This guide walks you through the process of creating a basic batch-driven solution.

What you’ll build

You’ll build a service that imports data from a CSV spreadsheet, transforms it with custom code, and stores the final results in a database.

What you’ll need

How to complete this guide

Like most Spring Getting Started guides, you can start from scratch and complete each step, or you can bypass basic setup steps that are already familiar to you. Either way, you end up with working code.

To start from scratch, move on to Build with Gradle.

To skip the basics, do the following:

When you’re finished, you can check your results against the code in gs-batch-processing/complete.

Build with Gradle

Build with Gradle

First you set up a basic build script. You can use any build system you like when building apps with Spring, but the code you need to work with Gradle and Maven is included here. If you’re not familiar with either, refer to Building Java Projects with Gradle or Building Java Projects with Maven.

Create the directory structure

In a project directory of your choosing, create the following subdirectory structure; for example, with mkdir -p src/main/java/hello on *nix systems:

└── src
    └── main
        └── java
            └── hello

Create a Gradle build file

Below is the initial Gradle build file.

build.gradle

buildscript {
    repositories {
        mavenCentral()
    }
    dependencies {
        classpath("org.springframework.boot:spring-boot-gradle-plugin:1.4.1.RELEASE")
    }
}

apply plugin: 'java'
apply plugin: 'eclipse'
apply plugin: 'idea'
apply plugin: 'spring-boot'

jar {
    baseName = 'gs-batch-processing'
    version =  '0.1.0'
}

repositories {
    mavenCentral()
}

sourceCompatibility = 1.8
targetCompatibility = 1.8

dependencies {
    compile("org.springframework.boot:spring-boot-starter-batch")
    compile("org.hsqldb:hsqldb")
    testCompile("junit:junit")
}

The Spring Boot gradle plugin provides many convenient features:

  • It collects all the jars on the classpath and builds a single, runnable "über-jar", which makes it more convenient to execute and transport your service.

  • It searches for the public static void main() method to flag as a runnable class.

  • It provides a built-in dependency resolver that sets the version number to match Spring Boot dependencies. You can override any version you wish, but it will default to Boot’s chosen set of versions.

Build with Maven

Build with Maven

First you set up a basic build script. You can use any build system you like when building apps with Spring, but the code you need to work with Maven is included here. If you’re not familiar with Maven, refer to Building Java Projects with Maven.

Create the directory structure

In a project directory of your choosing, create the following subdirectory structure; for example, with mkdir -p src/main/java/hello on *nix systems:

└── src
    └── main
        └── java
            └── hello

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>org.springframework</groupId>
    <artifactId>gs-batch-processing</artifactId>
    <version>0.1.0</version>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>1.4.1.RELEASE</version>
    </parent>

    <properties>
        <java.version>1.8</java.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-batch</artifactId>
        </dependency>
        <dependency>
            <groupId>org.hsqldb</groupId>
            <artifactId>hsqldb</artifactId>
        </dependency>
    </dependencies>


    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
            </plugin>
        </plugins>
    </build>

</project>

The Spring Boot Maven plugin provides many convenient features:

  • It collects all the jars on the classpath and builds a single, runnable "über-jar", which makes it more convenient to execute and transport your service.

  • It searches for the public static void main() method to flag as a runnable class.

  • It provides a built-in dependency resolver that sets the version number to match Spring Boot dependencies. You can override any version you wish, but it will default to Boot’s chosen set of versions.

Build with your IDE

Build with your IDE

Business Data

Typically your customer or a business analyst supplies a spreadsheet. In this case, you make it up.

src/main/resources/sample-data.csv

Jill,Doe
Joe,Doe
Justin,Doe
Jane,Doe
John,Doe

This spreadsheet contains a first name and a last name on each row, separated by a comma. This is a fairly common pattern that Spring handles out-of-the-box, as you will see.

Next, you write a SQL script to create a table to store the data.

src/main/resources/schema-all.sql

DROP TABLE people IF EXISTS;

CREATE TABLE people  (
    person_id BIGINT IDENTITY NOT NULL PRIMARY KEY,
    first_name VARCHAR(20),
    last_name VARCHAR(20)
);
Spring Boot runs schema-@@platform@@.sql automatically during startup. -all is the default for all platforms.

