{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 6 Reverse Lecture\n",
    "\n",
    "In this reverse lecture, we'll explore in more details a subset of the functionality of [dask](dask.org), a parallel data processing library for Python. We'll spend the majority of our time on dask's [`DataFrame`](https://docs.dask.org/en/latest/dataframe.html) API, as well as examine concepts such as lazy computation and its relationship with the different schedulers.\n",
    "\n",
    "We'll follow that by data processing using columnar binary data (Parquet), and end with a brief overview of dask's [`Bag`](https://docs.dask.org/en/latest/bag.html) API, using a JSON data \"scraper\" as working example.\n",
    "\n",
    "We close with a list of additional resources for you to learn more about dask, including examples on how to use it for ML tasks. Finally, for the take home portion of this lab, you'll solve two parallel programming assignments, which we detail on the [github repo](http://github.com/mitdbg/datascienceclass/tree/master/lab_6/README.md). \n",
    "\n",
    "## Running this notebook\n",
    "\n",
    "To execute code from a cell, you can either click `Run` at the top, or type `shift+Enter` after clicking a cell.  You can either run the entire notebook (`Restart & Run All` from the `Kernel` drop-down), or run each cell individually.  If you choose the latter, note that it is important that you run cells in order, as later cells depend on earlier ones. And to be able to `Run All` successfully, you'll have to write code for answering some of the questions in the notebook.\n",
    "\n",
    "Once you open your notebook on the browser, and check that the cells are rendering correctly (e.g., try running the \"Python packages\" cell below), we're good to go from there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python packages we'll need\n",
    "\n",
    "First, import the python packages we'll be using for the lab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from time import perf_counter\n",
    "\n",
    "# Data processing.\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Q6: For loading multiple CSVs into a single (pandas) dataframe.\n",
    "import glob\n",
    "import os\n",
    "\n",
    "# Plotting.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# \"Vanilla\" python parallelism.\n",
    "import multiprocessing\n",
    "\n",
    "# Scalable data analytics: dask.\n",
    "import dask\n",
    "import dask.bag as db\n",
    "import dask.dataframe as dd\n",
    "from dask.distributed import Client, LocalCluster\n",
    "import graphviz\n",
    "\n",
    "# Unused: scalable data analytics using Spark.\n",
    "#from pyspark.sql import SparkSession\n",
    "\n",
    "# For GC large pandas dataframes after use.\n",
    "import gc\n",
    "\n",
    "# Ignore warnings.\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: dask\n",
    "\n",
    "Dask is a python library aimed at parallel computation. It comprises two main components: a task scheduler, and a collection of data structures suited for different parallel programming tasks. Take a look at the [main page of their documentation](https://docs.dask.org/en/latest/) for more details.\n",
    "\n",
    "In this part of the reverse lecture, we'll introduce you to a subset of these features, as well as cover example tasks using the two different categories of dask schedulers.\n",
    "\n",
    "Additionally, of special interest to our course is the fact that dask provides an interface that almost exactly matches that of numpy arrays and pandas dataframes. We'll see that this is useful when speeding up some of the data analysis tasks we've covered so far in our course.\n",
    "\n",
    "\n",
    "### Example pandas vs dask comparison: data loading\n",
    "\n",
    "Before getting into too many details about the different task schedulers, let's take a look at a simple example runtime comparison between pandas and dask. Specifically, let's load the same spotify dataset from lab 4 in both, and see how long each one takes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 455 ms, sys: 22 ms, total: 477 ms\n",
      "Wall time: 450 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# load dataset using pandas:\n",
    "df = pd.read_csv('data/spotify_songs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.5 ms, sys: 7.9 ms, total: 47.4 ms\n",
      "Wall time: 41.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# load dataset using dask:\n",
    "dd_df = dd.read_csv('data/spotify_songs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we already see a 20x difference, which is impressive. However, this is also because dask does *lazy* computation.  That is, dask arrays have the required shape and data type, but they still point to data on disk. The loading is *lazy* in the sense that dask will load the array contents into memory (and in small chunks) only when necessary.\n",
    "\n",
    "We can observe this effect in action when we ask dask to compute an operation that requires inspecting the entire contents of the dataset.\n",
    "\n",
    "**Q1. Inspect the runtime each of the two dataframes (both pandas and dask) using `describe()`. How does the output in pandas compare to that of dask?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 94.7 ms, sys: 23.7 ms, total: 118 ms\n",
      "Wall time: 117 ms\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>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>2.281590e+05</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>44.209130</td>\n",
       "      <td>0.351200</td>\n",
       "      <td>0.554198</td>\n",
       "      <td>2.366092e+05</td>\n",
       "      <td>0.580967</td>\n",
       "      <td>0.137310</td>\n",
       "      <td>0.214638</td>\n",
       "      <td>-9.354658</td>\n",
       "      <td>0.122442</td>\n",
       "      <td>117.423062</td>\n",
       "      <td>0.444795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>17.276599</td>\n",
       "      <td>0.351385</td>\n",
       "      <td>0.183949</td>\n",
       "      <td>1.166787e+05</td>\n",
       "      <td>0.260577</td>\n",
       "      <td>0.292447</td>\n",
       "      <td>0.196977</td>\n",
