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MapReduce >> mail # user >> File formats in Hadoop: Sequence files vs AVRO vs RC vs ORC


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Re: File formats in Hadoop: Sequence files vs AVRO vs RC vs ORC
for xml files processing hadoop comes with a class for this purpose called
StreamXmlRecordReader,You can use it by setting your input format to
StreamInputFormat and setting the
stream.recordreader.class property to
org.apache.hadoop.streaming.StreamXmlRecordReader.

for Json files, an open-source project ElephantBird that contains some
useful utilities for working with LZO compression, has a
LzoJsonInputFormat, which can read JSON, but it requires that the input
file be LZOP compressed. We’ll use this code as a template for our own JSON
InputFormat, which doesn’t have the LZOP compression requirement.

if you are dealing with small files then sequence file format comes in
rescue, it stores sequences of binary key-value pairs. Sequence files are
well suited as a format for MapReduce data since they are
splittable,support compression.
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Raj K Singh
http://in.linkedin.com/in/rajkrrsingh
http://www.rajkrrsingh.blogspot.com
Mobile  Tel: +91 (0)9899821370
On Mon, Sep 30, 2013 at 1:10 PM, Wolfgang Wyremba <
[EMAIL PROTECTED]> wrote:

> Hello,
>
> the file format topic is still confusing me and I would appreciate if you
> could share your thoughts and experience with me.
>
> From reading different books/articles/websites I understand that
> - Sequence files (used frequently but not only for binary data),
> - AVRO,
> - RC (was developed to work best with Hive -columnar storage) and
> - ORC (a successor of RC to give Hive another performance boost - Stinger
> initiative)
> are all container file formats to solve the "small files problem" and all
> support compression and splitting.
> Additionally, each file format was developed with specific
> features/benefits
> in mind.
>
> Imagine I have the following text source data
> - 1 TB of XML documents (some millions of small files)
> - 1 TB of JSON documents (some hundred thousands of medium sized files)
> - 1 TB of Apache log files (some thousands of bigger files)
>
> How should I store this data in HDFS to process it using Java MapReduce and
> Pig and Hive?
> I want to use the best tool for my specific problem - with "best"
> performance of course - i.e. maybe one problem on the apache log data can
> be
> best solved using Java MapReduce, another one using Hive or Pig.
>
> Should I simply put the data into HDFS as the data comes from - i.e. as
> plain text files?
> Or should I convert all my data to a container file format like sequence
> files, AVRO, RC or ORC?
>
> Based on this example, I believe
> - the XML documents will be need to be converted to a container file format
> to overcome the "small files problem".
> - the JSON documents could/should not be affected by the "small files
> problem"
> - the Apache files should definitely not be affected by the "small files
> problem", so they could be stored as plain text files.
>
> So, some source data needs to be converted to a container file format,
> others not necessarily.
> But what is really advisable?
>
> Is it advisable to store all data (XML, JSON, Apache logs) in one specific
> container file format in the cluster- let's say you decide to use sequence
> files?
> Having only one file format in HDFS is of course a benefit in terms of
> managing the files and writing Java MapReduce/Pig/Hive code against it.
> Sequence files in this case is certainly not a bad idea, but Hive queries
> could probably better benefit from let's say RC/ORC.
>
> Therefore, is it better to use a mix of plain text files and/or one or more
> container file formats simultaneously?
>
> I know that there will be no crystal-clear answer here as it always
> "depends", but what approach should be taken here, or what is usually used
> in the community out there?
>
> I welcome any feedback and experiences you made.
>
> Thanks
>
>