spark BatchScanExec 源码
spark BatchScanExec 代码
文件路径:/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/BatchScanExec.scala
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* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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package org.apache.spark.sql.execution.datasources.v2
import com.google.common.base.Objects
import org.apache.spark.SparkException
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.QueryPlan
import org.apache.spark.sql.catalyst.plans.physical.{KeyGroupedPartitioning, SinglePartition}
import org.apache.spark.sql.catalyst.util.InternalRowSet
import org.apache.spark.sql.catalyst.util.truncatedString
import org.apache.spark.sql.connector.catalog.Table
import org.apache.spark.sql.connector.read.{HasPartitionKey, InputPartition, PartitionReaderFactory, Scan, SupportsRuntimeV2Filtering}
/**
* Physical plan node for scanning a batch of data from a data source v2.
*/
case class BatchScanExec(
output: Seq[AttributeReference],
@transient scan: Scan,
runtimeFilters: Seq[Expression],
keyGroupedPartitioning: Option[Seq[Expression]] = None,
ordering: Option[Seq[SortOrder]] = None,
@transient table: Table) extends DataSourceV2ScanExecBase {
@transient lazy val batch = scan.toBatch
// TODO: unify the equal/hashCode implementation for all data source v2 query plans.
override def equals(other: Any): Boolean = other match {
case other: BatchScanExec =>
this.batch == other.batch && this.runtimeFilters == other.runtimeFilters
case _ =>
false
}
override def hashCode(): Int = Objects.hashCode(batch, runtimeFilters)
@transient override lazy val inputPartitions: Seq[InputPartition] = batch.planInputPartitions()
@transient private lazy val filteredPartitions: Seq[Seq[InputPartition]] = {
val dataSourceFilters = runtimeFilters.flatMap {
case DynamicPruningExpression(e) => DataSourceV2Strategy.translateRuntimeFilterV2(e)
case _ => None
}
if (dataSourceFilters.nonEmpty) {
val originalPartitioning = outputPartitioning
// the cast is safe as runtime filters are only assigned if the scan can be filtered
val filterableScan = scan.asInstanceOf[SupportsRuntimeV2Filtering]
filterableScan.filter(dataSourceFilters.toArray)
// call toBatch again to get filtered partitions
val newPartitions = scan.toBatch.planInputPartitions()
originalPartitioning match {
case p: KeyGroupedPartitioning =>
if (newPartitions.exists(!_.isInstanceOf[HasPartitionKey])) {
throw new SparkException("Data source must have preserved the original partitioning " +
"during runtime filtering: not all partitions implement HasPartitionKey after " +
"filtering")
}
val newRows = new InternalRowSet(p.expressions.map(_.dataType))
newRows ++= newPartitions.map(_.asInstanceOf[HasPartitionKey].partitionKey())
val oldRows = p.partitionValuesOpt.get
if (oldRows.size != newRows.size) {
throw new SparkException("Data source must have preserved the original partitioning " +
"during runtime filtering: the number of unique partition values obtained " +
s"through HasPartitionKey changed: before ${oldRows.size}, after ${newRows.size}")
}
if (!oldRows.forall(newRows.contains)) {
throw new SparkException("Data source must have preserved the original partitioning " +
"during runtime filtering: the number of unique partition values obtained " +
s"through HasPartitionKey remain the same but do not exactly match")
}
groupPartitions(newPartitions).get.map(_._2)
case _ =>
// no validation is needed as the data source did not report any specific partitioning
newPartitions.map(Seq(_))
}
} else {
partitions
}
}
override lazy val readerFactory: PartitionReaderFactory = batch.createReaderFactory()
override lazy val inputRDD: RDD[InternalRow] = {
val rdd = if (filteredPartitions.isEmpty && outputPartitioning == SinglePartition) {
// return an empty RDD with 1 partition if dynamic filtering removed the only split
sparkContext.parallelize(Array.empty[InternalRow], 1)
} else {
new DataSourceRDD(
sparkContext, filteredPartitions, readerFactory, supportsColumnar, customMetrics)
}
postDriverMetrics()
rdd
}
override def doCanonicalize(): BatchScanExec = {
this.copy(
output = output.map(QueryPlan.normalizeExpressions(_, output)),
runtimeFilters = QueryPlan.normalizePredicates(
runtimeFilters.filterNot(_ == DynamicPruningExpression(Literal.TrueLiteral)),
output))
}
override def simpleString(maxFields: Int): String = {
val truncatedOutputString = truncatedString(output, "[", ", ", "]", maxFields)
val runtimeFiltersString = s"RuntimeFilters: ${runtimeFilters.mkString("[", ",", "]")}"
val result = s"$nodeName$truncatedOutputString ${scan.description()} $runtimeFiltersString"
redact(result)
}
override def nodeName: String = {
s"BatchScan ${table.name()}".trim
}
}
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