It has been a few months since we first released the Kubernetes operator for ArangoDB and started to brag about it. Since then, quite a few things have happened.

For example, we have done a lot of testing, fixed bugs, and by now the operator is declared to be production ready for three popular public Kubernetes offerings, namely Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS) and Pivotal Kubernetes Service (PKS) (see here for the current state of affairs).

The ability to see your data from various perspectives is the idea of a multi-model database. Having the freedom to combine these perspectives into a single query is the idea behind native multi-model in ArangoDB. Extending this freedom is the main thought behind the release of ArangoDB 3.4.

We’re always excited to put a new version of ArangoDB out there, but this time it’s something special. This new release includes two huge features: a C++ based full-text search and ranking engine called ArangoSearch; and largely extended capabilities for geospatial queries by integrating Google™ S2..

For ArangoDB 3.4 we already added 100,000 lines of code, happily deleted 50,000 lines and changed over 13,000 files until today. We merged countless PRs, invested months of problem solving, hacking, testing, hacking and testing again and are super excited to share the feature complete RC1 of ArangoDB 3.4 with you today.

Download the technical preview package for your OS: RC1 3.4 Community and RC1 3.4 Enterprise.

The team implemented over 40 new features, improvements and optimizations. In this short post, we will highlight just a few of them. You can find a full list in the release notes and..

Firstly, a huge thank you to all our customers that took the time to review ArangoDB for the Gartner Peer Insights “Voice of the Customer”: Operational Database Management Systems Market report. Without your help and assistance, the continued improvements and enhancements we make to our software wouldn’t be possible. We are overwhelmed to be listed as one of six OPDBMS solutions in the Customers’ Choice Zone. We believe this is a remarkable achievement.

Graph databases are often used to analyze relations within highly interconnected datasets. Social networks, recommendation engines, corporate hierarchies, fraud detection or querying a bill of materials are common use cases. But these datasets change over time and you as a developer or data scientist may want to time travel and analyze these changes.

While ArangoDB may not come with built-in support for managing the revision history of graph data, we’ll show in this article how to manage it in a performant manner for some general classes of graphs. Best of all, this won’t require any..

Many ArangoDB users rely on our `arangodump` and `arangorestore` tools as an integral part of their backup and recovery procedures. As such, we want to make the use of these tools, especially `arangodump`, as fast as possible. We’ve been working hard toward this goal in preparation for the upcoming 3.4 release.

We’ve made a number of low-level server-side changes to significantly reduce overhead and improve throughput. Additionally, we’ve put some work into rewriting much of the code for the client tools to allow dumping and restoring collections in parallel, using a number of worker threads..

Our recent release 3.3.9 includes several performance optimizations for data retrieval cases. Benefits can be expected for both storage engines, MMFiles and RocksDB, AQL batch lookup queries, and cluster AQL queries.

We were in search for some C++ reader/writer locks implementation that allows a thread to acquire a lock and then optionally pass it on to another thread. The C++11 and C++14 standard library lock implementations std::mutex and shared_mutex do not allow that (it would be undefined behaviour – by the way, it’s also undefined behaviour when doing this with the pthreads library).

In this article, we’re going to dive deeper into our recently released feature preview in Milestone ArangoDB 3.4 – ArangoSearch which provides a rich set of information retrieval capabilities. In particular, we’ll give you an idea of how our search engine works under the hood.

Anyone who has ever worked with our JavaScript framework Foxx was faced at some point with the challenge to install its Foxx service in its current ArangoDB instance or to replace the installed service with local code changes. This is not a big deal and can easily be done through ArangoDB’s WebUI. However, we developers always want to become more productive and clicking through a graphical UI is not the best way. Furthermore, this procedure is almost impossible to use in an automated deployment process. That’s why we decided to develop a node-based CLI tool to manage Foxx services, called ..