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Posted to issues-all@impala.apache.org by "ASF subversion and git services (Jira)" <ji...@apache.org> on 2022/03/02 02:02:00 UTC

[jira] [Commented] (IMPALA-9128) Improve debugging for slow sends in KrpcDataStreamSender

    [ https://issues.apache.org/jira/browse/IMPALA-9128?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17499840#comment-17499840 ] 

ASF subversion and git services commented on IMPALA-9128:
---------------------------------------------------------

Commit 719d3688950e9c22811ca2bb619b616f89679593 in impala's branch refs/heads/master from Riza Suminto
[ https://gitbox.apache.org/repos/asf?p=impala.git;h=719d368 ]

IMPALA-10049: Include RPC call_id in slow RPC logs

KRPC log slow RPC trace in the receiver side. The trace log has the
call_id info that matches with the sender. However, our slow RPC logging
in the sender side does not log this call_id. It is hard to associate
the slow RPC logs between sender and receiver.

With the recent KRPC rebase in IMPALA-10931, we can now log the call_id
on the sender side.

Testing:
I tested this with a low threshold and delays added (the same as we did
in IMPALA-9128):

  start-impala-cluster.py \
      --impalad_args=--impala_slow_rpc_threshold_ms=1 \
      --impalad_args=--debug_actions=END_DATA_STREAM_DELAY:JITTER@3000@1.0

The following is how the logs look like on the sender and receiver sides:

impalad_node1.INFO (sender):
I0217 10:29:36.278754  6606 krpc-data-stream-sender.cc:394] Slow TransmitData RPC (request call id 414) to 127.0.0.1:27002 (fragment_instance_id=d8453c2785c38df4:3473e28b00000041): took 343.279ms. Receiver time: 342.780ms Network time: 498.405us

impalad_node2.INFO (receiver):
I0217 10:29:36.278379  6775 rpcz_store.cc:269] Call impala.DataStreamService.TransmitData from 127.0.0.1:39702 (request call id 414) took 342ms. Trace:
I0217 10:29:36.278479  6775 rpcz_store.cc:270] 0217 10:29:35.935586 (+     0us) impala-service-pool.cc:179] Inserting onto call queue
0217 10:29:36.277730 (+342144us) impala-service-pool.cc:278] Handling call
0217 10:29:36.277859 (+   129us) krpc-data-stream-recvr.cc:397] Deserializing batch
0217 10:29:36.278330 (+   471us) krpc-data-stream-recvr.cc:424] Enqueuing deserialized batch
0217 10:29:36.278369 (+    39us) inbound_call.cc:171] Queueing success response
Metrics: {}

Change-Id: I7fb5746fa0be575745a8e168405d43115c425389
Reviewed-on: http://gerrit.cloudera.org:8080/18243
Reviewed-by: Wenzhe Zhou <wz...@cloudera.com>
Tested-by: Impala Public Jenkins <im...@cloudera.com>


> Improve debugging for slow sends in KrpcDataStreamSender
> --------------------------------------------------------
>
>                 Key: IMPALA-9128
>                 URL: https://issues.apache.org/jira/browse/IMPALA-9128
>             Project: IMPALA
>          Issue Type: Bug
>          Components: Distributed Exec
>            Reporter: Tim Armstrong
>            Assignee: Tim Armstrong
>            Priority: Major
>              Labels: observability
>             Fix For: Impala 3.4.0
>
>
> I'm trying to debug a problem that appears to be caused by a slow RPC:
> {noformat}
>     Fragment F00
>       Instance 754fc21ba4744310:d58fd04200000020 (host=xxxxxxxxx)
>         Hdfs split stats (<volume id>:<# splits>/<split lengths>): 0:1/120.48 MB 
>         - AverageThreadTokens: 1.00 (1.0)
>         - BloomFilterBytes: 0 B (0)
>         - InactiveTotalTime: 0ns (0)
>         - PeakMemoryUsage: 3.2 MiB (3337546)
>         - PeakReservation: 2.0 MiB (2097152)
>         - PeakUsedReservation: 0 B (0)
>         - PerHostPeakMemUsage: 6.7 MiB (6987376)
>         - RowsProduced: 7 (7)
>         - TotalNetworkReceiveTime: 0ns (0)
>         - TotalNetworkSendTime: 3.6m (215354065071)
>         - TotalStorageWaitTime: 4ms (4552708)
>         - TotalThreadsInvoluntaryContextSwitches: 2 (2)
>         - TotalThreadsTotalWallClockTime: 3.6m (215924079474)
>           - TotalThreadsSysTime: 24ms (24386000)
>           - TotalThreadsUserTime: 505ms (505714000)
>         - TotalThreadsVoluntaryContextSwitches: 3,623 (3623)
>         - TotalTime: 3.6m (215801961705)
>         Fragment Instance Lifecycle Event Timeline
>           Prepare Finished: 1ms (1812344)
>           Open Finished: 322ms (322905753)
>           First Batch Produced: 447ms (447050377)
>           First Batch Sent: 447ms (447054546)
>           ExecInternal Finished: 3.6m (215802284852)
>         Buffer pool
>           - AllocTime: 0ns (0)
>           - CumulativeAllocationBytes: 0 B (0)
>           - CumulativeAllocations: 0 (0)
>           - InactiveTotalTime: 0ns (0)
>           - PeakReservation: 0 B (0)
>           - PeakUnpinnedBytes: 0 B (0)
>           - PeakUsedReservation: 0 B (0)
>           - ReadIoBytes: 0 B (0)
>           - ReadIoOps: 0 (0)
>           - ReadIoWaitTime: 0ns (0)
>           - ReservationLimit: 0 B (0)
>           - TotalTime: 0ns (0)
>           - WriteIoBytes: 0 B (0)
>           - WriteIoOps: 0 (0)
>           - WriteIoWaitTime: 0ns (0)
>         Fragment Instance Lifecycle Timings
>           - ExecTime: 3.6m (215479380267)
>             - ExecTreeExecTime: 124ms (124299400)
>           - InactiveTotalTime: 0ns (0)
>           - OpenTime: 321ms (321088906)
>             - ExecTreeOpenTime: 572.04us (572045)
>           - PrepareTime: 1ms (1426412)
>             - ExecTreePrepareTime: 233.32us (233318)
>           - TotalTime: 0ns (0)
>         KrpcDataStreamSender (dst_id=3)
>           - EosSent: 58 (58)
>           - InactiveTotalTime: 3.6m (215354085858)
>           - PeakMemoryUsage: 464.4 KiB (475504)
>           - RowsSent: 7 (7)
>           - RpcFailure: 0 (0)
>           - RpcRetry: 0 (0)
>           - SerializeBatchTime: 99.87us (99867)
>           - TotalBytesSent: 207 B (207)
>           - TotalTime: 3.6m (215355336381)
>           - UncompressedRowBatchSize: 267 B (267)
> {noformat}
> We should add some diagnostics that will allow us to figure out which RPCs are slow and whether there's a pattern about which host is the problem. E.g. maybe we should log if the RPC time exceeds a configured threshold.
> It may also be useful to include some stats about the wait time, e.g. a histogram of the wait times, so that we can see if it's an outlier or general slowness.



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