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From Dmitriy Lyubimov <dlie...@gmail.com>
Subject Re: Crunch R word count example complete
Date Thu, 29 Nov 2012 05:52:33 GMT
Reducers (groupByKey) now working.

* GroupedPTable's doFun has two added functions -- FUN_INIT_GROUP,
FUN_CLEANUP_GROUP in addition to regular closure triad. The reason behind
push interface instead of pull interface (as in Crunch itself) is that
grouped values themselves are chunkified (can't serialize and materialize
the whole group on the R side as a single value due to memory concerns).

* note use of closures with a shared data environment.

* no use of combiners yet

* doFn fusion is an optimization issue as we discussed before.

===========================================
wordCountComplete <- function () {
 pipeline <- crunchR.MRPipeline$new("test-pipeline")
inputPCol <- pipeline$readTextFile("/crunchr-examples/input")

# this ptable contains pairs word, 1
wordsPTab <- inputPCol$parallelDo(
function(line) {
words<- strsplit(tolower(line),"[^[:alnum:]]+")[[1]]
sapply(words, function(x) emit(x,1))
},
keyType = crunchR.RString$new(),
valueType = crunchR.RVarUint32$new()
)

groupedPTab <- wordsPTab$groupByKey()
 key <- NULL
count <- 0
 wordCountsPTab <- groupedPTab$parallelDo(
FUN_INIT_GROUP = function (key) {
key<<-key
count <<- 0
},
FUN_PROCESS = function (values) count <<- count + sum(values),
FUN_CLEANUP_GROUP = function() emit(key,count),
keyType = crunchR.RString$new(),
valueType = crunchR.RVarUint32$new()
)
 wordCountsPTab$writeTextFile("/crunchr-examples/output")
 result <- pipeline$run()
if ( !result$succeeded() ) stop ("pipeline failed.")
}

library(crunchR)

wordCountComplete()


===========================================


On Tue, Nov 27, 2012 at 10:04 PM, Dmitriy Lyubimov <dlieu.7@gmail.com>wrote:

