AngersZhuuuu commented on a change in pull request #29401:
URL: https://github.com/apache/spark/pull/29401#discussion_r468590814
##########
File path:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveScriptTransformationSuite.scala
##########
@@ -195,75 +169,146 @@ class HiveScriptTransformationSuite extends
SparkPlanTest with SQLTestUtils with
val query = sql(
s"""
- |SELECT
- |TRANSFORM(a, b, c, d, e)
- |USING 'python $scriptFilePath' AS (a, b, c, d, e)
- |FROM v
+ |SELECT TRANSFORM(a, b, c, d, e)
+ |USING 'python ${scriptFilePath}'
+ |FROM v
""".stripMargin)
- // In Hive 1.2, the string representation of a decimal omits trailing
zeroes.
- // But in Hive 2.3, it is always padded to 18 digits with trailing
zeroes if necessary.
- val decimalToString: Column => Column = if (HiveUtils.isHive23) {
- c => c.cast("string")
- } else {
- c => c.cast("decimal(1, 0)").cast("string")
- }
- checkAnswer(query, identity, df.select(
- 'a.cast("string"),
- 'b.cast("string"),
- 'c.cast("string"),
- decimalToString('d),
- 'e.cast("string")).collect())
+ // In hive default serde mode, if we don't define output schema, it will
choose first
+ // two column as output schema (key: String, value: String)
+ checkAnswer(
+ query,
+ identity,
+ df.select(
+ 'a.cast("string").as("key"),
+ 'b.cast("string").as("value")).collect())
}
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(no serde)") {
+ testBasicInputDataTypesWith(hiveIOSchema, "hive serde")
+
+ test("SPARK-32106: TRANSFORM supports complex data types type (hive serde)")
{
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = defaultIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("c", ArrayType(IntegerType))(),
+ AttributeReference("d", MapType(StringType, IntegerType))(),
+ AttributeReference("e", StructType(
+ Seq(
+ StructField("col1", IntegerType, false),
+ StructField("col2", StringType, true))))()),
+ child = child,
+ ioschema = hiveIOSchema
+ ),
+ df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(with serde)") {
+ test("SPARK-32106: TRANSFORM supports complex data types end to end (hive
serde)") {
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = serdeIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ val query = sql(
+ """
+ |SELECT TRANSFORM (c, d, e)
+ |USING 'cat' AS (c array<int>, d map<string, int>, e
struct<col1:int, col2:string>)
+ |FROM v
+ """.stripMargin)
+ checkAnswer(query, identity, df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
-}
-private case class ExceptionInjectingOperator(child: SparkPlan) extends
UnaryExecNode {
- override protected def doExecute(): RDD[InternalRow] = {
- child.execute().map { x =>
- assert(TaskContext.get() != null) // Make sure that TaskContext is
defined.
- Thread.sleep(1000) // This sleep gives the external process time to
start.
- throw new IllegalArgumentException("intentional exception")
+ test("SPARK-32106: TRANSFORM doesn't support
CalenderIntervalType/UserDefinedType (hive serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))),
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))))
+ .toDF("a", "b", "c")
+ df.createTempView("v")
+
+ val e1 = intercept[SparkException] {
+ val plan = createScriptTransformationExec(
+ input = Seq(df.col("a").expr, df.col("b").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("b", CalendarIntervalType)()),
+ child = df.queryExecution.sparkPlan,
+ ioschema = hiveIOSchema)
+ SparkPlanTest.executePlan(plan, hiveContext)
+ }.getMessage
+ assert(e1.contains(
Review comment:
Done
##########
File path:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveScriptTransformationSuite.scala
##########
@@ -195,75 +169,146 @@ class HiveScriptTransformationSuite extends
SparkPlanTest with SQLTestUtils with
val query = sql(
s"""
- |SELECT
- |TRANSFORM(a, b, c, d, e)
- |USING 'python $scriptFilePath' AS (a, b, c, d, e)
- |FROM v
+ |SELECT TRANSFORM(a, b, c, d, e)
+ |USING 'python ${scriptFilePath}'
+ |FROM v
""".stripMargin)
- // In Hive 1.2, the string representation of a decimal omits trailing
zeroes.
