do not create DataSourceReader many times

cloud-fan · cloud-fan · commit ca6ccb24e8f2 · 2018-05-14T16:36:54.000+08:00
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Relation.scala
@@ -22,78 +22,57 @@ import scala.collection.JavaConverters._
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression}
-import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
-import org.apache.spark.sql.execution.datasources.DataSourceStrategy
-import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
+import org.apache.spark.sql.sources.DataSourceRegister
 import org.apache.spark.sql.sources.v2.{DataSourceOptions, DataSourceV2, ReadSupport, ReadSupportWithSchema}
-import org.apache.spark.sql.sources.v2.reader.{DataSourceReader, SupportsPushDownCatalystFilters, SupportsPushDownFilters, SupportsPushDownRequiredColumns, SupportsReportStatistics}
+import org.apache.spark.sql.sources.v2.reader.{DataSourceReader, SupportsReportStatistics}
 import org.apache.spark.sql.types.StructType
 
+/**
+ * A logical plan representing a data source v2 scan.
+ *
+ * @param source An instance of a [[DataSourceV2]] implementation.
+ * @param options The options for this scan. Used to create fresh [[DataSourceReader]].
+ * @param userSpecifiedSchema The user-specified schema for this scan. Used to create fresh
+ *                            [[DataSourceReader]].
+ * @param optimizedReader An optimized [[DataSourceReader]] which is produced by the optimizer rule
+ *                        [[PushDownOperatorsToDataSource]]. It is a temporary value and is excluded
+ *                        in the equality definition of this class. It is to avoid re-applying
+ *                        operators pushdown and re-creating [[DataSourceReader]] when we copy
+ *                        the relation during query plan transformation.
+ * @param pushedFilters The filters that are pushed down to the data source.
+ */
 case class DataSourceV2Relation(
+    output: Seq[AttributeReference],
     source: DataSourceV2,
     options: Map[String, String],
-    projection: Seq[AttributeReference],
-    filters: Option[Seq[Expression]] = None,
-    userSpecifiedSchema: Option[StructType] = None)
+    userSpecifiedSchema: Option[StructType],
+    optimizedReader: Option[DataSourceReader] = None,
+    pushedFilters: Seq[Expression] = Nil)
   extends LeafNode with MultiInstanceRelation with DataSourceV2StringFormat {
 
   import DataSourceV2Relation._
 
-  override def simpleString: String = "RelationV2 " + metadataString
-
-  override lazy val schema: StructType = reader.readSchema()
+  def createFreshReader: DataSourceReader = source.createReader(options, userSpecifiedSchema)
 
-  override lazy val output: Seq[AttributeReference] = {
-    // use the projection attributes to avoid assigning new ids. fields that are not projected
-    // will be assigned new ids, which is okay because they are not projected.
-    val attrMap = projection.map(a => a.name -> a).toMap
-    schema.map(f => attrMap.getOrElse(f.name,
-      AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()))
-  }
+  def reader: DataSourceReader = optimizedReader.getOrElse(createFreshReader)
 
-  private lazy val v2Options: DataSourceOptions = makeV2Options(options)
-
-  // postScanFilters: filters that need to be evaluated after the scan.
-  // pushedFilters: filters that will be pushed down and evaluated in the underlying data sources.
-  // Note: postScanFilters and pushedFilters can overlap, e.g. the parquet row group filter.
-  lazy val (
-      reader: DataSourceReader,
-      postScanFilters: Seq[Expression],
-      pushedFilters: Seq[Expression]) = {
-    val newReader = userSpecifiedSchema match {
-      case Some(s) =>
-        source.asReadSupportWithSchema.createReader(s, v2Options)
-      case _ =>
-        source.asReadSupport.createReader(v2Options)
-    }
-
-    DataSourceV2Relation.pushRequiredColumns(newReader, projection.toStructType)
-
-    val (postScanFilters, pushedFilters) = filters match {
-      case Some(filterSeq) =>
-        DataSourceV2Relation.pushFilters(newReader, filterSeq)
-      case _ =>
-        (Nil, Nil)
-    }
-    logInfo(s"Post-Scan Filters: ${postScanFilters.mkString(",")}")
-    logInfo(s"Pushed Filters: ${pushedFilters.mkString(", ")}")
+  override def simpleString: String = "RelationV2 " + metadataString
 
