Spring Batch Tutorial — Complete Guide

A complete, hands-on Spring Batch tutorial series for Java developers. Master enterprise batch processing with Spring Boot 3 and MySQL — from your first job to parallel processing and production deployments.

Spring Batch Tutorial — Complete Guide

This tutorial series takes you from zero to expert in Spring Batch. Every article uses Spring Boot 3.3+, Spring Batch 5.2+, Java 21, and MySQL 8.x. All code examples are complete and runnable, following a real-world e-commerce order processing domain throughout.

What you’ll build: A fully production-ready batch processing system — importing orders from CSV files and REST APIs, processing and enriching them in MySQL, scaling with parallel steps, handling failures gracefully, and deploying with monitoring.

Part 1: Foundations

  1. Introduction to Spring Batch: What, Why, and Architecture
  2. Chunk-Oriented Processing: The Core Batch Pattern
  3. Spring Boot + Spring Batch Setup: Your First Batch Job
  4. JobRepository and Batch Metadata: How Spring Batch Tracks Everything

Part 2: Readers — Getting Data In

  1. Reading Flat Files: CSV, Fixed-Width, and Delimited with FlatFileItemReader
  2. Reading from MySQL: JdbcCursorItemReader and JdbcPagingItemReader
  3. Reading with JPA: JpaPagingItemReader and Entity-Based Reading
  4. Reading from External Sources: REST APIs, S3, and Custom ItemReaders

Part 3: Writers — Getting Data Out

  1. Writing to Files and Databases: FlatFileItemWriter and JdbcBatchItemWriter
  2. Advanced Writers: JpaItemWriter, CompositeItemWriter, and Custom Writers

Part 4: ItemProcessor and Transformation

  1. Transforming Data with ItemProcessor: Validation, Filtering, and Enrichment
  2. Advanced Processing: CompositeItemProcessor, External APIs, and Async Processing

Part 5: Job and Step Configuration

  1. Configuring Jobs and Steps: Flows, Decisions, and Conditional Execution
  2. JobParameters, Execution Context, and Job Restartability
  3. Tasklets: Running Non-Chunk Operations in Batch Jobs

Part 6: Listeners and Observability

  1. Listeners: Hooking into Job, Step, and Chunk Lifecycle Events

Part 7: Error Handling and Fault Tolerance

  1. Retry Logic: Handling Transient Failures Gracefully
  2. Skip Logic, Dead Letter Patterns, and Job Restart Strategies

Part 8: Testing

  1. Testing Spring Batch Jobs: Unit Tests, Integration Tests, and @SpringBatchTest

Part 9: Scaling and Parallel Processing

  1. Multi-Threaded Steps and Async Processing for Performance
  2. Partitioning: Splitting Work Across Parallel Workers
  3. Remote Partitioning and Remote Chunking with Kafka

Part 10: Production and Operations

  1. Scheduling Batch Jobs: @Scheduled, Quartz, and Clustered Scheduling
  2. Performance Tuning: Chunk Size, Connection Pools, and Memory Management
  3. Best Practices Reference: The Complete Spring Batch Checklist