标签：spark sql catalyst storage in-memory
/** 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。
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 ==
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)
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
}
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。
——EOF——
原创文章，转载请注明出自：http://blog.csdn.net/oopsoom/article/details/39577419
Spark SQL 源码分析之 In-Memory Columnar Storage 之 in-memory query
标签：spark sql catalyst storage in-memory
原文地址：http://blog.csdn.net/oopsoom/article/details/39577419
