Highlights

Version 3.8

All Editions

  • Weighted traversals and k Paths: Two new AQL graph traversal methods to emit paths in order of increasing weights and to enumerate all paths between a source and a target vertex that match a given length.

  • ArangoSearch: New Pipeline Analyzer that allows you to combine multiple Analyzers, enabling case-insensitive n-gram-based fuzzy search and more. New AQL Analyzer so that you can use an AQL query to pre-process and filter your data for indexing. Support for geo-spatial queries through new Geo Analyzers and ArangoSearch Geo functions. A new Stopwords Analyzer that can be used standalone or in an Analyzer pipeline.

  • A WINDOW operation for aggregations over adjacent rows, value ranges or time windows.

Enterprise Edition

Also see What’s New in 3.8.

Version 3.7

All Editions

Enterprise Edition

Also see What’s New in 3.7.

Version 3.6

All Editions

Enterprise Edition

  • OneShard deployments offer a practicable solution that enables significant performance improvements by massively reducing cluster-internal communication. A database created with OneShard enabled is limited to a single DB-Server node but still replicated synchronously to ensure resilience. This configuration allows running transactions with ACID guarantees on shard leaders.

Also see What’s New in 3.6.

Version 3.5

All Editions

  • ArangoSearch: The search and ranking engine received an upgrade and now features Configurable Analyzers, Sorted Views and several improvements to the AQL integration.

  • AQL Graph Traversals: k Shortest Paths allows you to query not just for one shortest path between two documents but multiple, sorted by length or weight. With PRUNE you can stop walking down certain paths early in a graph traversal to improve its efficiency.

  • Stream Transaction API: Perform multi-document transactions with individual begin and commit / abort commands using the new HTTP endpoints or via a supported driver.

  • Time-to-Live Indexes: TTL indexes can be used to automatically remove documents in collections for use cases like expiring sessions or automatic purging of statistics or logs.

  • Index Hints & Named Indexes: Indexes can be given names and an optional AQL inline query option indexHint was added to override the internal optimizer decision on which index to utilize.

  • Data Masking: arangodump provides a convenient way to extract production data but mask critical information that should not be visible.

Enterprise Edition

  • Hot Backups: Create automatic, consistent backups of your cluster without noticeable impact on your production systems. In contrast to arangodump, hot backups are taken on the level of the underlying storage engine and hence both backup and restore are considerably faster.

  • SmartJoins: Run joins between identically sharded collections with performance close to that of a local join operation.

  • Advanced Data Masking: There are additional data masking functions available in the Enterprise Edition, such as for substituting email addresses and phone numbers with similar looking pseudo-data.

Also see What’s New in 3.5.

Version 3.4

All Editions

  • ArangoSearch: Search and similarity ranking engine integrated natively into ArangoDB and AQL. ArangoSearch combines Boolean retrieval capabilities with generalized ranking algorithms (BM25, TFDIF). Support of e.g. relevance-based searching, phrase and prefix-matching, complex boolean searches and query time relevance tuning. Search can be combined with all supported data models in a single query. Many specialized language Analyzers are already included for e.g. English, German, French, Chinese, Spanish and many other language.

  • GeoJSON Support and S2 Geo Index: ArangoDB now supports all geo primitives. (Multi-)Point, (Multi-)LineStrings, (Multi-)Polygons or intersections can be defined and queried for. The Google S2 geo index is optimized for RocksDB and enables efficient querying. Geo query results are automatically visualized with an OpenStreetMap integration within the Query Editor of the web interface.

  • Query Profiler: Enables the analysis of queries and adds additional information for the user to identify optimization potentials more easily. The profiler can be accessed via arangosh with db._profileQuery(...) or via the Profile button in the Query Editor of the web interface.

  • Streaming Cursors: Cursors requested with the stream option on make queries calculate results on the fly and make them available for the client in a streaming fashion, as soon as possible.

  • RocksDB as Default Storage Engine: With ArangoDB 3.4 the default storage engine for fresh installations will switch from MMFiles to RocksDB. Many optimizations have been made to RocksDB since the first release in 3.2. For 3.4 we optimized the binary storage format for improved insertion, implemented “optional caching”, reduced the replication catch-up time and much more.

Also see What’s New in 3.4.

Version 3.3

Enterprise Edition

  • Datacenter to Datacenter Replication: Replicate the entire structure and content of an ArangoDB cluster asynchronously to another cluster in a different datacenter with ArangoSync. Multi-datacenter support means you can fallback to a replica of your cluster in case of a disaster in one datacenter.

  • Encrypted Backups: arangodump can create backups encrypted with a secret key using AES256 block cipher.

All Editions

  • Server-level Replication: In addition to per-database replication, there is now an additional globalApplier. Start the global replication on the Follower once and all current and future databases will be replicated from the Leader to the Follower automatically.

  • Asynchronous Failover: Make a single server instance resilient with a second server instance, one as Leader and the other as asynchronously replicating Follower, with automatic failover to the Follower if the Leader goes down.

Also see What’s New in 3.3.

Version 3.2

All Editions

  • RocksDB Storage Engine: You can now use as much data in ArangoDB as you can fit on your disk. Plus, you can enjoy performance boosts on writes by having only document-level locks.

  • Pregel: We implemented distributed graph processing with Pregel to discover hidden patterns, identify communities and perform in-depth analytics of large graph data sets.

  • Fault-Tolerant Foxx: The Foxx management internals have been rewritten from the ground up to make sure multi-coordinator cluster setups always keep their services in sync and new Coordinators are fully initialized even when all existing Coordinators are unavailable.

Enterprise Edition

  • LDAP integration: Users and permissions can be managed from outside ArangoDB with an LDAP server in different authentication configurations.

  • Encryption at Rest: Let the server persist your sensitive data strongly encrypted to protect it even if the physical storage medium gets stolen.

  • SatelliteCollections: Faster join operations when working with sharded datasets by synchronously replicating selected collections to all DB-Servers in a cluster, so that joins can be executed locally.

Also see What’s New in 3.2.

Version 3.1

All Editions

  • Vertex-centric indices: AQL traversal queries can utilize secondary edge collection indexes for better performance against graphs with supernodes.

  • VelocyPack over HTTP: In addition to JSON, the binary storage format VelocyPack can now also be used in transport over the HTTP protocol, as well as streamed using the new bi-directional asynchronous binary protocol VelocyStream.

Enterprise Edition

  • SmartGraphs: Scale with graphs to a cluster and stay performant. With SmartGraphs you can use the “smartness” of your application layer to shard your graph efficiently to your machines and let traversals run locally.

  • Encryption Control: Choose your level of SSL encryption

  • Auditing: Keep a detailed log of all the important things that happened in ArangoDB.

Also see What’s New in 3.1.

Version 3.0

Also see What’s New in 3.0.