Hudi源码分析之使用Flink Table/SQL实现Hudi Sources

在文章Flink Table/SQL自定义Sources和Sinks全解析(附代码)中我们说到在Flink Table/SQL中如何自定义Sources和Sinks,有了上述文章的基础,我们再来理解Flink Table/SQL是如何实现Hudi的数据读取与写入就比较容易了。

Hudi源码分析之使用Flink Table/SQL实现Hudi Sources

动态表是自定义sources/sinks的核心,通过查阅源码我们可以知道在flink-hudi子模块中,org.apache.hudi.table.HoodieTableFactory类同时实现了DynamicTableSourceFactoryDynamicTableSinkFactory两个接口,该类为提供特定于连接器的逻辑,也就是说当我们在flink sql中指定connector为hudi时,会走该处的逻辑

create table t1 (
  col bigint
) with (
  connector = "hudi",
  ...
)

HoodieTableFactory类的UML图如下所示
Hudi源码分析之使用Flink Table/SQL实现Hudi Sources

静态变量FACTORY_ID和factoryIdentifier()方法指定了该connector的标志为hudi

  public static final String FACTORY_ID = "hudi";

    @Override
  public String factoryIdentifier() {
    return FACTORY_ID;
  }

createDynamicTableSource()函数用于定义读取hudi数据源对应的HoodieTableSource,createDynamicTableSource()函数内容如下:

  @Override
  public DynamicTableSource createDynamicTableSource(Context context) {
    Configuration conf = FlinkOptions.fromMap(context.getCatalogTable().getOptions());
    ResolvedSchema schema = context.getCatalogTable().getResolvedSchema();
    sanityCheck(conf, schema);
    setupConfOptions(conf, context.getObjectIdentifier().getObjectName(), context.getCatalogTable(), schema);

    Path path = new Path(conf.getOptional(FlinkOptions.PATH).orElseThrow(() ->
        new ValidationException("Option [path] should not be empty.")));
    return new HoodieTableSource(
        schema,
        path,
        context.getCatalogTable().getPartitionKeys(),
        conf.getString(FlinkOptions.PARTITION_DEFAULT_NAME),
        conf);
  }

在createDynamicTableSource函数中,首先使用sanityCheck(conf, schema)对flink sql DDL中的schema和配置参数进行校验;然后使用flink sql DDL设置配置参数,比如说表名,主键等;最后将上一步得到的配置参数传给HoodieTableSource。

HoodieTableSource实现了ScanTableSource、SupportsPartitionPushDown、SupportsProjectionPushDown、SupportsLimitPushDown以及SupportsFilterPushDown。定义了读取Hudi表的方式,我们在使用flink读取hudi数据的时候,配置流读、批量读取、从某个时间点读取则是在此进行配置。

首先我们分析一下流读的场景,也就是在flink sql DDL中指定read.streaming.enabled = true

我们来看一下getScanRuntimeProvider函数,

  @Override
  public ScanRuntimeProvider getScanRuntimeProvider(ScanContext scanContext) {
    return new DataStreamScanProvider() {

      @Override
      public boolean isBounded() {
        return !conf.getBoolean(FlinkOptions.READ_AS_STREAMING);
      }

      @Override
      public DataStream<RowData> produceDataStream(StreamExecutionEnvironment execEnv) {
        @SuppressWarnings("unchecked")
        TypeInformation<RowData> typeInfo =
            (TypeInformation<RowData>) TypeInfoDataTypeConverter.fromDataTypeToTypeInfo(getProducedDataType());
        if (conf.getBoolean(FlinkOptions.READ_AS_STREAMING)) {
          StreamReadMonitoringFunction monitoringFunction = new StreamReadMonitoringFunction(
              conf, FilePathUtils.toFlinkPath(path), maxCompactionMemoryInBytes, getRequiredPartitionPaths());
          InputFormat<RowData, ?> inputFormat = getInputFormat(true);
          OneInputStreamOperatorFactory<MergeOnReadInputSplit, RowData> factory = StreamReadOperator.factory((MergeOnReadInputFormat) inputFormat);
          SingleOutputStreamOperator<RowData> source = execEnv.addSource(monitoringFunction, getSourceOperatorName("split_monitor"))
              .setParallelism(1)
              .transform("split_reader", typeInfo, factory)
              .setParallelism(conf.getInteger(FlinkOptions.READ_TASKS));
          return new DataStreamSource<>(source);
        } else {
          InputFormatSourceFunction<RowData> func = new InputFormatSourceFunction<>(getInputFormat(), typeInfo);
          DataStreamSource<RowData> source = execEnv.addSource(func, asSummaryString(), typeInfo);
          return source.name(getSourceOperatorName("bounded_source")).setParallelism(conf.getInteger(FlinkOptions.READ_TASKS));
        }
      }
    };
  }

