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.
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.
`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.
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 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.
`@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.
