December 2015 Summaries

Lending Club's journey to microservices is a common story in the tech industry. They started with five production microservices and grew to 139, which can be overwhelming for many organizations. To manage this complexity, Lending Club uses MacGyver, a tool that hooks into various services such as GitHub, AWS, Splunk, HipChat, and VMware. MacGyver collects data from these services and saves it in Neo4j, a graph database that provides fast queries, great join and traversal capabilities, and scalability. The app instances report to MacGyver, which then saves them to Neo4j, creating virtual servers and pools. This allows Lending Club to automate deployments, monitor infrastructure, and gain visibility into their services. By using Neo4j, they can query the data in real-time, making it easier to answer questions like "What pool should I deploy to?" or "Are live pool revisions in sync in different environments?" The tool also helps with infrastructure mapping, allowing them to identify single points of failure and monitor their entire infrastructure. Lending Club's experience demonstrates the potential of Neo4j in managing microservices and promoting agility in the development process.

Telenor, a leading telecommunications provider in Norway, was facing a growing number of subscriptions and an increasingly complex identity and access management system that couldn't handle the provisioning of data for its larger customer base. The company's leaders projected that they would have to stop accepting new customers within one year unless they re-imagined their existing solution. Telenor then found Neo4j, a graph database, and started using it to give itself a sustainable competitive advantage. By leveraging Neo4j, Telenor was able to simplify its business traversal rules via a graph data model, resulting in a minutes-to-milliseconds performance increase for its three million customers. The secret to this success lies in the ease of understanding that came from modeling the resource graph in Neo4j, allowing Telenor to get fast and secure answers to important questions about user access and subscriptions.

This release of Neo4j features refinements to the Bolt driver API, as well as a range of fixes, improvements and small additions. A new example, known as the Reindeer Graph, is introduced, which models a team of reindeer arranged in pairs except for the lead, all pulling a sleigh. The graph can be created using Java code with Bolt to connect to Neo4j running on localhost. The code exercises the graph by querying for the strongest pair of reindeer and adds a new reindeer named "Bolt" to the team. Notable changes include an upgrade to Lucene 5, support for upgrading databases with older Lucene versions, and improvements to Bolt TCP, TLS, WebSocket, and Secure WebSocket. The release is part of Neo4j's Early Access Program and invites users to provide feedback through various channels.

Graph databases are changing the world by influencing world leaders, changing the way investigative journalists share data, improving food traceability, and increasing searchability of documents. The problem with traditional relational databases is that they struggle to model true hierarchies, leading to issues with recursion, master data management, network and IT operations, real-time recommendations, forensics and fraud, and resource optimization. Graphs are a better fit for these use cases because they can handle complex relationships and provide more accurate insights. The key to getting started with graphs is to read the O'Reilly Graph Databases book, take an online course, learn about RDBMS-to-graph concepts and tools, install and play with Neo4j, explore GraphGists, and discover new insights into existing data. By doing so, organizations can gain a competitive advantage based on data relationships in their existing data.

The eBay Now platform, a same-day delivery service, utilized Neo4j to gain competitive advantage in the market. After acquiring Shutl, eBay rebranded it as "eBay Now" and leveraged Neo4j to power its delivery routing solution, eliminating performance issues with traditional MySQL solutions. The graph database's flexibility, speed, and ease of use allowed for significant improvements in query performance, enabling fast transactions with constant performance, and supporting future expansion of the business. With Neo4j, eBay Now was able to achieve sustainable competitive advantage in the highly competitive same-day delivery market.

Suellen is a user who has been using Neo4j for various projects, including analyzing literary works and art books. She found it to be an effective tool that brought new insights into her research, particularly when visualizing data in graph form. Suellen's team chose Neo4j due to its user-friendly interface and sandbox environment, which allowed her to demonstrate its capabilities to others. However, she acknowledges that she initially struggled with Cypher errors, but eventually gained confidence in using the database. Suellen believes that Neo4j has the potential to augment humanistic research in digital humanities by breaking free from traditional structured databases and RDBMSs.

Neo4j can be used as an embedded library within your Java application, or it can be run in a separate server process. The server version is more common and provides a richer set of APIs for interacting with the graph database. The server can also be extended using unmanaged extensions to add custom functionality. When deploying Neo4j to production, it's essential to consider hardware and software requirements, such as choosing machines with multiple cores, sufficient RAM, and specialized SSDs. A good configuration includes assigning a significant amount of RAM to the page cache and configuring the garbage collector for optimal performance. Neo4j supports clustering for high availability and high read throughput, which involves configuring instances in a cluster to work together seamlessly. Clustering can be configured using HA endpoints, load balancers, and caching strategies like cache sharding. Backups are crucial for data protection, and Neo4j provides online backup capabilities that allow for non-disruptive backups of running instances. Monitoring the production environment is also vital, and Neo4j exposes various metrics and counters to help with this task. Testing at scale is essential before deploying an app into production, and Neo4j has been extensively tested in real-world scenarios to ensure its stability and performance.

The Neo4j GraphGist Challenge is a competition where participants create interactive, database-backed pages using AsciiDoc to model their domain, use case or problem. The challenge runs through January 31st, 2016, and includes various categories such as Investigative Journalism, Holidays, Sports and Recreation, Public Web APIs, Pop Culture, and more. Participants can win prizes including a $500 gift card to Amazon, tickets to the GraphConnect conference, Neo4j S'well bottles, and a Neo4j T-shirt for every participant who submits a non-duplicate GraphGist made with care. The challenge provides a forkable GraphGist template to get started and allows participants to showcase their skills in graph data modeling and visualization.

