关于大数据:Apache-hudi-源码分析-写时处理优化小文件问题flink

Flink : 0.12 (引擎版本影响不大)

hudi : 0.11.0-SNAPSHOT

Time: 2022/03/14

spark 适配同理

flink 对每一行数据进行解决，结构 recorderKey（蕴含分区门路）
通过 Hudi Metadata 获取指定分区门路所有满足条件的小文件（fileId）
对小文件进行结构生成 AssignState，通过计算历史均匀每一行数据的大小，计算每个小文件还能再存入多少条数据。将 AssignState 用分区门路缓存。
对每行数据反复上述操作，如果是曾经缓存过的分区门路，间接获取 AssginState，更新每个小文件残余存入个数
如果小文件残余容量有余，就会创立新的 fileId 进行写入
待 checkpoint 触发写出

BucketAssignFunction.java

flink processElement，hudi 会通过其计算的每一条数据的 recordKey 失去 partitionPath

private HoodieRecordLocation getNewRecordLocation(String partitionPath) {
    // // 通过 recordKey 失去 partitionPath，获取对应分区的小文件信息，察看下一个代码块
    final BucketInfo bucketInfo = this.bucketAssigner.addInsert(partitionPath);
    final HoodieRecordLocation location;
    switch (bucketInfo.getBucketType()) {
      case INSERT:
        // This is an insert bucket, use HoodieRecordLocation instant time as "I".
        // Downstream operators can then check the instant time to know whether
        // a record belongs to an insert bucket.
        location = new HoodieRecordLocation("I", bucketInfo.getFileIdPrefix());
        break;
      case UPDATE:
        location = new HoodieRecordLocation("U", bucketInfo.getFileIdPrefix());
        break;
      default:
        throw new AssertionError();}
    return location;
  }

BucketAssigner.java

public BucketInfo addInsert(String partitionPath) {
    // 获取小文件，察看下一个代码块，而后回来
    SmallFileAssign smallFileAssign = getSmallFileAssign(partitionPath);

    // assign 判断小文件是否还能再调配，不能超过 totalAssgin
    if (smallFileAssign != null && smallFileAssign.assign()) {return new BucketInfo(BucketType.UPDATE, smallFileAssign.getFileId(), partitionPath);
    }

    // 上面就是创立新的 fileId 写入
    ...
  }


private synchronized SmallFileAssign getSmallFileAssign(String partitionPath) {
    // 判断是否缓存了对应分区门路的小文件信息
    if (smallFileAssignMap.containsKey(partitionPath)) {return smallFileAssignMap.get(partitionPath);
    }
      // writeProfile.getSmallFiles 获取小文件，察看下一个代码块，而后回来
    List<SmallFile> smallFiles = smallFilesOfThisTask(writeProfile.getSmallFiles(partitionPath));
    if (smallFiles.size() > 0) {LOG.info("For partitionPath :" + partitionPath + "Small Files =>" + smallFiles);
      // 重点关注：// 
      // 留神这里：小文件返回后结构 assignState, 在初始化 SmallFileAssignState 时，会通过计算历史的均匀每行数据的大小，如果没有，默认 1024 byte 作为每一行大小。// 而后 (文件最大配置 - 小文件大小) / 均匀行大小 = 这个小文件还能调配的行数（totalUnassigned
      // 后续的 processElement 每次就会调用 SmallFileAssignState assigned 调配（如果 partitionUrl 雷同），直到调配完
      //
      // // 重点关注：SmallFileAssignState[] states = smallFiles.stream()
          .map(smallFile -> new SmallFileAssignState(config.getParquetMaxFileSize(), smallFile, writeProfile.getAvgSize()))
          .toArray(SmallFileAssignState[]::new);
      SmallFileAssign assign = new SmallFileAssign(states);
      smallFileAssignMap.put(partitionPath, assign);
      return assign;
    }
    smallFileAssignMap.put(partitionPath, null);
    return null;
  }

WriteProfile.java

public synchronized List<SmallFile> getSmallFiles(String partitionPath) {
    // lookup the cache first
    if (smallFilesMap.containsKey(partitionPath)) {return smallFilesMap.get(partitionPath);
    }

    List<SmallFile> smallFiles = new ArrayList<>();
    if (config.getParquetSmallFileLimit() <= 0) {this.smallFilesMap.put(partitionPath, smallFiles);
      return smallFiles;
    }

