🚀 Building Spring by Example V2: Completing Module 07 - Spring Batch

When learning about Spring, many tutorials demonstrated features without explaining the underlying reasons or how the components fit within a real-world application. To address this, I began a project called spring-by-example. The approach is straightforward: rather than constructing a single large application, I create a series of small, focused examples that each delve into a specific Spring concept, accompanied by clear explanations, tests, and documentation. I am pleased to announce that I have now completed Module 07 - Spring Batch.

This module progresses from the basics of batch processing to advanced topics such as job execution metadata, restartability, fault tolerance, and testing. The module encompasses:

Jobs and Steps: A Job represents the entire batch process, while Steps define the individual operations that comprise that process.

Job Parameters: Parameters enable values to be supplied to a job during runtime, allowing for runtime customization of job behavior.

Item Readers, Processors, and Writers: These constitute the cornerstone of chunk-oriented processing. An ItemReader retrieves input data, an ItemProcessor modifies or filters it, and an ItemWriter outputs the processed items. For instance, a processor might convert an order description to uppercase:

@Override

public ProcessedOrder process(Order order) {

return new ProcessedOrder(order.getId(), order.getDescription().toUpperCase(Locale.ROOT));

}

Chunk-Oriented Processing: This technique groups items into batches for processing and writing, enhancing transaction boundaries and controlling execution flow. For example, processing items in groups of three:

new ChunkOrientedStepBuilder(Order, ProcessedOrder) (processOrdersStep, jobRepository, 3)

.reader(orderItemReader)

.processor(orderItemProcessor)

.writer(orderItemWriter)

.build();

Tasklet Processing: Unlike chunk-oriented processing, a tasklet is suitable for a single operation or a small, self-contained task, such as archiving records or executing database operations.

Job Execution and Metadata: Spring Batch records information about job instances, job executions, and step executions within a JDBC-backed JobRepository, enabling the exposure of execution data via REST endpoints and understanding the outcome of a batch run.

Restartability: A key feature of Spring Batch, restartability allows a batch job to fail partway through execution without necessitating a complete restart. The job can leverage persisted execution metadata and execution contexts to resume processing from the point it left off.

Skip and Retry: Spring Batch offers policies for handling failures differently. Some records may be invalid and can be skipped, while temporary failures might be retried. For example:

.faultTolerant()

.skipPolicy(skipPolicy)

.retryPolicy(retryPolicy)

The module concludes with batch testing, utilizing @SpringBatchTest, JobOperatorTestUtils, and Spring Boot testing support. This covers testing individual components, executing jobs and steps, verifying execution metadata, and testing step-scoped components. It emphasizes that batch application testing requires more than just verifying the final output; it also entails checking execution status, read/write counts, commit counts, job parameters, and the behavior of individual steps.

The spring-by-example project is open source, and I continue to expand it as I learn more. You can find it on GitHub (https://github.com/dsAQrAU5) or GitLab (https://gitlab.com/gfDEyGH3).

Completing Module 07 - Spring Batch represents a significant milestone in my journey. The next module will be Spring Integration, where I will continue to explore the Spring ecosystem through small, focused, runnable examples. One focused example at a time.