It can hold a large-scale data set of hundreds of billions of points and trillions of edges, and provides millisecond-level queries. NebulaGraph is an open source, distributed, and easy-to-expandable native graph database. The picture comes from the official website of NebulaGraph
It is an ideal choice for open source graph databases. It has Chinese documents, comprehensive documents, and an active community.
#Razorsql import half the data free
NebulaGraph's import performance, response time, and stability meet the requirements, support data segmentation, the distributed version is free and open source, and it is used by many companies. The success rate is close to 5 9s, and the response practice is relatively stable, with an average of 18.81ms, p95 38ms, and p99 only 115.6ms, which meets the demand. NebulaGraph uses 120 threads to perform a second-degree neighbor query stress test, and the final QPS is 6000+, which is a little better than a single machine. It is currently investigating the SST import, which can greatly increase the import speed. NebulaGraph fully imports 1 billion nodes and 10 billion edges in only 10 hours, which meets the requirements. And as the amount of concurrency increases, the performance gap will further widen, and JanusGraph starts with 20 threads, and the third-degree neighbor query will have errors. In the above figure, the performance of JanusGraph is regarded as 1, the import performance of NebulaGraph is an order of magnitude faster than JanusGraph, and the query performance is 4-7 times that of JanusGraph. In the third step, in order to verify the performance of NebulaGraph, a performance comparison test was performed on NebulaGraph and JanusGraph. NebulaGraph has excellent performance both in import and query performance.
The first step is to collect common open source distributed attribute graph databases, as shown in the following table: Open source and support distributed attribute graph database.The average response time of the second query does not exceed 50ms, and the QPS can reach 5000+.Able to support a large-scale graph with 1 billion nodes, 10 billion edges, and 17 billion attributes.The new map database should meet the following requirements: Therefore, finding a better-performing open source attribute graph database has become an urgent task. With the continuous growth of business data such as knowledge graphs, the existing graph database JanusGraph has been more difficult to deal with, and the import time has not been able to meet the requirements of the business. Graph database survey 2.1 Research background Graph databases are widely used in the fields of social networks, knowledge graphs, financial risk control, personalized recommendations, and network security.
Compared with relational databases or other NoSQL databases, the data model of graph databases is also simpler and more expressive. This means that the application does not have to use foreign keys or out-of-band processing (such as MapReduce) to infer data connections. Unlike other databases, the relationship occupies the primary position in the graph database. Graph database is a database for storing and querying graph data structure.