Carbondata源碼系列（一）文件生成過程

時間 2019-11-06

標籤 carbondata 源碼系列文件生成過程简体版

原文原文鏈接

在滴滴的兩年一直在加班，人也變懶了，就不多再寫博客了，最近在進行Carbondata和hive集成方面的工做，因而乎須要對Carbondata進行深刻的研究。node

因而新開一個系列，記錄本身學習Carbondata的點點滴滴。git

一、環境準備

當前版本是1.2.0-SNAPSHOTgithub

git clone https://github.com/apache/carbondata.gitsql

先用IDEA打開carbondata的代碼，點擊上方的View -> Tool Windows -> Maven Projects, 先勾選一下須要的profile和編譯format工程，以下圖所示： apache

二、探尋代碼入口

咱們先打開入口類CarbonDataFrameWriter，找到writeToCarbonFile這個方法數組

  private def writeToCarbonFile(parameters: Map[String, String] = Map()): Unit = {
    val options = new CarbonOption(parameters)
    val cc = CarbonContext.getInstance(dataFrame.sqlContext.sparkContext)
    if (options.tempCSV) {
      loadTempCSV(options, cc)
    } else {
      loadDataFrame(options, cc)
    }
  }

它有兩個方式，loadTempCSV和loadDataFrame。多線程

loadTempCSV是先生成CSV文件，再調用LOAD DATA INPATH...的命令導入數據。異步

這裏咱們之研究loadDataFrame這種直接生成數據的方式。ide

一路點進去，目標落在carbonTableSchema的LoadTable的run方法裏，接着就是洋洋灑灑的二百行的set代碼。它是核心實際上是構造一個CarbonLoadModel類。學習

      val carbonLoadModel = new CarbonLoadModel()
      carbonLoadModel.setTableName(relation.tableMeta.carbonTableIdentifier.getTableName)
      carbonLoadModel.setDatabaseName(relation.tableMeta.carbonTableIdentifier.getDatabaseName)
      carbonLoadModel.setStorePath(relation.tableMeta.storePath)

      val table = relation.tableMeta.carbonTable
      carbonLoadModel.setAggTables(table.getAggregateTablesName.asScala.toArray)
      carbonLoadModel.setTableName(table.getFactTableName)
      val dataLoadSchema = new CarbonDataLoadSchema(table)
      // Need to fill dimension relation
      carbonLoadModel.setCarbonDataLoadSchema(dataLoadSchema)

這些代碼爲了Load一個文本文件準備的，若是是用dataframe的方式則不須要看了。直接略過，直接調到if (carbonLoadModel.getUseOnePass)這一句。

這個跟字典的生成方式有關，這個值默認是false，先忽略true的過程吧，看主流程就行，下面這哥倆纔是咱們要找的。

// 生成字典文件          
GlobalDictionaryUtil
            .generateGlobalDictionary(
              sparkSession.sqlContext,
              carbonLoadModel,
              relation.tableMeta.storePath,
              dictionaryDataFrame)
 // 生成數據文件      
CarbonDataRDDFactory.loadCarbonData(sparkSession.sqlContext,
            carbonLoadModel,
            relation.tableMeta.storePath,
            columnar,
            partitionStatus,
            None,
            loadDataFrame,
            updateModel)

三、字段生成過程

先看GlobalDictionaryUtil.generateGlobalDictionary方法

      if (StringUtils.isEmpty(allDictionaryPath)) {
        LOGGER.info("Generate global dictionary from source data files!")
        // load data by using dataSource com.databricks.spark.csv
        var df = dataFrame.getOrElse(loadDataFrame(sqlContext, carbonLoadModel))
        var headers = carbonLoadModel.getCsvHeaderColumns
        headers = headers.map(headerName => headerName.trim)
        val colDictFilePath = carbonLoadModel.getColDictFilePath
        if (colDictFilePath != null) {
          // generate predefined dictionary
          generatePredefinedColDictionary(colDictFilePath, carbonTableIdentifier,
            dimensions, carbonLoadModel, sqlContext, storePath, dictfolderPath)
        }
        if (headers.length > df.columns.length) {
          val msg = "The number of columns in the file header do not match the " +
                    "number of columns in the data file; Either delimiter " +
                    "or fileheader provided is not correct"
          LOGGER.error(msg)
          throw new DataLoadingException(msg)
        }
        // use fact file to generate global dict
        val (requireDimension, requireColumnNames) = pruneDimensions(dimensions,
          headers, df.columns)
        if (requireDimension.nonEmpty) {
          // select column to push down pruning
          df = df.select(requireColumnNames.head, requireColumnNames.tail: _*)
          val model = createDictionaryLoadModel(carbonLoadModel, carbonTableIdentifier,
            requireDimension, storePath, dictfolderPath, false)
          // combine distinct value in a block and partition by column
          val inputRDD = new CarbonBlockDistinctValuesCombineRDD(df.rdd, model)
            .partitionBy(new ColumnPartitioner(model.primDimensions.length))
          // generate global dictionary files
          val statusList = new CarbonGlobalDictionaryGenerateRDD(inputRDD, model).collect()
          // check result status
          checkStatus(carbonLoadModel, sqlContext, model, statusList)
        } else {
          LOGGER.info("No column found for generating global dictionary in source data files")
        }
      } else {
        generateDictionaryFromDictionaryFiles(sqlContext,
          carbonLoadModel,
          storePath,
          carbonTableIdentifier,
          dictfolderPath,
          dimensions,
          allDictionaryPath)
      }