    // 获取小文件，调用 smallFilesProfile
    smallFiles = smallFilesProfile(partitionPath);
    this.smallFilesMap.put(partitionPath, smallFiles);
    return smallFiles;
  }


// 非 MOR 表实现，MOR 表调用 DeltaWriteProfile.smallFIleProfile
protected List<SmallFile> smallFilesProfile(String partitionPath) {
    // smallFiles only for partitionPath
    List<SmallFile> smallFileLocations = new ArrayList<>();

    HoodieTimeline commitTimeline = metaClient.getCommitsTimeline().filterCompletedInstants();

    if (!commitTimeline.empty()) { // if we have some commits
      HoodieInstant latestCommitTime = commitTimeline.lastInstant().get();
      // 获取指定分区下的所有文件（应用 Metadata 获取的 fsView）List<HoodieBaseFile> allFiles = fsView
          .getLatestBaseFilesBeforeOrOn(partitionPath, latestCommitTime.getTimestamp()).collect(Collectors.toList());

      for (HoodieBaseFile file : allFiles) {
        // 过滤出满足条件的文件
        // 小于 hoodie.parquet.small.file.limit 默认 100M，并且大于 0 的文件
        if (file.getFileSize() < config.getParquetSmallFileLimit() && file.getFileSize() > 0) {String filename = file.getFileName();
          SmallFile sf = new SmallFile();
          sf.location = new HoodieRecordLocation(FSUtils.getCommitTime(filename), FSUtils.getFileId(filename));
          sf.sizeBytes = file.getFileSize();
          smallFileLocations.add(sf);
        }
      }
    }

    return smallFileLocations;
  }

如果是 MOR 表，DeltaWriteProfile.java

@Override
  protected List<SmallFile> smallFilesProfile(String partitionPath) {
    // smallFiles only for partitionPath
    List<SmallFile> smallFileLocations = new ArrayList<>();

    // Init here since this class (and member variables) might not have been initialized
    HoodieTimeline commitTimeline = metaClient.getCommitsTimeline().filterCompletedInstants();

    // Find out all eligible small file slices
    if (!commitTimeline.empty()) {HoodieInstant latestCommitTime = commitTimeline.lastInstant().get();
      // find the smallest file in partition and append to it
      List<FileSlice> allSmallFileSlices = new ArrayList<>();
      // If we can index log files, we can add more inserts to log files for fileIds including those under
      // pending compaction.
      // 获取 base_file + log_file 的文件偏
      List<FileSlice> allFileSlices = fsView.getLatestFileSlicesBeforeOrOn(partitionPath, latestCommitTime.getTimestamp(), true)
          .collect(Collectors.toList());
      for (FileSlice fileSlice : allFileSlices) {
        // 判断是否满足小文件的条件 
        // (baseFileSize + totalLogFileSize * ratio) < hoodie.parquet.max.file.size(120M)
        // 这里的 ratio 是 hoodie.logfile.to.parquet.compression.ratio，默认 0.35
        if (isSmallFile(fileSlice)) {allSmallFileSlices.add(fileSlice);
        }
      }
      // Create SmallFiles from the eligible file slices
      for (FileSlice smallFileSlice : allSmallFileSlices) {SmallFile sf = new SmallFile();
        if (smallFileSlice.getBaseFile().isPresent()) {
          // TODO : Move logic of file name, file id, base commit time handling inside file slice
          String filename = smallFileSlice.getBaseFile().get().getFileName();
          sf.location = new HoodieRecordLocation(FSUtils.getCommitTime(filename), FSUtils.getFileId(filename));
          sf.sizeBytes = getTotalFileSize(smallFileSlice);
          smallFileLocations.add(sf);
        } else {smallFileSlice.getLogFiles().findFirst().ifPresent(logFile -> {
            // in case there is something error, and the file slice has no log file
            sf.location = new HoodieRecordLocation(FSUtils.getBaseCommitTimeFromLogPath(logFile.getPath()),
                FSUtils.getFileIdFromLogPath(logFile.getPath()));
            sf.sizeBytes = getTotalFileSize(smallFileSlice);
            smallFileLocations.add(sf);
          });
        }
      }
    }
    return smallFileLocations;
  }

解决小文件的形式不是追加写文件，而是应用雷同的 fileId 生成新的版本号，所以可能会有文件数并没有升高的疑难。设置适合的版本历史和 clean service 主动清理历史版本数据

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关于大数据:Apache-hudi-源码分析-写时处理优化小文件问题flink

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