What Is Performance Engineering? A Comprehensive Guide

In today’s competitive market, modern software must meet all needed performance-related requirements. Traditional QA testing can help you with this, but it may not suffice to achieve the desired level of reliability and long-term software performance. Due to this, most IT organizations implement the performance engineering process. It offers more than just fixing issues; it ensures optimal performance of an application as it scales and expands throughout its lifecycle.

In this article, we provide you with a comprehensive guide on “what is performance engineering?”, its main phases, benefits and challenges, tools, and best practices to help you understand this approach better and succeed in quality software development.

What is performance engineering?

Performance engineering is a proactive, continuous, and collaborative approach\practice to testing and monitoring software performance throughout the software development life cycle (SDLC). This process allows organizations to identify performance issues early on in SDLC and conduct cost-effective testing. Its main goal is to deliver high-performance, reliable, and scalable software that meets both user and business needs. Also, performance engineering helps organizations to:

• Avoid system failures;
• Avoid unnecessary tuning efforts;
• Ensure timely deployment;
• Optimize hardware costs;
• Reduce maintenance costs.

Performance engineering goes beyond just testing software. It includes seamless collaboration between teams, processes, and tools to support optimal system performance through continuous feedback loops. Even though performance engineering and performance testing are two different processes, people often confuse them. So, let’s see how they differ. 

Performance engineering vs. performance testing

Software performance engineering and performance testing go hand in hand. However, the two methods have different purposes and cover different activities. Let’s see the detailed differences between both, starting with performance engineering first:

• Goal: to optimize app performance throughout the SDLC and maintain it at its optimal level;
• Focus: performance improvement and optimization; 
• Timeframe: continuous throughout the entire development lifecycle, from design to quality assurance;
• Scope: it covers all engineering activities and deliverables.

Performance testing includes:

• Goal: evaluate performance and scalability of an app under various conditions;
• Focus: performance evaluation and validation;
• Timeframe: it typically occurs after development and before deployment;
• Scope: limited to specific scenarios and test cases.

If we look at a performance engineer and performance tester, their responsibilities also differ. So what is performance engineering? Performance engineers ensure that software, hardware, and systems operate efficiently. In short, they oversee the app’s performance lifecycle and then recommend the right improvements. Their responsibilities include identify bottlenecks, analyze performance issues, give recommendations based on test results, and more. 

In contrast, performance testers are responsible for designing, executing, and analyzing performance tests (like loads, stress, scalability, and capacity testing) to evaluate the responsiveness, stability, and scalability of apps. They measure performance metrics, such as latency, response time, throughput, etc., and identify performance issues and bottlenecks (often in collaboration with developers) to improve app performance. 

Benefits of performance engineering 

Organizations can benefit from performance engineering in several ways:

• Reduced rework and refactoring: by detecting and resolving performance issues early (before they escalate), it prevents extensive rework and refactoring code later on during development;
• Improved user experience: when an app functions as users expected (fast response times, reduced latency), resulting in a seamless user experience;
• Improved users’ trust: high-performing systems enable companies not only to gain users’ trust but also to retain them in the long-term, building strong reputations;
• Reduced costs: by addressing functionality issues early in the development cycle, organizations can avoid costly rework and downtime post-deployment;
• Increased revenue: increased conversions, reduced downtime and excellent UX achieved by better performance contribute to high revenue generation; 
• Early detection of issues: it eliminates the risk of performance-related failures in production by detecting bottlenecks early on.

As you can see, the investment in performance engineering not only ensures better app performance outcomes but also contributes to overall customer satisfaction and keeps finances within budget. Although performance engineering brings numerous benefits to companies, it also poses some challenges.

Challenges of performance engineering 

Here are a couple of performance engineering challenges that companies may face:

A lack of understanding of the process.

A lack of understanding of the performance engineering process, including needed resources, tools, specialists, and best practices, can pose future problems for organizations. These include inadequate resource allocation, poorly fine-tuned processes, and failure to achieve the desired results.

Selecting the right tools.