Create a business class

Now that you see the format of data inputs and outputs, you write code to represent a row of data.

src/main/java/hello/Person.java

package hello;

public class Person {
    private String lastName;
    private String firstName;

    public Person() {

    }

    public Person(String firstName, String lastName) {
        this.firstName = firstName;
        this.lastName = lastName;
    }

    public void setFirstName(String firstName) {
        this.firstName = firstName;
    }

    public String getFirstName() {
        return firstName;
    }

    public String getLastName() {
        return lastName;
    }

    public void setLastName(String lastName) {
        this.lastName = lastName;
    }

    @Override
    public String toString() {
        return "firstName: " + firstName + ", lastName: " + lastName;
    }

}

You can instantiate the Person class either with first and last name through a constructor, or by setting the properties.

Create an intermediate processor

A common paradigm in batch processing is to ingest data, transform it, and then pipe it out somewhere else. Here you write a simple transformer that converts the names to uppercase.

src/main/java/hello/PersonItemProcessor.java

package hello;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import org.springframework.batch.item.ItemProcessor;

public class PersonItemProcessor implements ItemProcessor<Person, Person> {

    private static final Logger log = LoggerFactory.getLogger(PersonItemProcessor.class);

    @Override
    public Person process(final Person person) throws Exception {
        final String firstName = person.getFirstName().toUpperCase();
        final String lastName = person.getLastName().toUpperCase();

        final Person transformedPerson = new Person(firstName, lastName);

        log.info("Converting (" + person + ") into (" + transformedPerson + ")");

        return transformedPerson;
    }

}

PersonItemProcessor implements Spring Batch’s ItemProcessor interface. This makes it easy to wire the code into a batch job that you define further down in this guide. According to the interface, you receive an incoming Person object, after which you transform it to an upper-cased Person.

There is no requirement that the input and output types be the same. In fact, after one source of data is read, sometimes the application’s data flow needs a different data type.

Put together a batch job

Now you put together the actual batch job. Spring Batch provides many utility classes that reduce the need to write custom code. Instead, you can focus on the business logic.

src/main/java/hello/BatchConfiguration.java

package hello;

import javax.sql.DataSource;

import org.springframework.batch.core.Job;
import org.springframework.batch.core.JobExecutionListener;
import org.springframework.batch.core.Step;
import org.springframework.batch.core.configuration.annotation.EnableBatchProcessing;
import org.springframework.batch.core.configuration.annotation.JobBuilderFactory;
import org.springframework.batch.core.configuration.annotation.StepBuilderFactory;
import org.springframework.batch.core.launch.support.RunIdIncrementer;
import org.springframework.batch.item.database.BeanPropertyItemSqlParameterSourceProvider;
import org.springframework.batch.item.database.JdbcBatchItemWriter;
import org.springframework.batch.item.file.FlatFileItemReader;
import org.springframework.batch.item.file.mapping.BeanWrapperFieldSetMapper;
import org.springframework.batch.item.file.mapping.DefaultLineMapper;
import org.springframework.batch.item.file.transform.DelimitedLineTokenizer;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.io.ClassPathResource;
import org.springframework.jdbc.core.JdbcTemplate;

@Configuration
@EnableBatchProcessing
public class BatchConfiguration {

    @Autowired
    public JobBuilderFactory jobBuilderFactory;

    @Autowired
    public StepBuilderFactory stepBuilderFactory;

    @Autowired
    public DataSource dataSource;

    // tag::readerwriterprocessor[]
    @Bean
    public FlatFileItemReader<Person> reader() {
        FlatFileItemReader<Person> reader = new FlatFileItemReader<Person>();
        reader.setResource(new ClassPathResource("sample-data.csv"));
        reader.setLineMapper(new DefaultLineMapper<Person>() {{
            setLineTokenizer(new DelimitedLineTokenizer() {{
                setNames(new String[] { "firstName", "lastName" });
            }});
            setFieldSetMapper(new BeanWrapperFieldSetMapper<Person>() {{
                setTargetType(Person.class);
            }});
        }});
        return reader;
    }

    @Bean
    public PersonItemProcessor processor() {
        return new PersonItemProcessor();
    }

    @Bean
    public JdbcBatchItemWriter<Person> writer() {
        JdbcBatchItemWriter<Person> writer = new JdbcBatchItemWriter<Person>();
        writer.setItemSqlParameterSourceProvider(new BeanPropertyItemSqlParameterSourceProvider<Person>());
        writer.setSql("INSERT INTO people (first_name, last_name) VALUES (:firstName, :lastName)");
        writer.setDataSource(dataSource);
        return writer;
    }
    // end::readerwriterprocessor[]

