Graph Engine vs. HugeGraph vs. JanusGraph vs. python-sql Comparison Chart

Graph Engine (GE) is a distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set. The capability of fast data exploration and distributed parallel computing makes GE a natural large graph processing platform. GE supports both low-latency online query processing and high-throughput offline analytics on billion-node large graphs. Schema does matter when we need to process data efficiently. Strongly-typed data modeling is crucial for compact data storage, fast data access, and clear data semantics. GE is good at managing billions of run-time objects of varied sizes. One byte counts as the number of objects goes large. GE provides fast memory allocation and reallocation with high memory ratios.

JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. JanusGraph is a project under The Linux Foundation, and includes participants from Expero, Google, GRAKN.AI, Hortonworks, IBM and Amazon. Elastic and linear scalability for a growing data and user base. Data distribution and replication for performance and fault tolerance. Multi-datacenter high availability and hot backups. All functionality is totally free. No need to buy commercial licenses. JanusGraph is fully open source under the Apache 2 license. JanusGraph is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. Support for ACID and eventual consistency. In addition to online transactional processing (OLTP), JanusGraph supports global graph analytics (OLAP) with its Apache Spark integration.