-    (newReader, postScanFilters, pushedFilters)
+  override def equals(other: Any): Boolean = other match {
+    case other: DataSourceV2Relation =>
+      output == other.output && source.getClass == other.source.getClass &&
+        options == other.options && userSpecifiedSchema == other.userSpecifiedSchema &&
+        pushedFilters == other.pushedFilters
+    case _ => false
   }
 
-  override def doCanonicalize(): LogicalPlan = {
-    val c = super.doCanonicalize().asInstanceOf[DataSourceV2Relation]
-
-    // override output with canonicalized output to avoid attempting to configure a reader
-    val canonicalOutput: Seq[AttributeReference] = this.output
-        .map(a => QueryPlan.normalizeExprId(a, projection))
-
-    new DataSourceV2Relation(c.source, c.options, c.projection) {
-      override lazy val output: Seq[AttributeReference] = canonicalOutput
-    }
+  override def hashCode(): Int = {
+    Seq(output, source.getClass, options, userSpecifiedSchema, pushedFilters).hashCode()
   }
 
+  // `LogicalPlanStats` caches the computed statistics, so we are fine here even the
+  // `optimizedReader` is None. We won't create `DataSourceReader` many times.
   override def computeStats(): Statistics = reader match {
     case r: SupportsReportStatistics =>
       Statistics(sizeInBytes = r.getStatistics.sizeInBytes().orElse(conf.defaultSizeInBytes))
@@ -102,9 +81,7 @@ case class DataSourceV2Relation(
   }
 
   override def newInstance(): DataSourceV2Relation = {
-    // projection is used to maintain id assignment.
-    // if projection is not set, use output so the copy is not equal to the original
-    copy(projection = projection.map(_.newInstance()))
+    copy(output = output.map(_.newInstance()))
   }
 }
 
@@ -150,111 +127,57 @@ case class StreamingDataSourceV2Relation(
 }
 
 object DataSourceV2Relation {
+
   private implicit class SourceHelpers(source: DataSourceV2) {
-    def asReadSupport: ReadSupport = {
-      source match {
-        case support: ReadSupport =>
-          support
-        case _: ReadSupportWithSchema =>
-          // this method is only called if there is no user-supplied schema. if there is no
-          // user-supplied schema and ReadSupport was not implemented, throw a helpful exception.
-          throw new AnalysisException(s"Data source requires a user-supplied schema: $name")
-        case _ =>
-          throw new AnalysisException(s"Data source is not readable: $name")
-      }
-    }
 
-    def asReadSupportWithSchema: ReadSupportWithSchema = {
-      source match {
-        case support: ReadSupportWithSchema =>
-          support
-        case _: ReadSupport =>
-          throw new AnalysisException(
-            s"Data source does not support user-supplied schema: $name")
-        case _ =>
-          throw new AnalysisException(s"Data source is not readable: $name")
-      }
+    private def asReadSupport: ReadSupport = source match {
+      case support: ReadSupport =>
+        support
+      case _: ReadSupportWithSchema =>
+        // this method is only called if there is no user-supplied schema. if there is no
+        // user-supplied schema and ReadSupport was not implemented, throw a helpful exception.
+        throw new AnalysisException(s"Data source requires a user-supplied schema: $name")
+      case _ =>
+        throw new AnalysisException(s"Data source is not readable: $name")
     }
 
-    def name: String = {
-      source match {
-        case registered: DataSourceRegister =>
-          registered.shortName()
-        case _ =>
-          source.getClass.getSimpleName
-      }
+    private def asReadSupportWithSchema: ReadSupportWithSchema = source match {
+      case support: ReadSupportWithSchema =>
+        support
+      case _: ReadSupport =>
+        throw new AnalysisException(
+          s"Data source does not support user-supplied schema: $name")
+      case _ =>
+        throw new AnalysisException(s"Data source is not readable: $name")
     }
-  }
 
-  private def makeV2Options(options: Map[String, String]): DataSourceOptions = {
-    new DataSourceOptions(options.asJava)
-  }
 
