Graph Databases

What are Graph Databases?

Graph databases are a method of data organization that allow complex relationships in data to be prioritized and stored. Unlike other databases, graph databases highlight the relationships between data points as much as the points themselves. 

What are the differences between graph databases and relational databases?

There are two commonly used frameworks for data management in Java: graph databases and relational databases. Both frameworks can be used to build Java applications but differ in both their formatting and capabilities. A relational database is presented in a table format. Within the table, columns and rows are used to organize the data. For example, think of a group of people. Each person would be stored in a different column and the specific information gathered about each person, such as their phone numbers and emails, would be stored across each row. In the relational database, tables are connected by joins, which indicate relationships that exist between tables. This becomes increasingly difficult to model with many or complex relationships. 

Alternatively, graph databases highlight relationships between datapoints as much as the data itself. In graph databases, an entity is the instance of data, which is most comparable to the table in relational databases. A node is most like the row, which is used to represent the specific information collected within the entity. Properties can be compared to columns, which are the distinct data values collected within each entity. Nodes are connected by relationships in this format, rather than joins. In this model, each node is depicted as a circle and the relationships are modeled as lines extending between nodes.  

Relationships can be established across different nodes and are able to accommodate the complexity of many relationships. For an example of graph databases, think of your favorite recipe. Each node would represent a different ingredient and the properties would model their different attributes, such as color or measurement. The interactions between ingredients would be labeled as the relationships: sugar is mixed with butter but also used as a garnish in the end. The graph database can connect all the distinct relationships and isn’t limited to the confines of a table format. Graph databases in Java modeling have several advantages over relational database frameworks, including increased efficiency and communicability.  

Unlike relational databases, graph databases in Java are modeled to create relationships as the data is written, not as it is read. This means that when information is inputted into graph database software, both the data values and the relationships become available for the Java program to use as memory pointers. Data accessibility at the time of writing, not the point of use, increases the efficiency of Java modeling by allowing programs to identify shortcuts and learn patterns.  

Java models use formatting structures like graph databases software, allowing the two components to work efficiently as complements. Similarly, to graph databases, which use nodes and relationships, Java models are constructed with objects and linkages. The similarity of these components allows for information in the graph database to be converted more seamlessly for the Java model to interpret; information can be communicated across models without much translation or adaptation. Translations between relational databases and Java models result in more disconnect, as information must be interpreted and rewritten across the two formats. This increases the time and complexity of Java modeling, as well as the opportunity for error.  

Information in graph databases can also be more easily communicated to the different areas of a business. The idea of nodes and relationships is intuitive, making the information digestible to people unfamiliar with software development. Graph databases function closely to human thought processes. For example, modeling a family tree involves drawing different entities and relationships with circles and lines. The logic behind graph databases follows standard intuition, which can help people understand the information. Therefore, information presented in graph databases can be more easily understood across the business, to people throughout management and even customers. 

Neo4j is the world’s leading graph database, providing open-source graph database software for Java application development. Neo4j graph databases have the added efficiency, scalability and communicability associated with graph database frameworks. In Neo4j, these advantages are a tradeoff of horizontal scaling capabilities. Horizontal scaling is the reorganization of rows within a table to create different data configurations. Since Neo4j stores relationships at the time of input, horizontal scaling was made more complex, but this exchange was made for all the added benefits of a graph database.

What do graph databases look like in Java?

Neo4j is implemented into Java applications through the Java Virtual Machine (JVM). The benefits of graph databases can be limited by a JVM’s capabilities, so even the best graph databases can’t succeed without the best JVMs to run them. JVM’s were created in a pre-cloud world, but many vendors remain stuck in this pre-cloud mindset. The limitations imposed by a pre-cloud world are no longer necessary. When JVM’s are designed for the cloud, their value optimization potential is no longer limited. 

Azul’s role in graph databases

Azul utilizes its 20 years heritage of Java leadership to drive innovation for the Java community, helping companies achieve greater efficiency, higher performance and cost optimization. Azul Platform Prime, the world’s only cloud native JVM, uses resources more efficiently, resulting in optimized code that is more performant.  

When compared to other runtime environments, Azul Platform Prime, was shown to maximize the benefits of Neo4j. Renaissance Suite is a popular Java benchmark that tests the capabilities of various JVM’s on the market. When running Neo4j on Azul Platform Prime, the Renaissance Suite results showed that Azul Platform Prime outperformed OpenJDK by 161.47%. Azul Platform Prime ensures that the opportunities Neo4j brings to the cloud are no longer confined to the limitations of pre-cloud JVM technology.