In this post, we'll show how to integrate Spring Boot with Apache Spark for fast data processing.
Spark is a powerful tool for processing large data sets and is becoming increasingly popular in the big data community. Spring Boot is a popular Java framework that makes it easy to create stand-alone, production-grade Spring-based applications.
Integrating these two frameworks can provide a fast, efficient way to process data. In this post, we'll show you how to do just that.
We'll start by creating a Spring Boot application. Then, we'll add the necessary dependencies for Spark. Finally, we'll write a simple Spark job and run it on our local development environment.
First, we'll create a Spring Boot application. We'll use the Spring Initializr to generate our project.
Go to the Spring Initializr website (https://start.spring.io/).
Fill in the form with the following values:
Click "Generate Project."
You should now have a zip file containing the project structure. Unzip the file and open it in your favorite IDE.
Next, we'll add the necessary dependencies for Spark. We'll need the following dependencies:
We can add these dependencies to our project by adding the following to our pom.xml file:
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.4.4</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.4.4</version>
</dependency>
</dependencies>
Now that we have our project set up, we can write a simple Spark job. We'll start by creating a new Java class called SparkJob.java.
In our SparkJob class, we'll first create a SparkSession. A SparkSession is the entry point to Spark SQL. It allows us to read data from a variety of data sources and perform SQL queries.
Next, we'll read in a data set. We'll use a sample data set of airline flight data. The data set is in CSV format and is available here: http://stat-computing.org/dataexpo/2009/2008.csv.bz2.
Once we have the data set, we'll register it as a table. This will allow us to run SQL queries against it.
Finally, we'll run a simple SQL query to count the number of flights for each airline.
The complete code for our SparkJob class is shown below:
package com.example.demo;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
public class SparkJob {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("Spark Demo")
.getOrCreate();
String dataFile = "data/2008.csv.bz2";
Dataset<Row> df = spark.read().format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load(dataFile);
df.createOrReplaceTempView("flights");
Dataset<Row> airlines = spark.sql("SELECT DISTINCT Carrier FROM flights");
for (Row row : airlines.collectAsList()) {
String airline = row.getString(0);
Dataset<Row> flights = spark.sql("SELECT * FROM flights WHERE Carrier = '" + airline + "'");
long numFlights = flights.count();
System.out.println(airline + ": " + numFlights);
}
spark.stop();
}
}
Now that we have our Spark job written, we can run it on our local development environment.
To do this, we'll need to first start up a local Spark instance. We can do this using the following command:
$SPARK_HOME/bin/spark-shell --master local[*]
Once Spark is up and running, we can submit our Spark job. We can do this using the following command:
spark-submit --class com.example.demo.SparkJob --master local[*] target/demo-0.0.1-SNAPSHOT.jar
If everything goes well, you should see output similar to the following:
American: 12359
Continental: 7261
Delta: 20216
United: 13721
US Airways: 10429
And that's it! You've now successfully integrated Spring Boot with Apache Spark.