Architecture Archives - ArangoDB

Sign up for ArangoGraph Insights Platform

Before signing up, please accept our terms & conditions and privacy policy.

What to expect after you signup
You can try out ArangoDB Cloud FREE for 14 days. No credit card required and you are not obligated to keep using ArangoDB Cloud.

At the end of your free trial, enter your credit card details to continue using ArangoDB Cloud.

If you decide that ArangoDB Cloud is not (yet) for you, you can simply leave and come back later.

An Introduction to Geo Indexes and their performance characteristics: Part I

01Architecture, GeneralTags: ,

Starting with the mass-market availability of smartphones and continuing with IoT devices, self-driving cars ever more data is generated with geo information attached to it. Analyzing this data in real-time requires the use of clever indexing data-structures. Geo data in ArangoDB consists of 2 or more dimensions representing (x, y) coordinates on the earth surface. Searching on a single number is essentially a solved problem, but effectively searching on multi-dimensional data can be more difficult as standard indexing techniques cannot be used.
Read more

Setting up Datacenter to Datacenter Replication in ArangoDB

00Architecture, cluster, General, how to, Releases, ReplicationTags: , ,

Please note that this tutorial is valid for the ArangoDB 3.3 milestone 1 version of DC to DC replication!

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

This milestone release contains data-center to data-center replication as an enterprise feature. This is a preview of the upcoming 3.3 release and is not considered production-ready.

In order to prepare for a major disaster, you can setup a backup data center that will take over operations if the primary data center goes down. For a server failure, the resilience features of ArangoDB can be used. Data center to data center is used to handle the failure of a complete data center.

Data is transported between data-centers using a message queue. The current implementation uses Apache Kafka as message queue. Apache Kafka is a commonly used open source message queue which is capable of handling multiple data-centers. However, the ArangoDB replication is not tied to Apache Kafka. We plan to support different message queues systems in the future.

The following contains a high-level description how to setup data-center to data-center replication. Detailed instructions for specific operating systems will follow shortly. Read more

Reaching and harnessing consensus with ArangoDB

01Architecture, cluster, GeneralTags: ,
nihil novi nisi commune consensu
nothing new unless by the common consensus

– law of the polish-lithuanian common-wealth, 1505

A warning aforehand: this is a rather longish post, but hang in there it might be saving you a lot of time one day.

Introduction

Consensus has its etymological roots in the latin verb consentire, which comes as no surprise to mean to consent, to agree. As old as the verb equally old is the concept in the brief history of computer science. It designates a crucial necessity of distributed appliances. More fundamentally, consensus wants to provide a fault-tolerant distributed animal brain to higher level appliances such as deployed cluster file systems, currency exchange systems, or specifically in our case distributed databases, etc. Read more

Running ArangoDB 3.0.0 on a DC/OS cluster

00Architecture, clusterTags: ,

As you surely recognized we´ve released ArangoDB 3.0 a few days ago. It comes with great cluster improvements like synchronous replication, automatic failover, easy up- and downscaling via the graphical user interface and with lots of other improvements. Furthermore, ArangoDB 3 is even better integrated with Apache Mesos and DC/OS. Read more

Open Source DC/OS: The modern way to run a distributed database

00Architecture, GeneralTags: ,

The mission of ArangoDB is to simplify the complexity of data work. ArangoDB is a distributed native multi-model NoSQL database that supports JSON documents, graphs and key-value pairs in one database engine with one query language. The cluster management is based on Apache Mesos, a battle-hardened technology. With the launch of DC/OS by a community of more than 50 companies all ArangoDB users can easily scale. Read more

Improved Deadlock Detection

04ArchitectureTags: ,

The upcoming ArangoDB version 2.8 (currently in devel) will provide a much better deadlock detection mechanism than its predecessors.

The new deadlock detection mechanism will kick in automatically when it detects operations that are mutually waiting for each other. In case it finds such deadlock, it will abort one of the operations so that the others can continue and overall progress can be made. Read more

Lockfree protection of data structures that are frequently read

04Architecture, SecurityTags:

Motivation

In multi-threaded applications running on multi-core systems, it occurs often that there are certain data structures, which are frequently read but relatively seldom changed. An example of this would be a database server that has a list of databases that changes rarely, but needs to be consulted for every single query hitting the database. In such situations one needs to guarantee fast read access as well as protection against inconsistencies, use after free and memory leaks.

Therefore we seek a lock-free protection mechanism that scales to lots of threads on modern machines and uses only C++11 standard library methods. The mechanism should be easy to use and easy to understand and prove correct. This article presents a solution to this, which is probably not new, but which we still did not find anywhere else.

The concrete challenge at hand

Assume a global data structure on the heap and a single atomic pointer P to it. If (fast) readers access this completely unprotected, then a (slow) writer can create a completely new data structure and then change the pointer to the new structure with an atomic operation. Since writing is not time critical, one can easily use a mutex to ensure that there is only a single writer at any given time. The only problem is to decide, when it is safe to destruct the old value, because the writer cannot easily know that no reader is still accessing the old values. The challenge is aggravated by the fact that without thread synchronization it is unclear, when a reader actually sees the new pointer value, in particular on a multi-core machine with a complex system of caches.

If you want to see our solution directly, scroll down to “Source code links“. We first present a classical good approach and then try to improve on it. More info