With Benchmarking, we can compare our Neo4j process and performance metrics with stats from other players in the industry. It is a method to measure the quality, time, and cost of implementing a solution in a production scenario. It is also a useful measure to constantly evaluate your needs and the current system metrics to analyze when a change might be required to hardware or software tools.

Response time metrics from Gatling

Although the Neo4j dashboard gives a good idea of the database state and metrics, you can use Gatling, a load testing framework based on Scala, Netty, and Akka that can test your whole application to measure end-to-end performance. The only challenge of this tool is that you need to write your code in Scala. You can use the built-in HTTP library to send requests to the REST server and evaluate the response and create simulations in real time to measure performance. You can learn about Gatling in detail at http://gatling.io/. Gatling also provides a splendid interface to view your metrics graphically; the following are some examples:

The Gatling interface

What we take away from the entire performance testing and benchmarking processes is the prospect of finding loopholes and bottlenecks in the system. We can then apply tweaks and tools to get the Neo4j system run more efficiently. Like any complex system, Neo4j can be properly tuned to achieve optimal performance. When you view the time-varying performance of your graph data store based on the workload characteristics, you can figure out whether it is possible to amortize your setup steps across many queries.

There are several tweaks possible that can help in increasing the performance of the Neo4j system depending on the type of data stored, the size of data, or the type of query operations performed on the data. However, here are a couple of common generic techniques that can help improve the performance of the Neo4j subsystem: