I've been toying with this idea lately.  Not so much using Neo4j as the 
storage for Akka Persistence but rather using Akka Persistence to store the 
events that cause changes to Neo4j so that the graph can be rebuilt later 
on without going all the way back to source data (which takes a long time 
for us).

We use Play as an API/integration layer between Neo4j and Elasticsearch and 
sometimes there are bugs that cause them to get out of sync with each 
other.  Presently we use Rabbit to persist updates that fail for whatever 
reason but I'm thinking about using Akka Persistence.  

Updates come into Play -> sent as commands to a persistent actor -> Akka 
persists those commands -> On successful persistence, updates are applied 
to Neo4j -> Neo4j sends back its results (created/updated graph nodes) 
which are indexed in ElasticSearch.

The advantage over our current architecture is that we could essentially 
roll back our graph to a given point in time (we keep snapshots of ES/Neo 
at 24 hour intervals).  Right now we just have these "crawlers" that run in 
Play to identify and fix nodes that are broken or out of sync but it'd be 
nice to just roll back to before a bug was introduced and resend the 
updates to Neo and ES from scratch rather than fix them piecemeal later on.

On Saturday, July 26, 2014 8:38:05 PM UTC-6, Soumya Simanta wrote:
>
> I'm new to Akka persistence. I like the concept and would like to evaluate 
> it for a new application I want to build. 
>
> I've a stream of data coming in. My events will be derived from stream 
> elements in a window by time or number of elements. These events are best 
> represented as a graph. Any ideas about what's the best to way to store and 
> query these events into a graph DB using Akka persistence. Has anyone else 
> any experience with a similar use case? Currently I'm looking at Neo4J but 
> I'm open to other stores as well. 
>
> Thanks
> -Soumya
>
>
>

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