Financial systems process millions of records nightly after market hours, and doing this reliably at scale requires more than a simple read-transform-write loop. Spring Batch addresses challenges like crash recovery, duplicate prevention, and bulk database writes through chunk-oriented processing, persistent job tracking via JobRepository, and safe restartability. A practical implementation is shown covering all five components: Job configuration, FlatFileItemReader for CSV parsing, ItemProcessor for business logic transformation, ItemWriter for bulk database inserts, and a Tasklet for post-processing steps. Scaling options including multi-threaded steps, parallel steps, Remote Chunking with Kafka, and Remote Partitioning are also covered.
Table of contents
The Initial ApproachThe Real Challenges of ScaleWhy Spring Batch?Understanding the Core ConceptsGet Akshay Kumar Barya ’s stories in your inboxHow Restartability WorksA Simple Implementation1. The Job Configuration2. The Reader3. The Processor4. The Writer5. The Tasklet (Completion Step)6. Launching the JobConclusion1 Comment
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