JobExecutionListener可以使用A 在执行作业之前用参考数据填充高速缓存，并在作业完成后清除高速缓存。
这是一个例子：
import org.springframework.batch.core.Job;import org.springframework.batch.core.JobExecution;import org.springframework.batch.core.JobExecutionListener;import org.springframework.batch.core.JobParameters;import org.springframework.batch.core.Step;import org.springframework.batch.core.StepContribution;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.launch.JobLauncher;import org.springframework.batch.core.scope.context.ChunkContext;import org.springframework.batch.core.step.tasklet.Tasklet;import org.springframework.batch.repeat.RepeatStatus;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.cache.CacheManager;import org.springframework.cache.concurrent.ConcurrentMapCacheManager;import org.springframework.context.ApplicationContext;import org.springframework.context.annotation.AnnotationConfigApplicationContext;import org.springframework.context.annotation.Bean;import org.springframework.context.annotation.Configuration;@Configuration@EnableBatchProcessingpublic class MyJob { private JobBuilderFactory jobs; private StepBuilderFactory steps; public MyJob(JobBuilderFactory jobs, StepBuilderFactory steps) { this.jobs = jobs; this.steps = steps; } @Bean public CacheManager cacheManager() { return new ConcurrentMapCacheManager(); // return the implementation you want } @Bean public Tasklet tasklet() { return new MyTasklet(cacheManager()); } @Bean public Step step() { return steps.get("step") .tasklet(tasklet()) .build(); } @Bean public JobExecutionListener jobExecutionListener() { return new CachingJobExecutionListener(cacheManager()); } @Bean public Job job() { return jobs.get("job") .start(step()) .listener(jobExecutionListener()) .build(); } class MyTasklet implements Tasklet { private CacheManager cacheManager; public MyTasklet(CacheManager cacheManager) { this.cacheManager = cacheManager; } @Override public RepeatStatus execute(StepContribution contribution, ChunkContext chunkContext) throws Exception { String name = (String) cacheManager.getCache("referenceData").get("foo").get(); System.out.println("Hello " + name); return RepeatStatus.FINISHED; } } class CachingJobExecutionListener implements JobExecutionListener { private CacheManager cacheManager; public CachingJobExecutionListener(CacheManager cacheManager) { this.cacheManager = cacheManager; } @Override public void beforeJob(JobExecution jobExecution) { // populate cache as needed. Can use a jdbcTemplate to query the db here and populate the cache cacheManager.getCache("referenceData").put("foo", "bar"); } @Override public void afterJob(JobExecution jobExecution) { // clear cache when the job is finished cacheManager.getCache("referenceData").clear(); } } public static void main(String[] args) throws Exception { ApplicationContext context = new AnnotationConfigApplicationContext(MyJob.class); JobLauncher jobLauncher = context.getBean(JobLauncher.class); Job job = context.getBean(Job.class); jobLauncher.run(job, new JobParameters()); }}执行后，将打印：
Hello bar
这意味着可以从缓存中正确检索数据。您将需要调整样本以查询数据库并填充缓存（请参见代码中的注释）。
希望这可以帮助。
