Spark SQL 源码分析之 In-Memory Columnar Storage 之 in-memory query
/** Spark SQL源码分析系列文章*/
前面讲到了Spark SQL In-Memory Columnar Storage的存储结构是基于列存储的。
那么基于以上存储结构,我们查询cache在jvm内的数据又是如何查询的,本文将揭示查询In-Memory Data的方式。
一、引子
当我们将src表cache到了内存后,再次查询src,可以通过analyzed执行计划来观察内部调用。
即parse后,会形成InMemoryRelation结点,最后执行物理计划时,会调用InMemoryColumnarTableScan这个结点的方法。
如下:
scala> val exe = executePlan(sql("select value from src").queryExecution.analyzed) 14/09/26 10:30:26 INFO parse.ParseDriver: Parsing command: select value from src 14/09/26 10:30:26 INFO parse.ParseDriver: Parse Completed exe: org.apache.spark.sql.hive.test.TestHive.QueryExecution = == Parsed Logical Plan == Project [value#5] InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None) == Analyzed Logical Plan == Project [value#5] InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None) == Optimized Logical Plan == Project [value#5] InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None) == Physical Plan == InMemoryColumnarTableScan [value#5], (InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None)) //查询内存中表的入口 Code Generation: false == RDD ==
二、InMemoryColumnarTableScan
private[sql] case class InMemoryColumnarTableScan( attributes: Seq[Attribute], relation: InMemoryRelation) extends LeafNode { override def output: Seq[Attribute] = attributes override def execute() = { relation.cachedColumnBuffers.mapPartitions { iterator => // Find the ordinals of the requested columns. If none are requested, use the first. val requestedColumns = if (attributes.isEmpty) { Seq(0) } else { attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId)) //根据表达式exprId找出对应列的ByteBuffer的索引 } iterator .map(batch => requestedColumns.map(batch(_)).map(ColumnAccessor(_)))//根据索引取得对应请求列的ByteBuffer,并封装为ColumnAccessor。 .flatMap { columnAccessors => val nextRow = new GenericMutableRow(columnAccessors.length) //Row的长度 new Iterator[Row] { override def next() = { var i = 0 while (i < nextRow.length) { columnAccessors(i).extractTo(nextRow, i) //根据对应index和长度,从byterbuffer里取得值,封装到row里 i += 1 } nextRow } override def hasNext = columnAccessors.head.hasNext } } } } }
查询请求的列,如下:
scala> exe.optimizedPlan res93: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan = Project [value#5] InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None) scala> val relation = exe.optimizedPlan(1) relation: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan = InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None) scala> val request_relation = exe.executedPlan request_relation: org.apache.spark.sql.execution.SparkPlan = InMemoryColumnarTableScan [value#5], (InMemoryRelation [key#4,value#5], false, 1000, (HiveTableScan [key#4,value#5], (MetastoreRelation default, src, None), None)) scala> request_relation.output //请求的列,我们请求的只有value列 res95: Seq[org.apache.spark.sql.catalyst.expressions.Attribute] = ArrayBuffer(value#5) scala> relation.output //默认保存在relation中的所有列 res96: Seq[org.apache.spark.sql.catalyst.expressions.Attribute] = ArrayBuffer(key#4, value#5) scala> val attributes = request_relation.output attributes: Seq[org.apache.spark.sql.catalyst.expressions.Attribute] = ArrayBuffer(value#5)
//根据exprId找出对应ID scala> val attr_index = attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId)) attr_index: Seq[Int] = ArrayBuffer(1) //找到请求的列value的索引是1, 我们查询就从Index为1的bytebuffer中,请求数据 scala> relation.output.foreach(e=>println(e.exprId)) ExprId(4) //对应<span style="font-family: Arial, Helvetica, sans-serif;">[key#4,value#5]</span> ExprId(5) scala> request_relation.output.foreach(e=>println(e.exprId)) ExprId(5)
三、ColumnAccessor
ColumnAccessor对应每一种类型,类图如下:
最后返回一个新的迭代器:
new Iterator[Row] { override def next() = { var i = 0 while (i < nextRow.length) { //请求列的长度 columnAccessors(i).extractTo(nextRow, i)//调用columnType.setField(row, ordinal, extractSingle(buffer))解析buffer i += 1 } nextRow//返回解析后的row } override def hasNext = columnAccessors.head.hasNext }
四、总结
Spark SQL In-Memory Columnar Storage的查询相对来说还是比较简单的,其查询思想主要和存储的数据结构有关。
即存储时,按每列放到一个bytebuffer,形成一个bytebuffer数组。
查询时,根据请求列的exprId查找到上述数组的索引,然后使用ColumnAccessor对buffer中字段进行解析,最后封装为Row对象,返回。
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