on April 24, 2020
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:
- Read the CSV file (extract).
- Perform a basic data transformation.
- 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:
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