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From "Tom White (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-249) Improving Map -> Reduce performance and Task JVM reuse
Date Wed, 13 Aug 2008 14:48:44 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-249?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12622231#action_12622231

Tom White commented on HADOOP-249:

> You would still need to guess where the boundary was.

I was thinking that you would call System.setOut() and System.setErr() for each new task,
so that each stream was redirected to a new file. (Of course, if the task hasn't finished
producing output, it will go into the wrong output file. But this is a general problem, as
you point out.) But if the overhead for copying the process output to a file in Java is so
high then we should do the stream handling outside Java and track offsets.

> Improving Map -> Reduce performance and Task JVM reuse
> ------------------------------------------------------
>                 Key: HADOOP-249
>                 URL: https://issues.apache.org/jira/browse/HADOOP-249
>             Project: Hadoop Core
>          Issue Type: Improvement
>          Components: mapred
>    Affects Versions: 0.3.0
>            Reporter: Benjamin Reed
>            Assignee: Devaraj Das
>         Attachments: disk_zoom.patch, image001.png, task_zoom.patch
> These patches are really just to make Hadoop start trotting. It is still at least an
order of magnitude slower than it should be, but I think these patches are a good start.
> I've created two patches for clarity. They are not independent, but could easily be made
> The disk-zoom patch is a performance trifecta: less disk IO, less disk space, less CPU,
and overall a tremendous improvement. The patch is based on the following observation: every
piece of data from a map hits the disk once on the mapper, and 3 (+plus sorting) times on
the reducer. Further, the entire input for the reduce step is sorted together maximizing the
sort time. This patch causes:
> 1)  the mapper to sort the relatively small fragments at the mapper which causes two
hits to the disk, but they are smaller files.
> 2) the reducer copies the map output and may merge (if more than 100 outputs are present)
with a couple of other outputs at copy time. No sorting is done since the map outputs are
> 3) the reducer  will merge the map outputs on the fly in memory at reduce time.
> I'm attaching the performance graph (with just the disk-zoom patch) to show the results.
This benchmark uses a random input and null output to remove any DFS performance influences.
The cluster of 49 machines I was running on had limited disk space, so I was only able to
run to a certain size on unmodified Hadoop. With the patch we use 1/3 the amount of disk space.
> The second patch allows the task tracker to reuse processes to avoid the over-head of
starting the JVM. While JVM startup is relatively fast, restarting a Task causes disk IO and
DFS operations that have a negative impact on the rest of the system. When a Task finishes,
rather than exiting, it reads the next task to run from stdin. We still isolate the Task runtime
from TaskTracker, but we only pay the startup penalty once.
> This second patch also fixes two performance issues not related to JVM reuse. (The reuse
just makes the problems glaring.) First, the JobTracker counts all jobs not just the running
jobs to decide the load on a tracker. Second, the TaskTracker should really ask for a new
Task as soon as one finishes rather than wait the 10 secs.
> I've been benchmarking the code alot, but I don't have access to a really good cluster
to try the code out on, so please treat it as experimental. I would love to feedback.
> There is another obvious thing to change: ReduceTasks should start after the first batch
of MapTasks complete, so that 1) they have something to do, and 2) they are running on the
fastest machines.

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