Java Performance Testing: Best Practices & Tools

With the growing adoption of DevOps and CI/CD practices, performance testing has become a shared responsibility between developers, SREs, and performance engineers. If you are new to performance testing, this article is for you! It covers key aspects of performance testing cloud-native Java applications, including critical performance indicators, testing methodologies, and popular open-source tools. 

What Can Affect Java Application Performance

Java application performance refers to the efficiency of the Java application, i.e., how fast it performs the tasks and how many CPU and memory resources it consumes.It is important to understand which factors can affect it so that when you establish key performance indicators and accumulate required metrics, you know where to look for the culprit.

There are multiple aspects that can impact Java application performance in the cloud, from application code to cloud infrastructure limits. They can be briefly summarized as follows:

Garbage Collection

Java boasts automatic garbage collection that relieves the developers of the manual memory management headache. But automatic doesn’t mean carved in stone: Java offers seven garbage collectors, each fit for a specific purpose. In addition, each of the collectors can be configured to reach the best result.

Small applications can benefit from the default garbage collection settings, but in the case of enterprise projects, default or improperly configured GC can lead to performance deterioration. Frequent GC pauses and improper heap usage can lead to increased latency, decreased throughput, excessive memory and CPU overhead, or even application crashes. 

Thread Contention & Synchronization

Multithreaded programming can make the program highly efficient, but it is fraught with potential caveats such as thread contention, thread locks, and deadlocks, which may result in application slowing down or even hanging.

JVM Configuration

JVM configuration includes adjusting various parameters affecting application performance, including heap size, RAM consumption, GC implementation, GC tuning, compilation level, and many others. For instance, improper heap sizing may result in application crashing with the OutOfMemoryError, and implications of improperly set garbage collector were discussed above.

Cloud Infrastructure Limits

Numerous factors may impact the efficiency of the cloud infrastructure and the cloud costs. A cloud infrastructure built with out-of-the-box technologies and default Kubernetes settings may limit the application performance under peak loads or in the case of traffic increase. The containers may start consuming too many resources, leading to CPU throttling, when Kubernetes limits the amount of resources available to containers, slowing down the application.

In addition, the application may behave differently in the cloud than on the bare metal, leading to inadequate memory consumption, excessive auto-scaling, and pods shutting down unexpectedly.

Container configuration

Some of the common containerization mistakes include improper JVM resource limits in Docker and Kubernetes, inadequate CPU and memory constraints, or lack of container-aware GC tuning. As a result of using improper or default container settings, the application may over- or underutilize available resources, slow down, or even exit unexpectedly.

Key Performance Indicators (KPIs)

Key Performance Indicators or KPIs are quantifiable measures used to assess the application performance. Before starting any performance testing, it is essential to understand what is expected of your application. This can be done by defining Service Level Agreements (SLAs), which are agreements between a service provider and users or clients. SLAs can be seen as performance targets, whereas KPIs are the metrics that show the actual performance of the application in terms of meeting these targets.

In a perfect world, an application works super fast, consumes minimal resources, and can manage the heaviest loads. Unfortunately, we can’t have it all as these performance indicators often compete with each other Therefore, KPIs should be defined individually based on business requirements.

The most important KPIs for Java applications include:

• Response time or latency: Time taken to process a request.
• Throughput: How many user requests the application can handle in a given timeframe. Throughput is usually measured as Requests per Second or RPS.
• Startup time: Time the application needs to start processing incoming requests. Enterprise Java applications usually take several seconds to start. But this KPI should also include the warmup time, which is the time taken by the app to reach peak performance. During the warmup, the application processes fewer requests, but consumes more resources. Slow Java application startup and warmup can be a problem in the cloud.
• CPU & Memory Usage: The amount of CPU and memory consumed by the Java application.
• Error Rate & Fault Tolerance: The ability of the application to maintain stable work despite errors or faults in its components.

Methods of Performance Testing

There are several types of performance tests. Each of them serves a different purpose. Some identify performance bottlenecks, while others assess system resilience, scalability, or reliability. Below are the key performance testing methods that you should consider when evaluating application performance.

Some of the tools mentioned in this section are summarized in more detail further below.

Profiling & Bottleneck Analysis

Profiling is a method of assessing application behavior by gathering metrics on memory allocation, thread behavior, garbage collection, and other JVM operations.

Java profiling can help you identify performance bottlenecks, including code hotshots, thread synchronization problems, and even memory leaks. You may also need a profiler to gather metrics during the performance tests.

There are numerous tools for Java profiling, from easy-to-use profilers that gather essential JVM metrics to ~Application Performance Monitoring (APM) solutions that offer multiple features including infrastructure monitoring or inter-services communication evaluation. You can read a detailed guide to Java profiling to find out about profiling tools, techniques, and best practices.

