Run test 100 times.

apache · Sep 17, 2015 · b4bdef6 · b4bdef6
1 parent 8bde803
commit b4bdef6
Showing 1 changed file with 61 additions and 59 deletions.
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -551,67 +551,69 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Te
     }
   }
 
-  test("udaf with all data types") {
-    val struct =
-      StructType(
-        StructField("f1", FloatType, true) ::
-          StructField("f2", ArrayType(BooleanType), true) :: Nil)
-    val dataTypes = Seq(StringType, BinaryType, NullType, BooleanType,
-      ByteType, ShortType, IntegerType, LongType,
-      FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
-      DateType, TimestampType,
-      ArrayType(IntegerType), MapType(StringType, LongType), struct,
-      new MyDenseVectorUDT())
-    // Right now, we will use SortBasedAggregate to handle UDAFs.
-    // UnsafeRow.mutableFieldTypes.asScala.toSeq will trigger SortBasedAggregate to use
-    // UnsafeRow as the aggregation buffer. While, dataTypes will trigger
-    // SortBasedAggregate to use a safe row as the aggregation buffer.
-    Seq(dataTypes, UnsafeRow.mutableFieldTypes.asScala.toSeq).foreach { dataTypes =>
-      val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
-        StructField(s"col$index", dataType, nullable = true)
-      }
-      // The schema used for data generator.
-      val schemaForGenerator = StructType(fields)
-      // The schema used for the DataFrame df.
-      val schema = StructType(StructField("id", IntegerType) +: fields)
-
-      logInfo(s"Testing schema: ${schema.treeString}")
-
-      val udaf = new ScalaAggregateFunction(schema)
-      // Generate data at the driver side. We need to materialize the data first and then
-      // create RDD.
-      val maybeDataGenerator =
-        RandomDataGenerator.forType(
-          dataType = schemaForGenerator,
-          nullable = true,
-          seed = Some(System.nanoTime()))
-      val dataGenerator =
-        maybeDataGenerator
-          .getOrElse(fail(s"Failed to create data generator for schema $schemaForGenerator"))
-      val data = (1 to 50).map { i =>
-        dataGenerator.apply() match {
-          case row: Row => Row.fromSeq(i +: row.toSeq)
-          case null => Row.fromSeq(i +: Seq.fill(schemaForGenerator.length)(null))
-          case other =>
-            fail(s"Row or null is expected to be generated, " +
-              s"but a ${other.getClass.getCanonicalName} is generated.")
+  (1 to 100).foreach { i =>
+    test(s"udaf with all data types: run $i") {
+      val struct =
+        StructType(
+          StructField("f1", FloatType, true) ::
+            StructField("f2", ArrayType(BooleanType), true) :: Nil)
+      val dataTypes = Seq(StringType, BinaryType, NullType, BooleanType,
+        ByteType, ShortType, IntegerType, LongType,
+        FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
+        DateType, TimestampType,
+        ArrayType(IntegerType), MapType(StringType, LongType), struct,
+        new MyDenseVectorUDT())
+      // Right now, we will use SortBasedAggregate to handle UDAFs.
+      // UnsafeRow.mutableFieldTypes.asScala.toSeq will trigger SortBasedAggregate to use
+      // UnsafeRow as the aggregation buffer. While, dataTypes will trigger
+      // SortBasedAggregate to use a safe row as the aggregation buffer.
+      Seq(dataTypes, UnsafeRow.mutableFieldTypes.asScala.toSeq).foreach { dataTypes =>
+        val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
+          StructField(s"col$index", dataType, nullable = true)
+        }
+        // The schema used for data generator.
+        val schemaForGenerator = StructType(fields)
+        // The schema used for the DataFrame df.
+        val schema = StructType(StructField("id", IntegerType) +: fields)
+
+        logInfo(s"Testing schema: ${schema.treeString}")
+
+        val udaf = new ScalaAggregateFunction(schema)
+        // Generate data at the driver side. We need to materialize the data first and then
+        // create RDD.
+        val maybeDataGenerator =
+          RandomDataGenerator.forType(
+            dataType = schemaForGenerator,
+            nullable = true,
+            seed = Some(System.nanoTime()))
+        val dataGenerator =
+          maybeDataGenerator
+            .getOrElse(fail(s"Failed to create data generator for schema $schemaForGenerator"))
+        val data = (1 to 50).map { i =>
+          dataGenerator.apply() match {
+            case row: Row => Row.fromSeq(i +: row.toSeq)
+            case null => Row.fromSeq(i +: Seq.fill(schemaForGenerator.length)(null))
+            case other =>
+              fail(s"Row or null is expected to be generated, " +
+                s"but a ${other.getClass.getCanonicalName} is generated.")
+          }
         }
-      }
-
-      // Create a DF for the schema with random data.
-      val rdd = sqlContext.sparkContext.parallelize(data, 1)
-      val df = sqlContext.createDataFrame(rdd, schema)
 
-      val allColumns = df.schema.fields.map(f => col(f.name))
-      val expectedAnaswer =
-        data
-          .find(r => r.getInt(0) == 50)
-          .getOrElse(fail("A row with id 50 should be the expected answer."))
-      checkAnswer(
-        df.groupBy().agg(udaf(allColumns: _*)),
-        // udaf returns a Row as the output value.
-        Row(expectedAnaswer)
-      )
+        // Create a DF for the schema with random data.
+        val rdd = sqlContext.sparkContext.parallelize(data, 1)
+        val df = sqlContext.createDataFrame(rdd, schema)
+
+        val allColumns = df.schema.fields.map(f => col(f.name))
+        val expectedAnaswer =
+          data
+            .find(r => r.getInt(0) == 50)
+            .getOrElse(fail("A row with id 50 should be the expected answer."))
+        checkAnswer(
+          df.groupBy().agg(udaf(allColumns: _*)),
+          // udaf returns a Row as the output value.
+          Row(expectedAnaswer)
+        )
+      }
     }
   }
 }