That code will not run in parallel right? So, a map-reduce task would yield
better performance no?
On Fri, Feb 26, 2016 at 6:06 PM, Stephan Ewen <se...@apache.org> wrote:
> Then go for:
>
> input.timeWindowAll(Time.seconds(10)).fold(0, new
> FoldFunction<Tuple2<Integer, Integer>, Integer>() { @Override public
> Integer fold(Integer integer, Tuple2<Integer, Integer> o) throws Exception
> { return integer + 1; } });
>
> Try to explore the API a bit, most things should be quite intuitive.
> There are also some docs:
> https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/streaming_guide.html#windows-on-unkeyed-data-streams
>
> On Fri, Feb 26, 2016 at 4:07 PM, Saiph Kappa <saiph.ka...@gmail.com>
> wrote:
>
>> Why the ".keyBy"? I don't want to count tuples by Key. I simply want to
>> count all tuples that are contained in a window.
>>
>> On Fri, Feb 26, 2016 at 9:14 AM, Till Rohrmann <trohrm...@apache.org>
>> wrote:
>>
>>> Hi Saiph,
>>>
>>> you can do it the following way:
>>>
>>> input.keyBy(0).timeWindow(Time.seconds(10)).fold(0, new
>>> FoldFunction<Tuple2<Integer, Integer>, Integer>() {
>>> @Override
>>> public Integer fold(Integer integer, Tuple2<Integer, Integer> o) throws
>>> Exception {
>>> return integer + 1;
>>> }
>>> });
>>>
>>> Cheers,
>>> Till
>>>
>>>
>>> On Thu, Feb 25, 2016 at 7:58 PM, Saiph Kappa <saiph.ka...@gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> In Flink Stream what's the best way of counting the number of tuples
>>>> within a window of 10 seconds? Using a map-reduce task? Asking because in
>>>> spark there is the method rawStream.countByWindow(Seconds(x)).
>>>>
>>>> Thanks.
>>>>
>>>
>>>
>>
>