Walmart's retail competitiveness was significantly enhanced by leveraging Neo4j to power real-time product recommendations, enabling the company to provide finely tuned, highly personalized suggestions that react well to customer needs. By substituting a complex batch process with Neo4j, Walmart gained super-fast insight into customer behavior and product trends, allowing them to make data-driven decisions and outperform competitors in terms of online shopping experience. The use of graph databases provided Walmart's eCommerce team with the ability to quickly query customers' past purchases and capture new interests shown during their current online visit, making real-time recommendations a trivial task for Neo4j compared to other database management systems. With Neo4j's scalability and availability features, Walmart was able to achieve a sustainable competitive advantage over other online retailers, leaving them behind in terms of product relevance and customer satisfaction.

Neo4j is being used by Nulli, a company that offers extended identity access management policies within the ForgeRock product set. The company chose Neo4j due to its ability to handle large amounts of data, such as hundreds of millions of pieces of data from IoT devices. Nulli's CEO, Derek Small, found Neo4j intuitive and enjoyed modeling data with it. He was able to visualize information within his company using the data modeling capabilities of Neo4j, including modeling business processes and management responsibilities. If Nulli were to restart their project with Neo4j, they would power up the flux capacitor, go back in time, and not drop the project after 12 months, as this would have allowed them to maintain momentum.

We are grateful to the Neo4j community for publishing great articles about their projects and integrations with Neo4j. These articles showcase various use cases of Neo4j in different technologies such as Ruby on Rails, Meteor.js, Apache Spark, and more. The featured articles demonstrate innovative applications of Neo4j graph database technology, including a recommendation engine, big data analytics, event discovery app, and more. To be considered for the "From the Community" blog post, community members should follow Neo4j on Twitter and use the #Neo4j hashtag with their article submission. Additionally, the Neo4j team invites community members to submit their presentation ideas for GraphConnect Europe in London on April 26, 2016, with submissions due by January 29, 2015.

The text discusses how Gamesys, a company that operates online bingo, slots, and casino games, leveraged Neo4j graph database technology to gain a competitive advantage in the highly competitive online gaming market. By capturing social relationships between players, Gamesys was able to incentivize existing members to refer their friends, reducing churn and increasing engagement. Additionally, the use of Neo4j enabled the company to uncover fraud and detect potential referral abuse. The success of these projects led to the expansion of Neo4j usage within the company, allowing it to model complex economies in its games, such as "Here Be Monsters", and provide accurate predictive reports to reduce play testing, giving the company a competitive edge over others with traditional approaches.

The Neo4j 3.0 Milestone 1 release introduces Bolt, a new network protocol designed for high-performance access to graph databases, consolidating and refining existing ways of working with Neo4j. This milestone marks the start of a new major branch of development, bringing forward best features while leaving behind less useful ones, and is available for download as part of the Early Access Program. Bolt improves both performance and developer experience, using a compact binary encoding over TCP or web sockets, and provides uniform language drivers that encapsulate the protocol, freeing contributors to focus on unique integration. Cypher advances are also included, such as improved cost-based planning for queries mixing reads and writes, and expanded support for various Neo4j operations. The release includes additional features, notes about milestone 1, and a call for feedback from users.

Neo Technology, a Swedish company, has expanded its global presence by welcoming Singaporean developers to its Neo4j community. This move is significant as it reflects growing interest in graph databases among academic circles and the wider developer ecosystem, particularly around the Internet of Things (IoT) and smart cities. To facilitate this growth, Neo Technology has established a meetup group and online resources, including LinkedIn groups, Facebook groups, and a dedicated Facebook group for local discussions. Additionally, 1degreenorth, an official Neo4j partner in Singapore, is providing support and resources to developers, including a self-service web portal for accessing HPC-BDA infrastructure. This initiative will launch in the first quarter of 2016 and is open to Singapore-based companies and government agencies looking to start proof-of-concept projects using big data analytics.

In the realm of software development, a generalized Turing Machine can be used to model problem-solving as a search for sequences of instructions or services that transform an initial context into a target one. By expanding the tape into multiple dimensions associated with specific properties and reader/writer heads, this machine can be seen as similar to a robot with getters and setters methods that have costs equivalent to energy spent to operate them. The machine's operations can be decomposed into sequences of basic getters and setters, each with an overall duration and cost. This concept is relevant in the context of microservices, where annotations with Cypher queries provide a set of inputs for services, automating documentation and reuse. A video recognition project illustrates how this concept can be applied to real-world problems, requiring the identification of parse tree generation microservices that can be combined into an abstract syntax tree code solution.

We're Backstory, a news exploration website that uses Neo4j to create rich, interconnected timelines from hundreds of sources. Our goal is to empower people to consume news in a more informative and open-ended way. We've built an extensive "news graph" powered by Neo4j, which allows us to answer interesting questions about the news with simple Cypher queries. This model has propelled us forward in many ways, including using Neo4j clients across languages, leveraging the Neo4j Browser for experimentation, and achieving novel solutions due to graph flexibility's underrated capabilities. We're excited to see what the future holds for our Neo4j-powered news graph and are open to following along with our progress on Twitter and joining our mailing list.