This repository contains the Java labs as well as their Scala and Python ports of the code used in Manning Publication’s Spark in Action, 2nd edition, by Jean-Georges Perrin.

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Spark in Action, 2nd edition - chapter 2

Welcome to Spark in Action, 2nd edition, chapter 2. As we are building our mental model around Spark processing, we need a small but efficient example that does a whole flow (or data pipeline).

This code is designed to work with Apache Spark v3.1.2.

Lab

Each chapter has one or more labs. Labs are examples used for teaching in the book. You are encouraged to take ownership of the code and modify it, experiment with it, hence the use of the term lab.

Lab #100

The CsvToDatabaseApp application does the following:

1. The app acquires a session (a SparkSession).
2. It then asks Spark to load (ingest) a dataset in CSV format.
3. Spark performs a small transformation.
4. Spark stores the result in a database, in this example, it is PostgreSQL.

To create the PostgreSQL database, you can use the following SQL commands:

CREATE ROLE jgp WITH
    LOGIN
    NOSUPERUSER
    NOCREATEDB
    NOCREATEROLE
    INHERIT
    NOREPLICATION
    CONNECTION LIMIT -1
    PASSWORD 'Spark<3Java';
CREATE DATABASE spark_labs
    WITH 
    OWNER = jgp
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1;

Lab #110

The CsvToApacheDerbyApp application is similar to lab #100, however, instead of using PostgreSQL, it uses Apache Derby.

Special thanks to Kelvin Rawls for porting the code to Derby.

Apache Derby has a bit of a special place in my heart. Originally named Cloudscape, the eponym company was acquired by Informix in 1999, which was itself acquired by IBM in 2001. Informix's project Arrowhead had the ambition to align the SQL (and other features) between all their databases. Under the new ownership, Cloudscape was influenced by the Db2 SQL before going to the Open Source community as Apache Derby.

Running the lab in Java

For information on running the Java lab, see chapter 2 in Spark in Action, 2nd edition.

Running the lab using PySpark

Prerequisites:

You will need:

• git.
• Apache Spark (please refer Appendix P - 'Spark in production: installation and a few tips').

1. Clone this project

   git clone https://github.com/jgperrin/net.jgp.books.spark.ch02

2. Go to the lab in the Python directory

   cd net.jgp.books.spark.ch02/src/main/python/lab100_csv_to_db/

3. Execute the following spark-submit command to create a jar file to our this application

   spark-submit csvToRelationalDatabaseApp.py

Running the lab in Scala

Prerequisites:

You will need:

• git.
• Apache Spark (please refer Appendix P - 'Spark in production: installation and a few tips').

1. Clone this project

   git clone https://github.com/jgperrin/net.jgp.books.spark.ch02

2. Go to the correct directory:

   cd net.jgp.books.spark.ch02

3. Package application using sbt command

   sbt clean assembly

4. Run Spark/Scala application using spark-submit command as shown below:

   spark-submit --class net.jgp.books.spark.ch02.lab100_csv_to_db.CsvToRelationalDatabaseScalaApp target/scala-2.12/SparkInAction2-Chapter02-assembly-1.0.0.jar

News

1. [2020-06-07] Updated the pom.xml to support Apache Spark v3.1.2.
2. [2020-06-07] As we celebrate the first anniversary of Spark in Action, 2nd edition is the best-rated Apache Spark book on Amazon.

Notes:

1. [Java] Due to renaming the packages to match more closely Java standards, this project is not in sync with the book's MEAP prior to v10 (published in April 2019).
2. [Scala, Python] As of MEAP v14, we have introduced Scala and Python examples (published in October 2019).
3. The master branch contains the last version of the code running against the latest supported version of Apache Spark. Look in specifics branches for specific versions.

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