Batch processing remains a crucial component of many enterprise systems — from ETL jobs to scheduled data migrations and report generation. When applications need to process millions of records efficiently, Spring Batch is the go-to solution in the Java ecosystem.

Paired with Spring Boot, Spring Batch offers a robust, configurable, and highly scalable way to handle large data volumes with transaction management, retry policies, and fault tolerance built-in.

In this guide, you’ll learn how to implement batch jobs using Spring Boot and Spring Batch, including core concepts like Job, Step, ItemReader, ItemProcessor, and ItemWriter, and how to scale them to handle large data sets.

Why Use Spring Batch?

Spring Batch provides:

  • Declarative configuration with Spring
  • Chunk-oriented processing for efficiency
  • Transactional integrity and checkpointing
  • Built-in retry and skip mechanisms
  • Job restarts and history tracking
  • Scalability via parallel or remote partitioning

It’s ideal for use cases like:

  • Importing large CSV or XML files
  • Database-to-database transformations
  • Scheduled cleanup jobs
  • Data enrichment pipelines

Project Setup

Add the required dependencies:

<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-batch</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>

Spring Boot auto-configures an in-memory job repository, but for production, connect it to a persistent database (like PostgreSQL or MySQL) to store job metadata.

Basic Components of a Batch Job

A Spring Batch job consists of one or more Steps, and each step follows a Reader → Processor → Writer pattern.

Example configuration:

@Bean
public Job importUserJob(JobRepository jobRepository, Step step1) {
return new JobBuilder("importUserJob", jobRepository)
.start(step1)
.build();
}

@Bean
public Step step1(JobRepository jobRepository, PlatformTransactionManager txManager) {
return new StepBuilder("step1", jobRepository)
.<User, User>chunk(100, txManager)
.reader(userReader())
.processor(userProcessor())
.writer(userWriter())
.build();
}

Reading Large Files

To read a large CSV file:

@Bean
public FlatFileItemReader<User> userReader() {
return new FlatFileItemReaderBuilder<User>()
.name("userItemReader")
.resource(new FileSystemResource("users.csv"))
.delimited()
.names("id", "name", "email")
.targetType(User.class)
.build();
}

For databases, use JdbcCursorItemReader or JpaPagingItemReader depending on your needs.

Processing Logic

Use ItemProcessor to transform or filter data:

@Bean
public ItemProcessor<User, User> userProcessor() {
return user -> {
user.setEmail(user.getEmail().toLowerCase());
return user;
};
}

Return null to skip a record.

Writing to Database

Use a JDBC writer for large-scale insert/update operations:

@Bean
public JdbcBatchItemWriter<User> userWriter(DataSource dataSource) {
return new JdbcBatchItemWriterBuilder<User>()
.sql("INSERT INTO users (id, name, email) VALUES (:id, :name, :email)")
.beanMapped()
.dataSource(dataSource)
.build();
}

Batch writers offer excellent performance and can handle thousands of records per second.

Handling Errors and Skipping Records

Spring Batch allows you to skip or retry records without failing the entire job:

stepBuilderFactory.get("step")
.<User, User>chunk(100)
.reader(reader())
.processor(processor())
.writer(writer())
.faultTolerant()
.skipLimit(10)
.skip(ParseException.class)
.retryLimit(3)
.retry(SQLException.class)
.build();

This ensures robustness in jobs running over unreliable or inconsistent data.

Job Parameters and Scheduling

Pass runtime parameters:

JobParameters jobParameters = new JobParametersBuilder()
.addString("fileName", "data.csv")
.addLong("time", System.currentTimeMillis())
.toJobParameters();
jobLauncher.run(job, jobParameters);

Use @Scheduled or tools like Quartz for periodic execution.

Parallel and Partitioned Processing

To improve throughput:

  • Use multi-threaded steps (taskExecutor)
  • Partition jobs to distribute across nodes
  • Use remote chunking for horizontally scalable workloads

Example for parallel step execution:

@Bean
public TaskExecutor taskExecutor() {
return new SimpleAsyncTaskExecutor("batch-thread-");
}

Combine with step.taskExecutor(taskExecutor()) to enable concurrency.

Monitoring and Job Repository

Spring Batch uses a JobRepository to store:

  • Job instances
  • Job executions
  • Step executions
  • Parameters and status

You can query this data via Spring Batch Admin UI or custom monitoring dashboards.

For observability, integrate:

  • Spring Boot Actuator
  • Prometheus and Grafana
  • Logs and alerts for job failure or success

Conclusion

Spring Boot and Spring Batch provide a rock-solid framework for building scalable, fault-tolerant, and maintainable batch processing systems. Whether you’re processing millions of records from a flat file or orchestrating complex data pipelines, Spring Batch offers the tools you need to succeed.

By leveraging chunking, skip policies, parallel processing, and transaction management, you can confidently build Java batch jobs that handle massive datasets with grace and performance.