https://github.com/sandysanthosh/spring-batch
Spring Batch using Spring Boot
https://github.com/sandysanthosh/spring-batch
Last synced: 10 months ago
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Spring Batch using Spring Boot
- Host: GitHub
- URL: https://github.com/sandysanthosh/spring-batch
- Owner: sandysanthosh
- License: apache-2.0
- Created: 2024-11-20T13:06:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-20T13:06:28.000Z (about 1 year ago)
- Last Synced: 2025-01-25T13:25:41.174Z (12 months ago)
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Spring Batch is a lightweight, comprehensive batch framework designed for developing robust batch applications. It is part of the larger Spring ecosystem and provides reusable functions for processing large volumes of data.
Here’s an overview of Spring Batch and its key components:
---
### **Core Concepts**
1. **Job**:
- Represents the entire batch process.
- It is a container for steps.
- Can be configured to include multiple steps, execute sequentially or conditionally.
2. **Step**:
- A single task or phase in a batch job.
- Composed of three parts:
- **ItemReader**: Reads data (e.g., from a database, file, or queue).
- **ItemProcessor**: Processes the data (e.g., filtering, transforming).
- **ItemWriter**: Writes the data (e.g., to a database, file).
3. **Execution Context**:
- Stores metadata and maintains state between job and step executions.
4. **JobRepository**:
- Central component for storing job execution data (e.g., job metadata, status).
5. **JobLauncher**:
- Used to launch a batch job programmatically.
---
### **Key Features**
- **Chunk-Oriented Processing**:
- Reads data in chunks and processes them in batches.
- Efficient for handling large data sets.
- **Transaction Management**:
- Supports declarative transaction management to ensure consistency.
- **Retry and Skip Mechanisms**:
- Handles errors by retrying or skipping records based on rules.
- **Partitioning**:
- Splits a job into parallelizable partitions to optimize processing time.
- **Scalability**:
- Supports both multi-threading and remote processing for scalability.
---
### **Basic Example**
Here’s how you can configure a simple Spring Batch job:
```java
@Configuration
@EnableBatchProcessing
public class BatchConfig {
@Autowired
private JobBuilderFactory jobBuilderFactory;
@Autowired
private StepBuilderFactory stepBuilderFactory;
@Bean
public ItemReader reader() {
return new ListItemReader<>(List.of("Item1", "Item2", "Item3"));
}
@Bean
public ItemProcessor processor() {
return item -> "Processed " + item;
}
@Bean
public ItemWriter writer() {
return items -> items.forEach(System.out::println);
}
@Bean
public Step step1() {
return stepBuilderFactory.get("step1")
.chunk(1)
.reader(reader())
.processor(processor())
.writer(writer())
.build();
}
@Bean
public Job job() {
return jobBuilderFactory.get("job1")
.start(step1())
.build();
}
}
```
---
### **Common Use Cases**
1. **ETL Jobs**:
- Extract-Transform-Load processes for migrating or processing data.
2. **Data Integration**:
- Combine and harmonize data from multiple sources.
3. **Report Generation**:
- Generate reports based on complex logic.
4. **Data Cleanup**:
- Process and clean up stale or corrupted data.
---
Would you like help with a specific Spring Batch feature or issue? 😊