Multi-model database
ArangoDB is a graph database system developed by ArangoDB Inc. ArangoDB is a multi-model database system since it supports three data models (graphs, JSON documents, key/value)[1] with one database core and a unified query language AQL (ArangoDB Query Language). AQL is mainly a declarative language[2] and allows the combination of different data access patterns in a single query.[3]
ArangoDB is a NoSQL database system[4] but AQL is similar in many ways to SQL,[5] it uses RocksDB as a storage engine.
History
ArangoDB Inc. was founded in 2015 by Claudius Weinberger and Frank Celler.[6] They originally called the database system “A Versatile Object Container", or AVOC for short, leading them to call the database AvocadoDB.[7][8][9] Later, they changed the name to ArangoDB.[10] The word "arango" refers to a little-known avocado variety grown in Cuba.[11]
In January 2017 ArangoDB raised a seed round investment of 4.2 million Euros led by Target Partners. In March 2019 ArangoDB raised 10 million dollars in series A funding[12] led by Bow Capital. In October 2021 ArangoDB raised 27.8 million dollars in series B funding led by Iris Capital.[13]
Features
- JSON: ArangoDB uses JSON as a default storage format,[14] but internally it uses ArangoDB VelocyPack – a fast and compact binary format for serialization and storage.[15] ArangoDB can natively store a nested JSON object as a data entry inside a collection. Therefore, there is no need to disassemble the resulting JSON objects. Thus, the stored data would simply inherit the tree structure of the JSON data.
- Predictable performance: ArangoDB is written mainly in C++[16] and manages its own memory to avoid unpredictable performance arising from garbage collection.
- Scaling: ArangoDB provides scaling through clustering.[17]
- Reliability: ArangoDB provides datacenter-to-datacenter replication.[18]
- Kubernetes: ArangoDB runs on Kubernetes, including cloud-based Kubernetes services Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Microsoft Azure Kubernetes Service (AKS).[19]
- Microservices: ArangoDB provides integration with native JavaScript microservices directly on top of the DBMS using the Foxx framework.[20]
- Multiple query languages: The database has its own query language, AQL (ArangoDB Query Language), and also provides GraphQL to write flexible native web services directly on top of the DBMS.[21]
- Search: ArangoDB's search engine combines boolean retrieval capabilities with generalized ranking components allowing for data retrieval based on a precise vector space model.[22]
- Pregel algorithm: Pregel is a system for large scale graph processing.[23] Pregel is implemented in ArangoDB and can be used with predefined algorithms, e.g. PageRank, Single-Source Shortest Path and Connected components.[24]
- Transactions: ArangoDB supports user-definable transactions. Transactions in ArangoDB are atomic, consistent, isolated, and durable (ACID), but only if data is not sharded.[25]
AQL (ArangoDB Query Language) is the SQL-like query language[26] used in ArangoDB. It supports CRUD operations for both documents (nodes) and edges, but it is not a data definition language (DDL). AQL does support geospatial queries.
AQL is JSON-oriented:
// Return every document in a collection
FOR doc IN collection
RETURN doc
// Count the number of documents in a collection
FOR doc IN collection
COLLECT WITH COUNT INTO length
RETURN length
// Add a new document into our collection
INSERT { _key: "john", name: "John", age: 45 } INTO collection
// Update document with key of “john” to have age 46.
UPDATE { _key: "john", age: 46 } IN collection
// Add an attribute numberOfLogins for all users with status active:
FOR u IN users
FILTER u.active == true
UPDATE u WITH { numberOfLogins: 0 } IN users
