ArangoDB v3.10 reached End of Life (EOL) and is no longer supported.

This documentation is outdated. Please see the most recent stable version.

Enterprise Edition Features

The commercial version of ArangoDB offers performance, compliance, and security features for larger or more sensitive datasets, as well as additional query capabilities

The Enterprise Edition has all the features of the Community Edition and, on top of that, the features outlined below. For additional information, see .


  • SmartGraphs: Value-based sharding of large graph datasets for better data locality when traversing graphs.

  • EnterpriseGraphs: A specialized version of SmartGraphs, with an automatic sharding key selection.

  • SmartGraphs using SatelliteCollections: Collections replicated on all cluster nodes can be combined with graphs sharded by document attributes to enable more local execution of graph queries.

  • SatelliteGraphs: Graphs replicated on all cluster nodes to execute graph traversals locally.

  • SatelliteCollections: Collections replicated on all cluster nodes to execute joins with sharded data locally.

  • SmartJoins: Co-located joins in a cluster using identically sharded collections.

  • OneShard: Option to store all collections of a database on a single cluster node, to combine the performance of a single server and ACID semantics with a fault-tolerant cluster setup.

  • Traversal Parallelization: Parallel execution of traversal queries with many start vertices, leading to faster results.

  • Traversal Projections: Optimized data loading for AQL traversal queries if only a few document attributes are accessed.

  • Parallel index creation: Non-unique indexes can be created with multiple threads in parallel.

  • minhash Analyzer: Jaccard similarity approximation for entity resolution, such as for finding duplicate records, based on how many elements they have in common

  • geo_s2 Analyzer: Efficiently index geo-spatial data using different binary formats, tuning the size on disk, the precision, and query performance.

  • ArangoSearch column cache: Always cache field normalization values, Geo Analyzer auxiliary data, stored values, primary sort columns, and primary key columns in memory to improve the performance of Views and inverted indexes.

  • Read from followers in clusters: Allow dirty reads so that Coordinators can read from any shard replica and not only from the leader, for scaling reads.


  • Pregel in Cluster: Distributed iterative graph analytics for cluster deployments.

  • Search highlighting: Get the substring positions of matched terms, phrases, or n-grams.

  • Nested search: Match arrays of objects with all the conditions met by a single sub-object, and define for how many of the elements this must be true.

  • Skip inaccessible collections: Let AQL queries like graph traversals pretend that collections are empty if the user has no access to them instead of failing the query.