One of the lesser-known challenges is the memory consumed by storing identical string values over and over again. Think of common attributes like “city,” “category,” or “status” that might appear on thousands, or even millions, of nodes and edges. A powerful solution to this problem is string interning.
Hands-on look at Multigraph Topology: how key-space slots isolate customer graphs, why replicas sustain 120 K QPS, and when to scale masters vs replicas in graph ACL systems.
Graph Query Language demystified: learn how FalkorDB simplifies querying for LLM and GraphRAG workflows using Cypher-like syntax.
FalkorDB’s new v4.6 release introduces a powerful CSV loader that streamlines graph database imports with intelligent column mapping and built-in data transformation capabilities.
Discover the process of migrating from a relational database to a graph database. This guide covers schema analysis, data transformation, and optimization techniques for AI/ML workflows.
Discover how to leverage LlamaIndex RAG with FalkorDB to create efficient GraphRAG systems. Enhance LLM performance with knowledge graph-powered retrieval and generation.
Explore how AI agents leverage memory systems and graph databases to maintain context, process relationships, and make autonomous decisions. Analysis of architecture, implementation, and performance impacts.
Knowledge graphs have become a game-changer in building Retrieval-Augmented Generation (RAG) applications, often referred to as GraphRAG. These applications enhance the reasoning capabilities of large
Edges in FalkorDB enable efficient graph representation and traversal using GraphBLAS tensors. Learn how FalkorDB uses GraphBLAS to support advanced graph operations and scalable graph processing, making Edges in FalkorDB a useful tool for graph data management.
Modern software architectures are complex systems of interconnected components. As projects grow, keeping track of all their moving parts becomes increasingly challenging. Complex control flows,