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I'm excited seeing the progress being made in this space recently (this, redisgraph, etc) - the wider ecosystem is still immature but promising.
How does this compare to Dgraph? Most recent discussion about it: https://news.ycombinator.com/item?id=23031762 Also, does it support GraphQL?
How does this compare to RedisGraph?
1. Memgraph is a native GraphDB which means that it's specifically engineered to support graph and graph only whereas RedisGraph is a module that runs on top of key-value store (nothing wrong with that, but it might have some limitation when it comes to performance on complex graph algorithms and traversals)
2. Cypher coverage – We cover about 80% of the cypher query language, where I think RedisGraph covers only a small portion (But I hear that they're improving on that)
Apart from that, I guess you would really need to test both when for specific use-cases to judge performance and scalability. We'll try to provide some benchmarks in the next few weeks.
I hope this helps :D Have you used RedisGraph? Any thoughts?
How do you handle high-availability? Also, do you have some sort of persistency model for the community version which doesn't have HA?
All editions of Memgraph persist data to the disk via write-ahead logging (WAL) and periodic snapshots for log compaction so even though Memgraph is designed to be in-memory first, data is always backed up to disk. If you enable asynchronous (periodic fsync) WAL, you can trade off a small window of durability for better performance.
Before 1.0, Memgraph leveraged the RAFT consensus algorithm for HA which worked great but had some performance implications. Based on feedback from our users and customers, we have decided to switch our HA implementation to the streaming replication model (similar to PostgreSQL) with automatic failover.
How does this compare to Dgraph? Most recent discussion about it: https://news.ycombinator.com/item?id=23031762 Also, does it support GraphQL?
1. The graph model: Property graph for Memgraph vs RDF for DGraph (Depending on the use-case you have one model might be better than the other. E.g ontologies are better served with RDF, whereas graphs with a lot of properties and labels are better served by the property graph model)
2. Memgraph is an in-memory first system where is Dgraph is a disk-based system. Again depending on your use-case and the performance you are looking for, an in-memory system might be better suited.
3. Query language & Ecosystem: we support Cypher and the Bolt Protocol (Same as Neo4j) so we work with a lot of the existing graph tools.
In terms of performance, we don't have official benchmarks yet but we have a few clients that test Memgraph and Dgraph and reported a 3-5x in read performance and about an 8x in write performance for their specific workloads.
I hope this answers your question.
Congrats on reaching 1.0! I'm always interested in new GraphDB solutions and glad to see the Cypher integration and Neo4j wire compatibility here. I'm excited seeing the progress being made in this space recently (this, redisgraph, etc) - the wider ecosystem is still immature but promising.
Congrats on the launch! Which graph algorithms do you support?
Do you have any plans to release a NodeJs Driver?
Cool product, but the deal is unclear and inflexible, so DynamoDB or Postgres are still better options
When is someone going to provide an actual serverless graph db?
It’s unbelievable how much masturbatory self-congratulation goes on in the graph database community while Serverless Postgres and Dynamo are exponentially cheaper and better. Is Cypher really worth a 100x-1000x price hike?