This year we got a lot of requests from our customers to provide Spring Data support for ArangoDB. So we listened and teamed up with one of our bigger customers from the financial sector to develop a Spring Data implementation for ArangoDB. We have also made an extensive demo on how to use Spring Data ArangoDB with an example data set of Game of Thrones characters and locations. So, Spring is not only coming… it is already there!

The newest release 4.3.2 of the official ArangoDB Java driver comes with load balancing for cluster setups and advanced fallback mechanics.

Load balancing strategies

Round robin

There are two different strategies for load balancing that the Java driver provides. The first and most common strategy is the round robin way. Round robin does, what the name already assumes, a round robin load balancing where a list of known coordinators in the cluster is iterated through. Each database operation uses a different coordinator than the one before.

With the 3.2 release, ArangoDB comes with version 1.1 of the binary protocol VelocyStream. VelocyStream is a bi-directional async binary protocol which supports sending messages pipelined, multiplexed, uni-directional or bi-directional. The messages themselves are VelocyPack objects.

Currently we are diving deeper into the Apache Spark world. We started with an implementation of a Spark-Connector written in Scala. The connector supports loading of data from ArangoDB into Spark and vice-versa. Today we release a first prototype with an aim of including our community into the development process early to build a product that fits your needs. Your feedback is more than welcome!

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.

After defining a graph and filling it with some vertices and edges (see part 1), the time has come to retrieve information out of the graph.

Please take a look at the defined graph operations of ArangoDB. These will be the base for our next examples. (Yes, there may be other ways to get the results, this post does not claim completeness!)

We will start with some easy stuff and then smoothly advance in complexity.

Question: “How many edges are defined within the graph?”

Answer:

First we have to define an AQL-query:

String query = "for i in GRAPH_EDGES('Academical', null) return i";

In our..

This post is outdated, please see more recent infos below.

Please see a technical article about our current Pregel integration in our blog, details about the various Pregel algorithms ArangoDB supports in our documentation and a tutorial about Community Detection with real data in our training center.

Ever since Google introduced Pregel as a system for large-scale graph processing we thought of a way how to enable this feature in ArangoDB. So we set up an ArangoDB cluster, created some huge graphs and started evaluating the concept. We came up with a new ArangoDB module (called..

With ArangoDB 2.2 the new graph API was released featuring multi collection graphs (see blog). With the new version (2.2.1) of arangodb-java-driver the new graph API is supported. In the following you can find a small example of creating a graph with Java.

For the import via maven and configuring the driver, please read the Basics and Driver Setup. For the following we assume, that arangodbDriver is a configured instance of the driver.

So let’s start the whole thing…

In the database we need a graph containing collections for the vertices and edges (defined in edge definitions).

The current arangodb-java-driver supports the usage of ArangoDB’s batch and asynchronous interface. This post will guide you through the usage of these features.

The batch interface

The batch interface enables the user to stack a series of calls and execute them in a batch request. Each stacked request returns a request id that can be used to retrieve the single results from the batch response. So how do you use this feature in the java driver ?

First we create an instance of the java driver:

ArangoConfigure configure = new ArangoConfigure(); configure.init(); ArangoDriver driver = new..

A new arangodb-java-driver is out now, it’s on github. The driver is available for ArangoDB from version 2.2 onwards.

How to include the driver in your application ?

The driver is available as maven artifact. To add the driver to your project with maven, add the following code to your pom.xml:

<dependencies>
  <dependency>
    <groupId>com.arangodb</groupId>
    <artifactId>arangodb-java-driver</artifactId>
    <version>2.2</version>
  </dependency>
  ....
</dependencies>