Two great open-source tools for Java profiling are Java Flight Recorder built into the JVM and a lightweight GUI-less Async Profiler.

Load Testing

Load testing evaluates how the application behaves under normal and increased workloads. It simulates the interaction of a varying number of concurrent users with the system to determine response times, throughput, and memory utilization. Load testing helps to ensure that the application meets the SLAs under various traffic conditions.

Some tools for Java load testing are Apache JMeter, Gatling, and BlazeMeter.

Stress Testing

Stress testing is aimed at pushing the system beyond limits to identify breaking points and recovery tempo. The testing consists in increasing the number of concurrent users interacting with the application until the system fails. As a result, you can define the maximum load the application can handle, identify whether the system gracefully degrades or crashes, and see how fast it resumes normal operation after a failure.

Some popular tools for stress testing are Locust, wrk, and Siege. 

Scalability Testing

Scalability testing evaluates how well the application scales horizontally (adding more instances) or vertically (adding CPU/memory) under increased concurrent load. Scalability tests help you identify performance impact of scaling, how much more load the application can handle after scaling, and whether the auto-scaling strategies lead to delayed pod provisioning. 

If you use Kubernetes, you can take advantage of Kubernetes-specific tools for scalability testing such as K6 and Kubemark.

Spike testing

Spike testing helps you determine how the application behaves under sudden and extreme traffic surges. Unlike load testing where traffic increase is introduced gradually, these tests subject the application to sharp spikes to see how well the application adapts to them. Some performance issues you may reveal are slow response times, timeouts, or inadequate scaling policies.

Spike testing is a necessity for applications that experience unpredictable traffic splashes, such as e-commerce apps during Black Friday or other sales events.

You can configure Gatling and k6 to generate sharp traffic spikes.

Endurance testing

Endurance or soak testing evaluates application behavior over an extended period of time under a stable load. Endurance tests can help you identify memory leaks, resource exhaustion, or gradual performance degradation. 

You can use JMeter configured for long-duration tests or Locust that supports continuous load generation.

Chaos Engineering

Chaos engineering can help you assess the resilience of your application, i.e., its ability to function in the case of real-world disruptions and system failures. It is a testing approach when controlled failures such as server crashes or network failures are introduced into the system to evaluate its behavior under stress.

Some tools for performing chaos experiments are Chaos Monkey, Gremlin, and Chaos Mesh.

Chaos tests help teams to identify single points of failures and improve fault-tolerance of their system.

Open-Source Tools for Java Performance Testing, Monitoring, and Profiling

There’s no tool that can help you manage all tasks related to Java performance testing, which means that you have to equip yourself with several solutions for comprehensive performance evaluation. Many tools are free, some of them require a subscription or offer freemium licenses. 

Below is an overview of some of the most widely used open-source tools, categorized by their purpose:

• Performance testing tools that simulate load, analyze scalability, and measure system responsiveness or resilience;
• Profiling tools that help to monitor application behavior under the load and identify related CPU, memory, and thread bottlenecks;
• Monitoring and tracing tools that provide insights into distributed systems and application observability.

Note that you should use a combination of tools: a load testing tool for creating a load and a profiler for analyzing application behavior under this load. 

Tool	Category	Purpose	Features	Ease of use
JMeter	Performance testing	Load testing, Performance benchmarking, Endurance testing	GUI-based Detailed reporting	Moderate: needs time to get familiar with tool’s UI
k6	Performance testing	Load testing for APIs and microservices	Lightweight Clod-native Integrates into CI/CD	Easy: simple scripts, but requires JavaScript knowledge
Gatling	Performance testing	Load testing, Stress testing, Spike testing	Real-time performance metrics DSL for expressive test scenarios Integrates into CI/CD	Easy: simple setup, can write scripts in Java
wrk	Performance testing	HTTP benchmarking	Multi-threaded request generation Low overhead	Easy: CLI-based, but lacks built-in reporting
Chaos Monkey	Performance testing	Chaos engineering	Randomly terminates services or infrastructure to test failure handling Developed for testing in the cloud environment	Moderate: requires Kubernetes or cloud infrastructure for testing
Locust	Performance testing	Load testing, Stress testing	Can simulate millions of users with distributed execution Web-based UI for real-time test control and reporting	Easy: requires scripting, but readable and flexible
Java Flight Recorder (JFR)	Profiling	Low-overhead profiling and performance analysis	Integrated into JVM Supports hundreds of profiling events Supports custom events	Moderate: No setup needed, but requires time to understand the workflow
Async Profiler	Profiling	CPU & allocation profiling with flame graphs	Lightweight Can profile native calls Generates flame graphs	Easy: simple setup, clear CLI commands
Jaeger	Monitoring and tracing	Distributed tracing for microservices performance analysis	Tracks request flows across services Latency analysis in microservices OpenTelemetry integration	Moderate: requires integration
OpenTelemetry	Monitoring and tracing	Telemetry data generation	Distributed tracing, metric collection, and logging End-to-end observability for cloud-native applications	Moderate: requires manual instrumentation