> pipeline visualization sounds like the exposure of the planner graph, not
> the task graph.
>
> If task could be set up using visitor, crunchr could certainly extend a
> standard one.
>
>
> On Tue, Nov 27, 2012 at 9:55 PM, Dmitriy Lyubimov <dlieu.7@gmail.com>wrote:
>
>> i certainly haven't understood yet the entire code but my first pass on
>> the Crunch classes indicates that there are at least two DAGs built in
>> fact.
>>
>> One is the enitre thing (the entire MR planner), based on Graph etc. and
>> another one  is materialized DAG of RTNode's in a particular MR task.
>>
>> IMO R side doesn't need the former. it only needs to know of do function
>> fusions, nothing else.Which means it really needs the access to the setup
>> mechanism of RTNodes in the task. Ideally.
>>
>> Like i said, even that is probably excessive. It really needs an API to
>> setup DoFn fusions only (at this point. There are probably more functions
>> to fuse though). This api, sort of 3rd party sdk, doesn't even need to know
>> it is crunchR that is using it of course.
>>
>> Of course I am very pragmatically driven and thus favor quick and dirty
>> paths to make this thing usable.
>>
>> On another note, my process is ridiculously slow now. My lack of
>> knowledge of R unit testing best practices really kills me. There is a
>> concept of package unit tests in R but they still require package
>> recompilation which is still a way too long cycle to debug stuff. Plus lack
>> of a  completion tooling for R5 classes in StatEt at the same level as for
>> java and scala really wears me down...  :) oh well.
>>
>> i am close to push another milestone (complete work count without combine
>> function).
>>
>>
>> On Tue, Nov 27, 2012 at 9:09 PM, Josh Wills <josh.wills@gmail.com> wrote:
>>
>>> On Sat, Nov 24, 2012 at 10:17 PM, Dmitriy Lyubimov <dlieu.7@gmail.com
>>> >wrote:
>>>
>>> > it looks like easy and naive solution might be to detect whenever
>>> > IntermediateEmitter is used in a function and serialize one to R side
>>> > instead of actually using it on Java side.
>>> >
>>> > thoughts?
>>> >
>>>
>>> I can think of a few ways to do this, but none that I'm happy with yet.
>>> The
>>> overhead of the R-Java bridge is certainly something we would like to do
>>> away with when we can; the question is whether determining when to avoid
>>> it
>>> should live on the Crunch side via the optimizer/runner, or whether we
>>> should have the planner expose a data structure that explains the plan it
>>> is going to use and allow the R side to use that plan to do the function
>>> composition step itself before calling in to Crunch. That step could also
>>> be useful in other environments-- I think Gabriel went at least partway
>>> down this path already w/the pipeline visualization JIRA he did a few
>>> weeks
>>> back.
>>>
>>> The latter approach would mean that we would need to have RCrunch be it's
>>> own (more R-like) wrapper around the underlying R-Java bridge, which also
>>> has some appeal. But we'd need to play around with it a little and see
>>> what
>>> it looked like.
>>>
>>>
>>> >
>>> > On Sat, Nov 24, 2012 at 5:05 PM, Dmitriy Lyubimov <dlieu.7@gmail.com>
>>> > wrote:
>>> >
>>> > > Aha. I did a number of bug fixes so both examples (with 1 doFn and
2
>>> > > doFn's ) are working now. Good.
>>> > >
>>> > > Josh, please read the comment to the second example. The intermediate
>>> > > output of doFn #1 runs to java/Crunch and back just to be fed into
>>> doFn
>>> > #2.
>>> > > I would very much like to short-circuit those things on R side.
>>> Otherwise
>>> > > it will be very hard to optimize multi-tenant applications (multiple
>>> > > decoupled models encapsulated into bunch of doFn's distributed and
>>> > > optimized by Crunch). Which is actually my pattern for production.
>>> > >