View Code

包含了兩種狀況：不存在字典文件和已存在字段文件。

先看不存在的狀況

        // use fact file to generate global dict
        val (requireDimension, requireColumnNames) = pruneDimensions(dimensions,
          headers, df.columns)
        if (requireDimension.nonEmpty) {
          // 只選取標記爲字典的維度列
          df = df.select(requireColumnNames.head, requireColumnNames.tail: _*)
          val model = createDictionaryLoadModel(carbonLoadModel, carbonTableIdentifier,
            requireDimension, storePath, dictfolderPath, false)
          // 去重以後按列分區
          val inputRDD = new CarbonBlockDistinctValuesCombineRDD(df.rdd, model)
            .partitionBy(new ColumnPartitioner(model.primDimensions.length))
          // 生成全局字段文件
          val statusList = new CarbonGlobalDictionaryGenerateRDD(inputRDD, model).collect()
          // check result status
          checkStatus(carbonLoadModel, sqlContext, model, statusList)
        } else {
          LOGGER.info("No column found for generating global dictionary in source data files")
        }

先從源文件當中讀取全部維度列，去重以後按列分區，而後輸出，具體輸出的過程請看CarbonGlobalDictionaryGenerateRDD的internalCompute方法。

          val dictWriteTask = new DictionaryWriterTask(valuesBuffer,
            dictionaryForDistinctValueLookUp,
            model.table,
            model.columnIdentifier(split.index),
            model.hdfsLocation,
            model.primDimensions(split.index).getColumnSchema,
            model.dictFileExists(split.index)
          )
          // execute dictionary writer task to get distinct values
          val distinctValues = dictWriteTask.execute()
          val dictWriteTime = System.currentTimeMillis() - t3
          val t4 = System.currentTimeMillis()
          // if new data came than rewrite sort index file
          if (distinctValues.size() > 0) {
            val sortIndexWriteTask = new SortIndexWriterTask(model.table,
              model.columnIdentifier(split.index),
              model.primDimensions(split.index).getDataType,
              model.hdfsLocation,
              dictionaryForDistinctValueLookUp,
              distinctValues)
            sortIndexWriteTask.execute()
          }
          val sortIndexWriteTime = System.currentTimeMillis() - t4
          CarbonTimeStatisticsFactory.getLoadStatisticsInstance.recordDicShuffleAndWriteTime()
          // After sortIndex writing, update dictionaryMeta
          dictWriteTask.updateMetaData()

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字典文件在表目錄的下的Metadata目錄下，它須要生成三種文件

一、字段文件，命令方式爲列ID.dict

二、sort index文件，命令方式爲列ID.sortindex

三、字典列的meta信息，命令方式爲列ID.dictmeta

四、數據生成過程

請打開CarbonDataRDDFactory，找到loadCarbonData這個方法，方法裏面包括了從load命令和從dataframe加載的兩種方式，代碼看起來是有點兒又長又臭的感受。咱們只關注loadDataFrame的方式就好。

      def loadDataFrame(): Unit = {
        try {
          val rdd = dataFrame.get.rdd
　　　　　　// 獲取數據的位置
          val nodeNumOfData = rdd.partitions.flatMap[String, Array[String]]{ p =>
            DataLoadPartitionCoalescer.getPreferredLocs(rdd, p).map(_.host)
          }.distinct.size
　　　　　　// 確保executor數量要和數據的節點數同樣多
          val nodes = DistributionUtil.ensureExecutorsByNumberAndGetNodeList(nodeNumOfData,
            sqlContext.sparkContext)
          val newRdd = new DataLoadCoalescedRDD[Row](rdd, nodes.toArray.distinct)
　　　　　　// 生成數據文件
          status = new NewDataFrameLoaderRDD(sqlContext.sparkContext,
            new DataLoadResultImpl(),
            carbonLoadModel,
            currentLoadCount,
            tableCreationTime,
            schemaLastUpdatedTime,
            newRdd).collect()

