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https://issues.apache.org/jira/browse/HDDS-3155?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17058462#comment-17058462
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mingchao zhao commented on HDDS-3155:
-------------------------------------
By tested HDDS-2717 , I found that this PR did not solve my problem.
Through the HDFS API's flush operation, ozone still persists the new chunk file.
Except for the problem of getting stuck executing a MapReduce task. We
tested the write performance of HDFS and ozone with TestDFSIO. The performance
of ozone is much slower than that of HDFS. We found that the reason for the
above two problems is HDFS's optimization of flush:
1. If you do not actively call flush, after the client's buffer is full, it
will automatically brush to the DN's buffer through the DFSOutputStream's
hflush (no guarantee to write to disk). This is why HDFS behaves faster than
ozone.
2. If I actively call flush, as I did in my test program above. The data in
the client buffer will be flushed directly to the DN buffer via hflush (no
writing to disk is guaranteed). This is why HDFS Mapreduce doesn't get stuck.
Currently HDFS has two persistence mechanisms, hflush([no guarantee to
write to
disk|https://github.com/apache/hadoop/blob/ac4b556e2d44d3cd10b81c190ecee23e2dd66c10/hadoop-hdfs-project/hadoop-hdfs-client/src/main/java/org/apache/hadoop/hdfs/DFSOutputStream.java#L573])
and hsync(guarantee to write to disk[guarantee to write to
disk|https://github.com/apache/hadoop/blob/ac4b556e2d44d3cd10b81c190ecee23e2dd66c10/hadoop-hdfs-project/hadoop-hdfs-client/src/main/java/org/apache/hadoop/hdfs/DFSOutputStream.java#L599]).
I'm going to push a PR to add something like HDFS's hflush in ozone. I will
update the progress of this PR in this jira.
> Improved ozone client flush implementation to make it faster.
> -------------------------------------------------------------
>
> Key: HDDS-3155
> URL: https://issues.apache.org/jira/browse/HDDS-3155
> Project: Hadoop Distributed Data Store
> Issue Type: Improvement
> Reporter: mingchao zhao
> Priority: Major
> Attachments: amlog, image-2020-03-12-16-48-08-391.png,
> image-2020-03-12-17-47-57-770.png, stdout
>
>
> Background:
> When we execute mapreduce in the ozone, we find that the task will be
> stuck for a long time after the completion of Map and Reduce. The log is as
> follows:
> {code:java}
> //Refer to the attachment: stdout
> 20/03/05 14:43:30 INFO mapreduce.Job: map 100% reduce 33%
> 20/03/05 14:43:33 INFO mapreduce.Job: map 100% reduce 100%
> 20/03/05 15:29:52 INFO mapreduce.Job: Job job_1583385253878_0002 completed
> successfully{code}
> By looking at AM's log(Refer to the amlog for details), we found that the
> time of over 40 minutes is AM writing a task log into ozone.
> At present, after MR execution, the Task information is recorded into the
> log on HDFS or ozone by AM. Moreover, the task information is flush to HDFS
> or ozone one by one
> ([details|https://github.com/apache/hadoop/blob/a55d6bba71c81c1c4e9d8cd11f55c78f10a548b0/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/jobhistory/JobHistoryEventHandler.java#L1640]).
> The problem occurs when the number of task maps is large.
> Currently, each flush operation in ozone generates a new chunk file in
> real time on the disk. This approach is not very efficient at the moment. For
> this we can refer to the implementation of HDFS flush. Instead of writing to
> disk each time flush writes the contents of the buffer to the datanode's OS
> buffer. In the first place, we need to ensure that this content can be read
> by other datanodes.
>
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