Carbondata源码系列（一）文件生成过程
在滴滴的两年一直在加班，人也变懒了，就很少再写博客了，最近在进行Carbondata和hive集成方面的工作，于是乎需要对Carbondata进行深入的研究。
于是新开一个系列，记录自己学习Carbondata的点点滴滴。
1、环境准备
当前版本是1.2.0-SNAPSHOT
git clone https://github.com/apache/carbondata.git
先用IDEA打开carbondata的代码，点击上方的View -> Tool Windows -> Maven Projects, 先勾选一下需要的profile和编译format工程，如下图所示：
2、探寻代码入口
我们先打开入口类CarbonDataFrameWriter，找到writeToCarbonFile这个方法
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)
}
}它有两个方式，loadTempCSV和loadDataFrame。
loadTempCSV是先生成CSV文件，再调用LOAD DATA INPATH...的命令导入数据。
这里我们之研究loadDataFrame这种直接生成数据的方式。
一路点进去，目标落在carbonTableSchema的LoadTable的run方法里，接着就是洋洋洒洒的二百行的set代码。它是核心其实是构造一个CarbonLoadModel类。
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)这些代码为了Load一个文本文件准备的，如果是用dataframe的方式则不需要看了。直接略过，直接调到if (carbonLoadModel.getUseOnePass)这一句。
这个跟字典的生成方式有关，这个值默认是false，先忽略true的过程吧，看主流程就行，下面这哥俩才是我们要找的。
// 生成字典文件
GlobalDictionaryUtil
.generateGlobalDictionary(
sparkSession.sqlContext,
carbonLoadModel,
relation.tableMeta.storePath,
dictionaryDataFrame)
// 生成数据文件
CarbonDataRDDFactory.loadCarbonData(sparkSession.sqlContext,
carbonLoadModel,
relation.tableMeta.storePath,
columnar,
partitionStatus,
None,
loadDataFrame,
updateModel) 3、字段生成过程
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)
}包含了两种情况：不存在字典文件和已存在字段文件。
先看不存在的情况
// use fact file to generate global dict
val (requireDimension, requireColumnNames) = pruneDimensions(dimensions,
headers, df.columns)
if (requireDimension.nonEmpty) {
// 只选取标记为字典的维度列
df = df.select(requireColumnNames.head, requireColumnNames.tail: _*)
val model = createDictionaryLoadModel(carbonLoadModel, carbonTableIdentifier,
requireDimension, storePath, dictfolderPath, false)
// 去重之后按列分区
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")
}先从源文件当中读取所有维度列，去重之后按列分区，然后输出，具体输出的过程请看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()字典文件在表目录的下的Metadata目录下，它需要生成三种文件
1、字段文件，命令方式为 列ID.dict
2、sort index文件，命令方式为 列ID.sortindex
3、字典列的meta信息，命令方式为 列ID.dictmeta
4、数据生成过程
请打开CarbonDataRDDFactory，找到loadCarbonData这个方法，方法里面包括了从load命令和从dataframe加载的两种方式，代码看起来是有点儿又长又臭的感觉。我们只关注loadDataFrame的方式就好。
def loadDataFrame(): Unit = {
try {
val rdd = dataFrame.get.rdd
// 获取数据的位置
val nodeNumOfData = rdd.partitions.flatMap[String, Array[String]]{ p =>
DataLoadPartitionCoalescer.getPreferredLocs(rdd, p).map(_.host)
}.distinct.size
// 确保executor数量要和数据的节点数一样多
val nodes = DistributionUtil.ensureExecutorsByNumberAndGetNodeList(nodeNumOfData,
sqlContext.sparkContext)
val newRdd = new DataLoadCoalescedRDD[Row](rdd, nodes.toArray.distinct)
// 生成数据文件
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
}
}打开NewDataFrameLoaderRDD类，查看internalCompute方法，这个方法的核心是这句话
new DataLoadExecutor().execute(model, loader.storeLocation, recordReaders.toArray)打开DataLoadExecutor，execute方法里面的核心是DataLoadProcessBuilder的build方法，根据表不同的参数设置，DataLoadProcessBuilder的build过程会有一些不同
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)) {
// 没有排序列或者carbon.load.sort.scope设置为NO_SORT的
return buildInternalForNoSort(inputIterators, configuration);
} else if (configuration.getBucketingInfo() != null) {
// 设置了Bucket的表
return buildInternalForBucketing(inputIterators, configuration);
} else if (sortScope.equals(SortScopeOptions.SortScope.BATCH_SORT)) {
// carbon.load.sort.scope设置为BATCH_SORT
return buildInternalForBatchSort(inputIterators, configuration);
} else {
return buildInternal(inputIterators, configuration);
}
}下面仅介绍标准的导入过程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个步骤：
1、读取数据，并进行格式转换，这一步骤是读取csv文件服务的，dataframe的数据格式都已经处理过了
2、根据字段的数据类型和配置，替换掉字典列的值；非字典列会被替换成byte数组
3、按照Sort列进行排序
4、把数据用Carbondata的格式输出
下面我们从第二步DataConverterProcessorStepImpl开始说起，在getIterator方法当中，会发现每一个CarbonRowBatch都要经过localConverter的convert方法转换，localConverter中只有RowConverterImpl一个转换器。
RowConverterImpl由很多的FieldConverter组成，在initialize方法中可以看到它是由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);
}
}从这段代码当中可以看出来，它是分成了几种类型的
1、维度类型，编码方式为Encoding.DIRECT_DICTIONARY的非复杂列，采用DirectDictionaryFieldConverterImpl （主要是TIMESTAMP和DATE类型），换算成值和基准时间的差值
2、维度类型，编码方式为Encoding.DICTIONARY的非复杂列，采用DictionaryFieldConverterImpl （非高基数的字段类型），把字段换成字典中的key(int类型)
3、维度类型，复杂列，采用ComplexFieldConverterImpl (复杂字段类型，Sturct和Array类型），把字段转成二进制
4、维度类型，高基数列，采用NonDictionaryFieldConverterImpl，原封不动，原来是啥样，现在还是啥样
5、指标类型，采用MeasureFieldConverterImpl (值类型，float、double、int、bigint、decimal等），原封不动，原来是啥样，现在还是啥样
第三步SortProcessorStepImpl，关键点在SorterFactory.createSorter是怎么实现的
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;
}居然还可以使用堆外内存sort，设置enable.unsafe.sort为true就可以开启了。我们看默认的ParallelReadMergeSorterImpl吧。
超过100000条记录就要把数据排序，然后生成一个文件，文件数超过20个文件之后，就要做一次文件合并。
规则在NewRowComparator和NewRowComparatorForNormalDims当中
相关参数：
carbon.sort.size 100000
carbon.sort.intermediate.files.limit 20
到最后一步了，打开DataWriterProcessorStepImpl类，它是通过CarbonFactHandlerFactory.createCarbonFactHandler生成一个CarbonFactHandler，通过CarbonFactHandler的addDataToStore方法处理CarbonRow
addDataToStore的实现很简单，当row的数量达到一个blocklet的大小之后，就往线程池里提交一个异步的任务Producer进行处理
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);
}
}
}这里用到了生产者消费者的模式，Producer的处理是多线程的，Consumer是单线程的；Producer主要是负责数据的压缩，Consumer负责进行输出，数据的交换通过blockletDataHolder。
相关参数：
carbon.number.of.cores.while.loading 2 （Producer的线程数）
number.of.rows.per.blocklet.column.page 32000
文件生成主要包含以上过程，限于文章篇幅，下一章再继续接着写Carbondata的数据文件格式细节。