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From Martin Neumann <mneum...@spotify.com>
Subject Re: how load/group with large csv files
Date Tue, 21 Oct 2014 17:04:05 GMT
There was not enough time to clean it up and gold plate it. He got semi
horrible java code now with some explanation how it would look in scala.
My colleague was asking for a quick (and dirty) job, so taking more time on
it would have defied the purpose of the whole thing a bit.

In any case thanks for the advice, hopefully I found us another Flink
supporter.

On Tue, Oct 21, 2014 at 3:52 PM, Stephan Ewen <sewen@apache.org> wrote:

> Hej,
>
> Do you want to use Scala? You can use simple case classes there and use
> fields directly as keys, it will look very elegant...
>
> If you want to stick with Java, you can actually use POJOs (Robert just
> corrected me, expression keys should be available there)
>
> Can you define a class
>
> public class MyClass {
>     public String id;
>     public int someValue;
>     public double anotherValue;
>     ...
> }
>
> and then run a program like this:
>
> DataSet<MyClass> data = env.readAsText(...).map(new Parser());
>
> data.groupBy("id").sort("someValue").reduceGroup(new
> GroupReduceFunction(...));
>
>
> Feel free to post your program here so we can give you comments!
>
> Greetings,
> Stephan
>
>
>
> On Tue, Oct 21, 2014 at 3:32 PM, Martin Neumann <mneumann@spotify.com>
> wrote:
>
> > Nope,
> >
> > but I cant filter out the useless data since the program I'm comparing to
> > does not either. The point is to prove to one of my Colleague that Flink
> >
> > Spark.
> > The Spark program runs out of memory and crashes when just doing a simple
> > group and counting the number of items.
> >
> > This is also one of the reasons I ask for what is the best style of doing
> > this so I can get it as clean as possible to compare it to Spark.
> >
> > cheers Martin
> >
> >
> > On Tue, Oct 21, 2014 at 3:07 PM, Aljoscha Krettek <aljoscha@apache.org>
> > wrote:
> >
> > > By the way, do you actually need all those 54 columns in your job?
> > >
> > > On Tue, Oct 21, 2014 at 3:02 PM, Martin Neumann <mneumann@spotify.com>
> > > wrote:
> > > > I will go with that workaround, however I would have preferred if I
> > could
> > > > have done that directly with the API instead of doing Map/Reduce like
> > > > Key/Value tuples again :-)
> > > >
> > > > By the way is there a simple function to count the number of items
> in a
> > > > reduce group? It feels stupid to write a GroupReduce that just
> iterates
> > > and
> > > > increments a counter.
> > > >
> > > > cheers Martin
> > > >
> > > > On Tue, Oct 21, 2014 at 2:54 PM, Robert Metzger <rmetzger@apache.org
> >
> > > wrote:
> > > >
> > > >> Yes, for sorted groups, you need to use Pojos or Tuples.
> > > >> I think you have to split the input lines manually, with a mapper.
> > > >> How about using a TupleN<...> with only the fields you need?
> (returned
> > > by
> > > >> the mapper)
> > > >>
> > > >> if you need all fields, you could also use a Tuple2<String,
> String[]>
> > > where
> > > >> the first position is the sort key?
> > > >>
> > > >>
> > > >>
> > > >> On Tue, Oct 21, 2014 at 2:20 PM, Gyula Fora <gyfora@apache.org>
> > wrote:
> > > >>
> > > >> > I am not sure how you should go about that, let’s wait for
some
> > > feedback
> > > >> > from the others.
> > > >> >
> > > >> > Until then you can always map the array to (array, keyfield)
and
> use
> > > >> > groupBy(1).
> > > >> >
> > > >> >
> > > >> > > On 21 Oct 2014, at 14:17, Martin Neumann <mneumann@spotify.com>
> > > wrote:
> > > >> > >
> > > >> > > Hej,
> > > >> > >
> > > >> > > Unfortunately .sort() cannot take a key extractor, would
I have
> to
> > > do
> > > >> the
> > > >> > > sort myself then?
> > > >> > >
> > > >> > > cheers Martin
> > > >> > >
> > > >> > > On Tue, Oct 21, 2014 at 2:08 PM, Gyula Fora <gyfora@apache.org>
> > > wrote:
> > > >> > >
> > > >> > >> Hey,
> > > >> > >>
> > > >> > >> Using arrays is probably a convenient way to do so.
> > > >> > >>
> > > >> > >> I think the way you described the groupBy only works
for tuples
> > > now.
> > > >> To
> > > >> > do
> > > >> > >> the grouping on the array field, you would need to create
a key
> > > >> > extractor
> > > >> > >> for this and pass that to groupBy.
> > > >> > >>
> > > >> > >> Actually we have some use-cases like this for streaming
so we
> are
> > > >> > thinking
> > > >> > >> of writing a wrapper for the array types that would
behave as
> you
> > > >> > described.
> > > >> > >>
> > > >> > >> Regards,
> > > >> > >> Gyula
> > > >> > >>
> > > >> > >>> On 21 Oct 2014, at 14:03, Martin Neumann <
> mneumann@spotify.com>
> > > >> wrote:
> > > >> > >>>
> > > >> > >>> Hej,
> > > >> > >>>
> > > >> > >>> I have a csv file with 54 columns each of them is
string (for
> > > now). I
> > > >> > >> need
> > > >> > >>> to group and sort them on field 15.
> > > >> > >>>
> > > >> > >>> Whats the best way to load the data into Flink?
> > > >> > >>> There is no Tuple54 (and the <> would look
awful anyway with
> 54
> > > times
> > > >> > >>> String in it).
> > > >> > >>> My current Idea is to write a Mapper and split the
string to
> > > Arrays
> > > >> of
> > > >> > >>> Strings would grouping and sorting work on this?
> > > >> > >>>
> > > >> > >>> So can I do something like this or does that only
work on
> > tuples:
> > > >> > >>> Dataset<String[]> ds;
> > > >> > >>> ds.groupBy(15).sort(20. ANY)
> > > >> > >>>
> > > >> > >>> cheers Martin
> > > >> > >>
> > > >> > >>
> > > >> >
> > > >> >
> > > >>
> > >
> >
>

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