apache/geaflow

What it solves

Apache GeaFlow addresses the limitations of traditional stream processing engines when handling complex, multi-hop relationship data. While standard engines often struggle with deep joins and large-scale graph iterations, GeaFlow provides a specialized, distributed system for real-time graph computing that avoids the need for expensive full-graph re-calculations.

How it works

GeaFlow uses a graph-native data model (vertex-edge storage) rather than a traditional table model. It supports incremental computing, meaning that when new data arrives, it only triggers calculations for the affected parts of the graph, leaving unchanged nodes alone. It unifies stream, batch, and graph processing, allowing users to interact with data using a combination of SQL and GQL (Graph Query Language).

Who it’s for

It is designed for developers and data engineers working on large-scale, real-time data applications such as financial risk control, knowledge graphs, social network analysis, and data warehouse acceleration.

Highlights

• Trillion-level graph storage capacity.
• Incremental graph computing that keeps performance stable even as data volume grows.
• Hybrid processing capabilities that combine SQL and GQL.
• High availability with exactly-once processing semantics.
• Cloud-native deployment and a one-stop development console.