Hi Filip,
I have one question on the problem: what is the expected behavior
when the parallelism of the FlatMapFunction is increased to more than 1?
Should each subtask maintains the partial sum of all values received, and
whenever the barrier is received, then it just outputs the partial sum of the
received value ?
Another question is that I think in Flink the watermark mechanism has
provided the functionality similar to punctuation, therefore is it possible to
implement the same logic with the Flink Window directly?
Best,
Yun
------------------------------------------------------------------
From:Filip Niksic <fnik...@seas.upenn.edu>
Send Time:2019 Oct. 8 (Tue.) 08:56
To:user <user@flink.apache.org>
Subject:[QUESTION] How to parallelize with explicit punctuation in Flink?
Hi all,
What would be a natural way to implement a parallel version of the following
Flink program?
Suppose I have a stream of items of type DataItem with two concrete
implementations of DataItem: Value and Barrier. Let’s say that Value holds an
integer value, and Barrier acts as explicit punctuation.
public interface DataItem {}
public class Value implements DataItem {
private final int val;
public Value(int val) { this.val = val; }
public int getVal() { return val; }
}
public class Barrier implements DataItem {}
The program should maintain a sum of values seen since the beginning of the
stream. On each Barrier, the program should output the sum seen so far.
An obvious way to implement this would be with a FlatMapFunction, maintaining
the sum as state and emitting it on each Barrier.
StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<DataItem> stream = env.fromElements(DataItem.class,
new Value(1), new Barrier(), new Value(3), new Value(-1), new Barrier());
stream.flatMap(new FlatMapFunction<DataItem, Integer>() {
private int sum = 0;
@Override
public void flatMap(DataItem dataItem, Collector<Integer> collector) throws
Exception {
if (dataItem instanceof Value) {
sum += ((Value) dataItem).getVal();
} else {
collector.collect(sum);
}
}
}).setParallelism(1).print().setParallelism(1);
env.execute();
// We should see 1 followed by 3 as output
However, such an operator cannot be parallelized, since the order of Values and
Barriers matters. That’s why I need to set parallelism to 1 above. Is there a
way to rewrite this to exploit parallelism?
(Another reason to set parallelism to 1 above is that I’m assuming there is a
single instance of the FlatMapFunction. A proper implementation would take more
care in using state. Feel free to comment on that as well.)
Best regards,
Filip Niksic
