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 Set up the project.

To skip the basics, do the following:

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

Set up the project

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. But you can also use Maven. The pom.xml file is included right here. If you are using Spring Tool Suite (STS), you can import the guide directly.

build.gradle

buildscript {
    repositories {
        maven { url "http://repo.spring.io/libs-snapshot" }
        mavenLocal()
    }
    dependencies {
        classpath("org.springframework.boot:spring-boot-gradle-plugin:1.0.0.RC4")
    }
}

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

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

repositories {
    mavenCentral()
    maven { url "http://repo.spring.io/libs-snapshot" }
}

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

task wrapper(type: Wrapper) {
    gradleVersion = '1.11'
}

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.

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.springframework.batch.item.ItemProcessor;

public class PersonItemProcessor implements ItemProcessor<Person, Person> {

    @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);

        System.out.println("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.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.ItemProcessor;
import org.springframework.batch.item.ItemReader;
import org.springframework.batch.item.ItemWriter;
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.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 {

    // tag::readerwriterprocessor[]
    @Bean
    public ItemReader<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 ItemProcessor<Person, Person> processor() {
        return new PersonItemProcessor();
    }

    @Bean
    public ItemWriter<Person> writer(DataSource dataSource) {
        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::jobstep[]
    @Bean
    public Job importUserJob(JobBuilderFactory jobs, Step s1) {
        return jobs.get("importUserJob")
                .incrementer(new RunIdIncrementer())
                .flow(s1)
                .end()
                .build();
    }

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

    @Bean
    public JdbcTemplate jdbcTemplate(DataSource dataSource) {
        return new JdbcTemplate(dataSource);
    }

}

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 ItemReader<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 ItemProcessor<Person, Person> processor() {
        return new PersonItemProcessor();
    }

    @Bean
    public ItemWriter<Person> writer(DataSource dataSource) {
        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(JobBuilderFactory jobs, Step s1) {
        return jobs.get("importUserJob")
                .incrementer(new RunIdIncrementer())
                .flow(s1)
                .end()
                .build();
    }

    @Bean
    public Step step1(StepBuilderFactory stepBuilderFactory, ItemReader<Person> reader,
            ItemWriter<Person> writer, ItemProcessor<Person, Person> processor) {
        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.

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>.

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

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
import org.springframework.context.ApplicationContext;
import org.springframework.context.annotation.ComponentScan;
import org.springframework.jdbc.core.JdbcTemplate;
import org.springframework.jdbc.core.RowMapper;

@ComponentScan
@EnableAutoConfiguration
public class Application {

    public static void main(String[] args) {
        ApplicationContext ctx = SpringApplication.run(Application.class, args);

        List<Person> results = ctx.getBean(JdbcTemplate.class).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) {
            System.out.println("Found <" + person + "> in the database.");
        }
    }

}

The main() method defers to the SpringApplication helper class, providing Application.class as an argument to its run() method. This tells Spring to read the annotation metadata from Application and to manage it as a component in the Spring application context.

The @ComponentScan annotation tells Spring to search recursively through the hello package and its children for classes marked directly or indirectly with Spring’s @Component annotation. This directive ensures that Spring finds and registers BatchConfiguration, because it is marked with @Configuration, which in turn is a kind of @Component annotation.

The @EnableAutoConfiguration annotation switches on reasonable default behaviors based on the content of your classpath. For example, it looks for any class that implements the CommandLineRunner interface and invokes its run() method. In this case, it runs the demo code for this guide.

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 build a single executable JAR file that contains all the necessary dependencies, classes, and resources. This makes it easy to ship, version, and deploy the service as an application throughout the development lifecycle, across different environments, and so forth.

./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 mvn spring-boot:run. Or you can build the JAR file with mvn clean package and run the JAR by typing:

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.

Run the batch job

If you are using Gradle, you can run your batch job at the command line this way:

./gradlew clean build && java -jar build/libs/gs-batch-processing-0.1.0.jar
If you are using Maven, you can run your batch job by typing mvn clean package && java -jar target/gs-batch-processing-0.1.0.jar.

You can alternatively run the app directly from Gradle like this:

./gradlew bootRun
With mvn, you can run mvn spring-boot:run.

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.