| Open Source Ecology | The community version is open source, the commercial version is closed source | Open source, compatible with the Apache Tinkerpop ecosystem, mainly provided by IBM on cloud services.(Janus has added few features since 2015 forked from Titan) | Open source, compatible with the Apache Tinkerpop ecosystem. (HugeGraph has continued to add a large number of features with active state since 2017) |
| Technology Architecture | Stand-alone version architecture, graph storage structure adopts adjacency linked list, suitable for scenarios where small-scale graphs can be accommodated in memory (linked lists are not suitable for query on disk) | Share-Storage architecture, graph storage structure adopts adjacency sequence table, mainly adopts Hbase as back-end storage | Share-Storage architecture, Share-Nothing architecture (RocksDB), graph storage structure adopts adjacent sequence table, single-machine can support billion-level graphs, read and write performance is much higher than Titan/Janus |
| Data Scale | Community version billion-level, stand-alone version | Over 10 billions | Over 100 billion |
| Write Performance | online import speed is slow (~10k/s) , Offline import speed is faster (10~100k/s) | Slow (1~10k/s), especially for graphs above one billion level. | online import is fast (100~500k/s), and supports fast overwriting feature |
| Read Performance | 10~40 k/s，on 100 million scale graph | ~10k/s，with performance jitter | 20~100k, the performance of HugeGraph 0.12 is faster than Neo4j 2x+ on 100 million scales; HugeGraph is faster than Neo4j 5x on the 1 billion scale graph |
| Super Vertex | Adjacent edges query of super vertex is slow, and the cross-linked list storage structure is difficult to speed up the query partial of adjacent edges | Can be relieved by Vertex-Centric index | Can be relieved by Vertex-Centric index, and supports access all data by paging |
| Built-in Common Graph Algorithms | Provides an installation algorithm package, providing graph algorithms like path search, similarity, centrality, community detection, link prediction, etc. | Not Supported | Built-in provides basic graph algorithms, like path search, collaborative recommendation, centrality, community discovery, etc. |
| Support large-scale Graph Computing | Not Supported | Support the expansion of Spark GraphX, Giraph, etc. | Built-in HugeGraph-Computer, providing large-scale parallel graph computing, in addition to supporting the expansion of Spark GraphX |
| HA | Supported by Commercial Version | Not Supported | Supported |
