Spring Batch Overview: Architecture, Core Concepts, and Practical Usage

Spring Batch is a lightweight, full‑featured batch processing framework provided by Spring, designed for high‑volume, automated, and transactional data handling in enterprise environments. It is not a scheduling framework but offers reusable features like transaction management, job restart, skip, and resource management.

The typical batch flow reads large records from databases, files, or queues, processes them, and writes the transformed data back, often handling billions of transactions daily. Spring Batch integrates with Spring's POJO‑based development model while allowing access to advanced enterprise services.

Key architectural components include:

Job: the top‑level abstraction representing a batch process, composed of ordered Steps.

Step: a distinct phase of a Job, containing an ItemReader, ItemProcessor, and ItemWriter.

JobRepository: persists Job, Step, and execution metadata.

JobLauncher: launches a Job with specific JobParameters.

ExecutionContext: stores key‑value pairs for restartability.

Core interfaces are illustrated with Java code examples:

public interface Job {
    String getName();
    boolean isRestartable();
    void execute(JobExecution execution);
    JobParametersIncrementer getJobParametersIncrementer();
    JobParametersValidator getJobParametersValidator();
}

A simple job configuration using Java config:

@Bean
public Job footballJob() {
    return this.jobBuilderFactory.get("footballJob")
        .start(playerLoad())
        .next(gameLoad())
        .next(playerSummarization())
        .end()
        .build();
}

JobInstance represents a logical execution of a Job, distinguished by JobParameters (e.g., a run date). JobExecution captures a single attempt to run a JobInstance, while StepExecution does the same for each Step, storing statistics and context.

ItemReader, ItemWriter, and ItemProcessor abstract data input, output, and transformation. Examples include JdbcPagingItemReader and JdbcCursorItemReader with corresponding bean definitions.

@Bean
public JdbcPagingItemReader itemReader(DataSource dataSource, PagingQueryProvider queryProvider) {
    Map
parameterValues = new HashMap<>();
    parameterValues.put("status", "NEW");
    return new JdbcPagingItemReaderBuilder
()
        .name("creditReader")
        .dataSource(dataSource)
        .queryProvider(queryProvider)
        .parameterValues(parameterValues)
        .rowMapper(customerCreditMapper())
        .pageSize(1000)
        .build();
}

Chunk processing groups a configurable number of items (e.g., 10) before committing a transaction, improving performance for large data sets.

Skip policies allow a Step to ignore a limited number of exceptions (skipLimit) or specific exception types (skip/noSkip), preventing job failure for tolerable errors.

Practical tips include disabling automatic job launch with spring.batch.job.enabled=false , handling memory exhaustion by using paged readers, and following batch design principles such as minimizing I/O, avoiding duplicate work, and ensuring data integrity.