Quine Streaming Graph is a real-time event stream processor that combines a graph data model with the Cypher query language to analyze high-volume data streams. It ingests and consolidates multiple event feeds into a single unified graph, processing live data alongside historical context without imposing time window limitations. The system uses standing queries to continuously monitor for complex, multi-hop patterns as data streams in. It natively handles categorical data, supports unlimited streaming joins, and performs real-time entity resolution. The platform's purpose is to contextualize system behaviors by creating subject-predicate-object data structures from event streams.
The system is built on a distributed, event-driven architecture with backpressured stream processing to maintain stability during high-volume ingestion, processing over one million events per second per cluster. This architecture scales without requiring complex state management or checkpoints. Upon detecting a complete pattern, the engine provides millisecond-latency insights and triggers automated responses or alerts. Operators can construct logic using Cypher queries and extend functionality with externally built User Defined Functions and event-based triggers to dynamically update graph nodes.
