Do Graph Databases Scale? Yes? No? Let’s see!

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…

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The ArangoDB Operator for Kubernetes – Stateful Cluster Deployments in 5min

At ArangoDB we’ve got many requests for running our database on Kubernetes. This makes complete sense since Kubernetes is a highly popular system for deploying, scaling and managing containerized applications.

Running any stateful application on Kubernetes is a bit more involved than running a stateless application, because of the storage requirements and potentially other requirements such as static network addresses. Running a database on Kubernetes combines all the challenges of running a stateful application, combined with a quest for optimal performance.

This article explains what is needed to run ArangoDB on Kubernetes and what we’re doing to make it a lot easier.

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

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ArangoDB Cluster Administration Course Released

Cluster Administration course will take you all the way from concept and anatomy of the ArangoDB cluster to maintenance, resilience and troubleshooting of your distributed environment.

When data size or workload makes purchasing of a single database server prohibitive one needs to rethink the system architecture and consider to cluster a farm of more affordable machines. While ArangoDB clusters bring additional added value like seamless runtime scaling and thin provisioning, their management can look testy and challenging. Read more

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AWS Neptune: A New Vertex in the Graph World — But Where’s the Edge?

At AWS Re:Invent just a few days ago, Andy Jassy, the CEO of AWS, unveiled their newest database product offerings: AWS Neptune. It’s a fully managed, graph database which is capable of storing RDF and property graphs. It allows developers access to data via SPARQL or java-based TinkerPop Gremlin. As versatile and as good as this may sound, one has to wonder if another graph database will solve a key problem in modern application development and give Amazon an edge over its competition. Read More

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Setting up Datacenter to Datacenter Replication in ArangoDB

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 ArangoGraph: 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

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Reaching and harnessing consensus with ArangoDB

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

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Starting an ArangoDB cluster the easy way

Recently, we have got a lot of feedback about the fact that standing up an ArangoDB cluster “manually” is an awkward and error-prone affair. We have been aware of this for some time, but always expected that most users running ArangoDB clusters would do so on Apache Mesos or DC/OS, where deployment is a breeze due to our ArangoDB framework.

However, for various valid reasons people do not want to use Apache Mesos and thus are back to square one with the problem of deploying an ArangoDB cluster without Apache Mesos. Read more

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Deploying an ArangoDB 3 Cluster with 2 Clicks

Hurray! Last week finally saw the release of ArangoDB 3.0 with lots of new features and in particular various improvements for ArangoDB clusters. In this blog post, I want to talk about one aspect of this, which is deployment.

DC/OS

As of last Wednesday, deploying an ArangoDB 3.0 cluster on DC/OS has become even simpler, because the new version of our framework scheduler has been accepted to the DC/OS Universe. Therefore, deployment is literally only two clicks: Read more

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Running ArangoDB 3.0.0 on a DC/OS cluster

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

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ArangoDB 3.0 new Cluster features

The 3.0 release of ArangoDB will introduce a completely overhauled cluster and marks a major milestone on its road to “zero-maintenance” where you can keep focus on your product instead of your datacenter.

Synchronous replication

Earlier releases of ArangoDB already featured asynchronous replication. This was already a great method to do backups and allowed for failover in case of a disaster. However that was mostly a manual job and furthermore – due to its asynchronous nature – data loss could happen. Read more

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