We are building an app with one main dataset of object documents O Documents may have no more than twelve properties, each O with up to three similar size embedded documents
*Q1*. What would be the best way to model russian-dolls like taxonomy for embedded linked documents with an initial depth of 3 ? Now we constantly reassign two values to each object i.e. two separate *key variables* (based on clicks) : V1 and V2 *We aim to achieve the highest possible speed in V1/V2 direct queries for each object O * We can either model V1 and V2 as document properties of the same object O, or link separate key-value pairs KV1 and KV2 to O in graph model For V1 and V2 alone we expect hundreds of simultaneous queries on the same object O *Q2.* What would be faster : *document V1/V2 property queries *in O *or* *KV1/KV2 graph queries *with O ? *Q3*. In this case what would be the advantage of ArangoDB over Neo4j ? -- You received this message because you are subscribed to the Google Groups "ArangoDB" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