- // But in Hive 2.3, it is always padded to 18 digits with trailing
zeroes if necessary.
- val decimalToString: Column => Column = if (HiveUtils.isHive23) {
- c => c.cast("string")
- } else {
- c => c.cast("decimal(1, 0)").cast("string")
- }
- checkAnswer(query, identity, df.select(
- 'a.cast("string"),
- 'b.cast("string"),
- 'c.cast("string"),
- decimalToString('d),
- 'e.cast("string")).collect())
+ // In hive default serde mode, if we don't define output schema, it will
choose first
+ // two column as output schema (key: String, value: String)
+ checkAnswer(
+ query,
+ identity,
+ df.select(
+ 'a.cast("string").as("key"),
+ 'b.cast("string").as("value")).collect())
}
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(no serde)") {
+ testBasicInputDataTypesWith(hiveIOSchema, "hive serde")
+
+ test("SPARK-32106: TRANSFORM supports complex data types type (hive serde)")
{
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = defaultIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("c", ArrayType(IntegerType))(),
+ AttributeReference("d", MapType(StringType, IntegerType))(),
+ AttributeReference("e", StructType(
+ Seq(
+ StructField("col1", IntegerType, false),
+ StructField("col2", StringType, true))))()),
+ child = child,
+ ioschema = hiveIOSchema
+ ),
+ df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(with serde)") {
+ test("SPARK-32106: TRANSFORM supports complex data types end to end (hive
serde)") {
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = serdeIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ val query = sql(
+ """
+ |SELECT TRANSFORM (c, d, e)
+ |USING 'cat' AS (c array<int>, d map<string, int>, e
struct<col1:int, col2:string>)
+ |FROM v
+ """.stripMargin)
+ checkAnswer(query, identity, df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
-}
-private case class ExceptionInjectingOperator(child: SparkPlan) extends
UnaryExecNode {
- override protected def doExecute(): RDD[InternalRow] = {
- child.execute().map { x =>
- assert(TaskContext.get() != null) // Make sure that TaskContext is
defined.
- Thread.sleep(1000) // This sleep gives the external process time to
start.
- throw new IllegalArgumentException("intentional exception")
+ test("SPARK-32106: TRANSFORM doesn't support
CalenderIntervalType/UserDefinedType (hive serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))),
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))))
+ .toDF("a", "b", "c")
+ df.createTempView("v")
+
+ val e1 = intercept[SparkException] {
+ val plan = createScriptTransformationExec(
+ input = Seq(df.col("a").expr, df.col("b").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("b", CalendarIntervalType)()),
+ child = df.queryExecution.sparkPlan,
+ ioschema = hiveIOSchema)
+ SparkPlanTest.executePlan(plan, hiveContext)
+ }.getMessage
+ assert(e1.contains(
+ "CalendarIntervalType cannot be converted to Hive TypeInfo"))
+
+ val e2 = intercept[SparkException] {
+ val plan = createScriptTransformationExec(
+ input = Seq(df.col("a").expr, df.col("c").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("c", new TestUDT.MyDenseVectorUDT)()),
+ child = df.queryExecution.sparkPlan,
+ ioschema = hiveIOSchema)
+ SparkPlanTest.executePlan(plan, hiveContext)
+ }.getMessage
+
assert(e2.contains("org.apache.spark.sql.types.TestUDT$MyDenseVectorUDT") &&
+ e2.contains("cannot be converted to Hive TypeInfo"))
}
}
- override def output: Seq[Attribute] = child.output
+ test("SPARK-32106: TRANSFORM doesn't support" +
+ " CalenderIntervalType/UserDefinedType end to end (hive serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))),
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))))
+ .toDF("a", "b", "c")
+ df.createTempView("v")
- override def outputPartitioning: Partitioning = child.outputPartitioning
+ val e1 = intercept[SparkException] {
+ sql(
+ """
+ |SELECT TRANSFORM(a, b) USING 'cat' AS (a, b)
+ |FROM v
+ """.stripMargin).collect()
+ }.getMessage
+ assert(e1.contains(
Review comment:
Done
##########
File path:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveScriptTransformationSuite.scala
##########
@@ -195,75 +169,146 @@ class HiveScriptTransformationSuite extends
SparkPlanTest with SQLTestUtils with
val query = sql(
s"""
- |SELECT
- |TRANSFORM(a, b, c, d, e)
- |USING 'python $scriptFilePath' AS (a, b, c, d, e)
- |FROM v
+ |SELECT TRANSFORM(a, b, c, d, e)
+ |USING 'python ${scriptFilePath}'
+ |FROM v
""".stripMargin)
- // In Hive 1.2, the string representation of a decimal omits trailing
zeroes.
