-Re: File formats in Hadoop: Sequence files vs AVRO vs RC vs ORC
It is not recommended to keep the data at rest in sequences format, because
it is Java specific and you cannot share it with other none-java systems
easily, it is ideal for running map/reduce jobs. On approach would be to
bring all the data of different formats in HDFS as is and then convert them
to a single format that works best for you depending on whether you will
export this data out or not (in addition to many other considerations). But
as already mentioned Hive can directly read any of these formats.
On Mon, Sep 30, 2013 at 1:08 AM, Raj K Singh <[EMAIL PROTECTED]> wrote:
> 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
> 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.
> Raj K Singh
> Mobile Tel: +91 (0)9899821370
> On Mon, Sep 30, 2013 at 1:10 PM, Wolfgang Wyremba <
> [EMAIL PROTECTED]> wrote:
>> 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
>> 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
>> 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
>> 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
>> 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
>> to overcome the "small files problem".
>> - the JSON documents could/should not be affected by the "small files
>> - 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
>> 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.