       "      <td>5.940994</td>\n",
       "      <td>0.186264</td>\n",
       "      <td>30.712458</td>\n",
       "      <td>0.255397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.056900</td>\n",
       "      <td>1.550900e+04</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009670</td>\n",
       "      <td>-52.457000</td>\n",
       "      <td>0.022200</td>\n",
       "      <td>30.379000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.030900</td>\n",
       "      <td>0.437000</td>\n",
       "      <td>1.862530e+05</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097700</td>\n",
       "      <td>-11.287000</td>\n",
       "      <td>0.036800</td>\n",
       "      <td>92.734000</td>\n",
       "      <td>0.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>2.211730e+05</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>-7.515000</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>115.347000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>2.648400e+05</td>\n",
       "      <td>0.793000</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>0.263000</td>\n",
       "      <td>-5.415000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>138.887000</td>\n",
       "      <td>0.643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.987000</td>\n",
       "      <td>5.552917e+06</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.585000</td>\n",
       "      <td>0.967000</td>\n",
       "      <td>239.848000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          popularity   acousticness   danceability   duration_ms  \\\n",
       "count  228159.000000  228159.000000  228159.000000  2.281590e+05   \n",
       "mean       44.209130       0.351200       0.554198  2.366092e+05   \n",
       "std        17.276599       0.351385       0.183949  1.166787e+05   \n",
       "min         0.000000       0.000001       0.056900  1.550900e+04   \n",
       "25%        33.000000       0.030900       0.437000  1.862530e+05   \n",
       "50%        47.000000       0.205000       0.570000  2.211730e+05   \n",
       "75%        57.000000       0.689000       0.690000  2.648400e+05   \n",
       "max       100.000000       0.996000       0.987000  5.552917e+06   \n",
       "\n",
       "              energy  instrumentalness       liveness       loudness  \\\n",
       "count  228159.000000     228159.000000  228159.000000  228159.000000   \n",
       "mean        0.580967          0.137310       0.214638      -9.354658   \n",
       "std         0.260577          0.292447       0.196977       5.940994   \n",
       "min         0.000020          0.000000       0.009670     -52.457000   \n",
       "25%         0.405000          0.000000       0.097700     -11.287000   \n",
       "50%         0.618000          0.000037       0.128000      -7.515000   \n",
       "75%         0.793000          0.023400       0.263000      -5.415000   \n",
       "max         0.999000          0.999000       1.000000       1.585000   \n",
       "\n",
       "         speechiness          tempo        valence  \n",
       "count  228159.000000  228159.000000  228159.000000  \n",
       "mean        0.122442     117.423062       0.444795  \n",
       "std         0.186264      30.712458       0.255397  \n",
       "min         0.022200      30.379000       0.000000  \n",
       "25%         0.036800      92.734000       0.232000  \n",
       "50%         0.050600     115.347000       0.430000  \n",
       "75%         0.109000     138.887000       0.643000  \n",
       "max         0.967000     239.848000       1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Q1: YOUR CODE GOES HERE (pandas)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 98.5 ms, sys: 1.05 ms, total: 99.5 ms\n",
      "Wall time: 93.1 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: describe-numeric, 65 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "              popularity acousticness danceability duration_ms   energy instrumentalness liveness loudness speechiness    tempo  valence\n",
       "npartitions=1                                                                                                                           \n",
       "                 float64      float64      float64     float64  float64          float64  float64  float64     float64  float64  float64\n",
       "                     ...          ...          ...         ...      ...              ...      ...      ...         ...      ...      ...\n",
       "Dask Name: describe-numeric, 65 tasks"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Q1: YOUR CODE GOES HERE (dask)\n",
    "dd_df.describe()            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q2. Clicker question: just by running `describe()` (and nothing else) on both `df` (pandas) and `dd_df` (dask), which of the following is true?**\n",
    "\n",
    "**a)** both output the same\n",
    "\n",
    "**b)** pandas output is more informative\n",
    "\n",
    "**c)** dask output is more informative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if we want dask to actually perform computations for us, we need to call `compute()`. For example, in the case of `describe()` above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 729 ms, sys: 71.2 ms, total: 800 ms\n",
      "Wall time: 610 ms\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>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>2.281590e+05</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "      <td>228159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>44.209130</td>\n",
       "      <td>0.351200</td>\n",
       "      <td>0.554198</td>\n",
       "      <td>2.366092e+05</td>\n",
       "      <td>0.580967</td>\n",
       "      <td>0.137310</td>\n",
       "      <td>0.214638</td>\n",
       "      <td>-9.354658</td>\n",
       "      <td>0.122442</td>\n",
       "      <td>117.423062</td>\n",
       "      <td>0.444795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>17.276599</td>\n",
       "      <td>0.351385</td>\n",
       "      <td>0.183949</td>\n",
       "      <td>1.166787e+05</td>\n",
       "      <td>0.260577</td>\n",
