ArangoDB Java Driver for Graphs: Enhanced Functionality
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?”
Introduction to the Pregel Module in ArangoDB
This post is outdated, please see more recent infos below.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 pregelRunner) that enables the user to write own algorithms, pass them to ArangoDB and have them executed in a Pregel-like fashion.
This means that the user’s algorithm is executed step wise on each server in the cluster in parallel for all its local vertices. In each step the vertices can send messages to each other to distribute information. These messages can be received by the other vertex in the next step. The algorithm terminates when there are no more active vertices left and no message has been sent.
We started to implement an experimental version of Pregel in ArangoDB. You need to check-out the pregel branch of ArangoDB in order to play with the following examples. Please be advised that the implementation is still in an early phase and very like to change. In this post we will provide a brief introduction to ArangoDB’s Pregel module by guiding you through the implementation of an example algorithm.
ArangoDB Java Driver: Graph Data Manipulation & Queries
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…
ArangoDB Java Driver: Batch & Asynchronous Mode | ArangoDB Blog
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 ArangoDriver(configure);
ArangoDB Java Driver: Simplifying Database Interactions
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>
Tour Around the New AQL Query Optimizer | ArangoDB Blog
The major new feature in ArangoDB 2.3 is the shiny new AQL query optimizer and executor. These parts of ArangoDB have been rewritten in 2.3 to make AQL much better for our end users.
Since one of the initial releases, ArangoDB has been shipped with AQL, the ArangoDB Query Language. AQL has since then been ArangoDB’s most versatile way of executing simple and also the not-so-simple queries.
I’ll start with an overview of query execution in previous versions of ArangoDB, and then explain the new engine and explain the differences.
ArangoDB 2.3 Beta 2: Available for Testing | ArangoDB Blog
This version is deprecated. Download the new version of ArangoDB
A new beta release is available for testing.
Please note that there are issues with the keys generated by the OBS. We will sort this out as fast as possible. Currently you simple have to trust the keys during install.
This beta release contains various fixes which were found during testing beta1.
Improved Non-Unique Hash Indexes in ArangoDB 2.3
With ArangoDB 2.3 now getting into the beta stage, it’s time to spread the word about new features and improvements.
Today’s post will be about the changes made to non-unique hash indexes.
Data Modeling: MongoDB vs ArangoDB | ArangoDB Blog
MongoDB is a document DB whereas ArangoDB is a multi-model DB supporting documents, graphs and key/values within a single database. When it comes to data modeling and data querying, they pursue somewhat different approaches.
In a Nutshell: In MongoDB, data modeling is “aggregate-oriented”, avoiding relations and joins. On the other side, everybody has probably used relational databases which organize the data in tables with relations and try to avoid as much redundancy as possible. Both approaches have their pros and cons. ArangoDB is somewhat in-between: You can both model and query your data in a “relational way” but also in an “aggregate-oriented way”, depending on your use case. ArangoDB offers joins, nesting of sub-documents and multi-collection graphs. (more…)
Setting Up Test Data: ArangoDB Tutorial & Best Practices
Today I was asked to look at code that was supposed to read data from a MySQL data source, process it and then import it into ArangoDB.
To run and debug the code I had to have some MySQL data source. So I thought I’d quickly set up a simple example table with a few rows. It turned out that this took more time than what I had expected.
Maybe I’m spoilt by JavaScript-enabled, schema-free databases where creating such test setups is so much easier.
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