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The graph relationships are integrated into Transact-SQL and use SQL Server as the foundational database management system. A scalable open-source distributed graph database for storing and handling billions of vertices and trillions of edges with milliseconds of latency.
Graph databases are not as useful for operational use cases because they are not efficient at processing high volumes of transactions and they are not good at handling queries that span the entire database.
If someone builds a database on top of another database, such as twitter has done, does that database inherit the limitations and inefficiencies of the underlying database?
I'm specifically interested in titan db (http://thinkaurelius.com) because of their claim to support splitting the dataset efficiently across nodes.
They claim to support distributing data across nodes, because of the efficiency of cassandra. However, neo4j claims that the reason they aren't distributing data between nodes, but rather duplicating the whole dataset on every node, is because any graph traversal that leaves one node, and therefor has to move across an ethernet network, is way too slow to be practical.
Since cassandra has no knowledge of the graph, it cannot optimize to keep graph traversals on one node. Therefor, most graph traversals will be across node boundaries.
Is titans claim to scale efficiently across nodes true?
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