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Mateusz Pieniak edited comment on SPARK-24334 at 5/22/18 1:59 PM: ------------------------------------------------------------------ I came across with this issue while running my custom apply function on larger dataset. It works on smaller dataset. I got the exception: {code:java} SparkException: Job aborted due to stage failure: Task 0 in stage 43.0 failed 4 times, most recent failure: Lost task 0.3 in stage 43.0 (TID 3108, 10.217.183.141, executor 3): org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by query. Memory leaked: (482816) Allocator(stdout writer for /databricks/python/bin/python) 0/482816/482816/9223372036854775807 (res/actual/peak/limit) at org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:153) at org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:131) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:350) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748){code} was (Author: pi3ni0): I came across with this issue while running my custom apply function on larger dataset. I got the exception: {code:java} SparkException: Job aborted due to stage failure: Task 0 in stage 43.0 failed 4 times, most recent failure: Lost task 0.3 in stage 43.0 (TID 3108, 10.217.183.141, executor 3): org.apache.spark.util.TaskCompletionListenerException: Memory was leaked by query. Memory leaked: (482816) Allocator(stdout writer for /databricks/python/bin/python) 0/482816/482816/9223372036854775807 (res/actual/peak/limit) at org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:153) at org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:131) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:350) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748){code} > Race condition in ArrowPythonRunner causes unclean shutdown of Arrow memory > allocator > ------------------------------------------------------------------------------------- > > Key: SPARK-24334 > URL: https://issues.apache.org/jira/browse/SPARK-24334 > Project: Spark > Issue Type: Sub-task > Components: PySpark > Affects Versions: 2.3.0 > Reporter: Li Jin > Priority: Major > > Currently, ArrowPythonRunner has two thread that frees the Arrow vector > schema root and allocator - The main writer thread and task completion > listener thread. > Having both thread doing the clean up leads to weird case (e.g., negative ref > cnt, NPE, and memory leak exception) when an exceptions are thrown from the > user function. > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org