[jira] [Created] (FLINK-6394) GroupCombine reuses instances even though object reuse is disabled

Jaromir Vanek created FLINK-6394: Summary: GroupCombine reuses instances even though object reuse is disabled Key: FLINK-6394 URL: https://issues.apache.org/jira/browse/FLINK-6394 Project: Flink

Re: Flink gives incorrect result when event time windowing used

ontributors who are more familiar with watermarks and >> event-time (cc Aljoscha, Kostas K, Stephan). >> >> Best, Fabian >> >> [1] >> >> https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/streaming/event_time.html#event-time-and-watermarks >&g

Flink gives incorrect result when event time windowing used

null && timer.timestamp <= mark.getTimestamp()) { / Thanks Jaromir Vanek / public class WindowingTest { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment();

Re: Deterministic processing with out-of-order streams

Hi Till, thank you for your answer. I am afraid defining an allowed lateness won't help. It will just change the problem by constant time. If we agree an element can come in arbitrary time after watermark (depends on the network latency), it may be assigned to the window or may be not if comes be

Deterministic processing with out-of-order streams

Is Flink processing really repeatedly deterministic when incoming stream of elements is out-of-order? How is it ensured? I am aware of all the principles like event time and watermarking. But I can't understand how it works in case there are late elements in stream - that means there are elements

Re: DataStream.partitionCustom() - define parallelism

Aljoscha Krettek-2 wrote > Hi, > it should be possible to set the parallelism on the actual downstream > operation. The partitioning operation is just an intermediate. > > Cheers, > Aljoscha Are you sure about that? The mentioned discussion