       "      <td>0.292447</td>\n",
       "      <td>0.196977</td>\n",
       "      <td>5.940994</td>\n",
       "      <td>0.186264</td>\n",
       "      <td>30.712458</td>\n",
       "      <td>0.255397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.056900</td>\n",
       "      <td>1.550900e+04</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009670</td>\n",
       "      <td>-52.457000</td>\n",
       "      <td>0.022200</td>\n",
       "      <td>30.379000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.030900</td>\n",
       "      <td>0.437000</td>\n",
       "      <td>1.862530e+05</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097700</td>\n",
       "      <td>-11.287000</td>\n",
       "      <td>0.036800</td>\n",
       "      <td>92.734000</td>\n",
       "      <td>0.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>2.211730e+05</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>-7.515000</td>\n",
       "      <td>0.050600</td>\n",
       "      <td>115.347000</td>\n",
       "      <td>0.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>2.648400e+05</td>\n",
       "      <td>0.793000</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>0.263000</td>\n",
       "      <td>-5.415000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>138.887000</td>\n",
       "      <td>0.643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.987000</td>\n",
       "      <td>5.552917e+06</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.585000</td>\n",
       "      <td>0.967000</td>\n",
       "      <td>239.848000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          popularity   acousticness   danceability   duration_ms  \\\n",
       "count  228159.000000  228159.000000  228159.000000  2.281590e+05   \n",
       "mean       44.209130       0.351200       0.554198  2.366092e+05   \n",
       "std        17.276599       0.351385       0.183949  1.166787e+05   \n",
       "min         0.000000       0.000001       0.056900  1.550900e+04   \n",
       "25%        33.000000       0.030900       0.437000  1.862530e+05   \n",
       "50%        47.000000       0.205000       0.570000  2.211730e+05   \n",
       "75%        57.000000       0.689000       0.690000  2.648400e+05   \n",
       "max       100.000000       0.996000       0.987000  5.552917e+06   \n",
       "\n",
       "              energy  instrumentalness       liveness       loudness  \\\n",
       "count  228159.000000     228159.000000  228159.000000  228159.000000   \n",
       "mean        0.580967          0.137310       0.214638      -9.354658   \n",
       "std         0.260577          0.292447       0.196977       5.940994   \n",
       "min         0.000020          0.000000       0.009670     -52.457000   \n",
       "25%         0.405000          0.000000       0.097700     -11.287000   \n",
       "50%         0.618000          0.000037       0.128000      -7.515000   \n",
       "75%         0.793000          0.023400       0.263000      -5.415000   \n",
       "max         0.999000          0.999000       1.000000       1.585000   \n",
       "\n",
       "         speechiness          tempo        valence  \n",
       "count  228159.000000  228159.000000  228159.000000  \n",
       "mean        0.122442     117.423062       0.444795  \n",
       "std         0.186264      30.712458       0.255397  \n",
       "min         0.022200      30.379000       0.000000  \n",
       "25%         0.036800      92.734000       0.232000  \n",
       "50%         0.050600     115.347000       0.430000  \n",
       "75%         0.109000     138.887000       0.643000  \n",
       "max         0.967000     239.848000       1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "dd_df.describe().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also inspect the task graph that dask assembles and executes for the computation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd_df.describe().visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, dask will try and perform as many block computations as possible, only merging and aggregating block results as needed.\n",
    "\n",
    "**Q3. Clicker question: why do we have 11 \"pipelines\" in parallel for computing quantiles in the task graph above?**\n",
    "\n",
    "**a)** This is the number of workers dask is using for the computation.\n",
    "\n",
    "**b)** This is the number of categorical columns the dataframe has.\n",
    "\n",
    "**c)** This is the number of numerical columns the dataframe has.\n",
    "\n",
    "**d)** This is the number of threads dask is using for the computation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task scheduler\n",
    "\n",
    "Dask provides two categories of task schedulers: *single machine* and *distributed*. From the client setup [documentation](https://docs.dask.org/en/latest/setup.html?highlight=client#setup): \n",
    "\n",
    "> **Single machine scheduler:** This scheduler provides basic features on a local process or thread pool. This scheduler was made first and is the default. It is simple and cheap to use. It can only be used on a single machine and does not scale.\n",
    ">\n",
    "> **Distributed scheduler:** This scheduler is more sophisticated. It offers more features, but also requires a bit more effort to set up. It can run locally or distributed across a cluster.\n",
    "\n",
    "If you load data into a dask dataframe, and run some computation on it, when you call `compute()` to start the computation it will use the *single machine scheduler*. For example, the sum over `acousticness` below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.24 ms, sys: 0 ns, total: 2.24 ms\n",
      "Wall time: 1.48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80129.52932901"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# sum values for acousticness column using pandas\n",
    "df.acousticness.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 389 ms, sys: 63.1 ms, total: 452 ms\n",
      "Wall time: 451 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80129.52932901"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# sum values for acousticness column using dask.\n",
