Spring Batch chunk processing explained with examples — Complete Guide

A practical, in-depth guide to Spring Batch chunk processing explained with examples with examples.

INTRO

Every time a legacy system needs to migrate millions of rows, or a nightly ETL job must transform a massive CSV file, the first thing that trips up engineers is memory. Loading the whole dataset into a list, looping over it, and persisting each record sounds simple, but it quickly blows up the JVM heap and stalls the pipeline. The real problem isn’t the size of the data—it’s the lack of a disciplined way to read, process, and write it in manageable pieces.

Spring Batch was built to solve exactly that. Its chunk-oriented processing model lets you define a read‑process‑write cycle that operates on a configurable number of items at a time. The framework handles transaction boundaries, restartability, and parallelism for you, so you can focus on the business logic instead of plumbing. In practice, mastering chunks means turning a brittle, single‑threaded import into a robust, scalable job that can survive crashes and scale horizontally.

If you’ve ever stared at a FlatFileItemReader that stalls on a 10‑GB file, or tried to roll your own paging logic only to lose consistency on failure, you’ll recognize the pain points that chunk processing eliminates. The guide below walks through the core concepts, shows you how to tune chunk size, and demonstrates real‑world patterns like skip‑logic and multi‑threaded steps.

WHAT YOU'LL LEARN

How Spring Batch defines a chunk and why transaction boundaries matter.
Configuring ItemReader, ItemProcessor, and ItemWriter for a clean, testable pipeline.
Choosing the right chunk size for memory‑constrained vs. high‑throughput scenarios.
Implementing skip and retry policies to handle dirty data without aborting the job.
Scaling chunk processing with multi‑threaded steps and partitioning.
Common pitfalls (e.g., non‑idempotent writers, stateful processors) and how to avoid them.

A SHORT CODE SNIPPET

@Configuration
@EnableBatchProcessing
public class ChunkJobConfig {

@Bean
public Job importUserJob(JobBuilderFactory jobs, Step step) {
return jobs.get("importUserJob")
.start(step)
.build();
}

@Bean
public Step step(StepBuilderFactory steps,
ItemReader<User> reader,
ItemProcessor<User, User> processor,
ItemWriter<User> writer) {
return steps.get("step")
.<User, User>chunk(100) // read & write 100 items per transaction
.reader(reader)
.processor(processor)
.writer(writer)
.faultTolerant()
.skipLimit(10)
.skip(InvalidDataException.class)
.build();
}
}

The snippet shows a minimal chunk step: 100 records are read, processed, and written inside a single transaction. If an InvalidDataException occurs, the step skips the offending record up to ten times before failing.

KEY TAKEAWAYS

Chunk size is a lever, not a magic number – start small, monitor memory, then increase until you hit the sweet spot for throughput.
Transaction management is automatic – each chunk is committed as a unit, giving you atomicity and easy restart capability.
Skip/Retry policies keep jobs alive – isolate bad records without sacrificing the whole batch.
Parallelism is additive – once the single‑threaded chunk works, you can add multi‑threading or partitioning to scale further.

👉 Read the complete guide with step-by-step examples, common mistakes, and production tips:
Spring Batch chunk processing explained with examples — Complete Guide