ArangoDB

ArangoDB
Name	ArangoDB
Type	Multi-model database
Developer	ArangoDB GmbH
Initial release	2011
Written in	C++
License	Open Source / Enterprise

ArangoDB ArangoDB is an open-source multi-model database combining graph, document, and key-value models in a single core. It was created to support flexible data modeling for applications in industries such as finance, healthcare, and telecommunications, and to compete with systems designed by corporations like Oracle Corporation, Microsoft, Amazon Web Services, Google LLC, and IBM. The project evolved within the European startup ecosystem alongside companies such as SAP SE and Siemens AG, gaining adoption in projects that also use platforms from Red Hat and Canonical Ltd..

History

ArangoDB was founded amid the rising popularity of NoSQL systems following the publication of work by Amazon.com engineers on distributed databases and the influence of academic research from institutions like Massachusetts Institute of Technology, Stanford University, and University of California, Berkeley. Early development drew on contributions from engineers with backgrounds at MongoDB, Inc., Couchbase, and open-source projects such as Linux Foundation-hosted initiatives. The company behind the project engaged with accelerator programs and investors including firms similar to Sequoia Capital and Accel Partners, and later established partnerships with cloud providers such as DigitalOcean and Microsoft Azure. Over successive releases the product introduced features that mirrored advances from research groups at ETH Zurich and collaborations with standards bodies like The Apache Software Foundation.

Architecture and data model

ArangoDB adopts a hybrid architecture combining a document store, a property graph engine, and a key-value layer in a unified storage engine, influenced by design patterns from systems created by Facebook, Twitter, and the Apache Cassandra project. The core storage uses a custom engine implemented in C++ with support for transactions inspired by academic work at Princeton University and Carnegie Mellon University. Its graph model supports labeled edges and vertices similar to representations used in Neo4j and research from Stanford Network Analysis Project. The system implements JSON-like documents and binary serialization formats comparable to those used by Google Protocol Buffers and Apache Avro, and integrates indexing strategies known from Elasticsearch and PostgreSQL.

Query language and APIs

ArangoDB provides AQL, a declarative query language designed to express joins, traversals, and aggregations within a single query, conceptually related to languages developed in academic projects at University of Washington and corporate query engines such as Amazon Redshift and Microsoft SQL Server. The system exposes HTTP/REST APIs and native drivers for ecosystems maintained by Oracle Corporation and The Node.js Foundation, with client libraries in languages associated with organizations like Python Software Foundation, Eclipse Foundation (for Jakarta EE), and Ruby on Rails. AQL includes features enabling graph traversals comparable to query capabilities in Apache TinkerPop and standards discussed at meetings of the World Wide Web Consortium.

Deployment and scalability

Deployment options for ArangoDB include standalone servers, active-failover pairs, and distributed clusters modeled after architectures used by Google Cloud Platform, Amazon Web Services, and Microsoft Azure. High-availability patterns follow replication strategies similar to those in Redis and consensus protocols influenced by research on Paxos and Raft used in systems like etcd and Consul. Containerized deployments leverage tools from Docker, Inc. and orchestration platforms such as Kubernetes and OpenShift. Enterprises often deploy the database alongside data platforms like Hadoop and streaming systems from Confluent or Apache Kafka.

Security and authentication

Security features include role-based access control and support for authentication mechanisms aligned with standards promoted by Internet Engineering Task Force and identity providers such as Okta, Inc., Auth0 and enterprise directories like Microsoft Active Directory. Transport-layer encryption and certificate management practices follow guidance from organizations including OpenSSL Software Foundation and policies referenced by National Institute of Standards and Technology. Integration with secrets management solutions from HashiCorp and key management services from Amazon Web Services and Google Cloud Platform is common in regulated deployments alongside compliance frameworks influenced by ISO and guidance from European Union Agency for Cybersecurity.

Use cases and integrations

Typical use cases span knowledge graphs, recommendation engines, fraud detection, and content management systems used by firms similar to Netflix, Spotify Technology S.A., and Airbnb, Inc.. Integrations exist with analytics ecosystems such as Apache Spark, business intelligence tools from Tableau Software and Microsoft Power BI, and orchestration in platforms like Jenkins and GitLab. The database is embedded in applications that interface with APIs from Salesforce, SAP SE, and payment networks that adhere to standards established by SWIFT and regulatory bodies including European Central Bank.

Performance and benchmarking

Performance characteristics have been evaluated in comparisons alongside systems from MongoDB, Inc., Neo4j, Inc., Redis Labs, and Couchbase. Benchmarks often measure latency and throughput under workloads modeled after academic suites from Transaction Processing Performance Council and industry benchmarks created by Yahoo! and SPEC. Tuning recommendations reference operating systems and kernel features from Red Hat and Canonical Ltd. and hardware guidance similar to procurement by Dell Technologies and Hewlett Packard Enterprise.

Category:Databases