google-bigquery - Apache Beam/Google Dataflow PubSub 到 BigQuery 管道 : Handling Insert Errors and Unexpected Retry Behavior

Question

我已经下载了 Pub/Sub to BigQuery Dataflow template 的副本来自Google's github repository 。我正在本地计算机上使用 direct-runner 运行它.

在测试中，我确认，如果在 UDF 处理或从 JSON 到 TableRow 的转换期间发生错误，模板只会将失败写入“死信”表。

我还希望更优雅地处理插入 BigQuery 时发生的故障，方法是将它们发送到单独的 TupleTag 中，以便它们也可以发送到死信表或其他输出以进行审查和处理。目前，当使用 dataflow-runner 执行时这些错误只会写入 Stackdriver 日志，并继续无限期地重试，直到问题得到解决。

问题一:在本地测试并发布格式与目标表架构不匹配的消息时，会重试插入 5 次，然后管道崩溃，并出现 RuntimeException 以及从对 Google API 的 HTTP 响应。我相信这种行为是在BigQueryServices.Impl内设置的这里:

private static final FluentBackoff INSERT_BACKOFF_FACTORY =
        FluentBackoff.DEFAULT.withInitialBackoff(Duration.millis(200)).withMaxRetries(5);

但是，基于Google's documentation ,

"When running in streaming mode, a bundle including a failing item will be retried indefinitely, which may cause your pipeline to permanently stall."

作为 Beam 的 Pub/Sub.IO ,

create and consume unbounded PCollections

我的印象是，从 Pub/Sub 读取时应默认启用流模式。我什至在 writeTableRows() 调用中添加了 Streaming_Inserts 方法，但它并没有影响此行为。

.apply(
            "WriteSuccessfulRecords",      
            BigQueryIO.writeTableRows()
                .withMethod(Method.STREAMING_INSERTS)

1. 这种行为是否受到我所在运行者的影响 使用？如果不是，我的理解哪里有缺陷？

问题二:

使用 BigQueryIO.write 时性能是否有差异与 BigQueryIO.writeTableRows ？

我问这个问题是因为我不知道如何在不创建自己的静态类(覆盖扩展方法并使用 ParDo 和 DoFn )的情况下捕获与插入相关的错误，我可以在其中添加自己的自定义逻辑来为成功记录创建单独的 TupleTags和故障记录，类似于 JavascriptTextTransformer 中的操作方式对于 FailsafeJavascriptUdf。

更新:

public static PipelineResult run(DirectOptions options) {

options.setRunner(DirectRunner.class);

    Pipeline pipeline = Pipeline.create(options);

    // Register the coder for pipeline
    FailsafeElementCoder<PubsubMessage, String> coder =
        FailsafeElementCoder.of(PubsubMessageWithAttributesCoder.of(), StringUtf8Coder.of());

    CoderRegistry coderRegistry = pipeline.getCoderRegistry();
    coderRegistry.registerCoderForType(coder.getEncodedTypeDescriptor(), coder);

     PCollectionTuple transformOut =
        pipeline
             //Step #1: Read messages in from Pub/Sub
            .apply(
                "ReadPubsubMessages",
  PubsubIO.readMessagesWithAttributes().fromTopic(options.getInputTopic()))

             //Step #2: Transform the PubsubMessages into TableRows
            .apply("ConvertMessageToTableRow", new PubsubMessageToTableRow(options));

    WriteResult writeResult = null;

    try {
      writeResult = 
            transformOut
        .get(TRANSFORM_OUT)
        .apply(
            "WriteSuccessfulRecords",      
            BigQueryIO.writeTableRows()
                .withMethod(Method.STREAMING_INSERTS)
                .withoutValidation()
                .withCreateDisposition(CreateDisposition.CREATE_NEVER)
                .withWriteDisposition(WriteDisposition.WRITE_APPEND)
                .withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors())
                .to("myproject:MyDataSet.MyTable"));
    } catch (Exception e) {
        System.out.print("Cause of the Standard Insert Failure is: ");
        System.out.print(e.getCause());
    }

    try {
        writeResult
            .getFailedInserts()
            .apply(
                    "WriteFailedInsertsToDeadLetter",
                    BigQueryIO.writeTableRows()
                        .to(options.getOutputDeadletterTable())
                        .withCreateDisposition(CreateDisposition.CREATE_IF_NEEDED)
                        .withWriteDisposition(WriteDisposition.WRITE_APPEND));
    } catch (Exception e) {
        System.out.print("Cause of the Error Insert Failure is: ");
        System.out.print(e.getCause());
    }

