May 2015 Summaries

The community contributed a diverse range of interesting posts to the Neo4j blog in April 2015, including articles on using Neo4j for various applications such as movie profiles and network optimization, as well as insights into graph databases and big data. The community also shared slides and videos related to Neo4j, showcasing its capabilities in bootstrapping recommendations and fraud prevention. Additionally, a book review of O'Reilly's Graph Databases Second Edition was featured, highlighting the importance of graph databases in modern applications. Throughout April, the community demonstrated their expertise with Neo4j, providing valuable insights and practical examples that benefited others in the field.

Neo4j, an open-source graph database project, has recently released Neo4j 2.2 with significant enhancements to its internal architecture, improving performance and scalability. The new page cache uses an LRU-K algorithm, delivering vastly improved scalability in highly concurrent workloads. This results in up to 10 times higher read throughput compared to previous versions of Neo4j. Additionally, the cost-based query planner, Cypher, gathers statistics about data sets, enabling more efficient query paths and reducing development cycles by 90%. Graph databases excel where requirements and/or data have an element of uncertainty or unpredictability, particularly in problems related to relationships between data. They are also beneficial for businesses that build operational applications on the right graph database, experiencing measurable benefits such as better performance overall, more competitive applications, easier development cycles, and higher revenues. Neo4j is well-positioned with respect to RDBMS handling XML and RDF data and NoSQL databases handling graph-based data, as it offers a unique property graph model and associated query methods that are more suitable for persistent data inside an enterprise. Graph databases can help support the Internet of Things (IoT) by understanding and managing connections between devices, which brings forth latent possibilities. Real-time recommendations, fraud detection, and master data management are among Neo4j's top use cases, showcasing its capabilities in various industries such as finance, healthcare, government, gaming, telecommunications, insurance, agribusiness, and more.

XRebel 2 has been integrated with Neo4j, allowing developers to analyze and optimize the performance of their Neo4j-backed applications. The integration utilizes Neo4j's remote access protocol, enabling XRebel to provide detailed insights into application performance, database queries, and exceptions. Developers can use this information to identify areas for optimization and improve the overall performance of their applications. To demonstrate the capabilities of the integration, an example application was used, which connects to a Neo4j database via JDBC and serves three JSON endpoints to a JavaScript frontend page. The XRebel profiler was attached as a Java agent to the application, allowing developers to access profiling information through the XRebel UI or by accessing a special endpoint added to the application's URL. The integration provides a valuable tool for developers to analyze and improve the performance of their Neo4j-backed applications.