    // tag::listener[]

    @Bean
    public JobExecutionListener listener() {
        return new JobCompletionNotificationListener(new JdbcTemplate(dataSource));
    }

    // end::listener[]

    // tag::jobstep[]
    @Bean
    public Job importUserJob() {
        return jobBuilderFactory.get("importUserJob")
                .incrementer(new RunIdIncrementer())
                .listener(listener())
                .flow(step1())
                .end()
                .build();
    }

    @Bean
    public Step step1() {
        return stepBuilderFactory.get("step1")
                .<Person, Person> chunk(10)
                .reader(reader())
                .processor(processor())
                .writer(writer())
                .build();
    }
    // end::jobstep[]
}

For starters, the @EnableBatchProcessing annotation adds many critical beans that support jobs and saves you a lot of leg work. This example uses a memory-based database (provided by @EnableBatchProcessing), meaning that when it’s done, the data is gone.

Break it down:

src/main/java/hello/BatchConfiguration.java

    @Bean
    public FlatFileItemReader<Person> reader() {
        FlatFileItemReader<Person> reader = new FlatFileItemReader<Person>();
        reader.setResource(new ClassPathResource("sample-data.csv"));
        reader.setLineMapper(new DefaultLineMapper<Person>() {{
            setLineTokenizer(new DelimitedLineTokenizer() {{
                setNames(new String[] { "firstName", "lastName" });
            }});
            setFieldSetMapper(new BeanWrapperFieldSetMapper<Person>() {{
                setTargetType(Person.class);
            }});
        }});
        return reader;
    }

    @Bean
    public PersonItemProcessor processor() {
        return new PersonItemProcessor();
    }

    @Bean
    public JdbcBatchItemWriter<Person> writer() {
        JdbcBatchItemWriter<Person> writer = new JdbcBatchItemWriter<Person>();
        writer.setItemSqlParameterSourceProvider(new BeanPropertyItemSqlParameterSourceProvider<Person>());
        writer.setSql("INSERT INTO people (first_name, last_name) VALUES (:firstName, :lastName)");
        writer.setDataSource(dataSource);
        return writer;
    }

. The first chunk of code defines the input, processor, and output. - reader() creates an ItemReader. It looks for a file called sample-data.csv and parses each line item with enough information to turn it into a Person. - processor() creates an instance of our PersonItemProcessor you defined earlier, meant to uppercase the data. - write(DataSource) creates an ItemWriter. This one is aimed at a JDBC destination and automatically gets a copy of the dataSource created by @EnableBatchProcessing. It includes the SQL statement needed to insert a single Person driven by Java bean properties.

The next chunk focuses on the actual job configuration.

src/main/java/hello/BatchConfiguration.java

    @Bean
    public Job importUserJob() {
        return jobBuilderFactory.get("importUserJob")
                .incrementer(new RunIdIncrementer())
                .listener(listener())
                .flow(step1())
                .end()
                .build();
    }

    @Bean
    public Step step1() {
        return stepBuilderFactory.get("step1")
                .<Person, Person> chunk(10)
                .reader(reader())
                .processor(processor())
                .writer(writer())
                .build();
    }

. The first method defines the job and the second one defines a single step. Jobs are built from steps, where each step can involve a reader, a processor, and a writer.

In this job definition, you need an incrementer because jobs use a database to maintain execution state. You then list each step, of which this job has only one step. The job ends, and the Java API produces a perfectly configured job.

The listener() method lets you hook into the engine and detect when the job is complete, triggering the verification of results.

