Initial support for using ParquetTableScan to read HiveMetaStore tables.

sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala

@@ -303,3 +303,15 @@ case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode {
     left.execute().map(_.copy()).intersection(right.execute().map(_.copy()))
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ * A plan node that does nothing but lie about the output of its child.  Used to spice a
+ * (hopefully structurally equivalent) tree from a different optimization sequence into an already
+ * resolved tree.
+ */
+@DeveloperApi
+case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPlan {
+  def children = child :: Nil
+  def execute() = child.execute()
+}

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala

@@ -78,6 +78,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   // Change the default SQL dialect to HiveQL
   override private[spark] def dialect: String = getConf(SQLConf.DIALECT, "hiveql")
 
+  /**
+   * When true, enables an experimental feature where metastore tables that use the parquet SerDe
+   * are automatically converted to use the Spark SQL parquet table scan, instead of the Hive
+   * SerDe.
+   */
+  private[spark] def convertMetastoreParquet: Boolean =
+    getConf("spark.sql.hive.convertMetastoreParquet", "false") == "true"
+
   override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution { val logical = plan }
 
@@ -328,6 +336,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       TakeOrdered,
       ParquetOperations,
       InMemoryScans,
+      ParquetConversion, // Must be before HiveTableScans
       HiveTableScans,
       DataSinks,
       Scripts,

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala

@@ -17,21 +17,122 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LowerCaseSchema}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.columnar.InMemoryRelation
+import org.apache.spark.sql.parquet.ParquetTableScan
+
+import scala.collection.JavaConversions._
 
 private[hive] trait HiveStrategies {
   // Possibly being too clever with types here... or not clever enough.
   self: SQLContext#SparkPlanner =>
 
   val hiveContext: HiveContext
 
+  /**
+   * :: Experimental ::
+   * Finds table scans that would use the Hive SerDe and replaces them with our own native parquet
+   * table scan operator.
+   *
+   * TODO: Much of this logic is duplicated in HiveTableScan.  Ideally we would do some refactoring
+   * but since this is after the code freeze for 1.1 all logic is here to minimize disruption.
+   */
+  @Experimental
+  object ParquetConversion extends Strategy {
+    implicit class LogicalPlanHacks(s: SchemaRDD) {
+      def lowerCase =
+        new SchemaRDD(s.sqlContext, LowerCaseSchema(s.logicalPlan))
+    }
+
+    implicit class PhysicalPlanHacks(s: SparkPlan) {
+      def fakeOutput(newOutput: Seq[Attribute]) = OutputFaker(newOutput, s)
+    }
+
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case PhysicalOperation(projectList, predicates, relation: MetastoreRelation)
+          if relation.tableDesc.getSerdeClassName.contains("Parquet") &&
+             hiveContext.convertMetastoreParquet =>
+
+        // Filter out all predicates that only deal with partition keys
+        val partitionKeyIds = relation.partitionKeys.map(_.exprId).toSet
+        val (pruningPredicates, otherPredicates) = predicates.partition {
+          _.references.map(_.exprId).subsetOf(partitionKeyIds)
+        }
+
+        // We are going to throw the predicates and projection back at the whole optimization
+        // sequence so lets unresolve all the attributes, allowing them to be rebound to the
+        // matching parquet attributes.
+        val unresolvedOtherPredicates = otherPredicates.map(_ transform {
+          case a: AttributeReference => UnresolvedAttribute(a.name)
+        }).reduceOption(And).getOrElse(Literal(true))
+
+        val unresolvedProjection = projectList.map(_ transform {
+          case a: AttributeReference => UnresolvedAttribute(a.name)
+        })
+
+        if (relation.hiveQlTable.isPartitioned) {
+          val rawPredicate = pruningPredicates.reduceOption(And).getOrElse(Literal(true))
+          // Translate the predicate so that it automatically casts the input values to the correct
+          // data types during evaluation
+          val castedPredicate = rawPredicate transform {
+            case a: AttributeReference =>
+              val idx = relation.partitionKeys.indexWhere(a.exprId == _.exprId)
+              val key = relation.partitionKeys(idx)
+              Cast(BoundReference(idx, StringType, nullable = true), key.dataType)
+          }
+
+          val inputData = new GenericMutableRow(relation.partitionKeys.size)
+          val pruningCondition =
+            if(codegenEnabled) {
+              GeneratePredicate(castedPredicate)
+            } else {
+              InterpretedPredicate(castedPredicate)
+            }
+
+          val partitions = relation.hiveQlPartitions.filter { part =>
+            val partitionValues = part.getValues
+            var i = 0
+            while (i < partitionValues.size()) {
+              inputData(i) = partitionValues(i)
+              i += 1
+            }
+            pruningCondition(inputData)
+          }
+
+          org.apache.spark.sql.execution.Union(
+            partitions.par.map(p =>
+              hiveContext
+                .parquetFile(p.getLocation)
+                .lowerCase
+                .where(unresolvedOtherPredicates)
+                .select(unresolvedProjection:_*)
+                .queryExecution
+                .executedPlan
+                .fakeOutput(projectList.map(_.toAttribute))).seq) :: Nil
+
+        } else {
+          hiveContext
+            .parquetFile(relation.hiveQlTable.getDataLocation.getPath)
+            .lowerCase
+            .where(unresolvedOtherPredicates)
+            .select(unresolvedProjection:_*)
+            .queryExecution
+            .executedPlan
+            .fakeOutput(projectList.map(_.toAttribute)) :: Nil
+        }
+      case _ => Nil
+    }
+  }
+
   object Scripts extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.ScriptTransformation(input, script, output, child) =>

sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala

@@ -0,0 +1,126 @@
+
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * 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
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.io.File
+
+import org.scalatest.BeforeAndAfterAll
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.sql.{SQLConf, QueryTest}
+import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+
+case class ParquetData(intField: Int, stringField: String)
+
+/**
+ * Tests for our SerDe -> Native parquet scan conversion.
+ */
+class ParquetMetastoreSuite extends QueryTest with BeforeAndAfterAll {
+
+  override def beforeAll(): Unit = {
+    setConf("spark.sql.hive.convertMetastoreParquet", "true")
+  }
+
+  override def afterAll(): Unit = {
+    setConf("spark.sql.hive.convertMetastoreParquet", "false")
+  }
+
+  val partitionedTableDir = File.createTempFile("parquettests", "sparksql")
+  partitionedTableDir.delete()
+  partitionedTableDir.mkdir()
+
+  (1 to 10).foreach { p =>
+    val partDir = new File(partitionedTableDir, s"p=$p")
+    sparkContext.makeRDD(1 to 10)
+      .map(i => ParquetData(i, s"part-$p"))
+      .saveAsParquetFile(partDir.getCanonicalPath)
+  }
+
+  sql(s"""
+    create external table partitioned_parquet
+    (
+      intField INT,
+      stringField STRING
+    )
+    PARTITIONED BY (p int)
+    ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+     STORED AS
+     INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+     OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+    location '${partitionedTableDir.getCanonicalPath}'
+    """)
+
+  sql(s"""
+    create external table normal_parquet
+    (
+      intField INT,
+      stringField STRING
+    )
+    ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+     STORED AS
+     INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+     OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+    location '${new File(partitionedTableDir, "p=1").getCanonicalPath}'
+    """)
+
+  (1 to 10).foreach { p =>
+    sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)")
+  }
+
+  test("simple count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet"),
+      100)
+  }
+
+  test("pruned count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE p = 1"),
+      10)
+  }
+
+  test("multi-partition pruned count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE p IN (1,2,3)"),
+      30)
+  }
+
+  test("non-partition predicates") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE intField IN (1,2,3)"),
+      30)
+  }
+
+  test("sum") {
+    checkAnswer(
+      sql("SELECT SUM(intField) FROM partitioned_parquet WHERE intField IN (1,2,3) AND p = 1"),
+      1 + 2 + 3
+    )
+  }
+
+  test("non-part select(*)") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM normal_parquet"),
+      10
+    )
+  }
+}