Reaching and harnessing consensus with ArangoDB

00Architecture, 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, Security

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

Running V8 isolates in a multi-threaded ArangoDB database

00API, Architecture, C++, Documentation, nodejs

ArangoDB allows running user-defined JavaScript code in the database. This can be used for more complex, stored procedures-like database operations. Additionally, ArangoDB’s Foxx framework can be used to make any database functionality available via an HTTP REST API. It’s easy to build data-centric microservices with it, using the scripting functionality for tasks like access control, data validation, sanitation etc.

We often get asked how the scripting functionality is implemented under the hood. Additionally, several people have asked how ArangoDB’s JavaScript functionality relates to node.js.

This post tries to explain that in detail.

More info

Fulltext Index Enhancements

00API, Architecture

This post is about improvements for the fulltext index in ArangoDB 2.6. The improvements address the problem that non-string attributes were ignored when fulltext-indexing.

Effectively this prevented string values inside arrays or objects from being indexed. Though this behavior was documented, it was limited the usefulness of the fulltext index much. Several users requested the fulltext index to be able to index arrays and object attributes, too.

Finally this has been accomplished, so the fulltext index in 2.6 supports indexing arrays and objects!

Read on in Jan’s blog post about Fulltext Index Enhancements.

Do you like ArangoDB?
icon-githubStar this project on GitHub.
Star ArangoDB on GitHub