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Posted to issues@spark.apache.org by "Xiao Li (JIRA)" <ji...@apache.org> on 2018/06/21 17:04:00 UTC
[jira] [Created] (SPARK-24624) Can not mix vectorized and
non-vectorized UDFs
Xiao Li created SPARK-24624:
-------------------------------
Summary: Can not mix vectorized and non-vectorized UDFs
Key: SPARK-24624
URL: https://issues.apache.org/jira/browse/SPARK-24624
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 2.3.1
Reporter: Xiao Li
In the current impl, we have the limitation: users are unable to mix vectorized and non-vectorized UDFs in same Project. This becomes worse since our optimizer could combine continuous Projects into a single one. For example,
{code}
applied_df = df.withColumn('regular', my_regular_udf('total', 'qty')).withColumn('pandas', my_pandas_udf('total', 'qty'))
{code}
Returns the following error.
{code}
IllegalArgumentException: Can not mix vectorized and non-vectorized UDFs
java.lang.IllegalArgumentException: Can not mix vectorized and non-vectorized UDFs
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$6.apply(ExtractPythonUDFs.scala:170)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$6.apply(ExtractPythonUDFs.scala:146)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:146)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:118)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$6.apply(TreeNode.scala:312)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$6.apply(TreeNode.scala:312)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:77)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:309)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:309)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$8.apply(TreeNode.scala:331)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:309)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:309)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$8.apply(TreeNode.scala:331)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:114)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:94)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:113)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:113)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:113)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:100)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:99)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3312)
at org.apache.spark.sql.Dataset.collectResult(Dataset.scala:2750)
...
{code}
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