[ 
https://issues.apache.org/jira/browse/FLINK-20663?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17316920#comment-17316920
 ] 

Xintong Song commented on FLINK-20663:
--------------------------------------

[~zhou_yb],

Findings from the JM logs:
- The TM where the failure happen is newly started, registered at 19:14:30, 
with plenty of managed memory (1.7G).
- In the first stage, 3 source tasks are deployed onto the TM (19:14:30), and 
successfully finished (19:14:51)
- In the second stage, 3 hash join tasks are deployed onto the (19:14:52), and 
soon failed (19:14:55) due to not being able to allocate managed memory.
- From the task name ( {{Source: HiveSource-chloe.chloe_bus_hive_log -> 
Calc(select=[dataobj], where=[(bustype = 12)]) -> (BatchExecPythonCalc, 
BatchExecPythonCalc}} ), it seems the first stage tasks do not use memory 
segments. They should only use managed memory for the python process.

I think this is a different problem. The previous problem reported in this 
ticket was caused due to memory segment not being GC-ed timely. In your case, 
the first stage tasks do not use memory segments. 

With the provided JM lobs, I cannot explain how this happened. Checking the 
codes, memory for python operators should have been released by the time the 
first stage tasks finish.

Would you be able to provide the complete task manager logs? And one more 
question, can the problem be reproduced, steady or occasionally?

> Managed memory may not be released in time when operators use managed memory 
> frequently
> ---------------------------------------------------------------------------------------
>
>                 Key: FLINK-20663
>                 URL: https://issues.apache.org/jira/browse/FLINK-20663
>             Project: Flink
>          Issue Type: Bug
>          Components: Runtime / Task
>    Affects Versions: 1.12.0
>            Reporter: Caizhi Weng
>            Assignee: Xintong Song
>            Priority: Critical
>              Labels: pull-request-available
>             Fix For: 1.12.2, 1.13.0
>
>         Attachments: exception
>
>
> Some batch operators (like sort merge join or hash aggregate) use managed 
> memory frequently. When these operators are chained together and the cluster 
> load is a bit heavy, it is very likely that the following exception occurs:
> {code:java}
> 2020-12-18 10:04:32
> java.lang.RuntimeException: 
> org.apache.flink.runtime.memory.MemoryAllocationException: Could not allocate 
> 512 pages
>       at 
> org.apache.flink.table.runtime.util.LazyMemorySegmentPool.nextSegment(LazyMemorySegmentPool.java:85)
>       at 
> org.apache.flink.runtime.io.disk.SimpleCollectingOutputView.<init>(SimpleCollectingOutputView.java:49)
>       at 
> org.apache.flink.table.runtime.operators.aggregate.BytesHashMap$RecordArea.<init>(BytesHashMap.java:297)
>       at 
> org.apache.flink.table.runtime.operators.aggregate.BytesHashMap.<init>(BytesHashMap.java:103)
>       at 
> org.apache.flink.table.runtime.operators.aggregate.BytesHashMap.<init>(BytesHashMap.java:90)
>       at LocalHashAggregateWithKeys$209161.open(Unknown Source)
>       at 
> org.apache.flink.streaming.runtime.tasks.OperatorChain.initializeStateAndOpenOperators(OperatorChain.java:401)
>       at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.lambda$beforeInvoke$2(StreamTask.java:506)
>       at 
> org.apache.flink.streaming.runtime.tasks.StreamTaskActionExecutor$SynchronizedStreamTaskActionExecutor.runThrowing(StreamTaskActionExecutor.java:92)
>       at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.beforeInvoke(StreamTask.java:501)
>       at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:530)
>       at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:722)
>       at org.apache.flink.runtime.taskmanager.Task.run(Task.java:547)
>       at java.lang.Thread.run(Thread.java:834)
>       Suppressed: java.lang.NullPointerException
>               at LocalHashAggregateWithKeys$209161.close(Unknown Source)
>               at 
> org.apache.flink.table.runtime.operators.TableStreamOperator.dispose(TableStreamOperator.java:46)
>               at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.disposeAllOperators(StreamTask.java:739)
>               at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.runAndSuppressThrowable(StreamTask.java:719)
>               at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.cleanUpInvoke(StreamTask.java:642)
>               at 
> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:551)
>               ... 3 more
>               Suppressed: java.lang.NullPointerException
>                       at LocalHashAggregateWithKeys$209766.close(Unknown 
> Source)
>                       ... 8 more
> Caused by: org.apache.flink.runtime.memory.MemoryAllocationException: Could 
> not allocate 512 pages
>       at 
> org.apache.flink.runtime.memory.MemoryManager.allocatePages(MemoryManager.java:231)
>       at 
> org.apache.flink.table.runtime.util.LazyMemorySegmentPool.nextSegment(LazyMemorySegmentPool.java:83)
>       ... 13 more
> Caused by: org.apache.flink.runtime.memory.MemoryReservationException: Could 
> not allocate 16777216 bytes, only 9961487 bytes are remaining. This usually 
> indicates that you are requesting more memory than you have reserved. 
> However, when running an old JVM version it can also be caused by slow 
> garbage collection. Try to upgrade to Java 8u72 or higher if running on an 
> old Java version.
>       at 
> org.apache.flink.runtime.memory.UnsafeMemoryBudget.reserveMemory(UnsafeMemoryBudget.java:164)
>       at 
> org.apache.flink.runtime.memory.UnsafeMemoryBudget.reserveMemory(UnsafeMemoryBudget.java:80)
>       at 
> org.apache.flink.runtime.memory.MemoryManager.allocatePages(MemoryManager.java:229)
>       ... 14 more
> {code}
> It seems that this is caused by relying on GC to release managed memory, as 
> {{System.gc()}} may not trigger GC in time. See {{UnsafeMemoryBudget.java}}.



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

Reply via email to