We are pleased to announce the release of ArangoDB 3.2. Get it here. After an unusually long hackathon, we eliminated two large roadblocks, added a long overdue feature and integrated an interesting new one into this release. Furthermore, we’re proud to report that we increased performance of ArangoDB on average by 35%, while at the same time reduced the memory footprint compared to version 3.1. In combination with a greatly improved cluster management, we think ArangoDB 3.2 is by far our best work. (see release notes for more details)

One key goal of ArangoDB has always been to provide a rock solid platform for building ideas. Our users should always feel safe to try new things with minimal effort by relying on ArangoDB. Todays 3.2 release is an important milestone towards this goal. We’re excited to release such an outstanding product today.


With the integration of Facebook’s RocksDB, as a first pluggable storage engine in our architecture, users can now work with as much data as fits on disk. Together with the better locking behavior of RocksDB (i.e., document-level locks), write intensive applications will see significant performance improvements. With no memory limit and only document-level locks, we have eliminated two roadblocks for many users. If one chooses RocksDB as the storage engine, everything, including indexes will persist on disk. This will significantly reduce start-up time.
See this how-to on “Comparing new RocksDB and mmfiles engine” to test the new engine for your operating system and use case.


Distributed graph processing was a missing feature in ArangoDB’s graph toolbox. We’re willing to admit that, especially since we managed to fill this need by implementing the Pregel computing model.

With PageRank, Community Detection, Vertex Centrality Measures and further algorithms, ArangoDB can now be used to gain high-level insights into the hidden characteristics of graphs. For instance, you can use graph processing capabilities to detect communities. You can then use the results to shard your data efficiently to a cluster and thereby enable SmartGraph usage to its full potential. We’re confident that with the integration of distributed graph processing, users will now have one of the most complete graph toolsets available in a single database.

Test the new pregel integration with this Community Detection Tutorial and further sharpen advanced graph skills with this new tutorial about Using SmartGraphs in ArangoDB.

Fault-Tolerant Foxx Services

Many people already enjoy using our Foxx JavaScript framework for data-centric microservices. Defining your own highly configurable HTTP routes with full access to the ArangoDB core on the C++ level can be pretty handy. In version 3.2, our Foxx team completely rewrote the management internals to support fault-tolerant Foxx services. This ensures multi-coordinator clusters will always keep their services in sync, and new coordinators are fully initialized, even when all existing coordinators are unavailable.

Test the new fault-tolerant Foxx yourself or learn Foxx by following the brand new Foxx tutorial.

Powerful Graph Visualization

Managing and processing graph data may not be enough, causing visualizing insights to be important. No worries. With ArangoDB 3.2, this can be handled easily. You can use the open-source option via arangoexport to export the data and then import it into Cytoscape (check out the tutorial).

Or you can just plug in the brand new Keylines 3.5 via Foxx and install an on-demand connection. With this option, you will always have the latest data visualized neatly in Keylines without any export/import hassle. Just follow this tutorial to get started with ArangoDB and Keylines.

Read-Only Users

To enhance basic user management in ArangoDB, we added Read-Only Users. The rights of these users can be defined on database and collection levels. On the database level, users can be given administrator rights, read access or denied access. On the collection level, within a database, users can be given read/write, read only or denied access. If a user is not given access to a database or a collection, the databases and collections won’t be shown to that user. Take the tutorial about new User Management.

We also improved geo queries since this is becoming more important to our community. With geo_cursor, it’s now possible to sort documents by distance to a certain point in space (Take the tutorial). This makes queries simple like, “Where can I eat vegan in a radius of one mile around Times Square?” We plan to add support for other geo-spatial functions (e.g., polygons, multi-polygons) in the next minor release. So watch for that.

ArangoDB 3.2 Enterprise Edition: More Room for Secure Growth

The Enterprise Edition of ArangoDB is focused on solving enterprise-scale problems and secure work with data. In version 3.1, we introduced SmartGraphs to bring fast traversal response times to sharded datasets in a cluster. We also added auditing and enhanced encryption control. Download ArangoDB Enterprise Edition (forever free evaluation).

Working closely with one of our larger clients, we further explored and improved an idea we had about a year ago. Satellite Collections is the exciting result of this collaboration. It’s designed to enable faster join operations when working with sharded datasets. To avoid expensive network hops during join processing among machines, one has ‘only’ to find a solution to enable joins locally.

With Satellite Collections, you can define collections to shard to a cluster, as well as set collections to replicate to each machine. The ArangoDB query optimizer knows where each shard is located and sends requests to the DBServers involved, which then execute the query locally. The DBservers will then send the partial results back to the Coordinator which puts together the final result. With this approach, network hops during join operations on sharded collections can be avoided, hence query performance is increased and network traffic reduced. This can be more easily understood with an example. In the schema below, collection C is sharded to multiple machines, while the smaller satellites (i.e., S1 – S5) are replicated to each machine, orbiting the shards of C.

Use cases for Satellite Collection are plentiful. In this more in-depth blog post, we use the example of an IoT case. Personalized patient treatment based on genome sequencing analytics is another excellent example where efficient join operations involving large datasets, can help improve patient care and save infrastructure costs.

Security Enhancements

From the very beginning of ArangoDB, we have been concerned with security. AQL is already protected from injections. By using Foxx, sensitive data can be contained within in a database, with only the results being passed to other systems, thus minimizing security exposure. But this is not always enough to meet enterprise scale-security requirements. With version 3.1, we introduced Auditing and Enhanced Encryption Control and with ArangoDB 3.2, we added even more protection to safeguard data.

Encryption at Rest

With RocksDB, you can encrypt the data stored on disk using a highly secure AES algorithm. Even if someone steals one of your disks, they won’t be able to access the data. With this upgrade, ArangoDB takes another big step towards HIPAA compliance.

Enhanced Authentication with LDAP

Normally, users are defined and managed in ArangoDB itself. With LDAP, you can use an external server to manage your users. We have implemented a common schema which can be extended. If you have special requirements that don’t fit into this schema, please let us know.

Conclusion & You

The entire ArangoDB team is proud to release version 3.2 of ArangoDB — this should not be a surprise considering all of the improvements we made. We hope you will enjoy the upgrade. We invite you to take ArangoDB 3.2 for a spin and to let us know what you think. We look forward to your feedback!
Download ArangoDB 3.2