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https://issues.apache.org/jira/browse/SPARK-12981?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15197759#comment-15197759
]
Fabian Boehnlein commented on SPARK-12981:
------------------------------------------
Seems similar to what I'm hitting when filtering with UDF based on an
aggregated GroupedData dataframe. Is this related / fixed with your PR as well?
Thanks!
{code}
data = sqlContext.createDataFrame(sc.parallelize([
{"a": 1, "b": 1 },
{"a": 2, "b": 2 },
{"a": 1, "b": 3 }
]))
aggr = data.groupby('a').agg({'b':'sum'})
is_two_udf = udf(lambda s: s==2, BooleanType())
aggr.filter(is_two_udf(aggr.a)){code}
{code}
Py4JJavaError: An error occurred while calling o346.filter.
: java.lang.ClassCastException:
org.apache.spark.sql.catalyst.plans.logical.Project cannot be cast to
org.apache.spark.sql.catalyst.plans.logical.Aggregate
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions$$anonfun$apply$14.resolvedAggregateFilter$1(Analyzer.scala:624)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions$$anonfun$apply$14.applyOrElse(Analyzer.scala:630)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions$$anonfun$apply$14.applyOrElse(Analyzer.scala:614)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions$.apply(Analyzer.scala:614)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveAggregateFunctions$.apply(Analyzer.scala:613)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
at
scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
at scala.collection.immutable.List.foreach(List.scala:318)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
at
org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)
at
org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)
at
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
at
org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$withPlan(DataFrame.scala:2150)
at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:784)
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:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
{code}
> Dataframe distinct() followed by a filter(udf) in pyspark throws a casting
> error
> --------------------------------------------------------------------------------
>
> Key: SPARK-12981
> URL: https://issues.apache.org/jira/browse/SPARK-12981
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.6.0
> Environment: Running on Mac OSX (El Capitan) with Spark 1.6 (Java 1.8)
> Reporter: Tom Arnfeld
> Priority: Critical
>
> We noticed a regression when testing out an upgrade of Spark 1.6 for our
> systems, where pyspark throws a casting exception when using `filter(udf)`
> after a `distinct` operation on a DataFrame. This does not occur on Spark 1.5.
> Here's a little notebook that demonstrates the exception clearly...
> https://gist.github.com/tarnfeld/ab9b298ae67f697894cd
> Though for the sake of here... the following code will throw an exception...
> {code}
> data.select(col("a")).distinct().filter(my_filter(col("a"))).count()
> {code}
> {code}
> java.lang.ClassCastException:
> org.apache.spark.sql.catalyst.plans.logical.Project cannot be cast to
> org.apache.spark.sql.catalyst.plans.logical.Aggregate
> {code}
> Whereas not using a UDF does not throw any errors...
> {code}
> data.select(col("a")).distinct().filter("a = 1").count()
> {code}
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