Welcome to the cheerful world of batch processing where jobs run overnight and you only notice the chaos at 2 AM. If you need reliable Spring Batch pipelines with Spring Boot and Java this guide will get you from project skeleton to a production ready job without soul crushing surprises. Expect setup steps tests and tips for tuning chunk processing retry and scaling.

Setup a Spring Boot project

Create a new Spring Boot project with spring boot starter batch and a JDBC driver. Use Spring Initializr or declare the dependencies in your build file. Configure a DataSource and run the Spring Batch schema SQL against your database so the framework can store job metadata in the job repository tables.

Define jobs steps and flow

Use JobBuilderFactory and StepBuilderFactory to compose jobs and steps. Decide if a step is chunk oriented or a simple tasklet. Chunk processing is the usual choice for record oriented work where you read process and write items in a transaction sized batch.

Core pattern

• ItemReader to read source records such as files or a database
• ItemProcessor to transform validate or reject records
• ItemWriter to persist results often in batches for performance

This separation makes testing easier and makes your midnight debugging slightly less dramatic.

Configure JobRepository JobLauncher and transactions

Spring Batch uses a JobRepository to persist execution state so jobs can resume after failures. Wire up a JobLauncher to start jobs and configure transaction management that matches your database isolation and concurrency needs. If you skip this part the framework will still run but you will regret it when you need restarts or parallel execution.

Tune chunk size retry and skip policies

Pick a sensible chunk size based on record size and transaction cost. Too small and you drown in overhead. Too large and one bad record drags the whole transaction down. Add retry rules for transient errors and skip policies for bad data so you do not block the whole job because of one rotten CSV line.

Scale with partitioning or remote steps

If single node throughput is not enough add partitioning or remote step execution. Partitioning splits input into ranges that run in parallel so you scale horizontally without rewriting your reader and writer logic. Monitor for contention on the database and tune accordingly.

Run tests monitor and iterate

Execute jobs locally then run them in staging. Monitor logs and your chosen dashboard to observe throughput and failures. Use metrics and job instance history from the job repository to find hotspots. Adjust chunk size retry and skip settings until you strike a balance between throughput and stability.

Quick checklist

• Include spring boot starter batch and JDBC driver
• Apply the Spring Batch schema for meta tables
• Define Job and Steps with JobBuilderFactory and StepBuilderFactory
• Implement ItemReader ItemProcessor and ItemWriter
• Configure JobRepository JobLauncher and transactions
• Tune chunk size and add retry and skip policies
• Consider partitioning for horizontal scaling

Follow these steps and you will have a maintainable Spring Batch pipeline that behaves in production and only occasionally threatens your sleep. If something still explodes at 2 AM at least your restart logic will work.