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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2018/01/24 02:46:00 UTC
[jira] [Updated] (SPARK-23177) PySpark parameter-less UDFs raise
exception if applied after distinct
[ https://issues.apache.org/jira/browse/SPARK-23177?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-23177:
---------------------------------
Fix Version/s: 2.4.0
> PySpark parameter-less UDFs raise exception if applied after distinct
> ---------------------------------------------------------------------
>
> Key: SPARK-23177
> URL: https://issues.apache.org/jira/browse/SPARK-23177
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.2, 2.2.0, 2.2.1
> Reporter: Jakub Wasikowski
> Priority: Major
> Fix For: 2.4.0
>
>
> It seems there is an issue with UDFs that take no arguments, but only if UDF is applied after {{distinct()}} operation.
> Here is the short example, that allows reproduce an issue in PySpark shell:
> {code:java}
> import pyspark.sql.functions as f
> import uuid
> df = spark.createDataFrame([(1,2), (3,4)])
> f_udf = f.udf(lambda: str(uuid.uuid4()))
> df.distinct().withColumn("a", f_udf()).show()
> {code}
> and it raises the following exception:
> {noformat}
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "/opt/spark/python/pyspark/sql/dataframe.py", line 336, in show
> print(self._jdf.showString(n, 20))
> File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
> File "/opt/spark/python/pyspark/sql/utils.py", line 63, in deco
> return f(*a, **kw)
> File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o54.showString.
> : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#16
> at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
> at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
> at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:475)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:474)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.generateResultCode(HashAggregateExec.scala:474)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduceWithKeys(HashAggregateExec.scala:612)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduce(HashAggregateExec.scala:148)
> at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85)
> at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:80)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
> at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:80)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.produce(HashAggregateExec.scala:38)
> at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:331)
> at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:372)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
> at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:228)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:311)
> at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2861)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
> at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842)
> at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2841)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2150)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2363)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:241)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at py4j.Gateway.invoke(Gateway.java:280)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#16 in [_1#0L,_2#1L]
> at scala.sys.package$.error(package.scala:27)
> at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
> at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
> at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
> ... 63 more
> {noformat}
> It is also worth to mention, that the same code without {{distinct}} does not cause an error.
> Furthermore, if the UDF takes at least one argument then an exception is not raised as well.
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