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From Stephan Ewen <se...@apache.org>
Subject Re: how load/group with large csv files
Date Tue, 21 Oct 2014 13:52:30 GMT
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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