineorder' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 540.27 sec\n",
      "\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "======= LOADING TABLE: ** customer ** IN SCHEMA ==> dist =======\n",
      "\n",
      "copy customer from 's3://awssampledbuswest2/ssbgz/customer' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 14.30 sec\n",
      "\n",
      "======= LOADING TABLE: ** dwdate ** IN SCHEMA ==> dist =======\n",
      "\n",
      "copy dwdate from 's3://awssampledbuswest2/ssbgz/dwdate' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 1.07 sec\n",
      "\n",
      "======= LOADING TABLE: ** supplier ** IN SCHEMA ==> dist =======\n",
      "\n",
      "copy supplier from 's3://awssampledbuswest2/ssbgz/supplier' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 5.37 sec\n",
      "\n",
      "======= LOADING TABLE: ** part ** IN SCHEMA ==> dist =======\n",
      "\n",
      "copy part from 's3://awssampledbuswest2/ssbgz/part' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 6.04 sec\n",
      "\n",
      "======= LOADING TABLE: ** lineorder ** IN SCHEMA ==> dist =======\n",
      "\n",
      "copy lineorder from 's3://awssampledbuswest2/ssbgz/lineorder' \n",
      "credentials 'aws_iam_role=arn:aws:iam::712163294613:role/dwhRole'\n",
      "gzip region 'us-west-2';\n",
      "        \n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "=== DONE IN: 725.66 sec\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#-- List of the tables to be loaded\n",
    "tables = [\"customer\",\"dwdate\",\"supplier\", \"part\", \"lineorder\"]\n",
    "\n",
    "#-- Insertion twice for each schema (WARNING!! EACH CAN TAKE MORE THAN 10 MINUTES!!!)\n",
    "nodistStats = loadTables(\"nodist\", tables)\n",
    "distStats = loadTables(\"dist\", tables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Compare the load performance results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ea41d2b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-- Plotting of the timing results\n",
    "stats = distStats.join(nodistStats)\n",
    "stats.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 5: Compare Query Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "oneDim_SQL =\"\"\"\n",
    "set enable_result_cache_for_session to off;\n",
    "SET search_path TO {};\n",
    "\n",
    "select sum(lo_extendedprice*lo_discount) as revenue\n",
    "from lineorder, dwdate\n",
    "where lo_orderdate = d_datekey\n",
    "and d_year = 1997 \n",
    "and lo_discount between 1 and 3 \n",
    "and lo_quantity < 24;\n",
    "\"\"\"\n",
    "\n",
    "twoDim_SQL=\"\"\"\n",
    "set enable_result_cache_for_session to off;\n",
    "SET search_path TO {};\n",
    "\n",
    "select sum(lo_revenue), d_year, p_brand1\n",
    "from lineorder, dwdate, part, supplier\n",
    "where lo_orderdate = d_datekey\n",
    "and lo_partkey = p_partkey\n",
    "and lo_suppkey = s_suppkey\n",
    "and p_category = 'MFGR#12'\n",
    "and s_region = 'AMERICA'\n",
    "group by d_year, p_brand1\n",
    "\"\"\"\n",
    "\n",
    "drill_SQL = \"\"\"\n",
    "set enable_result_cache_for_session to off;\n",
    "SET search_path TO {};\n",
    "\n",
    "select c_city, s_city, d_year, sum(lo_revenue) as revenue \n",
    "from customer, lineorder, supplier, dwdate\n",
    "where lo_custkey = c_custkey\n",
    "and lo_suppkey = s_suppkey\n",
    "and lo_orderdate = d_datekey\n",
    "and (c_city='UNITED KI1' or\n",
    "c_city='UNITED KI5')\n",
    "and (s_city='UNITED KI1' or\n",
    "s_city='UNITED KI5')\n",
    "and d_yearmonth = 'Dec1997'\n",
    "group by c_city, s_city, d_year\n",
    "order by d_year asc, revenue desc;\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "oneDimSameDist_SQL =\"\"\"\n",
    "set enable_result_cache_for_session to off;\n",
    "SET search_path TO {};\n",
    "\n",
    "select lo_orderdate, sum(lo_extendedprice*lo_discount) as revenue  \n",
    "from lineorder, part\n",
    "where lo_partkey  = p_partkey\n",
    "group by lo_orderdate\n",
    "order by lo_orderdate\n",
    "\"\"\"\n",
    "\n",
    "def compareQueryTimes(schema):\n",
    "    queryTimes  =[] \n",
    "    for i,query in enumerate([oneDim_SQL, twoDim_SQL, drill_SQL, oneDimSameDist_SQL]):\n",
    "        t0 = time()\n",
    "        q = query.format(schema)\n",
    "        %sql $q\n",
    "        queryTime = time()-t0\n",
    "        queryTimes.append(queryTime)\n",
    "    return pd.DataFrame({\"query\":[\"oneDim\",\"twoDim\", \"drill\", \"oneDimSameDist\"], \"queryTime_\"+schema:queryTimes}).set_index('query')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "1 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "280 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "4 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "2406 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "1 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "280 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "4 rows affected.\n",
      " * postgresql://dwhuser:***@dwhcluster.cqjazn4iv7sr.us-west-2.redshift.amazonaws.com:5439/dwh\n",
      "Done.\n",
      "Done.\n",
      "2406 rows affected.\n"
     ]
    }
   ],
   "source": [
    "noDistQueryTimes = compareQueryTimes(\"nodist\")\n",
    "distQueryTimes   = compareQueryTimes(\"dist\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e76d660b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "queryTimeDF =noDistQueryTimes.join(distQueryTimes)\n",
    "queryTimeDF.plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8ea8263320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "improvementDF = queryTimeDF[\"distImprovement\"] =100.0*(queryTimeDF['queryTime_nodist']-queryTimeDF['queryTime_dist'])/queryTimeDF['queryTime_nodist']\n",
    "improvementDF.plot.bar(title=\"% dist Improvement by query\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}