>>> > > I'd be eternally grateful if you could give it a thought. It may
>>> require
>>> > > some exposure of Crunch optimizer internals IMO.
>>> > >
>>> > > thank you, sir.
>>> > >
>>> > >
>>> > > On Sat, Nov 24, 2012 at 11:53 AM, Dmitriy Lyubimov <
>>> dlieu.7@gmail.com
>>> > >wrote:
>>> > >
>>> > >> also one obvious optimization is that if we somehow could perform
>>> > >> extraction of DoFn's DAG for a particular task, we could re-connect
>>> that
>>> > >> DAG on the R side instead of piping that data back and forth from
R
>>> to
>>> > java
>>> > >> DAG of doFn's. But i would need a help from somebody with deep
inner
>>> > >> knowledge of Crunch optimizer to extract and materialize such DAGs
>>> of
>>> > >> functions on the R side.
>>> > >>
>>> > >>
>>> > >> On Sat, Nov 24, 2012 at 11:13 AM, Dmitriy Lyubimov <
>>> dlieu.7@gmail.com
>>> > >wrote:
>>> > >>
>>> > >>> Another perhaps useful piece of information is that process,
>>> initialize
>>> > >>> and cleanup R closures may share the same environment and this
is
>>> > handled
>>> > >>> corerctly at the backend, e.g.
>>> > >>>
>>> > >>> createClosures <- function () {
>>> > >>>    x <- 0, y<- 0
>>> > >>>   startup <- function () x <<- 1
>>> > >>>   process <- function(value) y <<- ifelse(x==1,2,0)
>>> > >>>   cleanup <- function() emit(x + y)
>>> > >>>
>>> > >>>   list(process,startup,cleanup)
>>> > >>> }
>>> > >>>
>>> > >>> this function will produce 3 closures that share same environment
>>> > >>> containing x and y and each task at backend should emit value
3.
>>> > >>>
>>> > >>>
>>> > >>> On Sat, Nov 24, 2012 at 10:54 AM, Dmitriy Lyubimov <
>>> dlieu.7@gmail.com
>>> > >wrote:
>>> > >>>
>>> > >>>>
>>> > >>>>
>>> > >>>>
>>> > >>>> On Sat, Nov 24, 2012 at 10:29 AM, Josh Wills <
>>> josh.wills@gmail.com
>>> > >wrote:
>>> > >>>>
>>> > >>>>> Hey Dmitriy,
>>> > >>>>>
>>> > >>>>> I'm up and running w/Example1.R on my Linux machine--
very cool!
>>> My
>>> > >>>>> Mac is
>>> > >>>>> having some sort of issue w/creating /tmp/crunch* directories
>>> that I
>>> > >>>>> need
>>> > >>>>> to sort out.
>>> > >>>>>
>>> > >>>>> In the example you sent of the broken chaining of DoFns,
why
>>> didn't
>>> > the
>>> > >>>>> first line (quoted below) require a PType?
>>> > >>>>
>>> > >>>>
>>> > >>>> Because the implementation assumes a default  type which
is
>>> character
>>> > >>>> vector as below. Also, if it detects
>>> > >>>> that key type was specified explicitly, it returns PTable
>>> > automatically
>>> > >>>> instead of PCollection.
>>> > >>>>
>>> > >>>> Further on, PTable's emits automatically assume emit(key,value)
>>> > >>>> invocation for concise of notation (instead of java's
>>> > Pair.of(key,value) )
>>> > >>>> and PCollections assume just emit(value).
>>> > >>>>
>>> > >>>>  parallelDo = function ( FUN_PROCESS,
>>> > >>>> FUN_INITIALIZE=NULL,FUN_CLEANUP=NULL,
>>> > >>>> valueType=crunchR.RStrings$new(), keyType) {
>>> > >>>>  if (missing(keyType)) {
>>> > >>>>
>>> > >>>>
>>> >
>>> .parallelDo.PCollection(FUN_PROCESS,FUN_INITIALIZE,FUN_CLEANUP,valueType)
>>> > >>>>  } else {
>>> > >>>>
>>> > >>>>
>>> >
>>> .parallelDo.PTable(FUN_PROCESS,FUN_INITIALIZE,FUN_CLEANUP,keyType,valueType)
>>> > >>>> }
>>> > >>>>  },
>>> > >>>>
>>> > >>>>
>>> > >>>>
>>> > >>>> Is there a shortcut for the case
>>> > >>>>> when the PType of the child is the same as the PType
of the
>>> parent?
>>> > >>>>>
>>> > >>>>
>>> > >>>> er... no. it kind of always assume RStrings (which assumes
>>> > >>>> PType<String> but corresponding R type is multi-emit,
i.e. you can
>>> > emit a
>>> > >>>> vector once and internally it will translate into bunch
of calls
>>> of
>>> > >>>> emit(String). This is a notion that i made specifically
for R
>>> since R
>>> > >>>> operates with vectors and lists, so i can emit just one
vector
>>> type
>>> > and
>>> > >>>> declare it a multi-emit. It is not clear to me if this
