batch_high_volume

If you want to optimize performance and avoid using JPA for large-scale data processing, you can leverage alternative approaches such as using JDBC for direct database operations, optimizing batch processing, and minimizing overhead. Here's how you can adjust the Spring Batch setup to use JDBC instead of JPA for improved performance:

### 1. **Using JDBC with Spring Batch**

**1.1 Dependencies**

First, ensure that you have the necessary dependencies in your `pom.xml`. You can use Spring's JDBC support for this purpose:

```xml
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework</groupId>
    <artifactId>spring-jdbc</artifactId>
</dependency>
<dependency>
    <groupId>com.h2database</groupId>
    <artifactId>h2</artifactId>
    <scope>runtime</scope>
</dependency>
```

**1.2 Configuration**

Set up your `DataSource` and `JdbcTemplate`:

```java
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.jdbc.core.JdbcTemplate;
import org.springframework.jdbc.datasource.DriverManagerDataSource;

import javax.sql.DataSource;

@Configuration
public class DataSourceConfig {

    @Bean
    public DataSource dataSource() {
        DriverManagerDataSource dataSource = new DriverManagerDataSource();
        dataSource.setDriverClassName("org.h2.Driver");
        dataSource.setUrl("jdbc:h2:mem:testdb;DB_CLOSE_ON_EXIT=FALSE");
        dataSource.setUsername("sa");
        dataSource.setPassword("");
        return dataSource;
    }

    @Bean
    public JdbcTemplate jdbcTemplate() {
        return new JdbcTemplate(dataSource());
    }
}
```

### 2. **Using JdbcCursorItemReader**

For reading data using JDBC, you can use `JdbcCursorItemReader`. This approach uses SQL queries and processes rows as they are fetched, which is efficient for large datasets.

**2.1 Setup JdbcCursorItemReader**

```java
import org.springframework.batch.item.database.JdbcCursorItemReader;
import org.springframework.batch.item.database.builder.JdbcCursorItemReaderBuilder;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class BatchConfig {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Bean
    public JdbcCursorItemReader<SourceEntity> reader() {
        return new JdbcCursorItemReaderBuilder<SourceEntity>()
                .dataSource(jdbcTemplate.getDataSource())
                .name("sourceReader")
                .sql("SELECT id, data FROM source_entity")
                .rowMapper((rs, rowNum) -> {
                    SourceEntity entity = new SourceEntity();
                    entity.setId(rs.getLong("id"));
                    entity.setData(rs.getString("data"));
                    return entity;
                })
                .build();
    }
}
```

### 3. **Using JdbcBatchItemWriter**

For writing data, `JdbcBatchItemWriter` provides efficient batch writes. This avoids the overhead of ORM frameworks and handles bulk insertions or updates directly.

**3.1 Setup JdbcBatchItemWriter**

```java
import org.springframework.batch.item.database.builder.JdbcBatchItemWriterBuilder;
import org.springframework.batch.item.database.JdbcBatchItemWriter;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class BatchConfig {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Bean
    public JdbcBatchItemWriter<DestinationEntity> writer() {
        return new JdbcBatchItemWriterBuilder<DestinationEntity>()
                .dataSource(jdbcTemplate.getDataSource())
                .sql("INSERT INTO destination_entity (id, data) VALUES (:id, :data)")
                .beanMapped()
                .build();
    }
}
```

### 4. **Processor and Auditing**

For the processor, handle processing logic and auditing directly:

**4.1 Processor**

```java
import org.springframework.batch.item.ItemProcessor;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;

@Component
public class MyItemProcessor implements ItemProcessor<SourceEntity, DestinationEntity> {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Override
    public DestinationEntity process(SourceEntity item) throws Exception {
        // Create and save audit record
        String sql = "INSERT INTO audit_entity (item_id, item_data, processed_time, status) VALUES (?, ?, ?, ?)";
        jdbcTemplate.update(sql, item.getId(), item.getData(), LocalDateTime.now(), "Processed");

        DestinationEntity destination = new DestinationEntity();
        destination.setId(item.getId());
        destination.setData(item.getData());

        return destination;
    }
}
```

### 5. **Complete Batch Configuration**

**5.1 Complete Batch Configuration**

```java
import org.springframework.batch.core.Job;
import org.springframework.batch.core.Step;
import org.springframework.batch.core.configuration.annotation.EnableBatchProcessing;
import org.springframework.batch.core.configuration.annotation.JobBuilderFactory;
import org.springframework.batch.core.configuration.annotation.StepBuilderFactory;
import org.springframework.batch.core.configuration.annotation.JobBuilderFactory;
import org.springframework.batch.core.step.tasklet.Tasklet;
import org.springframework.batch.core.step.tasklet.TaskletStep;
import org.springframework.batch.repeat.RepeatStatus;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
@EnableBatchProcessing
public class BatchConfig {

    @Autowired
    private JobBuilderFactory jobBuilderFactory;

    @Autowired
    private StepBuilderFactory stepBuilderFactory;

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Autowired
    private ItemProcessor<SourceEntity, DestinationEntity> processor;

    @Autowired
    private JdbcBatchItemWriter<DestinationEntity> writer;

    @Bean
    public JdbcCursorItemReader<SourceEntity> reader() {
        return new JdbcCursorItemReaderBuilder<SourceEntity>()
                .dataSource(jdbcTemplate.getDataSource())
                .name("sourceReader")
                .sql("SELECT id, data FROM source_entity")
                .rowMapper((rs, rowNum) -> {
                    SourceEntity entity = new SourceEntity();
                    entity.setId(rs.getLong("id"));
                    entity.setData(rs.getString("data"));
                    return entity;
                })
                .build();
    }

    @Bean
    public Step step1() {
        return stepBuilderFactory.get("step1")
                .<SourceEntity, DestinationEntity>chunk(1000) // Adjust chunk size for performance
                .reader(reader())
                .processor(processor)
                .writer(writer)
                .build();
    }

    @Bean
    public Job importUserJob() {
        return jobBuilderFactory.get("importUserJob")
                .flow(step1())
                .end()
                .build();
    }
}
```

### 6. **Adjust Chunk Size**

The chunk size is critical in optimizing performance:

```java
@Bean
public Step step1() {
    return stepBuilderFactory.get("step1")
            .<SourceEntity, DestinationEntity>chunk(1000) // Adjust based on performance tests
            .reader(reader())
            .processor(processor())
            .writer(writer())
            .build();
}
```

### 7. **Transaction Management**

Spring Batch handles transactions for you, but ensure that the database settings are optimized for batch processing.

### Summary

**Optimizing a Spring Batch project using JDBC includes:**

1. **Using `JdbcCursorItemReader`** for efficient data reading.
2. **Using `JdbcBatchItemWriter`** for bulk data writing.
3. **Configuring a `JdbcTemplate`** for efficient database operations.
4. **Handling data processing and auditing directly** in the processor.
5. **Adjusting chunk sizes** based on performance testing.

By leveraging these strategies, you can significantly enhance the performance of your Spring Batch application when working with large datasets, avoiding the overhead associated with JPA and ORM frameworks.