{
  "id": 5374184,
  "title": "Spring Batch: Processing Large Data Volumes Efficiently (2026-09-03 17:31)",
  "url": "https://urgent.news/2026/09/03/spring-batch-processing-large-data-volumes-efficiently-2026-09-03-17",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-03T17:31:30.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/said_olano/spring-batch-processing-large-data-volumes-efficiently-2026-09-03-1731-33kc"
  },
  "original_language": "en",
  "account": null,
  "summary": "Spring Batch offers a robust framework for processing large data volumes efficiently, with transaction management, chunk-based processing, restartability, and scaling primitives. Its core concepts involve a job composed of one or more steps, each following a read-process-write cycle with ItemReader, ItemProcessor, and ItemWriter components. The chunk-oriented processing allows items to be read one at a time, accumulated into a chunk, and then written together within a single transaction. A basic chunk-oriented step can be implemented using the @Bean method, specifying a chunk size of 1000 items, which strikes a balance between minimizing transaction overhead and avoiding memory pressure. Choosing the appropriate chunk size is crucial for optimal performance. When reading large datasets, Spring Batch provides two strategies: using a JdbcCursorItemReader for a database cursor approach, or employing a JdbcPagingItemReader for repeated paginated queries, ensuring better restartability and thread safety.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}