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From Josh Wills <>
Subject Re: Same processing in two m/r jobs
Date Tue, 12 Nov 2013 16:26:12 GMT
Hey Surbhi,

The planner is trying to minimize the amount of data it writes to disk at
the end of the first job; it doesn't usually worry so much about re-running
the same computation in two different jobs if it means that less data will
be written to disk overall, since most MR jobs aren't CPU bound.

While that's often a useful heuristic, there are many cases where it isn't
true, and this sounds like one of them. My advice would be to materialize
the output of the union of S2 and S3, at which point the planner should run
the processing of S2 and S3 once at the end of job 1, and then pick up that
materialized output for grouping in job 2.


On Mon, Nov 11, 2013 at 8:30 PM, Mungre,Surbhi <>wrote:

>  Background:
> We have a crunch pipeline which is used to normalize and standardize some
> entities represented as Avro. In our pipeline, we also capture some context
> information about the errors and warnings which we encounter during our
> processing. We pass a pair of context information and Avro entities in our
> pipeline. At the end of the pipeline, the context information is written to
> HDFS and Avro entities are written to HFiles.
> Problem:
> When we were trying to analyze DAG for our crunch pipeline we noticed that same processing
is done in two m/r jobs. Once it is done to capture context information and second time it
is done to generate HFiles. I wrote a test which replicates this issue with a simple example.
The test and a DAG created from this test are attached with the post. It is clear from the
DAG that S2 and S3 are processed twice. I am not sure why this processing is done twice and
if there is any way to avoid this behavior.
> Surbhi Mungre
> Software Engineer
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Director of Data Science
Cloudera <>
Twitter: @josh_wills <>

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