- You can also import the code from this guide as well as view the web page directly into [Spring Tool Suite (STS)][gs-sts] and work your way through it from there.
Like all Spring's [Getting Started guides](/guides/gs), 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](#scratch).
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](http://gradle.org) and [Maven](https://maven.apache.org) is included here. If you're not familiar with either, refer to [Building Java Projects with Gradle](/guides/gs/gradle/) or [Building Java Projects with Maven](/guides/gs/maven).
In a project directory of your choosing, create the following subdirectory structure; for example, with `mkdir -p src/main/java/hello` on *nix systems:
Below is the [initial Gradle build file](https://github.com/spring-guides/gs-batch-processing/blob/master/initial/build.gradle). But you can also use Maven. The pom.xml file is included [right here](https://github.com/spring-guides/gs-batch-processing/blob/master/initial/pom.xml). If you are using [Spring Tool Suite (STS)][gs-sts], you can import the guide directly.
This guide is using [Spring Boot's starter POMs](/guides/gs/spring-boot/).
### Create 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`
```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.
### Define the destination for your data
Next, you write a SQL script to create a table to store the data.
`src/main/resources/schema-all.sql`
```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)
);
```
> **Note:** Spring Boot runs `schema-@@platform@@.sql` automatically during startup. `-all` is the default for all platforms.
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.
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`.
> **Note:** 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.
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.
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.
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`
```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,
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.
> **Note:** 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>`.
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.
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][u-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.
Now that your `Application` class is ready, you simply instruct the build system to create a single, executable jar containing everything. This makes it easy to ship, version, and deploy the service as an application throughout the development lifecycle, across different environments, and so forth.
Below are the Gradle steps, but if you are using Maven, you can find the updated pom.xml [right here](https://github.com/spring-guides/gs-batch-processing/blob/master/complete/pom.xml) and build it by typing `mvn clean package`.
The [Spring Boot gradle plugin][spring-boot-gradle-plugin] collects all the jars on the classpath and builds a single "über-jar", which makes it more convenient to execute and transport your service.
It also searches for the `public static void main()` method to flag as a runnable class.
