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Posted to issues-all@impala.apache.org by "Zoltán Borók-Nagy (Jira)" <ji...@apache.org> on 2021/05/31 11:29:00 UTC

[jira] [Commented] (IMPALA-10578) Big Query influence other query seriously when hardware not reach limit

    [ https://issues.apache.org/jira/browse/IMPALA-10578?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17354392#comment-17354392 ] 

Zoltán Borók-Nagy commented on IMPALA-10578:
--------------------------------------------

Seems like it was a configuration problem. Can we close this issue?

> Big Query influence other query seriously when hardware not reach limit 
> ------------------------------------------------------------------------
>
>                 Key: IMPALA-10578
>                 URL: https://issues.apache.org/jira/browse/IMPALA-10578
>             Project: IMPALA
>          Issue Type: Bug
>          Components: Backend
>    Affects Versions: Impala 3.4.0
>         Environment: impala-3.4
> 80 machines with 96 cpu and 256GB mem
> scratch-dir is on separate disk different from HDFS data dir
>            Reporter: wesleydeng
>            Priority: Major
>         Attachments: big_query.txt.bz2, image-2021-03-10-19-59-24-188.png, image-2021-03-16-16-32-37-862.png, small_query_be_influenced_very_slow.txt.bz2
>
>
> When a big query is running(use mt_dop=8), other query is very difficult to start. 
> A small query (select distinct one field from a small table)  may take about 1 minutes, normallly it take only about 1~3 second.
>  From the impalad log, I found a incomprehensible log like this:
> !image-2021-03-16-16-32-37-862.png|width=836,height=189!
> !image-2021-03-10-19-59-24-188.png|width=892,height=435!
> ---------------
> About the gap between "Handling call" and "Deserializing Batch", I found another path : 
> --KrpcDataStreamRecvr::SenderQueue::AddBatch
>   ----EnqueueDeferredRpc(move(payload), l);   // after dequeue, will call KrpcDataStreamRecvr::SenderQueue::AddBatchWork
> --------------- 
>  
>  
> When the Big query is running, data spilled  has happened because mem_limit was set and this big query waste a lot of memory.
>  
> In the attchment, I append the profile of big query and small query. The small query can be finished in seconds normally. the timeline of small query show  as below:
> Query Timeline: 21m39s
>  - Query submitted: 48.846us (48.846us)
>  - Planning finished: 2.934ms (2.886ms)
>  - Submit for admission: 12.572ms (9.637ms)
>  - Completed admission: 13.622ms (1.050ms)
>  - Ready to start on 56 backends: 15.271ms (1.649ms)
>  -- All 56 execution backends (171 fragment instances) started: 18s505ms (18s489ms)*
>  - Rows available: 51s770ms (33s265ms)
>  - First row fetched: 57s220ms (5s449ms)
>  - Last row fetched: 59s119ms (1s899ms)
>  - Released admission control resources: 1m1s (2s223ms)
>  - AdmissionControlTimeSinceLastUpdate: 80.000ms
>  - ComputeScanRangeAssignmentTimer: 439.749us
>  
>  
>  
>  



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