- // But in Hive 2.3, it is always padded to 18 digits with trailing
zeroes if necessary.
- val decimalToString: Column => Column = if (HiveUtils.isHive23) {
- c => c.cast("string")
- } else {
- c => c.cast("decimal(1, 0)").cast("string")
- }
- checkAnswer(query, identity, df.select(
- 'a.cast("string"),
- 'b.cast("string"),
- 'c.cast("string"),
- decimalToString('d),
- 'e.cast("string")).collect())
+ // In hive default serde mode, if we don't define output schema, it will
choose first
+ // two column as output schema (key: String, value: String)
+ checkAnswer(
+ query,
+ identity,
+ df.select(
+ 'a.cast("string").as("key"),
+ 'b.cast("string").as("value")).collect())
}
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(no serde)") {
+ testBasicInputDataTypesWith(hiveIOSchema, "hive serde")
+
+ test("SPARK-32106: TRANSFORM supports complex data types type (hive serde)")
{
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = defaultIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("c", ArrayType(IntegerType))(),
+ AttributeReference("d", MapType(StringType, IntegerType))(),
+ AttributeReference("e", StructType(
+ Seq(
+ StructField("col1", IntegerType, false),
+ StructField("col2", StringType, true))))()),
+ child = child,
+ ioschema = hiveIOSchema
+ ),
+ df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
- test("SPARK-30973: TRANSFORM should wait for the termination of the script
(with serde)") {
+ test("SPARK-32106: TRANSFORM supports complex data types end to end (hive
serde)") {
assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", Array(0, 1, 2), Map("a" -> 1)),
+ (2, "2", Array(3, 4, 5), Map("b" -> 2)))
+ .toDF("a", "b", "c", "d")
+ .select('a, 'b, 'c, 'd, struct('a, 'b).as("e"))
+ df.createTempView("v")
- val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
- val e = intercept[SparkException] {
- val plan =
- new HiveScriptTransformationExec(
- input = Seq(rowsDf.col("a").expr),
- script = "some_non_existent_command",
- output = Seq(AttributeReference("a", StringType)()),
- child = rowsDf.queryExecution.sparkPlan,
- ioschema = serdeIOSchema)
- SparkPlanTest.executePlan(plan, hiveContext)
+ // Hive serde support ArrayType/MapType/StructType as input and output
data type
+ val query = sql(
+ """
+ |SELECT TRANSFORM (c, d, e)
+ |USING 'cat' AS (c array<int>, d map<string, int>, e
struct<col1:int, col2:string>)
+ |FROM v
+ """.stripMargin)
+ checkAnswer(query, identity, df.select('c, 'd, 'e).collect())
}
- assert(e.getMessage.contains("Subprocess exited with status"))
- assert(uncaughtExceptionHandler.exception.isEmpty)
}
-}
-private case class ExceptionInjectingOperator(child: SparkPlan) extends
UnaryExecNode {
- override protected def doExecute(): RDD[InternalRow] = {
- child.execute().map { x =>
- assert(TaskContext.get() != null) // Make sure that TaskContext is
defined.
- Thread.sleep(1000) // This sleep gives the external process time to
start.
