Hi Sachin,
We can optimize this problem in the following ways:
-
use
org.apache.flink.streaming.api.datastream.WindowedStream#aggregate(org.apache.flink.api.common.functions.AggregateFunction)
to reduce number of data
- use TTL to clean data which are not need
- enble incremental checkpoint
- us
Hi,
I am doing the following
1. Use reduce function where the data type of output after windowing is the
same as the input.
2. Where the output of data type after windowing is different from that of
input I use the aggregate function. For example:
SingleOutputStreamOperator data =
reducedPlaye
Hi Sachin,
`performing incremental aggregation using stateful processing` is same as
`windows with agg`, but former is more flexible.If flink window can not
satisfy your performance needs
,and your business logic has some features that can be customized for
optimization. You can choose the former
