Firebird Batch Processing and Job Scheduling in Spring Boot

January 15, 2026

Batch processing is useful when dealing with repetitive or heavy database operations. With Spring Boot and Firebird, you can easily schedule and execute batch jobs for data imports, reports, or cleanup tasks efficiently.

1. Setting Up Batch Jobs

Spring Boot provides the @Scheduled annotation for periodic jobs.
Example for running a cleanup job daily:

@Component
public class CleanupJob {
    
    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Scheduled(cron = "0 0 2 * * *")
    public void cleanupOldData() {
        String sql = "DELETE FROM logs WHERE created_at < CURRENT_DATE - 30";
        jdbcTemplate.update(sql);
        System.out.println("Old data cleaned up successfully");
    }
}

This job runs every day at 2 AM and deletes logs older than 30 days.

2. Batch Insert and Update with JdbcTemplate

Batch operations minimize round-trips to the Firebird server, improving performance.

jdbcTemplate.batchUpdate(
    "INSERT INTO employee (name, dept_id) VALUES (?, ?)",
    new BatchPreparedStatementSetter() {
        public void setValues(PreparedStatement ps, int i) throws SQLException {
            ps.setString(1, employees.get(i).getName());
            ps.setInt(2, employees.get(i).getDeptId());
        }
        public int getBatchSize() {
            return employees.size();
        }
    });

This approach is ideal for importing large CSV files or syncing data.

3. Using Spring Batch for Complex Workflows

For more control over batch steps and error handling, use Spring Batch.
Add dependency in your pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-batch</artifactId>
</dependency>

Example job definition:

@Bean
public Job importJob(JobBuilderFactory jobs, StepBuilderFactory steps, Step step) {
    return jobs.get("importJob")
               .incrementer(new RunIdIncrementer())
               .flow(step)
               .end()
               .build();
}

Each Step can process data in chunks, reducing memory load while maintaining transactional integrity.

4. Scheduling Batch Jobs

You can combine Spring Batch with Spring Scheduler:

@EnableScheduling
@SpringBootApplication
public class AppScheduler {}

Then trigger batch jobs periodically:

@Scheduled(fixedRate = 600000)
public void runBatch() throws Exception {
    jobLauncher.run(job, new JobParameters());
}

This executes the batch job every 10 minutes automatically.

5. Monitoring and Logging

Track job execution using Spring Batch tables such as BATCH_JOB_EXECUTION and BATCH_STEP_EXECUTION.
Enable job logging for easier debugging:

spring.batch.job.enabled=true
logging.level.org.springframework.batch=DEBUG

Logs help identify failed steps and track job performance over time.

6. Handling Failures Gracefully

When a step fails, configure retries or skips:

.step("processStep")
.faultTolerant()
.retryLimit(3)
.skip(Exception.class)
.skipLimit(10)

This ensures your job continues running even if a few records fail, maintaining overall data consistency.

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