Organizations may struggle to evaluate and choose tools that align with their objectives, budget constraints, and technical infrastructure. Also, a lack of expertise in using these tools can exacerbate this problem.

Time and resource costs.

Performance engineering requires significant investment in skilled personnel, specialized tools, and infrastructure. These resources are expensive to acquire and maintain. Also, optimizing this process takes time, further increasing the overall investment.

Scalability and load variability.

Scalability and load variability pose challenges in predicting and modeling realistic workloads, user interactions, and traffic. Systems must scale smoothly to cope with load fluctuations while delivering optimal performance. The inability to take scalability requirements and load variability into account can lead to the degradation of user experience during peak load periods.

DevOps optimization.

Continuous integration, delivery, and deployment are core to DevOps, so performance testing is necessary at multiple stages. However, integrating performance testing seamlessly into the iteration cycles of DevOps can be challenging. Organizations must strike a balance between speed and quality while effectively incorporating performance testing into their DevOps workflows.

Complex architecture.

Often, modern software systems have complex architectures consisting of microservices, cloud-native technologies, etc. Performance engineering of such complex systems can be challenging, as it requires understanding components’ interactions, identifying bottlenecks, and optimizing resources. This complexity makes predicting and resolving performance issues problematic, requiring robust testing strategies and tools.

Addressing these challenges requires a strategic approach, informed tool selection, and a clear understanding of the main phases of performance engineering. Let’s look at them.

The main phases of performance engineering

The main phases of performance engineering in software development include:

1. Requirement analysis 

In this initial phase, performance engineers gain an understanding of the overall system architecture, including its components, modules, and dependencies. They collaborate with stakeholders to pinpoint non-functional requirements (NFRs) related to performance. These NFRs go beyond functionality and include aspects like responsiveness, security, usability, and portability. Also, stakeholders gain insights into:

• Anticipated user numbers;
• Expected transaction volumes and traffic levels;
• Potential performance bottlenecks stemming from integrated systems, etc.

The purpose of this phase is to identify and address potential bottlenecks, thus reducing the need for rework later. The performance engineering phase revolves around making well-informed decisions regarding technology, tools, and further design consideration.

2. Architecture design

During this phase, performance engineers focus on creating a system architecture with performance considerations in mind. Engineers collaborate closely with architects and developers to ensure that performance requirements are integrated into the system’s architecture from the outset. This process involves considering factors such as load balancing, caching, data storage, and optimization for scalability and fault tolerance.

Moreover, engineers establish monitoring and observability strategies for proactive performance management, identify potential design flaws, and provide performance-related recommendations.

3. Performance modeling

At this stage, performance engineers create an accurate performance model that mimics real-world user loads and system responses. How does it work? Engineers use tools or mathematical models to set up a model to simulate real-world scenarios, including user loads, transaction volumes, and system interactions. This model serves as a representation of how the system is expected to perform under various conditions.

This approach allows engineers to identify areas that may require optimization, identify potential performance issues, and better understand their system’s behavior. 

4. Performance profiling 

Performance profiling involves analyzing software code to identify resource-intensive sections. It aims to understand how the application behaves in terms of execution time, memory usage, runtime behavior, and other relevant metrics. During this phase, engineers use specialized tools known as profilers to gather detailed data on the execution of the software code. 

Each programming language has its profiler; for example, for Python are cProfile, Pyflame. Profilers are integrated into the development environment or deployed alongside the application to monitor its execution. Profiling code helps engineers identify bottlenecks in an application and areas for performance optimization. This may involve code refactoring, algorithm optimization, caching strategies, or other techniques.

5. Testing and validation: performance testing

As we mentioned above, performance testing is one part of the performance engineering process where QA testers evaluate how a system functions under various conditions. They evaluate speed, responsiveness, stability, reliability, and stability of a software app. Unlike functional testing (which examines individual functions of software), performance testing is non-functional and aims to determine system readiness for deployment.

