September 2011 Summaries

In a recent blog post, John Weeler discusses the concept of a Multi-level Marketing (MLM) Engine, which enables easy compensation calculation despite complex sales networks. He explores how this structure fits within a graph and dives into building an MLM engine, traversing its graph components. The author concludes by providing a sneak peek at the second part of the blog series, highlighting the potential benefits of using graphs to solve complex connected challenges.

Marko Rodriguez provides a step-by-step guide on building a graph-based movie recommender engine using Neo4j and Gremlin, leveraging the MovieLens dataset to explore various queries such as user preferences, co-rated movies, and genre-related recommendations. The tutorial uses Toy Story as an example to illustrate graph traversal techniques, answering questions like which users gave high ratings to specific movies and identifying highly co-rated movies shared with Toy Story. This guide aims to introduce developers to the capabilities of graph databases in solving real-world problems, providing a free resource to learn more about graph technologies for application development.

This PhD student is developing a social news stream app using Neo4j built on GWT, which outperforms a MySQL database in terms of speed, with theNeo4j application completing the task in approximately 0.9 seconds compared to around 50 seconds for the MySQL database. The developer is excited about the results and eager to further improve the project, demonstrating enthusiasm for programming and exploring the potential of graph databases.

The author, inspired by the potential of both Scala and Neo4j, decided to experiment with them at FOSS4g 2011. He started by using sbt for the first time and tried out Scala's application trait in order to create a main method. The project was documented throughout on his blog, where he expressed excitement about the possibilities of both technologies. The author also shared his experience with graph databases in general, and invited readers to learn more about them through a free ebook from O'Reilly.

Neo4j is experiencing a surge in popularity, and the company is responding by dedicating full-time engineering resources to support the community. The new initiative aims to provide better engagement, issue resolution, and advocacy for the community's long-term product direction. The community engineering team will focus on proactively addressing community needs, updating documentation based on user feedback, and working with contributors to expand and support open-source committers.

Be sure to visit Py2neo, Nigel's project that provides bindings between Python and Neo4j via its RESTful web service interface. Want to learn more about graph databases? Click below to get your free copy of O'Reilly's Graph Databases ebook and discover how to use graph technologies for your application today.

The Neo4j community is gearing up for the release of version 1.5, which promises significant performance improvements and changes to the core database engine. The new release will introduce a utility to generate GraphViz output from a Neo4j database, update Gremlin to version 1.2, and open GitHub issue tracking for all Neo4j projects. These changes aim to speed up performance, reuse space, and decrease database file size, while also making it easier for the community to contribute to the project by reporting issues directly on GitHub.

A Neo4j user, Romiko Derbynew, recently shared his experience with full text indexing in Neo4j, outlining his goals which included controlling the pointers of the index, full text search capabilities, and integrating all operations through REST. He also aimed to create an index when creating a node, update and index existing nodes, check if an index exists, and perform query checks during bootstrapping in the cloud. Additionally, Derbynew wanted to utilize Lucene for full-text search queries using its query language.