Kinetica Graph is a distributed, hybrid graph database engineered to work in tandem with the Kinetica relational engine. By bridging the gap between property graphs and OLAP expression support, it allows for high-performance analytics within a unified ecosystem (SQL/Python/C++/Java etc.) that is GQL compliant and fully supports cypher query syntax.

• Core Technical Advantages

• Get Free Developer Edition Kinetica DB+Graph

• Quick Guide on Wikipedia Example

• Kinetica Graph Solvers

• Graph Restful Endpoints

• Examples

• Comprehensive document with 250+ slides

• Journal Papers

• Graph Embedding algorithm using Kinetica-Graph

• Kinetica-Graph Design technical journal paper

• Patented adapted markov chain solver for map matching

• Novel Data structures for labeled queries for billion+ graphs

• Optimal routing algorithms for trips involving thousands of ev-charging stations using Kinetica-Graph

• Predictable Memory Management: Unlike databases that struggle with unstructured vertex valences, Kinetica uses fixed, calculable storage. The memory footprint is roughly twice the size of a bare-minimum CSR format, following the formula: .
• True Dynamic Topology: Utilizing an inplace double links data structure, Kinetica supports constant-time insertions and deletions. This eliminates the need to recreate the entire graph structure during modifications—a major limitation of CSR-based competitors like Neo4j and TigerGraph.
• Unified SQL & OLAP Integration: Every endpoint supports OLAP expressions, allowing graph outputs to function as table functions. This enables complex analytics, including joins and "group by" operations, to be executed within a single, concise SQL statement.
• Efficient Sharding & Distribution: The graph topology is distributed across multiple servers and nodes. To maximize efficiency, only nodes at inter-graph boundaries are duplicated; partitioned graphs remain independent while graph solvers iterate seamlessly across the cluster.
• Cypher & GQL Compliance: The system supports multi-hop, many-to-many property graph queries using Cypher. The query planner is highly optimized to bridge relational and graph data access seamlessly.

Kinetica exposes a wide array of advanced graph algorithms through a few streamlined RESTful endpoints, supporting both classical graph theory and modern machine learning:

• Supply Chain & Geo-Graph Analytics: Best-in-class support for logistics, including isochrone coverages and Mixed Integer Programming (MIP) for complex optimizations.
• Advanced Graph Solvers: Includes Markov chain map matching, Louvain clustering, connected components, and Jaccard similarities for recommendation engines.
• Security & Pattern Detection: Eulerian loops for fraud detection, pattern matching, and spectral bisection clustering.
• Data Science Integration: Support for novel graph embeddings to facilitate downstream machine learning tasks.

• Distributed Rendering: Built-in support for rendering large-scale geo-graphs and generic visualizations within a Jupyter-style workbench.
• Rich UI Interaction: Automated visualization tools utilize UI widgets to filter and explore data based on labels, hops, and paths using the opensource Orb library.
• Automatic Ontology Generation: A unique feature that generates ontology schemas directly from CRUD statements. This allows billion-node graphs to be visualized in a simplified "dot" format with a schema graph of a much smaller labeled entity connections, making massive datasets instantly interpretable.