        } catch {
          case ex: Exception =>
            LOGGER.error(ex, "load data frame failed")
            throw ex
        }
      }

打開NewDataFrameLoaderRDD類，查看internalCompute方法，這個方法的核心是這句話

new DataLoadExecutor().execute(model, loader.storeLocation, recordReaders.toArray)

打開DataLoadExecutor，execute方法裏面的核心是DataLoadProcessBuilder的build方法，根據表不一樣的參數設置，DataLoadProcessBuilder的build過程會有一些不一樣

  public AbstractDataLoadProcessorStep build(CarbonLoadModel loadModel, String storeLocation,
      CarbonIterator[] inputIterators) throws Exception {
    CarbonDataLoadConfiguration configuration = createConfiguration(loadModel, storeLocation);
    SortScopeOptions.SortScope sortScope = CarbonDataProcessorUtil.getSortScope(configuration);
    if (!configuration.isSortTable() || sortScope.equals(SortScopeOptions.SortScope.NO_SORT)) {
      // 沒有排序列或者carbon.load.sort.scope設置爲NO_SORT的
      return buildInternalForNoSort(inputIterators, configuration);
    } else if (configuration.getBucketingInfo() != null) {
      // 設置了Bucket的表
      return buildInternalForBucketing(inputIterators, configuration);
    } else if (sortScope.equals(SortScopeOptions.SortScope.BATCH_SORT)) {
      // carbon.load.sort.scope設置爲BATCH_SORT
      return buildInternalForBatchSort(inputIterators, configuration);
    } else {
      return buildInternal(inputIterators, configuration);
    }
  }

下面僅介紹標準的導入過程buildInternal：

  private AbstractDataLoadProcessorStep buildInternal(CarbonIterator[] inputIterators,
      CarbonDataLoadConfiguration configuration) {
    // 1. Reads the data input iterators and parses the data.
    AbstractDataLoadProcessorStep inputProcessorStep =
        new InputProcessorStepImpl(configuration, inputIterators);
    // 2. Converts the data like dictionary or non dictionary or complex objects depends on
    // data types and configurations.
    AbstractDataLoadProcessorStep converterProcessorStep =
        new DataConverterProcessorStepImpl(configuration, inputProcessorStep);
    // 3. Sorts the data by SortColumn
    AbstractDataLoadProcessorStep sortProcessorStep =
        new SortProcessorStepImpl(configuration, converterProcessorStep);
    // 4. Writes the sorted data in carbondata format.
    return new DataWriterProcessorStepImpl(configuration, sortProcessorStep);
  }

主要是分4個步驟：

一、讀取數據，並進行格式轉換，這一步驟是讀取csv文件服務的，dataframe的數據格式都已經處理過了

二、根據字段的數據類型和配置，替換掉字典列的值；非字典列會被替換成byte數組

三、按照Sort列進行排序

四、把數據用Carbondata的格式輸出

下面咱們從第二步DataConverterProcessorStepImpl開始提及，在getIterator方法當中，會發現每個CarbonRowBatch都要通過localConverter的convert方法轉換，localConverter中只有RowConverterImpl一個轉換器。

RowConverterImpl由不少的FieldConverter組成，在initialize方法中能夠看到它是由FieldEncoderFactory的createFieldEncoder方法生成的。

  public FieldConverter createFieldEncoder(DataField dataField,
      Cache<DictionaryColumnUniqueIdentifier, Dictionary> cache,
      CarbonTableIdentifier carbonTableIdentifier, int index, String nullFormat,
      DictionaryClient client, Boolean useOnePass, String storePath, boolean tableInitialize,
      Map<Object, Integer> localCache, boolean isEmptyBadRecord)
      throws IOException {
    // Converters are only needed for dimensions and measures it return null.
    if (dataField.getColumn().isDimension()) {
      if (dataField.getColumn().hasEncoding(Encoding.DIRECT_DICTIONARY) &&
          !dataField.getColumn().isComplex()) {
        return new DirectDictionaryFieldConverterImpl(dataField, nullFormat, index,
            isEmptyBadRecord);
      } else if (dataField.getColumn().hasEncoding(Encoding.DICTIONARY) &&
          !dataField.getColumn().isComplex()) {
        return new DictionaryFieldConverterImpl(dataField, cache, carbonTableIdentifier, nullFormat,
            index, client, useOnePass, storePath, tableInitialize, localCache, isEmptyBadRecord);
      } else if (dataField.getColumn().isComplex()) {
        return new ComplexFieldConverterImpl(
            createComplexType(dataField, cache, carbonTableIdentifier,
                    client, useOnePass, storePath, tableInitialize, localCache), index);
      } else {
        return new NonDictionaryFieldConverterImpl(dataField, nullFormat, index, isEmptyBadRecord);
      }
    } else {
      return new MeasureFieldConverterImpl(dataField, nullFormat, index, isEmptyBadRecord);
    }
  }

