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In this article, we are going to discuss the different features and use-cases of Neo4j.
This article is an excerpt taken from the book ‘Seven NoSQL Databases in a Week‘ written by Aaron Ploetz et al.
Neo4j’s best features
Aside from its support of the property graph model, Neo4j has several other features that make it a desirable data store. Here, we will examine some of those features and discuss how they can be utilized in a successful Neo4j cluster.
Enterprise Neo4j offers horizontal scaling through two types of clustering. The first is the typical high-availability clustering, in which several slave servers process data overseen by an elected master. In the event that one of the instances should fail, a new master is chosen.
The second type of clustering is known as causal clustering. This option provides additional features, such as disposable read replicas and built-in load balancing, that help abstract the distributed nature of the clustered database from the developer. It also supports causal consistency, which aims to support Atomicity Consistency Isolation and Durability (ACID) compliant consistency in use cases where eventual consistency becomes problematic. Essentially, causal consistency is delivered with a distributed transaction algorithm that ensures that a user will be able to immediately read their own write, regardless of which instance handles the request.
Neo4j ships with Neo4j Browser, a web-based application that can be used for database management, operations, and the execution of Cypher queries. In addition to, monitoring the instance on which it runs, Neo4j Browser also comes with a few built-in learning tools designed to help new users acclimate themselves to Neo4j and graph databases. Neo4j Browser is a huge step up from the command-line tools that dominate the NoSQL landscape.
In most clustered Neo4j configurations, a single instance contains a complete copy of the data. At the moment, true sharding is not available, but Neo4j does have a feature known as cache sharding. This feature involves directing queries to instances that only have certain parts of the cache preloaded, so that read requests for extremely large data sets can be adequately served.
Help for beginners
One of the things that Neo4j does better than most NoSQL data stores is the amount of documentation and tutorials that it has made available for new users. The Neo4j website provides a few links to get started with in-person or online training, as well as meetups and conferences to become acclimated to the community. The Neo4j documentation is very well-done and kept up to date, complete with well-written manuals on development, operations, and data modeling. The blogs and videos by the Neo4j, Inc. engineers are also quite helpful in getting beginners started on the right path.
Additionally, when first connecting to your instance/cluster with Neo4j Browser, the first thing that is shown is a list of links directed at beginners. These links direct the user to information about the Neo4j product, graph modeling and use cases, and interactive examples. In fact, executing the play movies command brings up a tutorial that loads a database of movies. This database consists of various nodes and edges that are designed to illustrate the relationships between actors and their roles in various films.
Neo4j’s versatility demonstrated in its wide use cases
Because of Neo4j’s focus on node/edge traversal, it is a good fit for use cases requiring analysis and examination of relationships. The property graph model helps to define those relationships in meaningful ways, enabling the user to make informed decisions. Bearing that in mind, there are several use cases for Neo4j (and other graph databases) that seem to fit naturally.
Social networks seem to be a natural fit for graph databases. Individuals have friends, attend events, check in to geographical locations, create posts, and send messages. All of these different aspects can be tracked and managed with a graph database such as Neo4j.
Who can see a certain person’s posts? Friends? Friends of friends? Who will be attending a certain event? How is a person connected to others attending the same event? In small numbers, these problems could be solved with a number of data stores. But what about an event with several thousand people attending, where each person has a network of 500 friends? Neo4j can help to solve a multitude of problems in this domain, and appropriately scale to meet increasing levels of operational complexity.
Like social networks, Neo4j is also a good fit for solving problems presented by matchmaking or dating sites. In this way, a person’s interests, goals, and other properties can be traversed and matched to profiles that share certain levels of equality. Additionally, the underlying model can also be applied to prevent certain matches or block specific contacts, which can be useful for this type of application.
Working with an enterprise-grade network can be quite complicated. Devices are typically broken up into different domains, sometimes have physical and logical layers, and tend to share a delicate relationship of dependencies with each other. In addition, networks might be very dynamic because of hardware failure/replacement, organization, and personnel changes.
The property graph model can be applied to adequately work with the complexity of such networks. In a use case study with Enterprise Management Associates (EMA), this type of problem was reported as an excellent format for capturing and modeling the inter dependencies that can help to diagnose failures.
For instance, if a particular device needs to be shut down for maintenance, you would need to be aware of other devices and domains that are dependent on it, in a multitude of directions. Neo4j allows you to capture that easily and naturally without having to define a whole mess of linear relationships between each device. The path of relationships can then be easily traversed at query time to provide the necessary results.
Many scalable data store technologies are not particularly suitable for business analysis or online analytical processing (OLAP) uses. When working with large amounts of data, coalescing desired data can be tricky with relational database management systems (RDBMS). Some enterprises will even duplicate their RDBMS into a separate system for OLAP so as not to interfere with their online transaction processing (OLTP) workloads.
Neo4j can scale to present meaningful data about relationships between different enterprise-marketing entities, which is crucial for businesses.
Many brick-and-mortar and online retailers collect data about their customers’ shopping habits. However, many of them fail to properly utilize this data to their advantage. Graph databases, such as Neo4j, can help assemble the bigger picture of customer habits for searching and purchasing, and even take trends in geographic areas into consideration.
For example, purchasing data may contain patterns indicating that certain customers tend to buy certain beverages on Friday evenings. Based on the relationships of other customers to products in that area, the engine could also suggest things such as cups, mugs, or glassware. Is the customer also a male in his thirties from a sports-obsessed area? Perhaps suggesting a mug supporting the local football team may spark an additional sale. An engine backed by Neo4j may be able to help a retailer uncover these small troves of insight.
To summarize, we saw Neo4j is widely used across all enterprises and businesses, primarily due to its speed, efficiency and accuracy.
Check out the book Seven NoSQL Databases in a Week to learn more about Neo4j and the other popularly used NoSQL databases such as Redis, HBase, MongoDB, and more.