Best Practices for Java Performance Testing in the Cloud

Java performance testing is crucial for meeting the SLAs and gaining competitive advantage. As modern applications are adapted to the cloud environment or are cloud native by design, performance tests must adapt to these realities. Below are the best practices for testing Java performance for modern demands.

Automate Performance Tests in CI/CD Pipeline and use Canary Releases

Integrating performance tests into the CI/CD pipeline will help you pinpoint performance bottlenecks and regressions as soon as possible. This way, you can remediate the situation before it hits end users. 

In addition, use canary releases by rolling out product changes to a small portion of users. This way, you will be able to monitor performance metrics in a controlled manner and roll back quickly in case of issues.

Design Performance Tests with Serverless and Kubernetes in Mind

Serverless architecture and container orchestration systems such as Kubernetes introduce their own performance challenges that you should consider.

For example, the inheritance part of serverless computing where resources are allocated on demand is the cold start issue. Cold starts happen when the cloud service such as AWS Lambda invokes a function for the first time and needs time to initialize it. Cold starts may become a significant problem with Java due to the prolonged startup and warmup times. Therefore, Java startup time assessment and reduction is a must if you want to use FaaS solutions.

As for Kubernetes, improperly configured auto-scaling strategies can lead to significant — up to several minutes — delays in pod provisioning. As a result, you need to perform load testing to evaluate how Kubernetes Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) behave under the sudden traffic spike.

Use Real-World Traffic Simulation

Using synthetic workloads or testing the cloud-native application on bare metal isn’t very useful. You should simulate the real-world scenarios, traffic patterns, and the production environment to gain realistic insights into the application performance. In addition, you can integrate user behavior variations to gain even more accurate testing results.

Simulating real-world production environments, scenarios, and traffic surges will help you ensure that performance optimizations are aligned with the actual user interactions and expectations.

Use Distributed Tracing to Analyze Request Flows

In the microservices architecture, performance bottlenecks are often difficult to pinpoint because requests often pass through multiple services. Distributed tracing tools like OpenTelemetry and Jaeger help track how requests move across services. This will help you identify latency sources.

By visualizing service-to-service interactions, database queries, and external API calls, developers can pinpoint slow components and cascading failures. As a result, it will be easier to optimize critical execution paths and avoid issues in the case of dynamic scaling.

Conclusion

Java performance testing is an essential procedure that helps IT teams meet SLAs and ensure that applications can handle various situations and traffic spikes without crashing or eating away at resources.

The sooner you integrate performance testing in your workflows, the less performance issues you have to solve in production.

So, what should be your next steps?

• Define KPIs;
• Choose the testing tools and set up the tests;
• Set up continuous performance monitoring in production;
• Optimize the performance if test results are unsatisfactory. You can check out the Guide to optimize Java performance in Kubernetes and the Overview of Java performance optimization techniques to define a suitable performance optimization strategy.

FAQ

How can developers integrate performance testing into CI/CD pipelines?

Performance tests can be integrated into CI/CD pipelines by using tools like JMeter, k6, or Gatling. Tests can be triggered on each deployment, with threshold-based pass/fail criteria.

What are the key performance indicators (KPIs) for Java applications?

Key KPIs include response time, throughput (RPS), CPU and memory usage, startup time, and error rates.

Which open-source tools are best for Java performance testing?

The best open-source tools are Gatling, k6, and Chaos Monkey for performance testing, Async Profiler and JFR for profiling, Jaeger and OpenTelemetry for distributed tracing and observability.

How can I simulate real-world user behavior in Java performance testing?

You can use JMeter, Gatling, or Locust to create test scenarios that mimic real-world interactions, including randomized think times, variable request patterns, and concurrent users.

When should performance testing be conducted in the software development lifecycle?

Performance testing should be done continuously:

• During development using unit-level benchmarks;
• In CI/CD pipelines for regression testing;
• Before production releases under realistic load conditions;
• In production monitoring to detect real-world performance issues.

How do I measure the impact of third-party APIs and external services on application performance?

You can use distributed tracing tools like OpenTelemetry and Jaeger to track API call latencies. Load testing with JMeter or k6 can simulate third-party API failures to analyze their impact on your system.