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Hadoop, mail # user - Can I write to an compressed file which is located in hdfs?


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Re: Can I write to an compressed file which is located in hdfs?
bejoy.hadoop@... 2012-02-07, 07:53
Hi
    AFAIK I don't think it is possible to append into a compressed file.

If you have files in hdfs on a dir and you need to compress the same (like files for an hour) you can use MapReduce to do that by setting mapred.output.compress = true and mapred.output.compression.codec='theCodecYouPrefer'
You'd get the blocks compressed in the output dir.

You can use the API to read from standard input like
-get hadoop conf
-register the required compression codec
-write to CompressionOutputStream.

You should get a well detailed explanation on the same from the book 'Hadoop - The definitive guide' by Tom White.

Regards
Bejoy K S

From handheld, Please excuse typos.

-----Original Message-----
From: Xiaobin She <[EMAIL PROTECTED]>
Date: Tue, 7 Feb 2012 14:24:01
To: <[EMAIL PROTECTED]>; <[EMAIL PROTECTED]>; David Sinclair<[EMAIL PROTECTED]>
Subject: Re: Can I write to an compressed file which is located in hdfs?

hi Bejoy and David,

thank you for you help.

So I can't directly write logs or append logs into an compressed file in
hdfs, right?

Can I compress an file which is already in hdfs and has not been compressed?

If I can , how can I do that?

Thanks!

2012/2/6 <[EMAIL PROTECTED]>

> Hi
>       I agree with David on the point, you can achieve step 1 of my
> previous response with flume. ie load real time inflow of data in
> compressed format into hdfs. You can specify a time interval or data size
> in flume collector that determines when to flush data on to hdfs.
>
> Regards
> Bejoy K S
>
> From handheld, Please excuse typos.
>
> -----Original Message-----
> From: David Sinclair <[EMAIL PROTECTED]>
> Date: Mon, 6 Feb 2012 09:06:00
> To: <[EMAIL PROTECTED]>
> Cc: <[EMAIL PROTECTED]>
> Subject: Re: Can I write to an compressed file which is located in hdfs?
>
> Hi,
>
> You may want to have a look at the Flume project from Cloudera. I use it
> for writing data into HDFS.
>
> https://ccp.cloudera.com/display/SUPPORT/Downloads
>
> dave
>
> 2012/2/6 Xiaobin She <[EMAIL PROTECTED]>
>
> > hi Bejoy ,
> >
> > thank you for your reply.
> >
> > actually I have set up an test cluster which has one namenode/jobtracker
> > and two datanode/tasktracker, and I have make an test on this cluster.
> >
> > I fetch the log file of one of our modules from the log collector
> machines
> > by rsync, and then I use hive command line tool to load this log file
> into
> > the hive warehouse which  simply copy the file from the local filesystem
> to
> > hdfs.
> >
> > And I have run some analysis on these data with hive, all this run well.
> >
> > But now I want to avoid the fetch section which use rsync, and write the
> > logs into hdfs files directly from the servers which generate these logs.
> >
> > And it seems easy to do this job if the file locate in the hdfs is not
> > compressed.
> >
> > But how to write or append logs to an file that is compressed and located
> > in hdfs?
> >
> > Is this possible?
> >
> > Or is this an bad practice?
> >
> > Thanks!
> >
> >
> >
> > 2012/2/6 <[EMAIL PROTECTED]>
> >
> > > Hi
> > >     If you have log files enough to become at least one block size in
> an
> > > hour. You can go ahead as
> > > - run a scheduled job every hour that compresses the log files for that
> > > hour and stores them on to hdfs (can use LZO or even Snappy to
> compress)
> > > - if your hive does more frequent analysis on this data store it as
> > > PARTITIONED BY (Date,Hour) . While loading into hdfs also follow a
> > > directory - sub dir structure. Once data is in hdfs issue a Alter Table
> > Add
> > > Partition statement on corresponding hive table.
> > > -in Hive DDL use the appropriate Input format (Hive has some ApacheLog
> > > Input Format already)
> > >
> > >
> > > Regards
> > > Bejoy K S
> > >
> > > From handheld, Please excuse typos.
> > >
> > > -----Original Message-----
> > > From: Xiaobin She <[EMAIL PROTECTED]>
> > > Date: Mon, 6 Feb 2012 16:41:50