- throw new IllegalArgumentException("intentional exception")
+ test("SPARK-32106: TRANSFORM doesn't support
CalenderIntervalType/UserDefinedType (hive serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+ withTempView("v") {
+ val df = Seq(
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))),
+ (1, new CalendarInterval(7, 1, 1000), new
TestUDT.MyDenseVector(Array(1, 2, 3))))
+ .toDF("a", "b", "c")
+ df.createTempView("v")
+
+ val e1 = intercept[SparkException] {
+ val plan = createScriptTransformationExec(
+ input = Seq(df.col("a").expr, df.col("b").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("b", CalendarIntervalType)()),
+ child = df.queryExecution.sparkPlan,
+ ioschema = hiveIOSchema)
+ SparkPlanTest.executePlan(plan, hiveContext)
+ }.getMessage
+ assert(e1.contains(
+ "CalendarIntervalType cannot be converted to Hive TypeInfo"))
+
+ val e2 = intercept[SparkException] {
+ val plan = createScriptTransformationExec(
+ input = Seq(df.col("a").expr, df.col("c").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("c", new TestUDT.MyDenseVectorUDT)()),
+ child = df.queryExecution.sparkPlan,
+ ioschema = hiveIOSchema)
+ SparkPlanTest.executePlan(plan, hiveContext)
+ }.getMessage
+
assert(e2.contains("org.apache.spark.sql.types.TestUDT$MyDenseVectorUDT") &&
+ e2.contains("cannot be converted to Hive TypeInfo"))
}
}
- override def output: Seq[Attribute] = child.output
+ test("SPARK-32106: TRANSFORM doesn't support" +
Review comment:
Done
##########
File path:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
##########
@@ -1063,6 +1063,9 @@ private[hive] trait HiveInspectors {
case DateType => dateTypeInfo
case TimestampType => timestampTypeInfo
case NullType => voidTypeInfo
+ case dt =>
+ throw new AnalysisException(
+ s"${dt.toString} cannot be converted to Hive TypeInfo")
Review comment:
Done
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/execution/BaseScriptTransformationSuite.scala
##########
@@ -0,0 +1,382 @@
+/*
+ * 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.execution
+
+import java.sql.{Date, Timestamp}
+
+import org.json4s.DefaultFormats
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+import org.scalatest.Assertions._
+import org.scalatest.BeforeAndAfterEach
+import org.scalatest.exceptions.TestFailedException
+
+import org.apache.spark.{SparkException, TaskContext, TestUtils}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute,
AttributeReference, Expression, GenericInternalRow}
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
+
+abstract class BaseScriptTransformationSuite extends SparkPlanTest with
SQLTestUtils
+ with BeforeAndAfterEach {
+ import testImplicits._
+ import ScriptTransformationIOSchema._
+
+ protected val uncaughtExceptionHandler = new TestUncaughtExceptionHandler
+
+ private var defaultUncaughtExceptionHandler: Thread.UncaughtExceptionHandler
= _
+
+ protected override def beforeAll(): Unit = {
+ super.beforeAll()
+ defaultUncaughtExceptionHandler = Thread.getDefaultUncaughtExceptionHandler
+ Thread.setDefaultUncaughtExceptionHandler(uncaughtExceptionHandler)
+ }
+
+ protected override def afterAll(): Unit = {
+ super.afterAll()
+ Thread.setDefaultUncaughtExceptionHandler(defaultUncaughtExceptionHandler)
+ }
+
+ override protected def afterEach(): Unit = {
+ super.afterEach()
+ uncaughtExceptionHandler.cleanStatus()
+ }
+
+ def isHive23OrSpark: Boolean
+
+ def createScriptTransformationExec(
+ input: Seq[Expression],
+ script: String,
+ output: Seq[Attribute],
+ child: SparkPlan,
+ ioschema: ScriptTransformationIOSchema): BaseScriptTransformationExec
