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https://issues.apache.org/jira/browse/HADOOP-1707?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#action_12538867
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Doug Cutting commented on HADOOP-1707:
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This still appears to have the cascading timeout issue, no? Each stage in the
pipeline must have a smaller timeout than the prior stage or else the whole
pipeline will fail when any node fails. In particular, the client must use a
much larger timeout, since it must permit the primary to potentially replay the
entire block downstream. Perhaps there can be multiple kinds of acks, some
which just indicate that the primary is still alive and others that indicate
that replication is complete? (Acks might include the current level of
replication.) That might help distinguish the cases where the primary has
actually gone down from those where it is still doing productive work. Then
one timeout could be used for communications, and a substantially longer one
for awaiting replication.
I also wonder whether, instead of having so many threads, we might implement
this with async i/o. Much of the processing seems simple enough that
maintaining a state object for each file being written and using a single
thread that selects on sockets and then updates the state might be more
efficient. Perhaps it will be simpler to write these with threads, then
convert them to async?
We discussed offline last week a different approach from what you've described
here. In that, acks would only signal that the immediately downstream node had
written the data, not all downstream nodes. Only at block end or flush would
it check that sufficient replicas exist, with a different command. Why have
you abandoned this plan?
An intermediate approach might be to use buffer pools on each datanode in the
pipeline. Each would write the buffer locally and queue it to be written
downstream. The buffer would only be returned to the pool when both writes
complete. A datanode could block when no buffers are available. That might
improve throughput.
> Remove the DFS Client disk-based cache
> --------------------------------------
>
> Key: HADOOP-1707
> URL: https://issues.apache.org/jira/browse/HADOOP-1707
> Project: Hadoop
> Issue Type: Improvement
> Components: dfs
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
> Fix For: 0.16.0
>
>
> The DFS client currently uses a staging file on local disk to cache all
> user-writes to a file. When the staging file accumulates 1 block worth of
> data, its contents are flushed to a HDFS datanode. These operations occur
> sequentially.
> A simple optimization of allowing the user to write to another staging file
> while simultaneously uploading the contents of the first staging file to HDFS
> will improve file-upload performance.
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