数据流DoFn中的数据存储区查询在云中运行时会减慢管道

Question

我试图通过在DoFn步骤中查询数据存储来增强管道中的数据.来自Class CustomClass的对象中的字段用于对数据存储表执行查询,返回的值用于增强对象.

代码如下所示:

public class EnhanceWithDataStore extends DoFn<CustomClass, CustomClass> {

private static Datastore datastore = DatastoreOptions.defaultInstance().service();
private static KeyFactory articleKeyFactory = datastore.newKeyFactory().kind("article");

@Override
public void processElement(ProcessContext c) throws Exception {

    CustomClass event = c.element();

    Entity article = datastore.get(articleKeyFactory.newKey(event.getArticleId()));

    String articleName = "";
    try{
        articleName = article.getString("articleName");         
    } catch(Exception e) {}

    CustomClass enhanced = new CustomClass(event);
    enhanced.setArticleName(articleName);

    c.output(enhanced);
}

当它在本地运行时,这很快,但是当它在云中运行时,此步骤会显着减慢管道的速度.是什么导致了这个？有没有解决方法或更好的方法来做到这一点？

可以在此处找到管道的图片(​​最后一步是增强步骤): 管道架构

Answer 1

您在此处所做的是您的输入PCollection<CustomClass>与数据存储区中的增强功能之间的连接.

对于每个分区,您PCollection对数据存储区的调用将是单线程的,因此会产生大量延迟.我希望这是缓慢的DirectPipelineRunner,并InProcessPipelineRunner为好.通过自动调节和动态工作重新平衡,您应该在运行Dataflow服务时看到并行性,除非您的结构有些问题导致我们对其进行优化,因此您可以尝试增加--maxNumWorkers.但是你仍然无法从批量操作中受益.

在管道中表达此连接可能更好,DatastoreIO.readFrom(...)然后使用CoGroupByKey转换.通过这种方式,Dataflow将对所有增强功能进行批量并行读取,并使用高效的GroupByKey机制将它们与事件对齐.

// Here are the two collections you want to join
PCollection<CustomClass> events = ...;
PCollection<Entity> articles = DatastoreIO.readFrom(...);

// Key them both by the common id
PCollection<KV<Long, CustomClass>> keyedEvents =
    events.apply(WithKeys.of(event -> event.getArticleId()))

PCollection<KV<Long, Entity>> =
    articles.apply(WithKeys.of(article -> article.getKey().getId())

// Set up the join by giving tags to each collection
TupleTag<CustomClass> eventTag = new TupleTag<CustomClass>() {};
TupleTag<Entity> articleTag = new TupleTag<Entity>() {};
KeyedPCollectionTuple<Long> coGbkInput =
    KeyedPCollectionTuple
        .of(eventTag, keyedEvents)
        .and(articleTag, keyedArticles);

PCollection<CustomClass> enhancedEvents = coGbkInput
    .apply(CoGroupByKey.create())
    .apply(MapElements.via(CoGbkResult joinResult -> {
      for (CustomClass event : joinResult.getAll(eventTag)) {
        String articleName;
        try {
          articleName = joinResult.getOnly(articleTag).getString("articleName");
        } catch(Exception e) {
          articleName = "";
        }
        CustomClass enhanced = new CustomClass(event);
        enhanced.setArticleName(articleName);
        return enhanced;
      }
    });

另一种可能性是,如果只有很少的文章可以将查询存储在内存中,那就是使用DatastoreIO.readFrom(...)然后将它们全部作为地图侧输入读取,View.asMap()并在本地表中查找它们.

// Here are the two collections you want to join
PCollection<CustomClass> events = ...;
PCollection<Entity> articles = DatastoreIO.readFrom(...);

// Key the articles and create a map view
PCollectionView<Map<Long, Entity>> = articleView
    .apply(WithKeys.of(article -> article.getKey().getId())
    .apply(View.asMap());

// Do a lookup join by side input to a ParDo
PCollection<CustomClass> enhanced = events
    .apply(ParDo.withSideInputs(articles).of(new DoFn<CustomClass, CustomClass>() {
      @Override
      public void processElement(ProcessContext c) {
        Map<Long, Entity> articleLookup = c.sideInput(articleView);
        String articleName;
        try {
          articleName =
              articleLookup.get(event.getArticleId()).getString("articleName");
        } catch(Exception e) {
          articleName = "";
        }
        CustomClass enhanced = new CustomClass(event);
        enhanced.setArticleName(articleName);
        return enhanced;
      }
    });

根据您的数据,这些中的任何一个都可能是更好的选择.