This guide walks you through creating a basic batch-driven solution. You 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
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- About 15 minutes
-<@prereq_editor_jdk_buildtools/>
## <@how_to_complete_this_guide jump_ahead='Create a business class'/>
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.
You can instantiate the `Person` class either with first and last name through a constructor, or by setting the properties.
Create an intermediate processor
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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.
Put together a batch job
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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.
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.
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>`.
This example uses a memory-based database (provided by `@EnableBatchProcessing`), meaning that when it's done, the data is gone. For demonstration purposes, there is extra code to create a `JdbcTemplate`, query the database, and print out the names of people the batch job inserts.
