Graph Engine vs. HugeGraph vs. OrigoDB vs. Python 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.

OrigoDB enables you to build high quality, mission critical systems with real-time performance at a fraction of the time and cost. This is not marketing gibberish! Please read on for a no nonsense description of our features. Get in touch if you have questions or download and try it out today! In-memory operations are orders of magnitude faster than disk operations. A single OrigoDB engine can execute millions of read transactions per second and thousands of write transactions per second with synchronous command journaling to a local SSD. This is the #1 reason we built OrigoDB. A single object oriented domain model is far simpler than the full stack including a relational model, object/relational mapping, data access code, views and stored procedures. That's a lot of waste that can be eliminated! The OrigoDB engine is 100% ACID out of the box. Commands execute one at a time, transitioning the in-memory model from one consistent state to the next.