Thanks. Apparently there is nothing wrong with that :-)
I came to the same conclusion in an earlier post.
Would be good if someone having experience with Kafka and event sourcing
corrected this stackoverflow answer (i.e. update 2):
http://stackoverflow.com/questions/17708489/using-kafka-as-a-c
Thanks for your detailed reply. Viewing each partition as an ordered
event bus is helpful.
The problem then moves to working out a strategy for mapping individual
DDD aggregates to partitions in a cluster which distributes load and
also allows for growing the cluster.
On 30/03/16 01:07, Hel
What's wrong with multiple aggregates per partition? You'll still process
all events for each aggregate in order. If you want to just replay for a
single aggregate somewhere, Kafka can spit out events fast enough to allow
you to quickly skip through all the stuff you don't need...
On Mon, Mar 28,
Well, if a partition is too large of a unit of order for your tastes, you
can add publisher ID¹s to some metadata, or force partition mapping and
use the key as an extra level of partitioning. And, pick a topicName that
describes all the traffic on that topic. An example:
topicName=³ad.click.even
Thanks for sharing your experience.
I'm surprised no one else has responded. Maybe there are few people
using Kafka for event sourcing.
I did find one answer to my question
http://stackoverflow.com/questions/26060535/kafka-as-event-store-in-event-sourced-system?rq=1
I guess using a single top
Have you looked into using a relational database as the primary store, with
something like Maxwell or Bottled Water as a broadcast mechanism?
On Mon, 28 Mar 2016 at 17:28 Daniel Schierbeck wrote:
> I ended up abandoning the use of Kafka as a primary event store, for
> several reasons. One is the
I ended up abandoning the use of Kafka as a primary event store, for
several reasons. One is the partition granularity issue; another is the
lack of a way to guarantee exclusive write access, i.e. ensure that only a
single process can commit an event for an aggregate at any one time.
On Mon, 28 Mar
Hi all,
When using Kafka for event sourcing in a CQRS style app, what approach
do you recommend for mapping DDD aggregates to topic partitions?
Assigning a partition to each aggregate seems at first to be the right
approach: events can be replayed in correct order for each aggregate and
ther
