Apache Hudi初探(九)(与spark的结合)--非bulk

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背景

之前讨论的都是’hoodie.datasource.write.operation’:'bulk_insert’的前提下，在这种模式下，是没有json文件的已形成如下的文件：

/dt=1/.hoodie_partition_metadata
/dt=1/2ffe3579-6ddb-4c5f-bf03-5c1b5dfce0a0-0_0-41263-0_20230528233336713.parquet
/dt=1/30b7d5b2-12e8-415a-8ec5-18206fe601c0-0_0-22102-0_20230528231643200.parquet
/dt=1/4abc1c6d-a8aa-4c15-affc-61a35171ce69-0_4-22106-0_20230528231643200.parquet
/dt=1/513dee80-2e8c-4db8-baee-a767b9dba41c-0_2-22104-0_20230528231643200.parquet
/dt=1/57076f86-0a62-4f52-8b50-31a5f769b26a-0_1-22103-0_20230528231643200.parquet
/dt=1/84553727-be9d-4273-bad9-0a38d9240815-0_0-59818-0_20230528233513387.parquet
/dt=1/fecd6a84-9a74-40b1-bfc1-13612a67a785-0_0-26640-0_20230528231723951.parquet

因为是bulk insert操作，所以没有去重的需要，所以直接采用spark原生的方式，
以下我们讨论非spark原生的方式，

闲说杂谈

继续Apache Hudi初探(八)(与spark的结合)–非bulk_insert模式
剩下的代码：

   val writeResult = DataSourceUtils.doWriteOperation(client, hoodieRecords, instantTime, operation)
   val (writeSuccessful, compactionInstant, clusteringInstant) =
        commitAndPerformPostOperations(sqlContext.sparkSession, df.schema,
          writeResult, parameters, writeClient, tableConfig, jsc,
          TableInstantInfo(basePath, instantTime, commitActionType, operation))

doWriteOperation 最终调用的是SparkRDDWriteClient对应的方法，如bulkInsert/insert/upsert/insertOverwrite，这里我们以upsert为例：

 public JavaRDD<WriteStatus> upsert(JavaRDD<HoodieRecord<T>> records, String instantTime) {
  HoodieTable<T, HoodieData<HoodieRecord<T>>, HoodieData<HoodieKey>, HoodieData<WriteStatus>> table =
      initTable(WriteOperationType.UPSERT, Option.ofNullable(instantTime));
  table.validateUpsertSchema();
  preWrite(instantTime, WriteOperationType.UPSERT, table.getMetaClient());
  HoodieWriteMetadata<HoodieData<WriteStatus>> result = table.upsert(context, instantTime, HoodieJavaRDD.of(records));
  HoodieWriteMetadata<JavaRDD<WriteStatus>> resultRDD = result.clone(HoodieJavaRDD.getJavaRDD(result.getWriteStatuses()));
  if (result.getIndexLookupDuration().isPresent()) {
    metrics.updateIndexMetrics(LOOKUP_STR, result.getIndexLookupDuration().get().toMillis());
  }
  return postWrite(resultRDD, instantTime, table);
}

initTable 创建获取一个HoodieSparkMergeOnReadTable
validateSchema 校验Schema的兼容性
preWrite 写之前的操作，这个之前有说过，具体参考：Apache Hudi初探(五)(与spark的结合)

table.upsert 真正写入数据的操作
最终调用的是 SparkInsertDeltaCommitActionExecutor<>().execute() 方法，最后最调用到HoodieWriteHelper.write：

  public HoodieWriteMetadata<O> write(String instantTime,
                                  I inputRecords,
                                  HoodieEngineContext context,
                                  HoodieTable<T, I, K, O> table,
                                  boolean shouldCombine,
                                  int shuffleParallelism,
                                  BaseCommitActionExecutor<T, I, K, O, R> executor,
                                  WriteOperationType operationType) {
    try {
        // De-dupe/merge if needed
        I dedupedRecords =
            combineOnCondition(shouldCombine, inputRecords, shuffleParallelism, table);

        Instant lookupBegin = Instant.now();
        I taggedRecords = dedupedRecords;
        if (table.getIndex().requiresTagging(operationType)) {
          // perform index loop up to get existing location of records
          context.setJobStatus(this.getClass().getSimpleName(), "Tagging: " + table.getConfig().getTableName());
          taggedRecords = tag(dedupedRecords, context, table);
        }
        Duration indexLookupDuration = Duration.between(lookupBegin, Instant.now());