In the step definition, you define how much data to write at a time. In this case, it writes up to ten records at a time. Next, you configure the reader, processor, and writer using the injected bits from earlier.

chunk() is prefixed <Person,Person> because it’s a generic method. This represents the input and output types of each "chunk" of processing, and lines up with ItemReader<Person> and ItemWriter<Person>.

src/main/java/hello/JobCompletionNotificationListener.java

package hello;

import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.List;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import org.springframework.batch.core.BatchStatus;
import org.springframework.batch.core.JobExecution;
import org.springframework.batch.core.listener.JobExecutionListenerSupport;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.jdbc.core.JdbcTemplate;
import org.springframework.jdbc.core.RowMapper;
import org.springframework.stereotype.Component;

public class JobCompletionNotificationListener extends JobExecutionListenerSupport {

	private static final Logger log = LoggerFactory.getLogger(JobCompletionNotificationListener.class);

	private final JdbcTemplate jdbcTemplate;

	@Autowired
	public JobCompletionNotificationListener(JdbcTemplate jdbcTemplate) {
		this.jdbcTemplate = jdbcTemplate;
	}

	@Override
	public void afterJob(JobExecution jobExecution) {
		if(jobExecution.getStatus() == BatchStatus.COMPLETED) {
			log.info("!!! JOB FINISHED! Time to verify the results");

			List<Person> results = jdbcTemplate.query("SELECT first_name, last_name FROM people", new RowMapper<Person>() {
				@Override
				public Person mapRow(ResultSet rs, int row) throws SQLException {
					return new Person(rs.getString(1), rs.getString(2));
				}
			});

			for (Person person : results) {
				log.info("Found <" + person + "> in the database.");
			}

		}
	}
}

This code listens for when a job is BatchStatus.COMPLETED, and then uses JdbcTemplate to inspect the results.

Make the application executable

Although batch processing can be embedded in web apps and WAR files, the simpler approach demonstrated below creates a standalone application. You package everything in a single, executable JAR file, driven by a good old Java main() method.

src/main/java/hello/Application.java

package hello;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class Application {

    public static void main(String[] args) throws Exception {
        SpringApplication.run(Application.class, args);
    }
}

@SpringBootApplication is a convenience annotation that adds all of the following:

  • @Configuration tags the class as a source of bean definitions for the application context.

  • @EnableAutoConfiguration tells Spring Boot to start adding beans based on classpath settings, other beans, and various property settings.

  • Normally you would add @EnableWebMvc for a Spring MVC app, but Spring Boot adds it automatically when it sees spring-webmvc on the classpath. This flags the application as a web application and activates key behaviors such as setting up a DispatcherServlet.

  • @ComponentScan tells Spring to look for other components, configurations, and services in the the hello package, allowing it to find the HelloController.

The main() method uses Spring Boot’s SpringApplication.run() method to launch an application. Did you notice that there wasn’t a single line of XML? No web.xml file either. This web application is 100% pure Java and you didn’t have to deal with configuring any plumbing or infrastructure.

For demonstration purposes, there is code to create a JdbcTemplate, query the database, and print out the names of people the batch job inserts.

Build an executable JAR

You can run the application from the command line with Gradle or Maven. Or you can build a single executable JAR file that contains all the necessary dependencies, classes, and resources, and run that. This makes it easy to ship, version, and deploy the service as an application throughout the development lifecycle, across different environments, and so forth.

If you are using Gradle, you can run the application using ./gradlew bootRun. Or you can build the JAR file using ./gradlew build. Then you can run the JAR file:

java -jar build/libs/gs-batch-processing-0.1.0.jar

If you are using Maven, you can run the application using ./mvnw spring-boot:run. Or you can build the JAR file with ./mvnw clean package. Then you can run the JAR file:

java -jar target/gs-batch-processing-0.1.0.jar
The procedure above will create a runnable JAR. You can also opt to build a classic WAR file instead.

The job prints out a line for each person that gets transformed. After the job runs, you can also see the output from querying the database.

Converting (firstName: Jill, lastName: Doe) into (firstName: JILL, lastName: DOE)
Converting (firstName: Joe, lastName: Doe) into (firstName: JOE, lastName: DOE)
Converting (firstName: Justin, lastName: Doe) into (firstName: JUSTIN, lastName: DOE)
Converting (firstName: Jane, lastName: Doe) into (firstName: JANE, lastName: DOE)
Converting (firstName: John, lastName: Doe) into (firstName: JOHN, lastName: DOE)
Found <firstName: JILL, lastName: DOE> in the database.
Found <firstName: JOE, lastName: DOE> in the database.
Found <firstName: JUSTIN, lastName: DOE> in the database.
Found <firstName: JANE, lastName: DOE> in the database.
Found <firstName: JOHN, lastName: DOE> in the database.

Summary

Congratulations! You built a batch job that ingested data from a spreadsheet, processed it, and wrote it to a database.

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