AgensGraph Enterprise:
Optimized Graph Data Management

AgensGraph Enterprise is a leading multi-model graph database designed for businesses demanding the highest performance, reliability, and scalability levels. Built on the robust foundation of PostgreSQL, AgensGraph Enterprise elevates data management with advanced features, dedicated support, and robust security, making it the premier solution for complex data environments.

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How it works

AgensGraph's architecture seamlessly integrates the robustness of PostgreSQL with advanced graph database capabilities. Utilizing a hybrid model that combines relational (RDB) and graph (GDB) storage, AgensGraph optimizes both cost and performance. The architecture features a unified transaction and cache layer, enhancing data processing efficiency and enabling real-time analytics while ensuring ACID compliance and stability.

Agens Graph Architecture

Graph Query Processing Engine

SQL Query Optimizer

Graph Query Optimizer

SQL Execution Engine

Graph Query Execution Engine

Unified Transaction Layer

Unified Cache Layer

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Hybrid

  • World's first multi-model RDB + GDB graph database

Real-time

  • Provides an analytical framework usable in OLTP environments

Optimization

  • Guarantees excellent performance and supports parallel queries through optimized query modules

Stability

  • Ensures ACID compliance for data integrity
  • Built on the reliable and proven open-source DBMS, PostgreSQL, enabling stable operations

The multi-model hybrid database solution further separates RDB and GDB storage, reducing costs and enabling efficient data analysis. This dual-storage approach supports parallel queries and optimized query modules, offering a powerful and flexible solution for managing complex, connected data.

Multi-Model Hybrid Database Solution

The multi-model hybrid database solution further separates RDB and GDB storage, reducing costs and enabling efficient data analysis. This dual-storage approach supports parallel queries and optimized query modules, offering a powerful and flexible solution for managing complex, connected data.

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Capabilities of AgensGraph Enterprise

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Performance and Scalability

  • Optimized Hybrid Query Execution: Integrates SQL and Cypher within a single database to leverage both relational and graph models, enhancing performance.

  • Effortless Dynamic Scalability: Supports both horizontal and vertical scaling to seamlessly manage growing workloads and larger datasets.

  • Peak Performance with Tuning Tools: Features real-time performance monitoring, auto-tuning algorithms, and sophisticated query optimization based on statistics.

  • Performance Enhancements: Includes removing SPI at ModifyGraph, graph partitioning, table partitioning using pg_partman, large parallel distributed processing, and a robust graph analysis framework.

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Security and Reliability

  • Enterprise-Grade Security: Offers granular access controls, end-to-end encryption, and comprehensive auditing capabilities.

  • High Availability and Disaster Recovery: Features robust failover mechanisms, automated backups, quick restores, and high availability configurations using Stolon.

  • Stable Connection Management: Utilizes pgBouncer for connection pooling, enhancing management efficiency.

  • Data Backup and Restore: Ensures reliable data backup and restoration with pgBackRest.

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Analytics and Integration

  • Real-Time Graph Analytics: Enables real-time analysis and visualization of relationships for applications like fraud detection and network monitoring.

  • Seamless Integration: Provides a wide array of connectors and APIs, enhanced with a Hadoop Connector and advanced data mapping and modeling.

  • Data Extensibility: Offers flexible data integration through foreign data wrappers (FDWs), including hadoop_FDW.

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Usability and AI Integration

  • Comprehensive Usability with AgensGraphViewer: A visualization tool that supports advanced graph algorithms and procedural language capabilities.

  • Advanced AI Integration: Incorporates machine learning and generative AI technologies such as TensorFlow and PyTorch.

  • Development Convenience: Supports the PL/agCypher procedural language for user-defined functions within Cypher.

Use Cases