With ArangoDB 3.4 we finally made the RocksDB storage engine the default. This decision was made after a year of constant improvements to the engine to make it suitable for all our customer’s use cases. Read more
“I must have a prodigious amount of mind;
it takes me as much as a week, sometimes, to make it up!”
― Mark Twain
How many shards should one choose, when creating collections in ArangoDB clusters?
TLDR: Don’t be too shy with sharding your data in many shards across your cluster. Be mindful however that AQL-heavy applications might not profit as much from heavy distribution. Read more
This post uses the new
.tar.gz binary distribution of ArangoDB to run multiple versions of ArangoDB alongside each other on the same machines. We will do a production-ready deployment on 3 cloud instances with authentication, TLS encryption, (self-signed) certificates and
systemd service. In the end, we show how to perform a rolling upgrade for one of the clusters to a new version. Read more
When using a database like ArangoDB it is also important to explore how it behaves once it reaches system bottlenecks, or which KPIs (Key Performance Indicators) it can achieve in your benchmarks under certain limitations. One can achieve this by torturing the system by effectively saturating the resources using random processes. Read more
2018 has been a fantastic year for the ArangoDB project. The community has welcomed many new members, customers, supporters and friends. Together we’ve reached new “heights” – accomplished goals, shipped a big brand-new release and improved ArangoDB on all fronts. Read more
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). Read more
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 Geometry Library and GeoJSON. Read more
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. Read more