notion will
>>> > have a
>>> > >>>> benefit. Obviously, you still can emit R character vector
as a
>>> single
>>> > >>>> value, too, but you would have to select different RType
thing
>>> there
>>> > to
>>> > >>>> imply your intent.
>>> > >>>>
>>> > >>>> Word count is a good example where multi-emit RType serves
you
>>> well:
>>> > >>>> you output result of split[[1]] which is a character vector
as
>>> one R
>>> > call
>>> > >>>> emit(split...) but it translates into bunch of individual
emits
>>> (the
>>> > >>>> variant i had before this last one with PTable, or the
one
>>> commented
>>> > one
>>> > >>>> here :
>>> > >>>>
>>> > >>>> # wordsPCol <- inputPCol$parallelDo(
>>> > >>>> > # function(line) emit(
>>> strsplit(tolower(line),"[^[:alnum:]]+")[[1]]
>>> > )
>>> > >>>> > # )
>>> > >>>>
>>> > >>>>
>>> > >>>>
>>> > >>>>>
>>> > >>>>> # wordsPCol <- inputPCol$parallelDo(
>>> > >>>>> # function(line) emit(
>>> strsplit(tolower(line),"[^[:alnum:]]+")[[1]] )
>>> > >>>>> # )
>>> > >>>>>
>>> > >>>>> Josh
>>> > >>>>>
>>> > >>>>>
>>> > >>>>>
>>> > >>>>> On Fri, Nov 23, 2012 at 1:59 PM, Dmitriy Lyubimov <
>>> dlieu.7@gmail.com
>>> > >
>>> > >>>>> wrote:
>>> > >>>>>
>>> > >>>>> > ok support for PTable emission (key,value) pairs
work in the
>>> latest
>>> > >>>>> commit.
>>> > >>>>> >
>>> > >>>>> > My current problem is that composition of doFunctions
doesn't
>>> work,
>>> > >>>>> > probably because of the sequence of cleanup()
calls. I have to
>>> > >>>>> figure out:
>>> > >>>>> >
>>> > >>>>> > =============
>>> > >>>>> > this composition of 2 functions (PCollection,
PTable) is a
>>> problem
>>> > >>>>> >
>>> > >>>>> > # wordsPCol <- inputPCol$parallelDo(
>>> > >>>>> > # function(line) emit(
>>> > strsplit(tolower(line),"[^[:alnum:]]+")[[1]] )
>>> > >>>>> > # )
>>> > >>>>> > #
>>> > >>>>> > # wordsPTab <- wordsPCol$parallelDo(function(word)
>>> emit(word,1),
>>> > >>>>> > # keyType = crunchR.RString$new(),
>>> > >>>>> > # valueType = crunchR.RUint32$new())
>>> > >>>>> >
>>> > >>>>> > but this equivalent works:
>>> > >>>>> > wordsPTab <- inputPCol$parallelDo(
>>> > >>>>> > function(line) {
>>> > >>>>> > words<- strsplit(tolower(line),"[^[:alnum:]]+")[[1]]
>>> > >>>>> > sapply(words, function(x) emit(x,1))
>>> > >>>>> > },
>>> > >>>>> > keyType = crunchR.RString$new(),
>>> > >>>>> > valueType = crunchR.RUint32$new()
>>> > >>>>> > )
>>> > >>>>> >
>>> > >>>>> >
>>> > >>>>> >
>>> > >>>>> > On Thu, Nov 22, 2012 at 2:13 PM, Dmitriy Lyubimov
<
>>> > dlieu.7@gmail.com
>>> > >>>>> >
>>> > >>>>> > wrote:
>>> > >>>>> >
>>> > >>>>> > > Ok ,  I guess i am going to work on the next
milestone which
>>> is
>>> > >>>>> > PTableType
>>> > >>>>> > > serialization support between R and java
sides.
>>> > >>>>> > >
>>> > >>>>> > > once i am done with that, i guess i will
be able to add
>>> other api
>>> > >>>>> and
>>> > >>>>> > > complete word count example fairly easily.
>>> > >>>>> > >
>>> > >>>>> > > Example1.R in its current state works.
>>> > >>>>> > >
>>> > >>>>> > >
>>> > >>>>> > > On Wed, Nov 21, 2012 at 12:11 PM, Josh Wills
<
>>> > jwills@cloudera.com>
>>> > >>>>> > wrote:
>>> > >>>>> > >
>>> > >>>>> > >> I'm going to play with this again over
the break-- BTW, did
>>> you
>>> > >>>>> see
>>> > >>>>> > >> Renjin?
>>> > >>>>> > >> I somehow missed this, but it looks interesting.
>>> > >>>>> > >>
>>> > >>>>> > >> http://code.google.com/p/renjin/
>>> > >>>>> > >>
>>> > >>>>> > >>
>>> > >>>>> > >> On Sun, Nov 18, 2012 at 11:44 AM, Dmitriy
Lyubimov <
>>> > >>>>> dlieu.7@gmail.com
>>> > >>>>> > >> >wrote:
>>> > >>>>> > >>
>>> > >>>>> > >> > On Sun, Nov 18, 2012 at 9:37 AM,
Josh Wills <
>>> > >>>>> josh.wills@gmail.com>
>>> > >>>>> > >> wrote:
>>> > >>>>> > >> >
>>> > >>>>> > >> > > Dmitrity,
>>> > >>>>> > >> > >
>>> > >>>>> > >> > > Just sent you a pull request