-  private def schema(
-      source: DataSourceV2,
-      v2Options: DataSourceOptions,
-      userSchema: Option[StructType]): StructType = {
-    val reader = userSchema match {
-      case Some(s) =>
-        source.asReadSupportWithSchema.createReader(s, v2Options)
+    private def name: String = source match {
+      case registered: DataSourceRegister =>
+        registered.shortName()
       case _ =>
-        source.asReadSupport.createReader(v2Options)
+        source.getClass.getSimpleName
+    }
+
+    def createReader(
+        options: Map[String, String],
+        userSpecifiedSchema: Option[StructType]): DataSourceReader = {
+      val v2Options = new DataSourceOptions(options.asJava)
+      userSpecifiedSchema match {
+        case Some(s) =>
+          asReadSupportWithSchema.createReader(s, v2Options)
+        case _ =>
+          asReadSupport.createReader(v2Options)
+      }
     }
-    reader.readSchema()
   }
 
   def create(
       source: DataSourceV2,
       options: Map[String, String],
-      filters: Option[Seq[Expression]] = None,
-      userSpecifiedSchema: Option[StructType] = None): DataSourceV2Relation = {
-    val projection = schema(source, makeV2Options(options), userSpecifiedSchema).toAttributes
-    DataSourceV2Relation(source, options, projection, filters, userSpecifiedSchema)
-  }
-
-  private def pushRequiredColumns(reader: DataSourceReader, struct: StructType): Unit = {
-    reader match {
-      case projectionSupport: SupportsPushDownRequiredColumns =>
-        projectionSupport.pruneColumns(struct)
-      case _ =>
-    }
-  }
-
-  private def pushFilters(
-      reader: DataSourceReader,
-      filters: Seq[Expression]): (Seq[Expression], Seq[Expression]) = {
-    reader match {
-      case r: SupportsPushDownCatalystFilters =>
-        val postScanFilters = r.pushCatalystFilters(filters.toArray)
-        val pushedFilters = r.pushedCatalystFilters()
-        (postScanFilters, pushedFilters)
-
-      case r: SupportsPushDownFilters =>
-        // A map from translated data source filters to original catalyst filter expressions.
-        val translatedFilterToExpr = scala.collection.mutable.HashMap.empty[Filter, Expression]
-        // Catalyst filter expression that can't be translated to data source filters.
-        val untranslatableExprs = scala.collection.mutable.ArrayBuffer.empty[Expression]
-
-        for (filterExpr <- filters) {
-          val translated = DataSourceStrategy.translateFilter(filterExpr)
-          if (translated.isDefined) {
-            translatedFilterToExpr(translated.get) = filterExpr
-          } else {
-            untranslatableExprs += filterExpr
-          }
-        }
-
-        // Data source filters that need to be evaluated again after scanning. which means
-        // the data source cannot guarantee the rows returned can pass these filters.
-        // As a result we must return it so Spark can plan an extra filter operator.
-        val postScanFilters =
-          r.pushFilters(translatedFilterToExpr.keys.toArray).map(translatedFilterToExpr)
-        // The filters which are marked as pushed to this data source
-        val pushedFilters = r.pushedFilters().map(translatedFilterToExpr)
-
-        (untranslatableExprs ++ postScanFilters, pushedFilters)
-
-      case _ => (filters, Nil)
-    }
+      userSpecifiedSchema: Option[StructType]): DataSourceV2Relation = {
+    val reader = source.createReader(options, userSpecifiedSchema)
+    DataSourceV2Relation(
+      reader.readSchema().toAttributes, source, options, userSpecifiedSchema)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/PushDownOperatorsToDataSource.scala
@@ -17,48 +17,81 @@
 
 package org.apache.spark.sql.execution.datasources.v2
 
-import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference, AttributeSet}
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.expressions.{And, AttributeSet, Expression}
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan, Project}
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.DataSourceStrategy
+import org.apache.spark.sql.sources
+import org.apache.spark.sql.sources.v2.reader.{SupportsPushDownCatalystFilters, SupportsPushDownFilters, SupportsPushDownRequiredColumns}
 
 object PushDownOperatorsToDataSource extends Rule[LogicalPlan] {
   override def apply(plan: LogicalPlan): LogicalPlan = plan match {
     // PhysicalOperation guarantees that filters are deterministic; no need to check
     case PhysicalOperation(project, filters, relation: DataSourceV2Relation) =>
-      assert(relation.filters.isEmpty, "data source v2 should do push down only once.")
+      val newReader = relation.createFreshReader
+      var newRelation = relation.copy(optimizedReader = Some(newReader))
 