    "# This computation uses the single machine scheduler:\n",
    "dd_df.acousticness.sum().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the *distributed scheduler* (even if single node on the same machine), you need to explicitly create a dask [`Client`](https://docs.dask.org/en/latest/setup.html?highlight=client#setup).  Hence, starting a dask `Client` is optional. We'll go through this step, however, as it provides an example of how to setup a [`LocalCluster`](https://distributed.dask.org/en/latest/local-cluster.html). Creating a dask client will also provide a dashboard that we can use to monitor the dask workers while they're computing results for us.\n",
    "\n",
    "There are several ways to create a dask client that connects to a `LocalCluster`. We'll do each step explicitly, although if you create a `Client` without specifying which cluster to connect to, by default dask will create a task scheduler associated to a `LocalCluster` instance.\n",
    "\n",
    "First, we check how many cores we have in our server:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of cores we have:  8\n"
     ]
    }
   ],
   "source": [
    "n_cores = multiprocessing.cpu_count()\n",
    "print('number of cores we have: ', n_cores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use that to size the number of workers for our `LocalCluster` instance, and create a `Client` connected to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:45473</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>67.37 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:45473' processes=8 threads=8, memory=67.37 GB>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multithreaded:\n",
    "# cluster = LocalCluster(n_workers=1, processes=False, threads_per_worker=4)\n",
    "# multiprocessing:\n",
    "# cluster = LocalCluster(n_workers=n_cores, processes=True)\n",
    "\n",
    "# If we start out the dask-scheduler from CLI on the Docker container using:\n",
    "# $ dask-scheduler --host 0.0.0.0 --dashboard-address 8787\n",
    "#\n",
    "# Then we specify the address for the client explicitly:\n",
    "# client = Client(address='0.0.0.0:8786')\n",
    "\n",
    "# If we start out the dask-scheduler from this jupyter notebook, then need\n",
    "# to set \"ip=None\" for the status dashboard to work correctly via Docker.\n",
    "# See:\n",
    "# https://github.com/dask/distributed/issues/1875#issuecomment-387519880\n",
    "cluster = LocalCluster(ip=None, n_workers=n_cores, processes=True)\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tcp://127.0.0.1:34541': 1,\n",
       " 'tcp://127.0.0.1:35989': 1,\n",
       " 'tcp://127.0.0.1:37589': 1,\n",
       " 'tcp://127.0.0.1:38553': 1,\n",
       " 'tcp://127.0.0.1:38787': 1,\n",
       " 'tcp://127.0.0.1:43833': 1,\n",
       " 'tcp://127.0.0.1:44051': 1,\n",
       " 'tcp://127.0.0.1:44313': 1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Info on each worker, together with how many threads per worker.\n",
    "client.ncores()\n",
    "\n",
    "# To restart the client and scheduler\n",
    "#client.restart()\n",
    "#\n",
    "# To shutdown the client and scheduler\n",
    "#client.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can scale the cluster down:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scale cluster down: \n",
      "\n",
      "Wait a bit for scaling to take effect...\n",
      "\n",
      "Cluster workers:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'tcp://127.0.0.1:35989': 1,\n",
       " 'tcp://127.0.0.1:37589': 1,\n",
       " 'tcp://127.0.0.1:38787': 1,\n",
       " 'tcp://127.0.0.1:43833': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_workers = n_cores / 2\n",
    "print('Scale cluster down: ')\n",
    "cluster.scale(n_workers)\n",
    "\n",
    "print('\\nWait a bit for scaling to take effect...')\n",
    "time.sleep(1)\n",
    "\n",
    "print('\\nCluster workers:')\n",
    "client.ncores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And scale it back up again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scale cluster up again: \n",
      "\n",
      "Wait a bit for scaling to take effect...\n",
      "\n",
      "Cluster workers:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'tcp://127.0.0.1:35989': 1,\n",
       " 'tcp://127.0.0.1:36085': 1,\n",
       " 'tcp://127.0.0.1:37589': 1,\n",
       " 'tcp://127.0.0.1:38787': 1,\n",
       " 'tcp://127.0.0.1:39091': 1,\n",
       " 'tcp://127.0.0.1:39359': 1,\n",
       " 'tcp://127.0.0.1:40959': 1,\n",
       " 'tcp://127.0.0.1:43833': 1}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_workers = n_cores\n",
    "print('Scale cluster up again: ')\n",
    "cluster.scale(n_workers)\n",
    "\n",
    "print('\\nWait a bit for scaling to take effect...')\n",
    "time.sleep(1)\n",
    "\n",
    "print('\\nCluster workers:')\n",
    "client.ncores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q4. Measure the time to compute the total sum of `acousticness` for `dd_df` on a local cluster with varying number of workers. Start with 1 core, and double number of cores all the way up to 8, or however many cores you have available (if less than 8). Take a look at our usage of `scale()` above for how to scale your cluster up or down, and at [`time.perf_counter`](https://docs.python.org/3/library/time.html#time.perf_counter) to measure elapsed time.** \n",
    "\n",
    "**Compare it against the time you get from using pandas. Was dask faster or slower than pandas?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pandas:\n",
      "Pandas time (ms): 3.9757806807756424\n",
      "\n",
      "Using dask:\n",
      "Resizing cluster to 1 worker(s)...\n",
      "dask time (ms): 449.1260163486004\n",
      "Resizing cluster to 2 worker(s)...\n",
      "dask time (ms): 998.3218610286713\n",
      "Resizing cluster to 4 worker(s)...\n",
      "dask time (ms): 521.3408898562193\n",
      "Resizing cluster to 8 worker(s)...\n",
      "dask time (ms): 494.42957527935505\n"
     ]
    }
   ],
   "source": [
    "# Q4: YOUR CODE GOES HERE.\n",
    "print('Using pandas:')\n",
    "t1_start = perf_counter()\n",
    "df.acousticness.sum()\n",
