Estimated reading time: 7 minutes

When our founders realized that data models can be features, we at ArangoDB set ourselves the big goal of developing the most flexible database. With today’s GA release of ArangoDB 3.7, the project reached an important milestone on this journey.

Watch the the ArangoDB 3.7 Release Webinar.

Estimated reading time: 10 minutes

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see…

In the following, we will define what..

This is an update of the 2016 blog post How to put ArangoDB to Spartan-Mode.

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.

Interested in trying out ArangoDB? Fire up your cluster in just a few clicks with ArangoDB ArangoGraph: the Cloud Service for ArangoDB. Start your free 14-day trial here

The new `.tar.gz` binary archive

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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.

This however will drown your system effectively – it may hinder you from capturing statistics, do debugging, and all other sorts of things you’re used to from a normally running system. The more clever way is to tell your system to limit the available resources for..

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.

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..

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 the first part of this article we dived deep into what indexes are currently available in ArangoDB (3.2 and 3.3), also briefly looking at what improvements are coming with ArangoDB 3.4. Read Part I here.

In this Part II, we are going to focus on how to actually add indexes to a data model and speed up specific queries.

Adding indexes to the data model

The goal of adding an extra index to the data model is to speed up a certain query or even multiple queries.

One of the first things that should be done during development of AQL queries should be to review the output of the explain command...

Recently, we published the latest findings of our Performance Benchmark 2018 including Neo4j, PostgGreSQL, MongoDB, OrientDB and, of course, ArangoDB. We tested bread & butter tasks in a client/server setup for all databases like single read/write and aggregation, but also things like shortest path queries which are a speciality for graph databases. Our goal was and is to demonstrate that a native multi-model database like ArangoDB can at least compete with the leading single model databases on their home turf.