For the three graph queries, NebulaGraph shows clearly better performance compared to Neo4j and HugeGraph. However, when the data size is large, NebulaGraph is much faster than the other two. Seen from the above table, in terms of data import, NebulaGraph is a bit slower than Neo4j when the data size is small. The results are as below: Graph Data Size The test has been performed against various metrics, including data import efficiency, one-hop query, two-hop query, and shared friends query. The Tencent Cloud Security team has used graph data at different orders of magnitudes for testing purpose. Graph Database Performance Comparison Test Results It includes everything you'll need to create apps, connect your data, and scale your business. It features the capability of dealing with super large datasets with hundreds of billions of vertices and trillions of edges. Dgraph Graph Database (33 Ratings) Remove Neo4j Graph Database Graph Database (167 Ratings) Visit Website Visit Website Overview Summary Dgraph is a graph database that makes implementing a GraphQL backend for your apps a breeze. Dgraph is a tool in the Graph Databases category of a tech stack. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP. NebulaGraph is an open source distributed graph database developed by vesoft Inc. Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. It has pretty good read and write performance. HugeGraph is developed to address the needs of anti-fraud, threat intelligence collection, and underground economy attack with graph storage and analysis capabilities. HugeGraph is a distributed graph database developed by Baidu. For comparison in this article, the team has chosen the Community edition. It has a Community edition and an Enterprise edition. ![]() Neo4j is the most widely adopted graph database in the industrial world. To better serve the Tencent Cloud business scenarios, the Tencent Cloud Security team has to select a highly performant graph database which fits the business development well, which is how this performance comparison comes into play. UserIndex.get( "name", name ).This article describes how the Tencent Cloud team compares NebulaGraph with two other popular graph databases on the market from several perspectives.īy their nature of dealing with interconnections, graph databases are perfect for fraud detection and building knowledge graphs in the security field. UserIndex.get( "name", name ).getSingle() ) Public Iterator getFriends( String name ) Public FriendOfAFriendDepth4( GraphDatabaseService db ) evaluator( new Evaluation evaluate( Path path ) relationships( withName( "FRIEND" ), Direction.OUTGOING ) Private static final TraversalDescription traversalDescription = However, on a MacBook Air (1.8 GHz i7, 4 GB RAM) with a 2 GB heap, GCR cache, but no warming of caches, and no other tuning, with a similarly sized dataset (1 million users, 50 friends per person), I repeatedly get approx 900 ms using the Traversal Framework on 1.9.2: public class FriendOfAFriendDepth4 I'm sorry you can't reproduce the results. Is there anything I can do to speed neo4j up (to be faster then mysql)?Īnd also there is another benchmark in Stackoverflow with same problem. My query to neo4j looks like this (using the REST api): start person=node:node_auto_index(noscenda_name="person123") match (person)->()->(friend) return count(distinct friend) ![]() Using 1.9.2 on a 64bit ubuntu I have setup neo4j.properties with these values: .mapped_memory=250M "*": single run only My results for 1 million people Short version: while trying to verify the performance claims made in the 'Graph Database' book I came to the following results (querying a random dataset containing n people, with 50 friends each): My results for 100k people The whole story (including scripts etc) is on ![]() ![]() I have updated the setup and tests, and don't want to change the original question too much. This is a follow up to can't reproduce/verify the performance claims in graph databases and neo4j in action books.
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