Getting started with Apache Spark

Getting started with Apache Spark

Purpose

• Getting up and running with Apache Spark
• Using VS Code (or your IDE of choice) with Spark
• Writing and running your own first Spark instructions

For a general introduction, see the slides to Session 1 on Apache Spark. There is a useful tutorial at TutorialsPoint.

Preparations

In the first exercise, you will run Spark standalone on your own computer, first in a console/terminal window and then in your favourite IDE (Integrated Development Environment).

Run pyspark-shell from console

Open a console (or terminal) window. I will use a Linux console in the examples.

(If you are on a Windows computer, it is a very good idea to install WSL2 - Windows Subsystem for Linux - and use it as your console/terminal window. But it is not a priority right now.)

You need to have python3 and pip on your machine. On Linux:

sudo apt install python3 python3-dev python3-pip

(In addition, Spark needs a Java Runtime Environment - a JRE or JDK - somewhere on your PATH.)

mkdir info319-exercises
cd info319-exercises

Create a Python environment using pip, pipenv or Conda. I will use pip in the examples. It is simple and transparent.

$ which python3
$ python3 --version
$ python3 -m venv venv

I have used both Python 3.8 and 3.10, but other recent versions should be fine. (The examples will use 3.10.)

Activate the environment:

$ . venv/bin/activate
(venv) $ which python

This should return something like .../info319-exercises/venv/bin/python

(venv) $ python --version
(venv) $ pip --version

Upgrade pip if necessary and install pyspark:

(venv) $ python3 -m pip install --upgrade pip
(venv) $ pip --version
(venv) $ pip install pyspark

Check that pyspark was installed in the right place:

(venv) $ ls venv/lib/python3.10/site-packages

You should now see the pyspark folder.

Start pyspark:

(venv) $ pyspark
Python 3.8.10 (default, Jun 22 2022, 20:18:18)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information. 

Don't panic if you get a few warnings here...

Welcome to
     ____              __
    / __/__  ___ _____/ /__
   _\ \/ _ \/ _ `/ __/  '_/
  /__ / .__/\_,_/_/ /_/\_\   version 3.3.0
     /_/

Using Python version 3.8.10 (default, Jun 22 2022 20:18:18)
Spark context Web UI available at http://172.23.240.233:4040
Spark context available as 'sc' (master = local[*], app id = local-1661179410845).
SparkSession available as 'spark'.
>>>

The >>> prompt means you are ready to go, but first exit to download some data:

>>> exit() 

Processing tweets

Download this archive of random tweets, and unpack in your exercise folder:

(venv) $ tar xjf tweet-id-text-345.tar.bz2
(venv) $ ls tweet-id-text-345

The folder should contain 345 small text files, each representing a tweet.

(venv) $ pyspark

...

>>> folder = 'tweet-id-text-345'
>>> tweets = spark.read.text(folder)
>>> type(tweets)
<class 'pyspark.sql.dataframe.DataFrame'>

DataFrame is a very central data structure in Spark.

>>> tweets.count()
>>> tweet_list = tweets.collect()
>>> type(tweet_list)

We are back in Python, but not completely:

>>> tweet_list[13]
>>> type(tweet_list[13])
<class 'pyspark.sql.types.Row'>

DataFrame Rows are another central Spark data structure. Can you get the rows out as Python dicts?

Exploring tweets in Spark

In Session 1 we will look at more things to do with Spark DataFrames. Here are some possible things to do in Exercise 1 (this is not final):

• Load the tweets as json objects.
• Collect only the texts from the tweets.
• Split the texts into words and select all the hashtags.
• Build a graph of retweets
• Split the tweets into two sets of 80% and 20% size.
• Find URLs in the texts and download a few image files.
• Work on a folder with more tweets.
• Open the Spark context Web UI (see pyspark's start-up banner)
• Experiment with different numbers of partitioners and executors.

Of course, we will do these things _in Spark_, without going via plain Python.

Set up git (optional)

Log in to a git repository, such as github.com or UiB's own GitLab git.app.uib.no . (This can be hard to set up with private and public SSH keys, but you will need it later in the course anyway.)

Create a new project 'info319-exercises' (it is practical to use same name as your folder). Copy the SSH address, such as 'git@git.app.uib.no:yourname/info319-exercises.git'.

Go back to your exercises folder. Create a file '.gitignore' with at least this line: '/venv/'.

$ echo "/venv/" > .gitignore

You can now push your project to the git repository:

$ cd info319_exercises
$ git remote add origin https://git.app.uib.no/yourname/info319-exercises.git
$ git branch -M main
$ git push -uf origin main

(The push will be sparse since we haven't written any Spark _program_ yet.)

Running Spark in Jupyter and VS Code

You can also run pyspark in a Jupyter notebook, either standalone or inside an Integrated Development Environment (IDE).

The example below runs pyspark in Jupyter inside VS Code (Visual Studio Code). See Microsoft's own guide to Getting started with Visual Studio Code. See this guide for running Jupyter notebooks in VS Code.

The example code below should work on other Jupyters too, such as JupyterLab.

Install and start VS Code. Select File -> Open folder... and open 'info319-exercise' from before. Create a new file with extension .ipynb, for example exercise1.ipynb. Doubleclick the new file, which should open it in a Jupyter notebook inside VS Code.

Activate the new exercise1.ipynb tab and then click the 'Python' button near the upper right corner of the Python notebook. A window should pop up and ask you to Change kernel for 'exercise1.ipynb'. Type in or select ./venv/bin/python. Now you are ready to use Jupter inside VS Code.

In the first cell, enter (and run it with Shift-Return):

!pip install findspark

In the next:

import findspark
findspark.init()

In the third:

from pyspark.sql import SQLContext, SparkSession
spark = SparkSession \
        .builder \
        .appName("Jupyter Spark shell") \
        .getOrCreate()
sc = spark.sparkContext

You can now use the notebook as a pyspark shell like before. For example:

folder = 'tweet-id-text-345'
tweets = spark.read.text(folder)
tweets.count()

An important advantage over the plain pyspark shell run in a terminal window is that autocompletion and other help functions now work. You can also easily save and load notebooks, etc.