based on playing with the
>>> code
>>> > >>>>> on OS X.
>>> > >>>>> > >> It
>>> > >>>>> > >> > > contains a README about my
experience getting things
>>> > working.
>>> > >>>>> > >> > >
>>> > >>>>> > >> >
>>> > >>>>> > >> > Are you sure it is doxygen package?
I thought it was
>>> roxygen2
>>> > >>>>> package?
>>> > >>>>> > >> >
>>> > >>>>> > >> > Actually there seems currently no
best practice in
>>> existence
>>> > >>>>> for R5
>>> > >>>>> > >> classes
>>> > >>>>> > >> > + roxygen2 (and the guy ignores
@import order of files,
>>> too).
>>> > >>>>> Hence
>>> > >>>>> > the
>>> > >>>>> > >> > hacks with file names.
>>> > >>>>> > >> >
>>> > >>>>> > >> >
>>> > >>>>> > >> > > Unfortunately, I haven't succeeded
in getting crunchR
>>> > loaded,
>>> > >>>>> I'm
>>> > >>>>> > >> running
>>> > >>>>> > >> > > into some issues w/RProtoBuf
on OS X. I'll give it
>>> another
>>> > go
>>> > >>>>> this
>>> > >>>>> > >> week
>>> > >>>>> > >> > on
>>> > >>>>> > >> > > my Linux machine at work.
>>> > >>>>> > >> > >
>>> > >>>>> > >> > ok i removed @import RProtoBuf,
you should be able to
>>> install
>>> > >>>>> w/o it.
>>> > >>>>> > >> Maven
>>> > >>>>> > >> > still compiles protoc  stuff though.
>>> > >>>>> > >> >
>>> > >>>>> > >> > >
>>> > >>>>> > >> > > J
>>> > >>>>> > >> > >
>>> > >>>>> > >> > >
>>> > >>>>> > >> > > On Sat, Nov 17, 2012 at 12:49
PM, Dmitriy Lyubimov <
>>> > >>>>> > dlieu.7@gmail.com
>>> > >>>>> > >> > > >wrote:
>>> > >>>>> > >> > >
>>> > >>>>> > >> > > > Josh,
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > ok the following commit
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > ==============
>>> > >>>>> > >> > > > commit 67605360838f810fa5ddf99abb3ef2962d3f05e3
>>> > >>>>> > >> > > > Author: Dmitriy Lyubimov
<dlyubimov@inadco.com>
>>> > >>>>> > >> > > > Date:   Sat Nov 17 12:29:27
2012 -0800
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >     example1 succeeds
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > ====================
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > runs example 1 for me
successfully in a fully
>>> distributed
>>> > >>>>> way
>>> > >>>>> > which
>>> > >>>>> > >> is
>>> > >>>>> > >> > > > first step (map-only thing)
for the word count.
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > (I think there's a hickup
somewhere here because in
>>> the
>>> > >>>>> output i
>>> > >>>>> > >> also
>>> > >>>>> > >> > > seem
>>> > >>>>> > >> > > > to see some empty lines,
so the strsplit() part is
>>> perhaps
>>> > >>>>> set up
>>> > >>>>> > >> > > somewhat
>>> > >>>>> > >> > > > incorrectly here, but
it's not the point right now):
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > ====Example1.R===========
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > library(crunchR)
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > pipeline <- crunchR.MRPipeline$new("test-pipeline")
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > inputPCol <-
>>> > >>>>> pipeline$readTextFile("/crunchr-examples/input")
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > outputPCol <- inputPCol$parallelDo(
>>> > >>>>> > >> > > > function(line) emit(
>>> > >>>>> strsplit(tolower(line),"[^[:alnum:]]")[[1]] )
>>> > >>>>> > >> > > > )
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > outputPCol$writeTextFile("/crunchr-examples/output")
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > result <- pipeline$run()
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > if ( !result$succeeded()
) stop ("pipeline failed.")
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > ========================================
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > I think R-java communication
now should support
>>> multiple
>>> > >>>>> doFn ok
>>> > >>>>> > and
>>> > >>>>> > >> > they
>>> > >>>>> > >> > > > will be properly shut
