Hey guys,
We're currently storing entities and edges in Cassandra. The entities
are JSON, and edges are directed edges with a source---type-->target.
We're using ElasticSearch for indexing and I could really use a hand with
design.
What we're doing currently, is we take an entity, and turn it's JSON into a
document. We then create multiple copies of our document and change it's
type to match the index. For instance, Image the following use case.
bob(user) -- likes -- > Duo (restaurant) ===> Document Type = bob(user)
+ likes + restaurant ; bob(user) + likes
bob(user) -- likes -> Root Down (restaurant) ===> Document Type =
bob(user) + likes+ restaurant ; bob(user) + likes
bob(user) -- likes --> Coconut Porter (beer). ===> Document Types =
bob(user) + likes + beer; bob(user) + likes
When we index using this scheme we create 3 documents based on the
restaurants Duo and Root Down, and the beer Coconut Porter. We then store
this document 2x, one for it's specific type, and one in the "all" bucket.
Essentially, the document becomes a node in the graph. For each incoming
directed edge, we're storing 2x documents and changing the type. This
gives us fast seeks when we search by type, but a LOT of data bloat. Would
it instead be more efficient to keep an array of incoming edges in the
document, then add it to our search terms? For instance, should we instead
have a document like this?
docId: Duo(restaurant)
edges: [ "bob(user) + likes + restaurant", "bob(user) + likes" ]
When searching where edges = "bob(user) + likes + restaurant"?
I don't know internally what specifying type actually does, if it just
treats it as as field, or if it changes the routing of the response? In
a social situation millions of people can be connected to any one entity,
so we have to have a scheme that won't fall over when we get to that case.
Any help would be greatly appreciated!
Thanks,
Todd
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