     PCollectionList.of(transformOut.get(UDF_DEADLETTER_OUT))
        .and(transformOut.get(TRANSFORM_DEADLETTER_OUT))
        .apply("Flatten", Flatten.pCollections())
        .apply(
            "WriteFailedRecords",
            WritePubsubMessageErrors.newBuilder()
                .setErrorRecordsTable(
                    maybeUseDefaultDeadletterTable(
                        options.getOutputDeadletterTable(),
                        options.getOutputTableSpec(),
                        DEFAULT_DEADLETTER_TABLE_SUFFIX))
                .setErrorRecordsTableSchema(getDeadletterTableSchemaJson())
                .build());

    return pipeline.run();
  }

错误:

Cause of the Error Insert Failure is: null[WARNING] 
java.lang.NullPointerException: Outputs for non-root node WriteFailedInsertsToDeadLetter are null
    at org.apache.beam.repackaged.beam_sdks_java_core.com.google.common.base.Preconditions.checkNotNull(Preconditions.java:864)
    at org.apache.beam.sdk.runners.TransformHierarchy$Node.visit(TransformHierarchy.java:672)
    at org.apache.beam.sdk.runners.TransformHierarchy$Node.visit(TransformHierarchy.java:660)
    at org.apache.beam.sdk.runners.TransformHierarchy$Node.access$600(TransformHierarchy.java:311)
    at org.apache.beam.sdk.runners.TransformHierarchy.visit(TransformHierarchy.java:245)
    at org.apache.beam.sdk.Pipeline.traverseTopologically(Pipeline.java:458)
    at org.apache.beam.sdk.Pipeline.validate(Pipeline.java:575)
    at org.apache.beam.sdk.Pipeline.run(Pipeline.java:310)
    at org.apache.beam.sdk.Pipeline.run(Pipeline.java:297)
    at com.google.cloud.teleport.templates.PubSubToBigQuery.run(PubSubToBigQuery.java:312)
    at com.google.cloud.teleport.templates.PubSubToBigQuery.main(PubSubToBigQuery.java:186)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.codehaus.mojo.exec.ExecJavaMojo$1.run(ExecJavaMojo.java:282)
    at java.lang.Thread.run(Thread.java:748)

Answer 1

在最新版本的 Beam 中，BigQueryIO.Write转换返回 WriteResult对象，使您能够检索未能输出到 BigQuery 的 TableRows 的 PCollection。使用此功能，您可以轻松检索失败，将其格式化为死信输出的结构，然后将记录重新提交到 BigQuery。这样就不需要单独的类来管理成功和失败的记录。

下面是您的管道的示例。

// Attempt to write the table rows to the output table.
WriteResult writeResult =
    pipeline.apply(
        "WriteRecordsToBigQuery",
        BigQueryIO.writeTableRows()
            .to(options.getOutputTable())
            .withCreateDisposition(CreateDisposition.CREATE_NEVER)
            .withWriteDisposition(WriteDisposition.WRITE_APPEND)
            .withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors()));

/*
 * 1) Get the failed inserts
 * 2) Transform to the deadletter table format.
 * 3) Output to the deadletter table.
*/
writeResult
  .getFailedInserts()
    .apply("FormatFailedInserts", ParDo.of(new FailedInsertFormatter()))
    .apply(
        "WriteFailedInsertsToDeadletter",
        BigQueryIO.writeTableRows()
            .to(options.getDeadletterTable())
            .withCreateDisposition(CreateDisposition.CREATE_NEVER)
            .withWriteDisposition(WriteDisposition.WRITE_APPEND));

此外，回答您的问题:

1. 根据光束docs ，您必须设置流 对于 DirectRunner，将选项设置为 true。
2. 不应该有 性能差异。无论哪种情况，您都需要将 输入记录到TableRow对象。应该没有什么区别 如果您事先在 ParDo 中或在可序列化的内部执行此操作 使用 BigQueryIO.Write.withFormatFunction 的函数.