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The native multi-model database for graphs, documents, and integrated AI vector search.

ArangoDB is a high-performance, native multi-model database engine that seamlessly integrates documents, graphs, and key-value pairs into a single core. In the 2026 market landscape, ArangoDB distinguishes itself through its 'ArangoGraph' ecosystem, which bridges the gap between traditional data structures and Generative AI requirements. Its technical architecture relies on the RocksDB storage engine, enabling it to handle massive datasets with ACID atomicity. Unlike 'polyglot persistence' models that require multiple database systems, ArangoDB allows users to perform complex graph traversals, full-text searches, and JSON document queries using a single, declarative language called AQL (ArangoDB Query Language). Its 2026 positioning emphasizes 'Graph Machine Learning' (GraphML) and integrated Vector Search, making it a critical infrastructure component for Retrieval-Augmented Generation (RAG) and fraud detection systems. By providing horizontal scalability for graphs through its proprietary 'SmartGraphs' technology, ArangoDB solves the 'sharding problem' that typically plagues distributed graph databases, allowing enterprises to scale their relationship-heavy data across multiple nodes without sacrificing join performance.
ArangoDB is a high-performance, native multi-model database engine that seamlessly integrates documents, graphs, and key-value pairs into a single core.
Explore all tools that specialize in graph traversal. This domain focus ensures ArangoDB delivers optimized results for this specific requirement.
A sharding technology that ensures related graph data resides on the same physical node.
An integrated C++ search engine for information retrieval, full-text search, and ranking.
Allows small collections to be replicated to all nodes in a cluster.
A single declarative query language supporting graph, document, and search operations.
Integrated adapter for PyTorch Geometric and DeepGraphLibrary.
Distributed graph processing for algorithms like PageRank and Connected Components.
Combination of sharded and local graph processing logic.
Sign up for ArangoGraph Cloud or download the ArangoDB Community/Enterprise Edition Docker image.
Initialize a new deployment (single-server or cluster) and configure the storage engine (RocksDB).
Create a Database and define Collections (Document or Edge collections).
Use the ArangoDB Web UI (Dashboard) to import sample datasets via JSON or CSV.
Define Graph definitions by linking Document collections with Edge collections.
Construct AQL queries to test data retrieval and relationship traversals.
Configure ArangoSearch views for full-text and vector-based indexing.
Integrate application code using official drivers (Java, Python, Node.js, Go, or C#).
Implement security protocols (TLS/SSL encryption and LDAP/Active Directory integration).
Deploy monitoring using Prometheus or Grafana via the integrated metrics endpoint.
All Set
Ready to go
Verified feedback from other users.
"Users praise the flexibility of the multi-model approach and the power of AQL, though some find the initial learning curve and cluster management complex."
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