[
https://issues.apache.org/jira/browse/SPARK-35717?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-35717.
----------------------------------
Resolution: Cannot Reproduce
no feed back from the author.
> pandas_udf crashes in conjunction with .filter()
> ------------------------------------------------
>
> Key: SPARK-35717
> URL: https://issues.apache.org/jira/browse/SPARK-35717
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.0, 3.1.1, 3.1.2
> Environment: Centos 8 with PySpark from conda
> Reporter: F. H.
> Priority: Major
>
> I wrote the following UDF that always returns some "byte"-type array:
>
> {code:python}
> from typing import Iterator
> @f.pandas_udf(returnType=t.ByteType())
> def spark_gt_mapping_fn(batch_iter: Iterator[pd.Series]) ->
> Iterator[pd.Series]:
> mapping = dict()
> mapping[(-1, -1)] = -1
> mapping[(0, 0)] = 0
> mapping[(0, 1)] = 1
> mapping[(1, 0)] = 1
> mapping[(1, 1)] = 2
> def gt_mapping_fn(v):
> if len(v) != 2:
> return -3
> else:
> a, b = v
> return mapping.get((a, b), -2)
>
> for x in batch_iter:
> yield x.apply(gt_mapping_fn).astype("int8")
> {code}
>
> However, every time I'd like to filter on the resulting column, I get the
> following error:
> {code:python}
> # works:
> (
> df
> .select(spark_gt_mapping_fn(f.col("genotype.calls")).alias("GT"))
> .limit(10).toPandas()
> )
> # fails:
> (
> df
> .select(spark_gt_mapping_fn(f.col("genotype.calls")).alias("GT"))
> .filter("GT > 0")
> .limit(10).toPandas()
> )
> {code}
> {code:java}
> Py4JJavaError: An error occurred while calling o672.collectToPython. :
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 9.0 failed 4 times, most recent failure: Lost task 0.3 in stage 9.0
> (TID 125) (ouga05.cmm.in.tum.de executor driver):
> org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by
> query. Memory leaked: (16384) Allocator(stdin reader for python3)
> 0/16384/34816/9223372036854775807 (res/actual/peak/limit) at
> org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:145)
> at
> org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:124)
> at org.apache.spark.scheduler.Task.run(Task.scala:147) at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)
> at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)
> at scala.Option.foreach(Option.scala:407) at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2196) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2217) at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2236) at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472) at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425) at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
> at
> org.apache.spark.sql.Dataset.$anonfun$collectToPython$1(Dataset.scala:3519)
> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687) at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775) at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685) at
> org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3516) 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:282) at
> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at
> py4j.commands.CallCommand.execute(CallCommand.java:79) at
> py4j.GatewayConnection.run(GatewayConnection.java:238) at
> java.lang.Thread.run(Thread.java:748) Caused by:
> org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by
> query. Memory leaked: (16384) Allocator(stdin reader for python3)
> 0/16384/34816/9223372036854775807 (res/actual/peak/limit) at
> org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:145)
> at
> org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:124)
> at org.apache.spark.scheduler.Task.run(Task.scala:147) at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> ... 1 more
> {code}
> I tried this with different versions of PySpark and PyArrow, always with the
> same result.
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