During this phase, performance engineers provide guidance and expertise in designing performance test scenarios and selecting appropriate testing tools. They analyze performance test results in-depth to identify root causes of performance issues. Based on testing results, they make needed adjustments to improve the performance and reliability of a system. 

6. Optimization: analysis and fine-tuning

Here, engineers analyze the results of performance tests to provide valuable feedback and suggestions for improving code or a system. These suggestions may include recommendations for:

• Architectural changes;
• Code refactoring;
• Adjusting configurations;
• Database tuning;
• Resource allocation (CPU, memory, disk I/O);
• Caching strategies, etc.

Also, engineers develop customized fine-tuning strategies to target specific problem areas and maximize performance improvements. Once these optimizations are implemented, it is necessary to conduct performance testing to determine whether they have improved performance.

7. Monitoring and maintenance

Once the system is deployed, performance engineers need to regularly monitor and maintain a high level of system performance. This involves keeping track of key performance metrics, such as response times and error rates, in real-time. Based on the monitoring feedback, they must perform patches, updates, and optimizations as needed. Observability and monitoring tools are crucial in this phase, as they provide insights over the long run.

Moreover, it helps the performance engineering team identify trends, such as increased or decreased system usage. Regular maintenance and updates are necessary to keep the system performing and reliable over time.

Best practices for performance engineering

To make the performance engineering process more effective, you need to be aware of the best practices from the start. Here are some of them:

Integrate performance engineering as early as possible

By integrating performance engineering early on, engineers can identify and address potential performance issues before they occur. This allows for better optimization of the system’s architecture, code, and design from the start, resulting in a more efficient and scalable system. 

In contrast, without this process, performance issues may go unnoticed until later stages. This can lead to significant rework, refactoring, and optimization efforts, resulting in project delays and increased development costs. Moreover, it can lead to poor performance, scalability issues, and user experience as well.

Refactor rather than tune

To improve the performance of a system, it is better to make structural changes to the code or architecture, rather than tweaking or adjusting existing components\ functions. By refactoring, engineers can eliminate inefficiencies, reduce technical debt, and improve overall system performance in a more systematic and comprehensive manner. This approach leads to more sustainable improvements and the long-term maintainability of a system.

Because a tuning project focuses on adjusting existing components or functions within the current architecture, it cannot provide the same efficiency as refactoring. While it might save time for your organization, it is not an effective strategy in the long-run.

Select the right tools

Companies must stay up-to-date on emerging technologies, tools, and performance optimization strategies to automate processes and meet business needs. To achieve this, they must evaluate and invest in the appropriate performance engineering tools. These investments allow engineers to better:

• Analyze vast amounts of data rapidly;
• Collect precise metrics;
• Identify and rectify performance issues quickly;
• Streamline performance testing, monitoring, and analysis.

Continuous performance monitoring

Continuous performance monitoring throughout the app’s lifecycle is essential for stable results. By monitoring key metrics regularly, engineers can stay ahead of potential issues, implement necessary optimizations, and maintain a high level of user satisfaction.

Run each test multiple times

Another recommended best practice is running tests multiple times. Engineers can verify the consistency of the results by repeating tests over and over again. Also, this practice allows engineers to identify any potential fluctuations or anomalies in the system’s behavior over time.

Use visualization for anomalies

Engineers use data visualization techniques to understand the behavior of software systems. By comparing the performance of an app under test to that of a perfectly optimized version, they can detect irregularities or anomalies in various system metrics. Since anomalies may not always be apparent in raw data or reports, this method is particularly effective. It allows engineers to detect performance-related issues faster and address them more promptly.

Use realistic test setups 

Realistic test setups allow engineers to evaluate how the system behaves under real-life conditions. This process should be similar to the one used in a real production environment. For this, engineers must be certain of the following: 

• Hardware: ensure that the test environment matches the specific hardware used in production;
• Software versions: use the same software versions as in the live system;
• Network configurations: replicate network conditions, latency, and bandwidth;
• User volume: simulate the expected user load accurately;
• Third-party integrations: include all third-party services or integrations used in production.