+
+ test("cat without SerDe") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+ val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+ checkAnswer(
+ rowsDf,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(rowsDf.col("a").expr),
+ script = "cat",
+ output = Seq(AttributeReference("a", StringType)()),
+ child = child,
+ ioschema = defaultIOSchema
+ ),
+ rowsDf.collect())
+ assert(uncaughtExceptionHandler.exception.isEmpty)
+ }
+
+ test("script transformation should not swallow errors from upstream
operators (no serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+ val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+ val e = intercept[TestFailedException] {
+ checkAnswer(
+ rowsDf,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(rowsDf.col("a").expr),
+ script = "cat",
+ output = Seq(AttributeReference("a", StringType)()),
+ child = ExceptionInjectingOperator(child),
+ ioschema = defaultIOSchema
+ ),
+ rowsDf.collect())
+ }
+ assert(e.getMessage().contains("intentional exception"))
+ // Before SPARK-25158, uncaughtExceptionHandler will catch
IllegalArgumentException
+ assert(uncaughtExceptionHandler.exception.isEmpty)
+ }
+
+ test("SPARK-25990: TRANSFORM should handle different data types correctly") {
+ assume(TestUtils.testCommandAvailable("python"))
+ val scriptFilePath = getTestResourcePath("test_script.py")
+
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", 1.0, BigDecimal(1.0), new Timestamp(1)),
+ (2, "2", 2.0, BigDecimal(2.0), new Timestamp(2)),
+ (3, "3", 3.0, BigDecimal(3.0), new Timestamp(3))
+ ).toDF("a", "b", "c", "d", "e") // Note column d's data type is
Decimal(38, 18)
+ df.createTempView("v")
+
+ val query = sql(
+ s"""
+ |SELECT
+ |TRANSFORM(a, b, c, d, e)
+ |USING 'python $scriptFilePath' AS (a, b, c, d, e)
+ |FROM v
+ """.stripMargin)
+
+ // In Hive 1.2, the string representation of a decimal omits trailing
zeroes.
+ // But in Hive 2.3, it is always padded to 18 digits with trailing
zeroes if necessary.
+ val decimalToString: Column => Column = if (isHive23OrSpark) {
+ c => c.cast("string")
+ } else {
+ c => c.cast("decimal(1, 0)").cast("string")
+ }
+ checkAnswer(query, identity, df.select(
+ 'a.cast("string"),
+ 'b.cast("string"),
+ 'c.cast("string"),
+ decimalToString('d),
+ 'e.cast("string")).collect())
+ }
+ }
+
+ test("SPARK-25990: TRANSFORM should handle schema less correctly (no
serde)") {
+ assume(TestUtils.testCommandAvailable("python"))
+ val scriptFilePath = getTestResourcePath("test_script.py")
+
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", 1.0, BigDecimal(1.0), new Timestamp(1)),
+ (2, "2", 2.0, BigDecimal(2.0), new Timestamp(2)),
+ (3, "3", 3.0, BigDecimal(3.0), new Timestamp(3))
+ ).toDF("a", "b", "c", "d", "e") // Note column d's data type is
Decimal(38, 18)
+
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("a").expr,
+ df.col("b").expr,
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr),
+ script = s"python $scriptFilePath",
+ output = Seq(
+ AttributeReference("key", StringType)(),
+ AttributeReference("value", StringType)()),
+ child = child,
+ ioschema = defaultIOSchema.copy(schemaLess = true)
+ ),
+ df.select(
+ 'a.cast("string").as("key"),
+ 'b.cast("string").as("value")).collect())
+ }
+ }
+
+ test("SPARK-30973: TRANSFORM should wait for the termination of the script
(no serde)") {
+ assume(TestUtils.testCommandAvailable("/bin/bash"))
+
+ val rowsDf = Seq("a", "b", "c").map(Tuple1.apply).toDF("a")
+ val e = intercept[SparkException] {
+ val plan =
+ createScriptTransformationExec(