当指定read.streaming.enabled = true时,该函数走流读逻辑,也就是

          StreamReadMonitoringFunction monitoringFunction = new StreamReadMonitoringFunction(
              conf, FilePathUtils.toFlinkPath(path), maxCompactionMemoryInBytes, getRequiredPartitionPaths());
          InputFormat<RowData, ?> inputFormat = getInputFormat(true);
          OneInputStreamOperatorFactory<MergeOnReadInputSplit, RowData> factory = StreamReadOperator.factory((MergeOnReadInputFormat) inputFormat);
          SingleOutputStreamOperator<RowData> source = execEnv.addSource(monitoringFunction, getSourceOperatorName("split_monitor"))
              .setParallelism(1)
              .transform("split_reader", typeInfo, factory)
              .setParallelism(conf.getInteger(FlinkOptions.READ_TASKS));
          return new DataStreamSource<>(source);

在上述流读逻辑中,StreamReadMonitoringFunction类扩展了RichSourceFunction和实现了CheckpointedFunction,该类指定了hudi数据源读取。我们再来看一下StreamReadMonitoringFunction类中的monitorDirAndForwardSplits方法,该方法具体执行了hudi数据源的读取方式。其方式为增量分片读取。


  @Override
  public void run(SourceFunction.SourceContext<MergeOnReadInputSplit> context) throws Exception {
    checkpointLock = context.getCheckpointLock();
    while (isRunning) {
      synchronized (checkpointLock) {
        monitorDirAndForwardSplits(context);
      }
      TimeUnit.SECONDS.sleep(interval);
    }
  }

  @VisibleForTesting
  public void monitorDirAndForwardSplits(SourceContext<MergeOnReadInputSplit> context) {
    HoodieTableMetaClient metaClient = getOrCreateMetaClient();
    if (metaClient == null) {
      // table does not exist
      return;
    }
    IncrementalInputSplits.Result result =
        incrementalInputSplits.inputSplits(metaClient, this.hadoopConf, this.issuedInstant);
    if (result.isEmpty()) {
      // no new instants, returns early
      return;
    }

    for (MergeOnReadInputSplit split : result.getInputSplits()) {
      context.collect(split);
    }
    // update the issues instant time
    this.issuedInstant = result.getEndInstant();
    LOG.info("\n"
            + "------------------------------------------------------------\n"
            + "---------- consumed to instant: {}\n"
            + "------------------------------------------------------------",
        this.issuedInstant);
  }

数据读取的时候也受checkpoint的影响,假如处于checkpoint,那么会停止读取直到chk结束,chk表示将当前读取的位置记录到状态中。由此我们可以知道当任务失败时,从上次chk点重启便可以从上次读取位置继续读取数据。

我们在定义hudi数据读取时,还可以定义read.start-commit,从某个commit开始消费数据,这些配置参数具体在这里进行处理

    IncrementalInputSplits.Result result =
        incrementalInputSplits.inputSplits(metaClient, this.hadoopConf, this.issuedInstant);

具体函数如下:

/**
   * Returns the incremental input splits.
   *
   * @param metaClient    The meta client
   * @param hadoopConf    The hadoop configuration
   * @param issuedInstant The last issued instant, only valid in streaming read
   * @return The list of incremental input splits or empty if there are no new instants
   */
  public Result inputSplits(
      HoodieTableMetaClient metaClient,
      org.apache.hadoop.conf.Configuration hadoopConf,
      String issuedInstant) {
    metaClient.reloadActiveTimeline();
    HoodieTimeline commitTimeline = metaClient.getCommitsAndCompactionTimeline().filterCompletedAndCompactionInstants();
    if (commitTimeline.empty()) {
      LOG.warn("No splits found for the table under path " + path);
      return Result.EMPTY;
    }
    List<HoodieInstant> instants = filterInstantsWithRange(commitTimeline, issuedInstant);
    // get the latest instant that satisfies condition
    final HoodieInstant instantToIssue = instants.size() == 0 ? null : instants.get(instants.size() - 1);
    final InstantRange instantRange;
    if (instantToIssue != null) {
      if (issuedInstant != null) {
        // the streaming reader may record the last issued instant, if the issued instant is present,
        // the instant range should be: (issued instant, the latest instant].
        instantRange = InstantRange.getInstance(issuedInstant, instantToIssue.getTimestamp(),
            InstantRange.RangeType.OPEN_CLOSE);
      } else if (this.conf.getOptional(FlinkOptions.READ_START_COMMIT).isPresent()) {
        // first time consume and has a start commit
        final String startCommit = this.conf.getString(FlinkOptions.READ_START_COMMIT);
        instantRange = startCommit.equalsIgnoreCase(FlinkOptions.START_COMMIT_EARLIEST)
            ? null
            : InstantRange.getInstance(startCommit, instantToIssue.getTimestamp(), InstantRange.RangeType.CLOSE_CLOSE);
      } else {
        // first time consume and no start commit, consumes the latest incremental data set.
        instantRange = InstantRange.getInstance(instantToIssue.getTimestamp(), instantToIssue.getTimestamp(),
            InstantRange.RangeType.CLOSE_CLOSE);
      }
    } else {
      LOG.info("No new instant found for the table under path " + path + ", skip reading");
      return Result.EMPTY;
    }