    "t1_stop = perf_counter()\n",
    "print('Pandas time (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "print('\\nUsing dask:')\n",
    "w=1;\n",
    "while w <= n_cores:\n",
    "    print('Resizing cluster to %s worker(s)...' % w)\n",
    "    cluster.scale(w)\n",
    "    time.sleep(2)\n",
    "    \n",
    "    t1_start = perf_counter()\n",
    "    dd_df.acousticness.sum().compute()\n",
    "    t1_stop = perf_counter()\n",
    "    \n",
    "    print('dask time (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "    w*=2;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the Spotify 218k songs dataset is not large enough to justify the additional scheduler overhead from dask. Specifically, on datasets that comfortably fit within available memory, pandas is expected to be faster than dask for most operations other than loading from disk. Take a look at [\"Best Practices\"](https://docs.dask.org/en/latest/dataframe-best-practices.html) section of dask documentation for more details.\n",
    "\n",
    "So let's use a larger dataset instead. We can use dask's `demo` package to create a synthetic timeseries dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=3741</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-02</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-30</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: make-timeseries, 3741 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                     id        x        y\n",
       "npartitions=3741                         \n",
       "2008-01-01        int64  float64  float64\n",
       "2008-01-02          ...      ...      ...\n",
       "...                 ...      ...      ...\n",
       "2018-03-29          ...      ...      ...\n",
       "2018-03-30          ...      ...      ...\n",
       "Dask Name: make-timeseries, 3741 tasks"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This lazily creates a timeseries dataset for us with around 7.6M rows.\n",
    "dd_df = dd.demo.make_timeseries(#start='2018-01-01',\n",
    "                                #end='2018-03-30',\n",
    "                                # NOTE: Use the smaller dataset (start_time above)\n",
    "                                # for the take home part. I'll use the earlier start time\n",
    "                                # below to obtain a larger dataset for demo'ing in class:\n",
    "                                start='2008-01-01',\n",
    "                                end='2018-03-30',\n",
    "                                dtypes={'x': float, 'y': float, 'id': int},\n",
    "                                freq='1s',\n",
    "                                partition_freq='24h')\n",
    "\n",
    "dd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've learned so far, dask computations are lazy up until we either explicitly call `compute()`, or call computations that themselves call `compute()`.\n",
    "\n",
    "This is why creating a large dataset above is almost instant. When we call `make_timeseries()`, calculates the number of partitions required for the parallel computation (we asked for one partition every 24h on that date range). It will only perform the actual computation required to create the dataset or brings required data chunks into memory once we actually need it to.\n",
    "\n",
    "We can see below that dask uses lazy computations even for the shape attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas shape:  (228159, 18)\n",
      "dask shape:  (Delayed('int-1c4f8e6d-0953-4372-85a6-d5f314cb60b6'), 3)\n"
     ]
    }
   ],
   "source": [
    "print('pandas shape: ', df.shape)\n",
    "print('dask shape: ', dd_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can retrieve the actual shape by again calling `compute()`, or by calling `len()`, which itself calls `compute()` behind the scenes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dask shape (compute):  DatetimeIndex(['2008-01-01 00:00:00', '2008-01-01 00:00:01',\n",
      "               '2008-01-01 00:00:02', '2008-01-01 00:00:03',\n",
      "               '2008-01-01 00:00:04', '2008-01-01 00:00:05',\n",
      "               '2008-01-01 00:00:06', '2008-01-01 00:00:07',\n",
      "               '2008-01-01 00:00:08', '2008-01-01 00:00:09',\n",
      "               ...\n",
      "               '2018-03-29 23:59:50', '2018-03-29 23:59:51',\n",
      "               '2018-03-29 23:59:52', '2018-03-29 23:59:53',\n",
      "               '2018-03-29 23:59:54', '2018-03-29 23:59:55',\n",
      "               '2018-03-29 23:59:56', '2018-03-29 23:59:57',\n",
      "               '2018-03-29 23:59:58', '2018-03-29 23:59:59'],\n",
      "              dtype='datetime64[ns]', name='timestamp', length=323222400, freq='S')\n",
      "dask shape (compute):  323222400\n"
     ]
    }
   ],
   "source": [
    "print('dask shape (compute): ', dd_df.index.compute())\n",
    "print('dask shape (compute): ', len(dd_df.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, computing memory usage for the dataframe, as well as the number of rows in it will both call `compute()` behind the scenes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dask.dataframe.core.DataFrame'>\n",
      "Columns: 3 entries, id to y\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 9.6 GB\n",
      "Number of rows:  323222400\n"
     ]
    }
   ],
   "source": [
    "dd_df.info(memory_usage='deep')\n",
    "print('Number of rows: ', len(dd_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q5. Compare the runtimes of running `head()` and `len(dd_df.index)` on the dask dataframe. Which one was faster? Why do you think that was the case?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dd_df.head() runtime (ms): 47.135571017861366\n",
      "len(dd_df.index) runtime (ms): 12103.850588202477\n"
     ]
    }
   ],
   "source": [
    "# Q5: YOUR CODE GOES HERE.\n",
    "t1_start = perf_counter()\n",
    "dd_df.head()\n",
    "t1_stop = perf_counter()\n",
    "print('dd_df.head() runtime (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "t1_start = perf_counter()\n",
    "len(dd_df.index)\n",
    "t1_stop = perf_counter()\n",
