This is Part I of Performance analysis using pyArango blog series. Please refer here for: Part II (cluster) and Part III (measuring system capacity).

Usually, your application will persist of a set of queries on ArangoDB for one scenario (i.e. displaying your user’s account information etc.) When you want to make your application scale, you’d fire requests on it, and see how it behaves. Depending on internal processes execution times of these scenarios vary a bit.

We will take intervals of 10 seconds, and graph the values we will get there:

• average – all times measured during the interval,..

With billions of objects and people connected to the internet and your precious data sometimes exposed publicly security is one of the most important topics to discuss. In light of recent ransomware attacks, databases exposed and other breaches we’d like to share a quick reminder on how to secure your ArangoDB environment.

Thinking about your data as a highly connected set of information is a powerful way to gain insights, solve problems and bring products faster into the hands of your users.

Unlike other databases, relationships take the first priority in graph databases and with ArangoDBs multi-model approach for graphs, documents and key/value pairs you can even switch between models or combine them in a single query.

The graph concept is booming but still new to many. So we invested a few bazillion coffees and some night shifts to come up with a good plan for a Graph Course:

The next alpha release of the upcoming ArangoDB 3.2 is available for testing. You can download and install alpha3 here.

Moving forward

As ArangoDB 3.2 will include several new features and improvements, we realized that the release model that we currently follow has room for improvement. Going forward we will introduce milestone releases with ArangoDB 3.3. For this major release you will see a bit more alphas. You can read detailed info about the new structure model here.

Pregel computing model

In this alpha we introduce support for incremental graph processing algorithms in a single mode..

Today we are glad to announce the start of ArangoDB Online meetup. As our international open-source community is growing with every passing day, we keep getting requests from members around the world on doing a tech meet or a short demo on ArangoDB. Quite a few members have already taken the initiative of presenting at conferences and local meetups – big “thank you” for that! Adding to that effort, it’s high time that we all moved to that one place where we can all connect and everyone has a chance to give/ participate in a talk. And what better way is there to bring us all together than..

The ArangoDB team gave me an opportunity to write a tutorial about arangochair. Arangochair is the first attempt to listen for changes in the database and execute actions like pushing a document to the client or execute an AQL query. Currently it is limited to single nodes.

This tutorial is loosely based on the example at baslr/arangochair-serversendevents-demo

arangochair is a Node.js module hosted on npm which make it fairly easy to install. Just runnpm install arangochair and its installed.

With the release of the initial alpha of ArangoDB version 3.2 we also include the preview of the new export tool arangoexport. Alpha2 of ArangoDB 3.2 can be downloaded here. An export functionality was initially requested by one of our community members to view an ArangoDB graph view the Cytoscape visualizer.

Arangoexport is capable of exporting a graph or certain collections of a graph to xgmml, Cytoscape’s graph format. But arangoexport is not limited to this. It can also generate JSON or JSONL data exports of arbitrary collections.

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.

The upcoming 3.1 release comes with a binary protocol – VelocyStream – to transport VelocyPack (internal storage format of ArangoDB introduced with the 3.0 release) data between ArangoDB and client applications. VelocyPack stores a superset of JSON, is more compact and has a fast attribute lookup. On the other hand, VelocyStream allows to send VelocyPack in an optimized form over the network. We think it would be the right time to update our official Java Driver to modernize it and to let it be the first to fully support VelocyStream.

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:

Choose ArangoDB3 in the DC/OS Universe 

Hit “Install Package” and you are done!