        HoodieWriteMetadata<O> result = executor.execute(taggedRecords);
        result.setIndexLookupDuration(indexLookupDuration);
        return result;
      } catch (Throwable e) {
        if (e instanceof HoodieUpsertException) {
          throw (HoodieUpsertException) e;
        }
        throw new HoodieUpsertException("Failed to upsert for commit time " + instantTime, e);
      }
    }

combineOnCondition 数据去重
最终是调用HoodieRecordPayload.preCombine（默认是OverwriteWithLatestAvroPayload.preCombine）
taggedRecords = tag(dedupedRecords, context, table) 因为默认的index是HoodieSimpleIndex,所以这个时候会调用到打标记这个操作
最终调用到的是HoodieSimpleIndex的tagLocationInternal，此时获得的是带有location的记录（如果没有索引到，则 record中的location为null）

executor.execute(taggedRecords) 该方法最终调用到BaseSparkCommitActionExecutor.execute方法：

public HoodieWriteMetadata<HoodieData<WriteStatus>> execute(HoodieData<HoodieRecord<T>> inputRecords) {
   // Cache the tagged records, so we don't end up computing both
   // TODO: Consistent contract in HoodieWriteClient regarding preppedRecord storage level handling
   JavaRDD<HoodieRecord<T>> inputRDD = HoodieJavaRDD.getJavaRDD(inputRecords);
   if (inputRDD.getStorageLevel() == StorageLevel.NONE()) {
     inputRDD.persist(StorageLevel.MEMORY_AND_DISK_SER());
   } else {
     LOG.info("RDD PreppedRecords was persisted at: " + inputRDD.getStorageLevel());
   }

   WorkloadProfile workloadProfile = null;
   if (isWorkloadProfileNeeded()) {
     context.setJobStatus(this.getClass().getSimpleName(), "Building workload profile: " + config.getTableName());
     workloadProfile = new WorkloadProfile(buildProfile(inputRecords), operationType, table.getIndex().canIndexLogFiles());
     LOG.info("Input workload profile :" + workloadProfile);
   }

   // partition using the insert partitioner
   final Partitioner partitioner = getPartitioner(workloadProfile);
   if (isWorkloadProfileNeeded()) {
     saveWorkloadProfileMetadataToInflight(workloadProfile, instantTime);
   }

   // handle records update with clustering
   Set<HoodieFileGroupId> fileGroupsInPendingClustering =
       table.getFileSystemView().getFileGroupsInPendingClustering().map(Pair::getKey).collect(Collectors.toSet());
   HoodieData<HoodieRecord<T>> inputRecordsWithClusteringUpdate = fileGroupsInPendingClustering.isEmpty() ? inputRecords : clusteringHandleUpdate(inputRecords, fileGroupsInPendingClustering);

   context.setJobStatus(this.getClass().getSimpleName(), "Doing partition and writing data: " + config.getTableName());
   HoodieData<WriteStatus> writeStatuses = mapPartitionsAsRDD(inputRecordsWithClusteringUpdate, partitioner);
   HoodieWriteMetadata<HoodieData<WriteStatus>> result = new HoodieWriteMetadata<>();
   updateIndexAndCommitIfNeeded(writeStatuses, result);
   return result;
 }

inputRDD.persist持久化当前的RDD,因为该RDD会被使用多次，便于加速
workloadProfile = new WorkloadProfile(buildProfile(inputRecords)
构建一个状态信息，主要是记录一下插入的记录数量和更新的记录数量其中主要形成了以filedId为key, *Pair<instantTime,count>*为value的Map数据
final Partitioner partitioner = getPartitioner(workloadProfile) 这里针对于upsert操作会返回UpsertPartitioner(因为默认hoodie.storage.layout.type为DEFAULT),
其中该UpsertPartitioner实例的构造方法中会进行一些额外的操作 assignUpdates和assignInserts（这里暂时忽略），主要是对数据进行分区处理，设计到小文件的处理
mapPartitionsAsRDD(inputRecordsWithClusteringUpdate, partitioner)
这里会根据hoodie.table.base.file.format的值（默认是parquet）,如果是hfile，则会要求排序，如果没有则只是按照partitioner进行重分区，
之后再进行数据insert或者update，具体的方法为handleUpsertPartition，会根据之前的partitoner信息进行插入或者更新（里面的细节有点复杂）
updateIndexAndCommitIfNeeded(writeStatuses, result)
该操作会首先会更新索引信息，对于HoodieSimpleIndex来说，什么也不操作（因为该index每次都会从parquet文件中读取信息从而组装成index），
其次如果hoodie.auto.commit为true（默认是true）会进行元数据的commit操作

postWrite 这里的postCommit 进行自动clean和Archive操作

commitAndPerformPostOperations
这里主要是异步Compcation和Clustering以及同步hive元数据，类似Apache Hudi初探(七)(与spark的结合)文章来源地址https://www.toymoban.com/news/detail-477671.html