    "print('len(dd_df.index) runtime (ms): %s' % ((t1_stop - t1_start)*1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's compare the total runtime of a sequence of operations over the dask and pandas dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14 s, sys: 1.19 s, total: 15.1 s\n",
      "Wall time: 28.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "873    0.573404\n",
       "880    0.572328\n",
       "886    0.585443\n",
       "887    0.570185\n",
       "890    0.583818\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Filter and groupby using dask.\n",
    "dd_df2 = dd_df[dd_df.y > 0]\n",
    "dd_df3 = dd_df2.groupby('id').x.std()\n",
    "\n",
    "# Calling head() will also call compute() behind the scenes.\n",
    "dd_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.4 s, sys: 13.9 s, total: 32.3 s\n",
      "Wall time: 38 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "75543"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# This gives us a pandas dataframe from the dask dataframe.\n",
    "df = dd_df.compute()\n",
    "\n",
    "# Filter and groupby using pandas.\n",
    "df2 = df[df.y > 0]\n",
    "df3 = df2.groupby('id').x.std()\n",
    "\n",
    "df3.head()\n",
    "\n",
    "# Garbage collect the pandas dataframes we created.\n",
    "#\n",
    "# XXX: pandas seems to be a lot less memory efficient than dask.\n",
    "# Even with explicit GC, perf monitors still show leaky behavior.\n",
    "# https://github.com/pandas-dev/pandas/issues?utf8=%E2%9C%93&q=is%3Aissue+leak+\n",
    "del df, df2, df3\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operations over the index is where dask usually does a lot better than pandas (see [\"Best Practices\"](https://docs.dask.org/en/latest/dataframe-best-practices.html)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 311 ms, sys: 37.5 ms, total: 348 ms\n",
      "Wall time: 519 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-01-25 14:53:22')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Dask: rolling standard deviation of 'x' column on 1m windows over the Datetime index.\n",
    "# Return index of first occurrence of max value out of those rolling 1m stddev.\n",
    "dd_df.x.rolling('1min').std().loc['2018-01-01':'2018-02-15'].idxmax().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialize as pandas df (ms): 28588.089296594262\n",
      "query over the index (ms): 15072.02062010765\n",
      "CPU times: user 27.5 s, sys: 11.1 s, total: 38.6 s\n",
      "Wall time: 43.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "298"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Dask: rolling standard deviation of 'x' column on 1m windows over the Datetime index.\n",
    "# Return index of first occurrence of max value out of those rolling 1m stddev.\n",
    "t1_start = perf_counter()\n",
    "df = dd_df.compute()\n",
    "t1_stop = perf_counter()\n",
    "print('materialize as pandas df (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "t1_start = perf_counter()\n",
    "df.x.rolling('1min').std().loc['2018-01-01':'2018-02-15'].idxmax()\n",
    "t1_stop = perf_counter()\n",
    "print('query over the index (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "# GC pandas df.\n",
    "del df\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and loading data\n",
    "\n",
    "Let's take a look at processing data in column-oriented binary format using [`Parquet`](https://en.wikipedia.org/wiki/Apache_Parquet). As we've seen in class, Parquet is an open-source column-oriented data storage format. In addition to allowing us to store our dataframe data in a column-wise fashion, it also supports a number of compression techniques (e.g., dictionary encoding, RLE, and bit packing, as we've seen in class).\n",
    "\n",
    "In this part of the lab, we'll briefly look at how to store and read data written in Parquet format, and compare it to loading the same dataset from CSV.\n",
    "\n",
    "First, we can save the larger timeseries dataset as both Parquet and CSV format. We'll do this for a smaller subset of the data, as it takes a really long time to save the entire dataset as CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to_parquet() (ms): 2878.202900290489\n",
      "to_csv() (ms): 8593.980561941862\n"
     ]
    }
   ],
   "source": [
    "subset_df = dd_df.loc['2018-01-01':'2018-03-30']\n",
    "\n",
    "# Measure time for saving as Parquet files.\n",
    "t1_start = perf_counter()\n",
    "subset_df.to_parquet('data/parquet/')\n",
    "t1_stop = perf_counter()\n",
    "print('to_parquet() (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "# Measure time for saving as CSV files.\n",
    "t1_start = perf_counter()\n",
    "subset_df.to_csv('data/csv/timeseries-*.csv')\n",
    "t1_stop = perf_counter()\n",
    "print('to_csv() (ms): %s' % ((t1_stop - t1_start)*1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though we used dask to write to Parquet, both pandas and dask support writing to / reading from Parquet files. The method interface is the same: [pandas's `read_parquet`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html) vs [dask's `read_parquet`](https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.read_parquet). You can also specify which Parquet engine to use.  There are some [`performance differences between both`](https://stackoverflow.com/questions/51361356/a-comparison-between-fastparquet-and-pyarrow), but for the time being we'll use the default (if you're using our Docker container, you have both installed and the default is `fastparquet`).\n",
    "\n",
    "**Q6. Measure the runtime of reading a dataframe from the same Parquet and CSV data we saved above. Compare using both pandas and dask, as well as fetching only column `x` vs the entire dataset.**\n",
    "\n",
    "**NOTE: When we saved our data as Parquet and CSV, we saved into multiple files (#files = #dask partitions). While dask supports reading multiple CSV files directly into a dask dataframe, pandas doesn't support the same for pandas dataframes. Take a look at [`glob`](https://docs.python.org/3/library/glob.html) for expanding the `timeseries-*.csv` into multiple files and [`pd.concat`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html) to create a single pandas dataframe from multiple CSVs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dask + parquet (ms): 1203.9695642888546\n",