-      val projectAttrs = project.map(_.toAttribute)
-      val projectSet = AttributeSet(project.flatMap(_.references))
-      val filterSet = AttributeSet(filters.flatMap(_.references))
+      val postScanFilters: Seq[Expression] = newReader match {
+        case r: SupportsPushDownCatalystFilters =>
+          val postScanFilters = r.pushCatalystFilters(filters.toArray)
+          newRelation.copy(pushedFilters = r.pushedCatalystFilters())
+          postScanFilters
 
-      val projection = if (filterSet.subsetOf(projectSet) &&
-          AttributeSet(projectAttrs) == projectSet) {
-        // When the required projection contains all of the filter columns and column pruning alone
-        // can produce the required projection, push the required projection.
-        // A final projection may still be needed if the data source produces a different column
-        // order or if it cannot prune all of the nested columns.
-        projectAttrs
-      } else {
-        // When there are filter columns not already in the required projection or when the required
-        // projection is more complicated than column pruning, base column pruning on the set of
-        // all columns needed by both.
-        (projectSet ++ filterSet).toSeq
+        case r: SupportsPushDownFilters =>
+          // A map from translated data source filters to original catalyst filter expressions.
+          val translatedFilterToExpr = mutable.HashMap.empty[sources.Filter, Expression]
+          // Catalyst filter expression that can't be translated to data source filters.
+          val untranslatableExprs = mutable.ArrayBuffer.empty[Expression]
+
+          for (filterExpr <- filters) {
+            val translated = DataSourceStrategy.translateFilter(filterExpr)
+            if (translated.isDefined) {
+              translatedFilterToExpr(translated.get) = filterExpr
+            } else {
+              untranslatableExprs += filterExpr
+            }
+          }
+
+          // Data source filters that need to be evaluated again after scanning. which means
+          // the data source cannot guarantee the rows returned can pass these filters.
+          // As a result we must return it so Spark can plan an extra filter operator.
+          val postScanFilters =
+          r.pushFilters(translatedFilterToExpr.keys.toArray).map(translatedFilterToExpr)
+          // The filters which are marked as pushed to this data source
+          val pushedFilters = r.pushedFilters().map(translatedFilterToExpr)
+          newRelation = newRelation.copy(pushedFilters = pushedFilters)
+          untranslatableExprs ++ postScanFilters
+
+        case _ => filters
       }
 
-      val newRelation = relation.copy(
-        projection = projection.asInstanceOf[Seq[AttributeReference]],
-        filters = Some(filters))
+      newReader match {
+        case r: SupportsPushDownRequiredColumns =>
+          val requiredColumns = AttributeSet(
+            project.flatMap(_.references) ++ postScanFilters.flatMap(_.references))
+          val neededOutput = relation.output.filter(requiredColumns.contains)
+          if (neededOutput != relation.output) {
+            r.pruneColumns(neededOutput.toStructType)
+            val nameToAttr = relation.output.map(_.name).zip(relation.output).toMap
+            val newOutput = r.readSchema().toAttributes.map {
+              // We have to keep the attribute id during transformation.
+              a => a.withExprId(nameToAttr(a.name).exprId)
+            }
+            newRelation = newRelation.copy(output = newOutput)
+          }
 
-      // Add a Filter for any filters that need to be evaluated after scan.
-      val postScanFilterCond = newRelation.postScanFilters.reduceLeftOption(And)
-      val filtered = postScanFilterCond.map(Filter(_, newRelation)).getOrElse(newRelation)
+        case _ =>
+      }
 
-      // Add a Project to ensure the output matches the required projection
-      if (newRelation.output != projectAttrs) {
-        Project(project, filtered)
+      val filterCondition = postScanFilters.reduceLeftOption(And)
+      val withFilter = filterCondition.map(Filter(_, newRelation)).getOrElse(newRelation)
+      if (withFilter.output == project) {
+        withFilter
       } else {
-        filtered
+        Project(project, withFilter)
       }
 
     case other => other.mapChildren(apply)
