By default, Kafka uses the record metadata timestamp. Thus, I assume
that you use a custom timestamp extractor? If yes, what timestamp do you
extract, as it seems you have multiple?
To make it work, you will need to write the data back to Kafka into a
new topic after the flatpMap() and read each r
This is a specific example:
We are sending metrics to Kafka Stream with the following layout:
Record Key:
--
|metric name|tags|
--
Record Value:
---
Not sure what you mean by "does not reset recordContext".
Note, that the "contract" for `flatMapValues` is that the output records
inherit the timestamp of the input record.
Not sure what behavior you expect? Maybe you can elaborate?
-Matthias
On 7/9/18 7:27 PM, Sicheng Liu wrote:
> Hi,
>
> I
Hi,
I found out that doing windowed aggregation on records generated by
flatMapValues gets incorrect result.
Take this topology as an example:
myStream.flatMapValues(...)
.groupByKey(...)
.aggregate(...)
Since inside KStreamWindowAggregateProcessor, the timestamp
