[SPARK-10339] [SPARK-10334] [SPARK-10301] [SQL]Partitioned table scan can OOM driver and throw a better error message when users need to enable parquet schema merging

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala

@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
@@ -121,7 +122,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       projections: Seq[NamedExpression],
       filters: Seq[Expression],
       partitionColumns: StructType,
-      partitions: Array[Partition]) = {
+      partitions: Array[Partition]): SparkPlan = {
     val relation = logicalRelation.relation.asInstanceOf[HadoopFsRelation]
 
     // Because we are creating one RDD per partition, we need to have a shared HadoopConf.
@@ -130,49 +131,51 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     val confBroadcast =
       relation.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf))
 
-    // Builds RDD[Row]s for each selected partition.
-    val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
-      // The table scan operator (PhysicalRDD) which retrieves required columns from data files.
-      // Notice that the schema of data files, represented by `relation.dataSchema`, may contain
-      // some partition column(s).
-      val scan =
-        pruneFilterProject(
-          logicalRelation,
-          projections,
-          filters,
-          (columns: Seq[Attribute], filters) => {
-            val partitionColNames = partitionColumns.fieldNames
-
-            // Don't scan any partition columns to save I/O.  Here we are being optimistic and
-            // assuming partition columns data stored in data files are always consistent with those
-            // partition values encoded in partition directory paths.
-            val needed = columns.filterNot(a => partitionColNames.contains(a.name))
-            val dataRows =
-              relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
-
-            // Merges data values with partition values.
-            mergeWithPartitionValues(
-              relation.schema,
-              columns.map(_.name).toArray,
-              partitionColNames,
-              partitionValues,
-              toCatalystRDD(logicalRelation, needed, dataRows))
-          })
-
-      scan.execute()
-    }
+    // Now, we create a scan builder, which will be used by pruneFilterProject. This scan builder
+    // will union all partitions and attach partition values if needed.
+    val scanBuilder = {
+      (columns: Seq[Attribute], filters: Array[Filter]) => {
+        // Builds RDD[Row]s for each selected partition.
+        val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
+          val partitionColNames = partitionColumns.fieldNames
+
+          // Don't scan any partition columns to save I/O.  Here we are being optimistic and
+          // assuming partition columns data stored in data files are always consistent with those
+          // partition values encoded in partition directory paths.
+          val needed = columns.filterNot(a => partitionColNames.contains(a.name))
+          val dataRows =
+            relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
+
+          // Merges data values with partition values.
+          mergeWithPartitionValues(
+            relation.schema,
+            columns.map(_.name).toArray,
+            partitionColNames,
+            partitionValues,
+            toCatalystRDD(logicalRelation, needed, dataRows))
+        }
+
+        val unionedRows =
+          if (perPartitionRows.length == 0) {
+            relation.sqlContext.emptyResult
+          } else {
+            new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+          }
 
-    val unionedRows =
-      if (perPartitionRows.length == 0) {
-        relation.sqlContext.emptyResult
-      } else {
-        new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+        unionedRows
       }
+    }
+
+    // Create the scan operator. If needed, add Filter and/or Project on top of the scan.
+    // The added Filter/Project is on top of the unioned RDD. We do not want to create
+    // one Filter/Project for every partition.
+    val sparkPlan = pruneFilterProject(
+      logicalRelation,
+      projections,
+      filters,
+      scanBuilder)
 
-    execution.PhysicalRDD.createFromDataSource(
-      projections.map(_.toAttribute),
-      unionedRows,
-      logicalRelation.relation)
+    sparkPlan
   }
 
   // TODO: refactor this thing. It is very complicated because it does projection internally.

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala

@@ -196,6 +196,13 @@ private[parquet] class CatalystRowConverter(
       }
     }
 
+    if (paddedParquetFields.length != catalystType.length) {
+      throw new UnsupportedOperationException(
+        "A Parquet file's schema has different number of fields with the table schema. " +
+          "Please enable schema merging by setting \"mergeSchema\" to true when load " +
+          "a Parquet dataset or set spark.sql.parquet.mergeSchema to true in SQLConf.")
+    }
+
     paddedParquetFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`

sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala

@@ -23,7 +23,7 @@ import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, SaveMode}
+import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
@@ -136,4 +136,17 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       assert(fs.exists(commonSummaryPath))
     }
   }
+
+  test("SPARK-10334 Projections and filters should be kept in physical plan") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      sqlContext.range(2).select('id as 'a, 'id as 'b).write.partitionBy("b").parquet(path)
+      val df = sqlContext.read.parquet(path).filter('a === 0).select('b)
+      val physicalPlan = df.queryExecution.executedPlan
+
+      assert(physicalPlan.collect { case p: execution.Project => p }.length === 1)
+      assert(physicalPlan.collect { case p: execution.Filter => p }.length === 1)
+    }
+  }
 }