DataFriday: basic ETL ops with Apache Spark

In this episode, you will learn about doing a basic ETL (extract, transform, and load) operation using Apache Spark. You will load a basic CSV file with Apache Spark, make a basic transformation using static functions, and save the result in a PostgreSQL. The tools used are Eclipse and Java.

If anyone can explain me why the L in ETL stands for load, please educate me… I would have gone for ETS with S as Save or ETP with P standing for Publish. It is a mistery for me…

The code is available on GitHub. The full description of the process is available in the second chapter of Spark in Action, 2nd edition. You can have a look at it on Manning’s live book website.

For convenience, the Java code is added here. You will see the main steps of this small application. After getting a Spark session, I will:

1. Read the CSV file (extract).
2. Perform a basic data transformation.
3. Save the result to a PostgreSQL database (load).

package net.jgp.books.spark.ch02.lab100_csv_to_db;

import static org.apache.spark.sql.functions.concat;
import static org.apache.spark.sql.functions.lit;

import java.util.Properties;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;

/**
 * CSV to a relational database.
 * 
 * @author jgp
 */
public class CsvToRelationalDatabaseApp {

  /**
   * main() is your entry point to the application.
   * 
   * @param args
   */
  public static void main(String[] args) {
    CsvToRelationalDatabaseApp app = new CsvToRelationalDatabaseApp();
    app.start();
  }

  /**
   * The processing code.
   */
  private void start() {
    // Creates a session on a local master
    SparkSession spark = SparkSession.builder()
        .appName("CSV to DB")
        .master("local")
        .getOrCreate();

    // Step 1: Ingestion
    // ---------

    // Reads a CSV file with header, called authors.csv, stores it in a
    // dataframe
    Dataset<Row> df = spark.read()
        .format("csv")
        .option("header", true)
        .load("data/authors.csv");

    // Step 2: Transform
    // ---------

    // Creates a new column called "name" as the concatenation of lname, a
    // virtual column containing ", " and the fname column
    df = df.withColumn(
        "name",
        concat(df.col("lname"), lit(", "), df.col("fname")));

    // Step 3: Save
    // ----

    // The connection URL, assuming your PostgreSQL instance runs locally on
    // the
    // default port, and the database we use is "spark_labs"
    String dbConnectionUrl = "jdbc:postgresql://localhost/spark_labs";

    // Properties to connect to the database, the JDBC driver is part of our
    // pom.xml
    Properties prop = new Properties();
    prop.setProperty("driver", "org.postgresql.Driver");
    prop.setProperty("user", "jgp");
    prop.setProperty("password", "Spark<3Java");

    // Write in a table called ch02
    df.write()
        .mode(SaveMode.Overwrite)
        .jdbc(dbConnectionUrl, "ch02", prop);

    System.out.println("Process complete");
  }
}

This is a basic ETL process, but it illustrates the basics and simplicity with which Apache Spark can transfer and modify data.

More resources:

• Loading CSV in Spark.
• File Ingestion in Apache Spark.

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