Job: A job is the highest-level concept in Spring Batch. It represents a complete batch process, consisting of multiple steps. A job typically has a start and end point and may include decision points and conditional branching.
Step: A step is a fundamental unit of work within a job. It represents an individual task or operation, such as reading data, processing it, and writing the results. Steps can be linear or conditional.
Item: Items are the pieces of data that are processed by batch jobs. In Spring Batch, items can be any Java objects, and you can define how items are read, processed, and written.
ItemReader: An ItemReader is responsible for reading items from a data source, such as a database, file, or REST API. Spring Batch provides various built-in ItemReaders for common data sources, and you can create custom readers for specialized cases.
ItemProcessor: An ItemProcessor is used to process and transform items before they are written to an output source. Processors are optional and can be used to perform data validation, enrichment, or other operations.
ItemWriter:An ItemWriter is responsible for writing items to an output source, such as a database, file, or message queue. Spring Batch offers various built-in ItemWriters, and you can create custom writers as needed.
JobRepository:The JobRepository is responsible for managing job metadata, such as job and step execution status, restartability, and job parameters. It stores this information in a persistent storage (e.g., a database) to ensure that jobs can be restarted in case of failure.
JobLauncher:The JobLauncher is responsible for starting and running batch jobs. It takes a job definition and job parameters as input and initiates the execution of the job.
Listeners: Spring Batch provides listeners that allow you to intercept and respond to various events during the batch processing lifecycle. You can use listeners to perform actions before and after steps, jobs, and item processing.
Listeners: Spring Batch provides listeners that allow you to intercept and respond to various events during the batch processing lifecycle. You can use listeners to perform actions before and after steps, jobs, and item processing.
Listeners: Spring Batch promotes chunk-based processing, where items are read, processed, and written in chunks rather than individually. This approach helps optimize performance and resource usage.
Retry and Skip: Spring Batch provides mechanisms for handling errors and exceptions during batch processing. You can configure retry and skip policies to manage exceptions and continue processing.
Partitioning: Spring Batch supports the partitioning of batch jobs, allowing you to split a large job into smaller, parallelizable chunks for better performance.
To use Spring Batch, you typically include the necessary Spring Batch dependencies in your project and configure batch jobs, steps, item readers, processors, and writers using XML or Java-based configuration. Spring Batch can be used with Spring Boot to simplify the setup and configuration of batch processing applications.