    String tableName = conf.getString(FlinkOptions.TABLE_NAME);

    Set<String> writePartitions;
    final FileStatus[] fileStatuses;

    if (instantRange == null) {
      // reading from the earliest, scans the partitions and files directly.
      FileIndex fileIndex = FileIndex.instance(new org.apache.hadoop.fs.Path(path.toUri()), conf);
      if (this.requiredPartitions != null) {
        // apply partition push down
        fileIndex.setPartitionPaths(this.requiredPartitions);
      }
      writePartitions = new HashSet<>(fileIndex.getOrBuildPartitionPaths());
      if (writePartitions.size() == 0) {
        LOG.warn("No partitions found for reading in user provided path.");
        return Result.EMPTY;
      }
      fileStatuses = fileIndex.getFilesInPartitions();
    } else {
      List<HoodieCommitMetadata> activeMetadataList = instants.stream()
          .map(instant -> WriteProfiles.getCommitMetadata(tableName, path, instant, commitTimeline)).collect(Collectors.toList());
      List<HoodieCommitMetadata> archivedMetadataList = getArchivedMetadata(metaClient, instantRange, commitTimeline, tableName);
      if (archivedMetadataList.size() > 0) {
        LOG.warn("\n"
            + "--------------------------------------------------------------------------------\n"
            + "---------- caution: the reader has fall behind too much from the writer,\n"
            + "---------- tweak 'read.tasks' option to add parallelism of read tasks.\n"
            + "--------------------------------------------------------------------------------");
      }
      List<HoodieCommitMetadata> metadataList = archivedMetadataList.size() > 0
          // IMPORTANT: the merged metadata list must be in ascending order by instant time
          ? mergeList(archivedMetadataList, activeMetadataList)
          : activeMetadataList;

      writePartitions = HoodieInputFormatUtils.getWritePartitionPaths(metadataList);
      // apply partition push down
      if (this.requiredPartitions != null) {
        writePartitions = writePartitions.stream()
            .filter(this.requiredPartitions::contains).collect(Collectors.toSet());
      }
      if (writePartitions.size() == 0) {
        LOG.warn("No partitions found for reading in user provided path.");
        return Result.EMPTY;
      }
      fileStatuses = WriteProfiles.getWritePathsOfInstants(path, hadoopConf, metadataList, metaClient.getTableType());
    }

    if (fileStatuses.length == 0) {
      LOG.warn("No files found for reading in user provided path.");
      return Result.EMPTY;
    }

    HoodieTableFileSystemView fsView = new HoodieTableFileSystemView(metaClient, commitTimeline, fileStatuses);
    final String endInstant = instantToIssue.getTimestamp();
    final AtomicInteger cnt = new AtomicInteger(0);
    final String mergeType = this.conf.getString(FlinkOptions.MERGE_TYPE);
    List<MergeOnReadInputSplit> inputSplits = writePartitions.stream()
        .map(relPartitionPath -> fsView.getLatestMergedFileSlicesBeforeOrOn(relPartitionPath, endInstant)
            .map(fileSlice -> {
              Option<List<String>> logPaths = Option.ofNullable(fileSlice.getLogFiles()
                  .sorted(HoodieLogFile.getLogFileComparator())
                  .map(logFile -> logFile.getPath().toString())
                  .collect(Collectors.toList()));
              String basePath = fileSlice.getBaseFile().map(BaseFile::getPath).orElse(null);
              return new MergeOnReadInputSplit(cnt.getAndAdd(1),
                  basePath, logPaths, endInstant,
                  metaClient.getBasePath(), maxCompactionMemoryInBytes, mergeType, instantRange);
            }).collect(Collectors.toList()))
        .flatMap(Collection::stream)
        .collect(Collectors.toList());
    return Result.instance(inputSplits, endInstant);
  }

当然,针对hudi数据源的读取方式是有很多种方式的,上述分析仅仅针对流读的简单分析,不过相信通过上述分析,也能够掌握其他读取方式的代码分析方法,并对问题分析起到一定的帮助。

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本文为从大数据到人工智能博主「xiaozhch5」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。

原文链接:https://lrting.top/backend/3519/

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