Realistic test setups in a production-like environment ensure more accurate results. When the system behaves as expected in testing, deployment can be done with confidence.

Performance engineering tools

The different phases of the performance process require different tools. So, it may be challenging to choose between available options in the market. To help you make this process easier, we’ve put together the main performance engineering tools:

Load testing tools:

• Apache JMeter: an open-source Java-based load testing and performance measurement tool. With capabilities like graphical reporting and scripting, it facilitates the testing of databases, FTP servers, web apps, and more;
• LoadRunner: a load testing tool available for purchase that is compatible with many protocols and technologies, such as mobile, API, database, etc. It offers features like correlation, analysis, and script recording,etc;
• Gatling: a scala-based, open-source load testing framework constructed on top of Akka. It offers real-time result visualization and lets users construct scenarios using a domain-specific language (DSL);
• Blazemeter: a commercial load testing platform that provides cloud-based load testing services, and is based on JMeter. It offers advanced reporting features, scalability, and connection with CI/CD processes.

Performance profiling tools:

• YourKit Java Profiler: for analyzing CPU and memory usage;
• JProfiler: a Java profiler with an intuitive UI.
• VisualVM: free Java profiler that analyzes memory usage, CPU performance, and thread behavior;
• Xdebug: a PHP extension that gives PHP programs the ability to be profiled and debugged. It can generate profiling reports showing function execution times and memory usage.

Real-time performance monitoring tools:

• New Relic: offers application performance monitoring (APM) and infrastructure monitoring;
• AppDynamics: monitors app performance, user experience, and business metrics.
• Grafana: an open-source visualization tool that can be used for real-time monitoring and observability. It supports integration with various data sources and provides customizable dashboards for visualizing metrics and logs in real-time.

Tools for log analysis and query tracing:

• ELK Stack: Elasticsearch is a distributed search and analytics engine that can be used for log analysis and query tracing. It allows users to index and search large volumes of log data in real-time, perform complex queries, and visualize results in Kibana;
• Graylog: an open-source log management platform that enables users to collect, index, and analyze log data from various sources. It offers features like full-text search, stream processing, and alerting to facilitate log analysis and query tracing.

Tools to automate the performance testing process:

• Jenkins: is an open-source automation server that supports building, deploying, and automating any project, providing hundreds of plugins to enhance its functionality;
• TeamCity: is a general-purpose CI/CD platform offering flexible workflows, collaboration features, and integration with popular build and test tools;
• Travis CI: is a hosted CI service that builds and tests software projects hosted on platforms like GitHub, GitLab, and more. It offers parallel builds, clean VMs, and auto-deployment on passing builds;
• GitLab CI/CD: is an integral part of the GitLab DevSecOps platform that automates software development workflows, including building, testing, and deploying;
• CircleCI: is a cloud-based CI/CD solution that provides real-time performance insights, allowing automated validation, integration, and deployment tasks with one-command automation.

Conclusion 

Performance engineering is a fundamental practice in modern software development, enhancing system performance and reliability from the beginning to beyond. By addressing performance issues proactively, you will ensure the highest level of performance, user experience, and scalability. We do not say that performance engineers replace QA testers. Instead, they complement each other to make a forward-looking solution rapidly and reliably. The answer to “what is performance engineering?” is how organizations view their core processes toward optimal performance.

Expert Opinion

Performance engineering is a strategic approach to ensuring software applications deliver optimal performance, scalability, and reliability. It is necessary to include performance concerns in the software development process to identify and address performance issues early on and save time and money. 

One of the key benefits of performance engineering is its ability to prevent bottlenecks and scalability limitations before they affect users. Companies may detect possible problems, make well-informed decisions, and apply focused optimizations to make sure applications can scale to meet increasing user needs by carrying out comprehensive performance testing and analysis.

In conclusion, performance engineering aids businesses in producing high-quality software products that meet user expectations, optimize return on investment (ROI), and give them a competitive advantage in the current digital environment.

QA Engineer at SoftTeco

Anastassia Svistunova