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From "Adaryl \"Bob\" Wakefield, MBA" <adaryl.wakefi...@hotmail.com>
Subject Re: Merging small files
Date Sun, 20 Jul 2014 16:37:54 GMT
“Even if we kept the discussion to the mailing list's technical Hadoop usage focus, any company/organization
looking to use a distro is going to have to consider the costs, support, platform, partner
ecosystem, market share, company strategy, etc.”

Yeah good point.

Adaryl "Bob" Wakefield, MBA
Mass Street Analytics

From: Shahab Yunus 
Sent: Sunday, July 20, 2014 11:32 AM
To: user@hadoop.apache.org 
Subject: Re: Merging small files

Why it isn't appropriate to discuss too much vendor specific topics on a vendor-neutral apache
mailing list? Checkout this thread: 

You can always discuss vendor specific issues in their respective mailing lists.

As for merging files, Yes one can use HBase but then you have to keep in mind that you are
adding overhead of development and maintenance of a another store (i.e. HBase). If your use
case could be satisfied with HDFS alone then why not keep it simple? And given the knowledge
of the requirements that the OP provided, I think Sequence File format should work as I suggested
initially. Of course, if things get too complicated from requirements perspective then one
might try out HBase.


On Sun, Jul 20, 2014 at 12:24 PM, Adaryl "Bob" Wakefield, MBA <adaryl.wakefield@hotmail.com>

  It isn’t? I don’t wanna hijack the thread or anything but it seems to me that MapR is
an implementation of Hadoop and this is a great place to discuss it’s merits vis a vis the
Hortonworks or Cloudera offering. 

  A little bit more on topic: Every single thing I read or watch about Hadoop says that many
small files is a bad idea and that you should merge them into larger files. I’ll take this
a step further. If your invoice data is so small, perhaps Hadoop isn’t the proper solution
to whatever it is you are trying to do and a more traditional RDBMS approach would be more
appropriate. Someone suggested HBase and I was going to suggest maybe one of the other NoSQL
databases, however, I remember that Eddie Satterly of Splunk says that financial data is the
ONE use case where a traditional approach is more appropriate. You can watch his talk here:


  Adaryl "Bob" Wakefield, MBA
  Mass Street Analytics

  From: Kilaru, Sambaiah 
  Sent: Sunday, July 20, 2014 3:47 AM
  To: user@hadoop.apache.org 
  Subject: Re: Merging small files

  This is not place to discuss merits or demerits of MapR, Small files screw up very badly
with Mapr.
  Small files go into one container (to fill up 256MB or what ever container size) and with
locality most
  Of the mappers go to three datanodes.

  You should be looking into sequence file format.


  From: "M. C. Srivas" <mcsrivas@gmail.com>
  Reply-To: "user@hadoop.apache.org" <user@hadoop.apache.org>
  Date: Sunday, July 20, 2014 at 8:01 AM
  To: "user@hadoop.apache.org" <user@hadoop.apache.org>
  Subject: Re: Merging small files

  You should look at MapR .... a few 100's of billions of small files is absolutely no problem.
(disc: I work for MapR)

  On Sat, Jul 19, 2014 at 10:29 AM, Shashidhar Rao <raoshashidhar123@gmail.com> wrote:

    Hi ,

    Has anybody worked in retail use case. If my production Hadoop cluster block size is 256
MB but generally if we have to process retail invoice data , each invoice data is merely let's
say 4 KB . Do we merge the invoice data to make one large file say 1 GB . What is the best
practice in this scenario



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