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This guide walks you through the process of creating a basic batch-driven solution.
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.
Typically your customer or a business analyst supplies a spreadsheet. In this case, you make it up.
src/main/resources/sample-data.csv
link:initial/src/main/resources/sample-data.csv[role=include]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
link:initial/src/main/resources/schema-all.sql[role=include]|
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Spring Boot runs schema-@@platform@@.sql automatically during startup. -all is the default for all platforms.
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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
link:complete/src/main/java/hello/Person.java[role=include]You can instantiate the Person class either with first and last name through a constructor, or by setting the properties.
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
link:complete/src/main/java/hello/PersonItemProcessor.java[role=include]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.
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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.
src/main/java/hello/BatchConfiguration.java
link:complete/src/main/java/hello/BatchConfiguration.java[role=include]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
link:/complete/src/main/java/hello/BatchConfiguration.java[role=include].
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
link:/complete/src/main/java/hello/BatchConfiguration.java[role=include]. 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.
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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>.
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src/main/java/hello/JobCompletionNotificationListener.java
link:/complete/src/main/java/hello/JobCompletionNotificationListener.java[role=include]This code listens for when a job is BatchStatus.COMPLETED, and then uses JdbcTemplate to inspect the results.
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
link:complete/src/main/java/hello/Application.java[role=include]For demonstration purposes, there is code to create a JdbcTemplate, query the database, and print out the names of people the batch job inserts.
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.
Congratulations! You built a batch job that ingested data from a spreadsheet, processed it, and wrote it to a database.