大成多线程与Spring事务

public List processObjects(List objectIds) {

// Process and save to DB

logger.info("Running in thread " + Thread.currentThread().getName() + " with object ids " + objectIds.toString());

return objectIds.stream().collect(Collectors.toList());

}

}

第三步：也是最整体的一步，通过分块然后顺利完成并行处置。当然为了保持编译器的整齐开放性和隔离开放性，我们将在紧接著完全一致充分利用之前常用Decorator结构上模式。

克隆

/**

* Service implementation for parallel chunk processing

*/

@Service

@Primary

@ConditionalOnProperty(prefix = "service", name = "parallel", havingValue = "true")

public class ProcessingServiceParallelRunDecorator implements ProcessingService {

private ProcessingService delegate;

public ProcessingServiceParallelRunDecorator(ProcessingService delegate) {

this.delegate = delegate;

}

/**

* In a real scenario it should be an external configuration

*/

private int batchSize = 10;

@Override

public List processObjects(List objectIds) {

List chuncks = getBatches(objectIds, batchSize);

List processedObjectIds = chuncks.parallelStream().map(delegate::processObjects)

.collect(Collectors.toList());

return flatList(processedObjectIds);

}

private List> getBatches(List collection, int batchSize) {

return IntStream.iterate(0, i -> i i + batchSize)

.mapToObj(i -> collection.subList(i, Math.min(i + batchSize, collection.size())))

.collect(Collectors.toList());

}

private List flatList(List> listOfLists) {

return listOfLists.stream().collect(ArrayList::new, List::addAll, List::addAll);

}

最后，我们通过一个简单的程式设计可验证一下执行者的结果。

克隆

private List> getBatches(List collection, int batchSize) {

return IntStream.iterate(0, i -> i i + batchSize)

.mapToObj(i -> collection.subList(i, Math.min(i + batchSize, collection.size())))

.collect(Collectors.toList());

}

private List flatList(List> listOfLists) {

return listOfLists.stream().collect(ArrayList::new, List::addAll, List::addAll);

通过输出日志，我们见到如下的执行者结果：

克隆

ProcessingDBService: Running in thread ForkJoinPool.commonPool-worker-3 with object ids [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

ProcessingDBService: Running in thread main with object ids [11, 12]

执行者结果也是符合期望目标的。List某类组后，除了主线许又通过ForkJoin启动时另外线许顺利完成并行处置。ProcessingServiceParallelRunDecorator 的parallelStream().map的并行处置大大提高了处置耐用开放性，而ProcessingDBService之前processObjects这个public法则上@Transactional的注解保证了的业务连续开放性，缺陷得以近乎化解。

译者介绍

胥磊，51CTO社区校对，某头部电商关键技术副总监，关注Java后端开发计划，关键技术管理制度，整体优化，分布式开发计划等领域。

原文标题：Multi-Threading and Spring Transactions，作者：Daniela Kolarova