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From Sandy Ryza <sandy.r...@cloudera.com>
Subject Re: hadoop input/output format advanced control
Date Thu, 26 Mar 2015 02:06:44 GMT
Regarding Patrick's question, you can just do "new Configuration(oldConf)"
to get a cloned Configuration object and add any new properties to it.

-Sandy

On Wed, Mar 25, 2015 at 4:42 PM, Imran Rashid <irashid@cloudera.com> wrote:

> Hi Nick,
>
> I don't remember the exact details of these scenarios, but I think the user
> wanted a lot more control over how the files got grouped into partitions,
> to group the files together by some arbitrary function.  I didn't think
> that was possible w/ CombineFileInputFormat, but maybe there is a way?
>
> thanks
>
> On Tue, Mar 24, 2015 at 1:50 PM, Nick Pentreath <nick.pentreath@gmail.com>
> wrote:
>
> > Imran, on your point to read multiple files together in a partition, is
> it
> > not simpler to use the approach of copy Hadoop conf and set per-RDD
> > settings for min split to control the input size per partition, together
> > with something like CombineFileInputFormat?
> >
> > On Tue, Mar 24, 2015 at 5:28 PM, Imran Rashid <irashid@cloudera.com>
> > wrote:
> >
> > > I think this would be a great addition, I totally agree that you need
> to
> > be
> > > able to set these at a finer context than just the SparkContext.
> > >
> > > Just to play devil's advocate, though -- the alternative is for you
> just
> > > subclass HadoopRDD yourself, or make a totally new RDD, and then you
> > could
> > > expose whatever you need.  Why is this solution better?  IMO the
> criteria
> > > are:
> > > (a) common operations
> > > (b) error-prone / difficult to implement
> > > (c) non-obvious, but important for performance
> > >
> > > I think this case fits (a) & (c), so I think its still worthwhile.  But
> > its
> > > also worth asking whether or not its too difficult for a user to extend
> > > HadoopRDD right now.  There have been several cases in the past week
> > where
> > > we've suggested that a user should read from hdfs themselves (eg., to
> > read
> > > multiple files together in one partition) -- with*out* reusing the code
> > in
> > > HadoopRDD, though they would lose things like the metric tracking &
> > > preferred locations you get from HadoopRDD.  Does HadoopRDD need to
> some
> > > refactoring to make that easier to do?  Or do we just need a good
> > example?
> > >
> > > Imran
> > >
> > > (sorry for hijacking your thread, Koert)
> > >
> > >
> > >
> > > On Mon, Mar 23, 2015 at 3:52 PM, Koert Kuipers <koert@tresata.com>
> > wrote:
> > >
> > > > see email below. reynold suggested i send it to dev instead of user
> > > >
> > > > ---------- Forwarded message ----------
> > > > From: Koert Kuipers <koert@tresata.com>
> > > > Date: Mon, Mar 23, 2015 at 4:36 PM
> > > > Subject: hadoop input/output format advanced control
> > > > To: "user@spark.apache.org" <user@spark.apache.org>
> > > >
> > > >
> > > > currently its pretty hard to control the Hadoop Input/Output formats
> > used
> > > > in Spark. The conventions seems to be to add extra parameters to all
> > > > methods and then somewhere deep inside the code (for example in
> > > > PairRDDFunctions.saveAsHadoopFile) all these parameters get
> translated
> > > into
> > > > settings on the Hadoop Configuration object.
> > > >
> > > > for example for compression i see "codec: Option[Class[_ <:
> > > > CompressionCodec]] = None" added to a bunch of methods.
> > > >
> > > > how scalable is this solution really?
> > > >
> > > > for example i need to read from a hadoop dataset and i dont want the
> > > input
> > > > (part) files to get split up. the way to do this is to set
> > > > "mapred.min.split.size". now i dont want to set this at the level of
> > the
> > > > SparkContext (which can be done), since i dont want it to apply to
> > input
> > > > formats in general. i want it to apply to just this one specific
> input
> > > > dataset i need to read. which leaves me with no options currently. i
> > > could
> > > > go add yet another input parameter to all the methods
> > > > (SparkContext.textFile, SparkContext.hadoopFile,
> > SparkContext.objectFile,
> > > > etc.). but that seems ineffective.
> > > >
> > > > why can we not expose a Map[String, String] or some other generic way
> > to
> > > > manipulate settings for hadoop input/output formats? it would require
> > > > adding one more parameter to all methods to deal with hadoop
> > input/output
> > > > formats, but after that its done. one parameter to rule them all....
> > > >
> > > > then i could do:
> > > > val x = sc.textFile("/some/path", formatSettings =
> > > > Map("mapred.min.split.size" -> "12345"))
> > > >
> > > > or
> > > > rdd.saveAsTextFile("/some/path, formatSettings =
> > > > Map(mapred.output.compress" -> "true",
> > "mapred.output.compression.codec"
> > > ->
> > > > "somecodec"))
> > > >
> > >
> >
>

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