      "dask + parquet, only x column (ms): 15.065854415297508\n",
      "\n",
      "dask + csv (ms): 3377.552270889282\n",
      "dask + csv, only x column (ms): 125.79466216266155\n",
      "\n",
      "pandas + parquet (ms): 769.6563620120287\n",
      "pandas + parquet, only x column (ms): 783.3001855760813\n",
      "\n",
      "pandas + csv glob (ms): 4536.528194323182\n",
      "pandas + csv glob, only x column (ms): 2041.719451546669\n",
      "\n",
      "pandas + csv from dask (ms): 3240.1663530617952\n",
      "pandas + csv from dask, only x column (ms): 1055.9092368930578\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2281"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q6: YOUR CODE GOES HERE.\n",
    "# Dask + Parquet\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_parquet('data/parquet/').compute()\n",
    "t1_stop = perf_counter()\n",
    "print('\\ndask + parquet (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_parquet('data/parquet/', usecols=['x'])\n",
    "t1_stop = perf_counter()\n",
    "print('dask + parquet, only x column (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "# Dask + CSV\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_csv('data/csv/timeseries-*.csv').compute()\n",
    "t1_stop = perf_counter()\n",
    "print('\\ndask + csv (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_csv('data/csv/timeseries-*.csv', usecols=['x'])\n",
    "t1_stop = perf_counter()\n",
    "print('dask + csv, only x column (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "\n",
    "# Pandas + Parquet \n",
    "t1_start = perf_counter()\n",
    "df = pd.read_parquet('data/parquet/')\n",
    "t1_stop = perf_counter()\n",
    "print('\\npandas + parquet (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()\n",
    "t1_start = perf_counter()\n",
    "df = pd.read_parquet('data/parquet/', columns=['x'])\n",
    "t1_stop = perf_counter()\n",
    "print('pandas + parquet, only x column (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()\n",
    "\n",
    "# Pandas + CSV (glob)\n",
    "t1_start = perf_counter()\n",
    "csv_files = glob.glob(os.path.join('', 'data/csv/timeseries-*.csv'))\n",
    "df = pd.concat(map(pd.read_csv, csv_files))\n",
    "t1_stop = perf_counter()\n",
    "print('\\npandas + csv glob (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()\n",
    "t1_start = perf_counter()\n",
    "csv_files = glob.glob(os.path.join('', 'data/csv/timeseries-*.csv'))\n",
    "df = pd.concat(map(lambda file: pd.read_csv(file, usecols=['x']), csv_files))\n",
    "t1_stop = perf_counter()\n",
    "print('pandas + csv glob, only x column (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()\n",
    "\n",
    "# Pandas + CSV (from dask)\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_csv('data/csv/timeseries-*.csv')\n",
    "df = dd_df.compute()\n",
    "t1_stop = perf_counter()\n",
    "print('\\npandas + csv from dask (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()\n",
    "t1_start = perf_counter()\n",
    "dd_df = dd.read_csv('data/csv/timeseries-*.csv', usecols=['x'])\n",
    "df = dd_df.compute()\n",
    "t1_stop = perf_counter()\n",
    "print('pandas + csv from dask, only x column (ms): %s' % ((t1_stop - t1_start)*1000))\n",
    "del df\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask bag\n",
    "\n",
    "Next, we'll look at processing JSON data hosted on the web using the dask [`Bag`](https://docs.dask.org/en/latest/bag.html) data structure ([full API](https://docs.dask.org/en/latest/bag-api.html)). \n",
    "\n",
    "There is some overlap between what you can do with bags and dataframes, as we'll see below. However, bags are useful when the data you're analyzing is more naturally represented as Python objects (e.g., dicts) than as tabular data (dataframe). That's often the case with JSON data, which has an almost 1:1 mapping with Python dicts.\n",
    "\n",
    "From the official documentation:\n",
    "\n",
    ">Dask Bag implements operations like `map`, `filter`, `groupby` and `aggregations` on collections of Python objects. It does this in parallel and in small memory using Python iterators. It is similar to a parallel version of itertools or a Pythonic version of the PySpark RDD.\n",
    "\n",
    "\n",
    "### Example: using Bag to process JSON data\n",
    "\n",
    "Below we'll use events JSON data from a web service that runs Jupyter notebooks called [mybinder.org](http://mybinder.org). Every time a user launches a notebook on platforms such as GitHub or GitLab, mybinder publishes an event and stores it in publicly accessible JSON files (one per day).\n",
    "\n",
    "For example, we can look at the first 5 events published this past Monday by running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 228 ms, sys: 131 ms, total: 359 ms\n",
      "Wall time: 1.39 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('{\"timestamp\": \"2019-10-28T00:00:00+00:00\", \"schema\": \"binderhub.jupyter.org/launch\", \"version\": 3, \"provider\": \"GitHub\", \"spec\": \"GLAM-Workbench/trove-unpublished/master\", \"status\": \"success\", \"origin\": \"gke.mybinder.org\"}\\n',\n",
       " '{\"timestamp\": \"2019-10-28T00:00:00+00:00\", \"schema\": \"binderhub.jupyter.org/launch\", \"version\": 3, \"provider\": \"GitHub\", \"spec\": \"ipython/ipython-in-depth/master\", \"status\": \"success\", \"origin\": \"gke.mybinder.org\"}\\n',\n",
       " '{\"timestamp\": \"2019-10-28T00:00:00+00:00\", \"schema\": \"binderhub.jupyter.org/launch\", \"version\": 3, \"provider\": \"GitHub\", \"spec\": \"ericmjl/Network-Analysis-Made-Simple/master\", \"status\": \"success\", \"origin\": \"gke.mybinder.org\"}\\n',\n",
       " '{\"timestamp\": \"2019-10-28T00:00:00+00:00\", \"schema\": \"binderhub.jupyter.org/launch\", \"version\": 3, \"provider\": \"GitHub\", \"spec\": \"ipython/ipython-in-depth/master\", \"status\": \"success\", \"origin\": \"gke.mybinder.org\"}\\n',\n",
