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From "Jay Hacker (JIRA)" <j...@apache.org>
Subject [jira] [Created] (MAPREDUCE-5018) Support raw binary data with Hadoop streaming
Date Thu, 21 Feb 2013 14:52:13 GMT
Jay Hacker created MAPREDUCE-5018:
-------------------------------------

             Summary: Support raw binary data with Hadoop streaming
                 Key: MAPREDUCE-5018
                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-5018
             Project: Hadoop Map/Reduce
          Issue Type: New Feature
          Components: contrib/streaming
            Reporter: Jay Hacker
            Priority: Minor


People often have a need to run older programs over many files, and turn to Hadoop streaming
as a reliable, performant batch system.  There are good reasons for this:

1. Hadoop is convenient: they may already be using it for mapreduce jobs, and it is easy to
spin up a cluster in the cloud.
2. It is reliable: HDFS replicates data and the scheduler retries failed jobs.
3. It is reasonably performant: it moves the code to the data, maintaining locality, and scales
with the number of nodes.

Historically Hadoop is of course oriented toward processing key/value pairs, and so needs
to interpret the data passing through it.  Unfortunately, this makes it difficult to use Hadoop
streaming with programs that don't deal in key/value pairs, or with binary data in general.
 For example, something as simple as running md5sum to verify the integrity of files will
not give the correct result, due to Hadoop's interpretation of the data.  

There have been several attempts at binary serialization schemes for Hadoop streaming, such
as TypedBytes (HADOOP-1722); however, these are still aimed at efficiently encoding key/value
pairs, and not passing data through unmodified.  Even the "RawBytes" serialization scheme
adds length fields to the data, rendering it not-so-raw.

I often have a need to run a Unix filter on files stored in HDFS; currently, the only way
I can do this on the raw data is to copy the data out and run the filter on one machine, which
is inconvenient, slow, and unreliable.  It would be very convenient to run the filter as a
map-only job, allowing me to build on existing (well-tested!) building blocks in the Unix
tradition instead of reimplementing them as mapreduce programs.

However, most existing tools don't know about file splits, and so want to process whole files;
and of course many expect raw binary input and output.  The solution is to run a map-only
job with an InputFormat and OutputFormat that just pass raw bytes and don't split.  It turns
out to be a little more complicated with streaming; I have attached a patch with the simplest
solution I could come up with.  I call the format "JustBytes" (as "RawBytes" was already taken),
and it should be usable with most recent versions of Hadoop.


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