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從這段代碼當中能夠看出來，它是分紅了幾種類型的

一、維度類型，編碼方式爲Encoding.DIRECT_DICTIONARY的非複雜列，採用DirectDictionaryFieldConverterImpl （主要是TIMESTAMP和DATE類型），換算成值和基準時間的差值

二、維度類型，編碼方式爲Encoding.DICTIONARY的非複雜列，採用DictionaryFieldConverterImpl （非高基數的字段類型），把字段換成字典中的key(int類型)

三、維度類型，複雜列，採用ComplexFieldConverterImpl (複雜字段類型，Sturct和Array類型），把字段轉成二進制

四、維度類型，高基數列，採用NonDictionaryFieldConverterImpl，原封不動，原來是啥樣，如今仍是啥樣

五、指標類型，採用MeasureFieldConverterImpl (值類型，float、double、int、bigint、decimal等），原封不動，原來是啥樣，如今仍是啥樣

第三步SortProcessorStepImpl，關鍵點在SorterFactory.createSorter是怎麼實現的

  public static Sorter createSorter(CarbonDataLoadConfiguration configuration, AtomicLong counter) {
    boolean offheapsort = Boolean.parseBoolean(CarbonProperties.getInstance()
        .getProperty(CarbonCommonConstants.ENABLE_UNSAFE_SORT,
            CarbonCommonConstants.ENABLE_UNSAFE_SORT_DEFAULT));
    SortScopeOptions.SortScope sortScope = CarbonDataProcessorUtil.getSortScope(configuration);
    Sorter sorter;
    if (offheapsort) {
      if (configuration.getBucketingInfo() != null) {
        sorter = new UnsafeParallelReadMergeSorterWithBucketingImpl(configuration.getDataFields(),
            configuration.getBucketingInfo());
      } else {
        sorter = new UnsafeParallelReadMergeSorterImpl(counter);
      }
    } else {
      if (configuration.getBucketingInfo() != null) {
        sorter =
            new ParallelReadMergeSorterWithBucketingImpl(counter, configuration.getBucketingInfo());
      } else {
        sorter = new ParallelReadMergeSorterImpl(counter);
      }
    }
    if (sortScope.equals(SortScopeOptions.SortScope.BATCH_SORT)) {
      if (configuration.getBucketingInfo() == null) {
        sorter = new UnsafeBatchParallelReadMergeSorterImpl(counter);
      } else {
        LOGGER.warn(
            "Batch sort is not enabled in case of bucketing. Falling back to " + sorter.getClass()
                .getName());
      }
    }
    return sorter;
  }

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竟然還可使用堆外內存sort，設置enable.unsafe.sort爲true就能夠開啓了。咱們看默認的ParallelReadMergeSorterImpl吧。

超過100000條記錄就要把數據排序，而後生成一個文件，文件數超過20個文件以後，就要作一次文件合併。

規則在NewRowComparator和NewRowComparatorForNormalDims當中

相關參數：

carbon.sort.size 100000

carbon.sort.intermediate.files.limit 20

到最後一步了，打開DataWriterProcessorStepImpl類，它是經過CarbonFactHandlerFactory.createCarbonFactHandler生成一個CarbonFactHandler，經過CarbonFactHandler的addDataToStore方法處理CarbonRow

addDataToStore的實現很簡單，當row的數量達到一個blocklet的大小以後，就往線程池裏提交一個異步的任務Producer進行處理

  public void addDataToStore(CarbonRow row) throws CarbonDataWriterException {
    dataRows.add(row);
    this.entryCount++;
    // if entry count reaches to leaf node size then we are ready to write
    // this to leaf node file and update the intermediate files
    if (this.entryCount == this.blockletSize) {
      try {
        semaphore.acquire();

        producerExecutorServiceTaskList.add(
            producerExecutorService.submit(
                new Producer(blockletDataHolder, dataRows, ++writerTaskSequenceCounter, false)
            )
        );
        blockletProcessingCount.incrementAndGet();
        // set the entry count to zero
        processedDataCount += entryCount;
        LOGGER.info("Total Number Of records added to store: " + processedDataCount);
        dataRows = new ArrayList<>(this.blockletSize);
        this.entryCount = 0;
      } catch (InterruptedException e) {
        LOGGER.error(e, e.getMessage());
        throw new CarbonDataWriterException(e.getMessage(), e);
      }
    }
  }