Spring Batch Job Execution Guide | Generated by AI

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Spring Batch is a lightweight, open-source framework from the Spring ecosystem designed for batch processing — executing jobs that involve reading, processing, and writing large volumes of data in a reliable and scalable way.

Here’s a comprehensive breakdown of how a Spring Batch job works:

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1. Core Concepts

• Job A job is the top-level entity. It represents the entire batch process and is made up of one or more steps.

• Step Each step represents a phase in the job (e.g., read → process → write). Steps can be chunk-oriented or tasklet-oriented.

• JobLauncher Component responsible for starting a job, usually triggered via code, a scheduler (Quartz, cron), or command line.

• JobRepository A database-backed store that keeps metadata about job executions, such as status, start/end times, exit codes, and restart points.

• JobExecution & StepExecution Represent the runtime state of a job and its steps, including success, failure, or partial completion.

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2. Execution Flow

1. Job Launch A JobLauncher starts the job by passing it a Job instance and JobParameters.

2. Job Instance & Metadata The framework checks the JobRepository to see if the job instance with those parameters already exists.

   • If yes, it may continue/restart.
   • If no, it creates a new instance.

3. Step Execution Each step runs sequentially (unless you configure parallel flows).

   • If one step fails and isn’t set to “allow restart,” the job stops.
   • Otherwise, the job continues to the next step.

4. Chunk-Oriented Processing (most common)

   • Reader: Reads a chunk of data (e.g., 100 records from a file/DB).
   • Processor: Optionally transforms or validates the data.
   • Writer: Writes the processed chunk to a target (e.g., DB, file, API).
   • After commit, Spring Batch persists the checkpoint in the JobRepository.

5. Fault Tolerance

   • Retry logic, skip policies, and restart capabilities are built-in.
   • If the job crashes, it can restart from the last committed checkpoint.

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3. Example

Imagine processing a CSV file of transactions:

• Reader: FlatFileItemReader loads rows from the CSV.
• Processor: A custom class validates and converts them into domain objects.
• Writer: JdbcBatchItemWriter inserts them into a database in batches.

• Job Flow:

  • Step 1: Read/process/write transactions.
  • Step 2: Generate a summary report.

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4. Advantages

• Declarative configuration (Java config or XML).
• Scalable (parallel steps, partitioning, remote chunking).
• Robust error handling and restartability.
• Integration with Spring ecosystem (Spring Boot, Spring Data, etc.).

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✅ In short: A Spring Batch job is a well-structured pipeline of steps where data flows from input → processing → output, with built-in transaction management, fault tolerance, and restartability.

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Do you want me to also show you a minimal Java config code example of a Spring Batch job (with reader, processor, writer) so you can see how it looks in practice?

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