@santand84 called it out. We have a graph that is ~32M nodes/1.7B edges that we load from Apache spark. We've had to work our way through quite a number of performance issues on the loading side, mostly tuning the batch size and partitioning/execut...
Not sure if this is still an issue for you, but it has to do with the exclusive node locking in Neo4J when writing relationships combined with parallelization in the spark writer. If two relationships in different executors from spark have the same ...
