On 19.08.14 18:48, delasoul wrote:
As I am no Spark expert - will it be used only as kind of
messaging(streaming) middleware to sync write and read store or also
to somehow change/merge/
filter the events it gets/pulls from the write store
usually, to process (transform/aggregate/filter/...) these events.
or is this all done via the plugin for PersistentViews?
(I guess it has to be like this, otherwise using only one backend
store cannot be supported?)
thanks,
michael
On Tuesday, 19 August 2014 17:48:16 UTC+2, Martin Krasser wrote:
On 19.08.14 17:41, Ashley Aitken wrote:
On Tuesday, 19 August 2014 19:33:55 UTC+8, Martin Krasser wrote:
If so, it sounds like a great solution but why would that
require an extension to the Akka Persistence design/API?
Because transformed/joined/... event streams in backend store
on the read side must be consumable by PersistentViews (for
creating read models). I still see this backend store to
maintain changes (= transformed/joined/... events) instead of
current state.
I am sorry I still don't see this.
This suggests to me that spark is talking directly to the read
model datastore (e.g. graph database, MongoDB, SQL database).
So are you suggesting:
1. journal -> spark -> Akka actors (like PersistentView) -> read
model data store
or
2. journal -> spark -> read model data store (like graph
database, MongoDb, SQL database) -> Akka actors <- Queries
I was suggesting 2.
I see PersistentView(for generalised topics) as the glue between
the Akka journal (write store) and read stores (1.).
Thanks for your patience.
Cheers,
Ashley.
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