On 13.07.14 16:03, Richard Rodseth wrote:
Thanks for the detailed reply. I might have been forgetting that Akka
persistence can be used for more than persisting DDD aggregates. I had
also forgotten that the event store and snapshot store can be different.
You can even use Kafka to implement a snapshot store. You just need to
enable log compaction
<http://kafka.apache.org/documentation.html#compaction> which will
always keep the last snapshot (entry) for each persistent actor (key). I
also plan to implement a snapshot store backed by Kafka but I'm not sure
at the moment how well Kafka supports large log entries.
On Sun, Jul 13, 2014 at 12:51 AM, Martin Krasser
<[email protected] <mailto:[email protected]>> wrote:
Hi Richard,
when using the Kafka journal with default/typical retention times,
your application is responsible for storing snapshots at intervals
that are significantly smaller than the retention time (for
example, with a retention time of 7 days, you may want to take
snapshots of your persistent actors every 3 days or so).
Alternatively, configure Kafka to keep messages "forever" (i.e.
set the retention time to the maximum value) if needed. I don't go
into Kafka partitioning details here but it is possible to
implement the journal driver in a way that both a single
persistent actor's data are partitioned *and* kept in order.
However, with the initial implementation, all data for a single
persistent actor must fit on a single Kafka node (different
persistent actors are of course distributed over a Kafka cluster).
Hence, deleting old data after a few weeks and taking snapshots at
regular interval is the way to go (which is good enough for many
applications I think).
The real value of the Kafka journal IMO comes with the many
external integrations it supports. For example, you can can use
the it as an input source for Spark streaming
<http://spark.apache.org/docs/latest/streaming-programming-guide.html>
and can do (scalable) stream processing of events generated by
persistent actors i.e. you can easily create Akka -> Kafka ->
Spark Streaming pipelines. This is an alternative to Akka's
PersistentView and even allows processing of events generated by
several/all persistent actors with a single consumer such as a
single Spark DStream (which is currently a limitation
<https://github.com/akka/akka/issues/15004> when using
PersistentViews).
I just see this as a starting point for what akka-persistence may
require from all journal implementations in later releases:
provide a persistent event stream generated several persistent
actors in a scalable way. This stream could then be consumed with
akka-streams or Spark Streaming, using a generic connector rather
than a journal-backend-specific, for example.
Initially I just wanted to implement the Kafka integration as
interceptor for journal commands so that events are stored in
Kafka in addition to another journal backend. This may be ok for
some projects, others may think that operational complexity gets
too high when you have to administer a Kafka/Zookeeper cluster in
addition to a Cassandra or MongoDB cluster, for example.
Hope that clarifies things a bit.
Cheers,
Martin
On 12.07.14 15:35, Richard Rodseth wrote:
I saw a tweet from Martin Krasser that he was working on an Akka
Persistence journal plug-in for Kafka. This puzzled me a bit
since Kafka messages are "durable" rather than "persistent" -
they are stored for a configurable time.
Could anyone comment on a typical usage? Assuming that your
persistent actor is going to get recovered before the Kafka topic
expires seems odd.
While the Akka/Kafka combination seems great, I always pictured
it would just involve ordinary actors playing the role of Kafka
producers and consumers.
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