Merge pull request #253 from harveyfeng/tablescan

Correctly detect in-memory RDDs for table scans.

src/main/scala/shark/execution/TableScanOperator.scala

@@ -37,7 +37,9 @@ import org.apache.spark.rdd.{PartitionPruningRDD, RDD}
 
 import shark.{LogHelper, SharkConfVars, SharkEnv}
 import shark.execution.optimization.ColumnPruner
-import shark.memstore2.{CacheType, ColumnarSerDe, MemoryMetadataManager}
+import shark.memstore2.CacheType
+import shark.memstore2.CacheType._
+import shark.memstore2.{ColumnarSerDe, MemoryMetadataManager}
 import shark.memstore2.{TablePartition, TablePartitionStats}
 import shark.util.HiveUtils
 
@@ -70,22 +72,25 @@ class TableScanOperator extends TopOperator[TableScanDesc] {
 
   @BeanProperty var tableDesc: TableDesc = _
 
+  // True if table data is stored the Spark heap.
   @BeanProperty var isInMemoryTableScan: Boolean = _
 
+  @BeanProperty var cacheMode: CacheType.CacheType = _
+
 
   override def initializeOnMaster() {
     // Create a local copy of the HiveConf that will be assigned job properties and, for disk reads,
     // broadcasted to slaves.
     localHConf = new HiveConf(super.hconf)
+    cacheMode = CacheType.fromString(
+      tableDesc.getProperties().get("shark.cache").asInstanceOf[String])
     isInMemoryTableScan = SharkEnv.memoryMetadataManager.containsTable(
       table.getDbName, table.getTableName)
   }
 
   override def outputObjectInspector() = {
-    val cacheMode = CacheType.fromString(
-      tableDesc.getProperties().get("shark.cache").asInstanceOf[String])
     if (parts == null) {
-      val serializer = if (CacheType.shouldCache(cacheMode)) {
+      val serializer = if (isInMemoryTableScan || cacheMode == CacheType.TACHYON) {
         new ColumnarSerDe
       } else {
         tableDesc.getDeserializerClass().newInstance()
@@ -94,7 +99,7 @@ class TableScanOperator extends TopOperator[TableScanDesc] {
       serializer.getObjectInspector()
     } else {
       val partProps = firstConfPartDesc.getProperties()
-      val partSerDe = if (CacheType.shouldCache(cacheMode)) {
+      val partSerDe = if (isInMemoryTableScan || cacheMode == CacheType.TACHYON) {
         new ColumnarSerDe
       } else {
         firstConfPartDesc.getDeserializerClass().newInstance()
@@ -115,8 +120,6 @@ class TableScanOperator extends TopOperator[TableScanDesc] {
     // 1. Spark heap (block manager), accessed through the Shark MemoryMetadataManager
     // 2. Tachyon table
     // 3. Hive table on HDFS (or other Hadoop storage)
-    val cacheMode = CacheType.fromString(
-      tableDesc.getProperties().get("shark.cache").asInstanceOf[String])
     // TODO(harvey): Pruning Hive-partitioned, cached tables isn't supported yet.
     if (isInMemoryTableScan || cacheMode == CacheType.TACHYON) {
       if (isInMemoryTableScan) {
@@ -147,8 +150,6 @@ class TableScanOperator extends TopOperator[TableScanDesc] {
     // the input table and we have statistics on the table.
     val columnsUsed = new ColumnPruner(this, table).columnsUsed
 
-    val cacheMode = CacheType.fromString(
-      tableDesc.getProperties().get("shark.cache").asInstanceOf[String])
     if (!table.isPartitioned && cacheMode == CacheType.TACHYON) {
       SharkEnv.tachyonUtil.pushDownColumnPruning(rdd, columnsUsed)
     }

src/test/scala/shark/SQLSuite.scala

@@ -1042,6 +1042,10 @@ class SQLSuite extends FunSuite {
       val cachedCount = cachedTableCounts(i)
       assert(onDiskCount == cachedCount, """Num rows for %s differ across Shark metastore restart. 
         (rows cached = %s, rows on disk = %s)""".format(tableName, cachedCount, onDiskCount))
+      // Check that we're able to materialize a row - i.e., make sure that table scan operator
+      // doesn't try to use a ColumnarSerDe when scanning contents on disk (for our test tables,
+      // LazySimpleSerDes should be used).
+      sc.sql("select * from %s limit 1".format(tableName))
     }
     // Finally, reload all tables.
     SharkRunner.loadTables()