Mikael,
by "growing out of bounds" we refer to the fact, that the changelog
encodes the keys as pair of . Thus, over time as
we create more and more window, storage requirement grows and grows and
will eventually hit a wall. How fast this happens, depends mainly on
your window advance time (and nu
Hi Matthias,
kind of :)
I'm interested in the retention mechanisms and my use case is to keep old
windows around for a long time (up to a year or longer) and access them via
interactive queries. As I understand from the documentation, the retention
mechanism is used to avoid changelogs from "grow
I am not sure if I can follow.
However, in Kafka Streams using window aggregation, the windowed KTable
uses a key-value store internally -- it's only called windowed store
because it encodes the key for the store as pair of
and also applies a couple of other mechanism with
regard to retention tim
Hi,
I'm wondering about the tradeoffs when implementing a tumbling window with
a long retention, e.g. 1 year. Is it better to use a normal key value store
and aggregate the time bucket using a group by instead of a window store?
Best,
Mikael
