Re: General Data questions - streams vs batch

s arrived (can cause high latency if the arrival > rate of elements is low or varies). > The flatmap function can be executed in parallel and does not require a > keyed stream. > > Best, Fabian > > > 2016-04-25 18:58 GMT+02:00 Konstantin Kulagin <kkula...@gmail.com>: >

General Data questions - streams vs batch

Hi guys, I have some kind of general question in order to get more understanding of stream vs final data transformation. More specific - I am trying to understand 'entities' lifecycle during processing. 1) For example in a case of streams: suppose we start with some key-value source, parallel it

Re: Count windows missing last elements?

I was trying to implement this (force flink to handle all values from input) but had no success... Probably I am not getting smth with flink windowing mechanism I've created my 'finishing' trigger which is basically a copy of purging trigger But was not able to make it work:

Re: Count windows missing last elements?

No problems at all, there is not much flink people and a lot of asking guys - it should be hard to understand each person's issues :) Yes, it is not as easy as 'contains' operator: I need to collect some amount of tuples in order to create a in-memory lucene index. After that I will filter

java.lang.ClassCastException: org.apache.flink.streaming.api.watermark.Watermark cannot be cast to org.apache.flink.streaming.runtime.streamrecord.StreamRecord

Hi guys, trying to run this example: StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStreamSource> source = env.addSource(new SourceFunction>() { @Override public void