Hi,
I’m afraid there is no other solution besides calling keyBy() again if you
require a keyed data stream. This has to do with the data model of Flink, where
the key is part of the regular data element. This is different from systems
that have a (Key, Value) data model. Those systems can provid
Here is some code of my current solution, if there is some better way of
doing it let me know.
KeyedStream hostStream = inStream
.keyBy(t -> t.getHost());
KeyedStream,String> freqStream = hostStreams
.timeWindow(Time.of(WINDOW_SIZE, TimeUnit.MILLISECONDS))
.fold(new Tuple2(0, ""), new Count()
Hi,
where are you storing the results of each window computation to? Maybe you
could also store it from inside a custom WindowFunction where you just count
the elements and then store the results.
On the other hand, adding a (1) field and doing a window reduce (à la
WordCount) is going to be wa
Hej,
I want to do the following thing:
1. Split a Stream of incoming Logs by host address.
2. For each Key, create time based windows
3. Count the number of items in the window
4. Feed it into a statistical model that is maintained for each host
Since I don't have fields to sum upon, I use a (win
