Intro to Spring Batch and ETL

Welcome to the gentle art of moving mountains of data without losing your mind. Spring Batch with Spring Boot gives you a tired but reliable set of tools for ETL and Java Batch jobs. If your use case involves reading from files or databases transforming rows and writing to a sink then this is the playground.

Project setup and job configuration

Create a Spring Boot project and add Spring Batch plus a JDBC driver for your job repository. Spring Boot will wire sensible defaults so you do not have to fight configuration noise. Declare Job and Step beans and pick a chunk size that matches your memory and latency goals. Chunk Processing controls how many items are read before a write and where transaction boundaries land.

Readers processors and writers

Choose an ItemReader that matches the source format. FlatFileItemReader works great for CSV files while JdbcPagingItemReader is a solid choice for database extracts. Map incoming fields to a simple DTO so the transformer gets clean input and does not have to guess what you meant.

The ItemProcessor handles transformation validation and enrichment. Keep processors small and stateless. Small focused processors are easy to unit test and much less likely to produce spooky production behavior at 3am.

ItemWriter implementations batch writes for efficiency. Use JdbcBatchItemWriter for relational targets or write a custom ItemWriter for APIs or message queues. Design your writers to be idempotent whenever possible so retries and restarts do not create duplicates.

Job repository and transactional behavior

Use a dedicated schema for job metadata and tune transaction timeouts based on chunk size and downstream latency. Spring Batch stores execution state so restarts pick up where they left off and do not reprocess already completed chunks. That is the magic that saves you from rerunning the whole world after a single bad row.

Testing failure scenarios and observability

Simulate partial failures to verify restart semantics and idempotency. Expose metrics for step durations processed counts and error rates so the operations team can make informed choices other than panicking. Log chunk boundaries and record counts to make troubleshooting less painful.

• Test multiple chunk sizes to find the sweet spot between throughput and memory
• Verify restart behavior with corrupted or partial input
• Ensure writers handle retries and duplicate suppression

Practical tips and common gotchas

Keep business logic in the processor and not in the reader. Stateless processors scale better and cause fewer weird race conditions. If your job calls external APIs be ready for retries backoff and circuit breaking. Monitor memory usage and tune chunk processing rather than guessing numbers.

With careful Job Configuration and a pragmatic Batch Architecture you get reliable ETL that survives crashes and awkward data. In short use the right ItemReader ItemProcessor and ItemWriter and Spring Batch will do the heavy lifting while you go have coffee and think about a nicer job name.