       " '{\"timestamp\": \"2019-10-28T00:00:00+00:00\", \"schema\": \"binderhub.jupyter.org/launch\", \"version\": 3, \"provider\": \"GitHub\", \"spec\": \"jupyterlab/jupyterlab-demo/try.jupyter.org\", \"status\": \"success\", \"origin\": \"gke.mybinder.org\"}\\n')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "db.read_text('https://archive.analytics.mybinder.org/events-2019-10-28.jsonl').take(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`mybinder.org`](mybinder.org) also publishes an index containing all other JSON files that they're currently hosting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'name': 'events-2018-11-03.jsonl', 'date': '2018-11-03', 'count': '7057'},\n",
       " {'name': 'events-2018-11-04.jsonl', 'date': '2018-11-04', 'count': '7489'},\n",
       " {'name': 'events-2018-11-05.jsonl', 'date': '2018-11-05', 'count': '13590'},\n",
       " {'name': 'events-2018-11-06.jsonl', 'date': '2018-11-06', 'count': '13920'},\n",
       " {'name': 'events-2018-11-07.jsonl', 'date': '2018-11-07', 'count': '12766'})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load index JSON file, inspect its contents.\n",
    "db.read_text('https://archive.analytics.mybinder.org/index.jsonl').map(json.loads).take(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using bag's [`pluck`](https://docs.dask.org/en/latest/bag-api.html#dask.bag.Bag.pluck), we can filter out for only the name attributes. We'll use that to retrieve a list of URLs of the index contents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://archive.analytics.mybinder.org/events-2018-11-03.jsonl',\n",
       " 'https://archive.analytics.mybinder.org/events-2018-11-04.jsonl',\n",
       " 'https://archive.analytics.mybinder.org/events-2018-11-05.jsonl']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urls = (db.read_text('https://archive.analytics.mybinder.org/index.jsonl')\n",
    "                    .map(json.loads)\n",
    "                    .pluck('name')\n",
    "                    .compute())\n",
    "urls = ['https://archive.analytics.mybinder.org/' + u for u in urls]\n",
    "urls[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a bag on the list of urls, we can automatically parse the JSON data into Python dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'timestamp': '2018-11-03T00:00:00+00:00',\n",
       "  'schema': 'binderhub.jupyter.org/launch',\n",
       "  'version': 1,\n",
       "  'provider': 'GitHub',\n",
       "  'spec': 'Qiskit/qiskit-tutorial/master',\n",
       "  'status': 'success'},\n",
       " {'timestamp': '2018-11-03T00:00:00+00:00',\n",
       "  'schema': 'binderhub.jupyter.org/launch',\n",
       "  'version': 1,\n",
       "  'provider': 'GitHub',\n",
       "  'spec': 'ipython/ipython-in-depth/master',\n",
       "  'status': 'success'})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notebook_runs = db.read_text(urls).map(json.loads)\n",
    "notebook_runs.take(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q7. Using the Python dict data we just saved above in `notebook_runs`, filter it for runs whose provider was \"GitHub\", and that happened in September 2019. What were the top-3 most run notebooks in that month, and how many times were they run?**\n",
    "\n",
    "**To answer this question, you have two options. You can either use only `Bag` functions, such as [`filter`](https://docs.dask.org/en/latest/bag-api.html#dask.bag.Bag.filter) and [`frequencies`](https://docs.dask.org/en/latest/bag-api.html#dask.bag.Bag.frequencies) (take a look at some [usage examples here](https://examples.dask.org/bag.html)). Alternatively, you can also use `Bag`'s [`to_dataframe`](https://docs.dask.org/en/latest/bag-api.html#dask.bag.Bag.to_dataframe) and do your processing as dataframe-style computations.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-3 using dask bag (ms): 21387.124905362725\n",
      "\n",
      "results:\n",
      "[('ipython/ipython-in-depth/master', 182307), ('jupyterlab/jupyterlab-demo/try.jupyter.org', 103784), ('binder-examples/requirements/master', 22625)]\n",
      "\n",
      "Top-3 using dask dataframe (ms): 33789.69642519951\n",
      "\n",
      "results:\n",
      "ipython/ipython-in-depth/master               182307\n",
      "jupyterlab/jupyterlab-demo/try.jupyter.org    103784\n",
      "binder-examples/requirements/master            22625\n",
      "Name: spec, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Q7: YOUR CODE GOES HERE.\n",
    "# Dask bag: top-3.\n",
    "t1_start = perf_counter()\n",
    "top_runs = (notebook_runs\n",
    "          .filter(lambda n: n['provider'] == 'GitHub' and n['timestamp'].startswith('2019-09'))\n",
    "          .pluck('spec')\n",
    "          .frequencies(sort=True)\n",
    "          .take(3))\n",
    "t1_stop = perf_counter()\n",
    "print('Top-3 using dask bag (ms): %s\\n' % ((t1_stop - t1_start)*1000))\n",
    "print('results:')\n",
    "print(list(top_runs))\n",
    "\n",
    "# Getting top-3: dask dataframe.\n",
    "t1_start = perf_counter()\n",
    "dd_df = notebook_runs.to_dataframe()\n",
    "dd_df = dd_df[(dd_df['provider'] == 'GitHub') & (dd_df['timestamp'].str.startswith('2019-09'))]\n",
    "top_runs = (dd_df.spec.value_counts()\n",
    "                 .nlargest(3)\n",
    "                 .compute())\n",
    "t1_stop = perf_counter()\n",
    "print('\\nTop-3 using dask dataframe (ms): %s\\n' % ((t1_stop - t1_start)*1000))\n",
    "print('results:')\n",
    "print(top_runs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Resources\n",
    "\n",
    "## Dask examples and tutorials\n",
    "\n",
    "* Dask examples: https://examples.dask.org/\n",
    "* Dask for ML tasks (e.g., incremental learning and hyperparameter tuning): https://ml.dask.org/examples.html\n",
    "* YouTube playlist with introductory Dask concepts: https://www.youtube.com/playlist?list=PLTgRMOcmRb3OlkfAdqJWyGGrQM7eU-mi7\n",
    "\n",
    "## Dask benchmark codes\n",
    "* On a 512 core server: https://matthewrocklin.com/blog/work/2017/07/03/scaling\n",
    "* On GCS: https://gist.github.com/mrocklin/4c198b13e92f881161ef175810c7f6bc#file-scaling-gcs-ipynb\n"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}