down and executed and
>>> synchronized
>>> > >>>>> even if
>>> > >>>>> > >> they
>>> > >>>>> > >> > > emit
>>> > >>>>> > >> > > > in the cleanup phase.
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > This example assumes a
lot of defaults (such as RTypes
>>> > >>>>> which are
>>> > >>>>> > by
>>> > >>>>> > >> > > default
>>> > >>>>> > >> > > > character vector singleton
in and character vector out
>>> > for a
>>> > >>>>> > DoFn).
>>> > >>>>> > >> > Also
>>> > >>>>> > >> > > > obviously uses text in-text
out at this point only.
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > To run, install the package
and upload the test input
>>> > >>>>> > (test-prep.sh)
>>> > >>>>> > >> > > > Assuming you have compiled
the maven part, the R
>>> package
>>> > >>>>> snapshot
>>> > >>>>> > >> could
>>> > >>>>> > >> > > be
>>> > >>>>> > >> > > > installed by running "install-snapshot-rpkg.sh".
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > You also need to make
sure your backend tasks see JRI
>>> > >>>>> library.
>>> > >>>>> > there
>>> > >>>>> > >> > are
>>> > >>>>> > >> > > > multiple ways to do it
i guess but for the purposes of
>>> > >>>>> testing the
>>> > >>>>> > >> > > > following just works for
me in my mapred-site:
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > <property>
>>> > >>>>> > >> > > >    <name>mapred.child.java.opts</name>
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > >
>>> > >>>>> > >> >
>>> > >>>>> > >>
>>> > >>>>> >
>>> > >>>>>
>>> >
>>>  <value>-Djava.library.path=/home/dmitriy/R/x86_64-pc-linux-gnu-library/2/rJava/jri
>>> > >>>>> > >> > > > </value>
>>> > >>>>> > >> > > >    <final>false</final>
>>> > >>>>> > >> > > > </property>
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > I think at this point
you guys might help me by doing
>>> > >>>>> review of
>>> > >>>>> > that
>>> > >>>>> > >> > > stuff,
>>> > >>>>> > >> > > > asking questions and making
suggestions how to go by
>>> > >>>>> incorporating
>>> > >>>>> > >> > other
>>> > >>>>> > >> > > > types of doFn and perhaps
a way to complete the word
>>> count
>>> > >>>>> > example,
>>> > >>>>> > >> > > perhaps
>>> > >>>>> > >> > > > running comparative benchmarks
with a java-only word
>>> > count,
>>> > >>>>> how
>>> > >>>>> > much
>>> > >>>>> > >> > > > overhead we seem to be
suffering here.
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > I use StatEt in eclipse.
Although it is a huge way
>>> > forward,
>>> > >>>>> the
>>> > >>>>> > >> process
>>> > >>>>> > >> > > is
>>> > >>>>> > >> > > > still extremely tedious
since I don't know unit
>>> testing
>>> > >>>>> framework
>>> > >>>>> > >> in R
>>> > >>>>> > >> > > well
>>> > >>>>> > >> > > > (so i just scribble some
stuff on the side to
>>> unit-test
>>> > >>>>> this and
>>> > >>>>> > >> that)
>>> > >>>>> > >> > > and
>>> > >>>>> > >> > > > the integration test running
cycle is significant
>>> enough.
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > Which is why any help
and suggestions are very
>>> welcome!
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > I will definitely add
support for reading/writing
>>> sequence
>>> > >>>>> files
>>> > >>>>> > and
>>> > >>>>> > >> > > > Protobufs, as well as
Mahout DRM's .
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > > > Thanks.
>>> > >>>>> > >> > > > -Dmitrity
>>> > >>>>> > >> > > >
>>> > >>>>> > >> > >
>>> > >>>>> > >> >
>>> > >>>>> > >>
>>> > >>>>> > >>
>>> > >>>>> > >>
>>> > >>>>> > >> --
>>> > >>>>> > >> Director of Data Science
>>> > >>>>> > >> Cloudera <http://www.cloudera.com>
>>> > >>>>> > >> Twitter: @josh_wills <http://twitter.com/josh_wills>
>>> > >>>>> > >>
>>> > >>>>> > >
>>> > >>>>> > >
>>> > >>>>> >
>>> > >>>>>
>>> > >>>>
>>> > >>>>
>>> > >>>
>>> > >>
>>> > >
>>> >
>>>
>>
>>
>

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