+ input = Seq(rowsDf.col("a").expr),
+ script = "some_non_existent_command",
+ output = Seq(AttributeReference("a", StringType)()),
+ child = rowsDf.queryExecution.sparkPlan,
+ ioschema = defaultIOSchema)
+ SparkPlanTest.executePlan(plan, spark.sqlContext)
+ }
+ assert(e.getMessage.contains("Subprocess exited with status"))
+ assert(uncaughtExceptionHandler.exception.isEmpty)
+ }
+
+ def testBasicInputDataTypesWith(serde: ScriptTransformationIOSchema,
testName: String): Unit = {
+ test(s"SPARK-32106: TRANSFORM should support basic data types as input
($testName)") {
+ assume(TestUtils.testCommandAvailable("python"))
+ withTempView("v") {
+ val df = Seq(
+ (1, "1", 1.0f, 1.0, 11.toByte, BigDecimal(1.0), new Timestamp(1),
+ new Date(2020, 7, 1), true),
+ (2, "2", 2.0f, 2.0, 22.toByte, BigDecimal(2.0), new Timestamp(2),
+ new Date(2020, 7, 2), true),
+ (3, "3", 3.0f, 3.0, 33.toByte, BigDecimal(3.0), new Timestamp(3),
+ new Date(2020, 7, 3), false)
+ ).toDF("a", "b", "c", "d", "e", "f", "g", "h", "i")
+ .withColumn("j", lit("abc").cast("binary"))
+
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("a").expr,
+ df.col("b").expr,
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr,
+ df.col("f").expr,
+ df.col("g").expr,
+ df.col("h").expr,
+ df.col("i").expr,
+ df.col("j").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", IntegerType)(),
+ AttributeReference("b", StringType)(),
+ AttributeReference("c", FloatType)(),
+ AttributeReference("d", DoubleType)(),
+ AttributeReference("e", ByteType)(),
+ AttributeReference("f", DecimalType(38, 18))(),
+ AttributeReference("g", TimestampType)(),
+ AttributeReference("h", DateType)(),
+ AttributeReference("i", BooleanType)(),
+ AttributeReference("j", BinaryType)()),
+ child = child,
+ ioschema = serde
+ ),
+ df.select('a, 'b, 'c, 'd, 'e, 'f, 'g, 'h, 'i, 'j).collect())
+ }
+ }
+ }
+
+ testBasicInputDataTypesWith(defaultIOSchema, "no serde")
+
+ test("SPARK-32106: TRANSFORM should support more data types (interval,
array, map, struct " +
+ "and udt) as input (no serde)") {
+ assume(TestUtils.testCommandAvailable("python"))
+ withTempView("v") {
+ val df = Seq(
+ (new CalendarInterval(7, 1, 1000), Array(0, 1, 2), Map("a" -> 1), (1,
2),
+ new SimpleTuple(1, 1L)),
+ (new CalendarInterval(7, 2, 2000), Array(3, 4, 5), Map("b" -> 2), (3,
4),
+ new SimpleTuple(1, 1L)),
+ (new CalendarInterval(7, 3, 3000), Array(6, 7, 8), Map("c" -> 3), (5,
6),
+ new SimpleTuple(1, 1L))
+ ).toDF("a", "b", "c", "d", "e")
+
+ // Can't support convert script output data to
ArrayType/MapType/StructType now,
+ // return these column still as string.
+ // For UserDefinedType, if user defined deserialize method to support
convert string
+ // to UserType like [[SimpleTupleUDT]], we can support convert to this
UDT, else we
+ // will return null value as column.
+ checkAnswer(
+ df,
+ (child: SparkPlan) => createScriptTransformationExec(
+ input = Seq(
+ df.col("a").expr,
+ df.col("b").expr,
+ df.col("c").expr,
+ df.col("d").expr,
+ df.col("e").expr),
+ script = "cat",
+ output = Seq(
+ AttributeReference("a", CalendarIntervalType)(),
+ AttributeReference("b", StringType)(),
+ AttributeReference("c", StringType)(),
+ AttributeReference("d", StringType)(),
+ AttributeReference("e", new SimpleTupleUDT)()),
+ child = child,
+ ioschema = defaultIOSchema
+ ),
+ df.select('a, 'b.cast("string"), 'c.cast("string"), 'd.cast("string"),
'e).collect())
+ }
+ }
+
+ test("SPARK-32106: TRANSFORM should respect DATETIME_JAVA8API_ENABLED (no
serde)") {
Review comment:
Done
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