What is performance engineering?

Performance engineering is the practice of optimizing IT systems to meet benchmarks for speed and efficiency.

Performance engineering is not just a single action, but a DevOps and shift-left methodology that enables businesses to track and optimize performance at every step of the software development lifecycle (SDLC). Its goal is to ensure that systems meet performance metrics for criteria such as speed, reliability, efficiency and response time.

Performance engineering teams first establish baseline system performance through stress testing. Then, they use that baseline to identify network issues and opportunities for improvement. When the benchmarks are set, engineers can begin to reconfigure the network, put fixes in place and continually monitor the network for performance issues and future capacity planning.

Observability is at the foundation of performance engineering. Observability tools collect the raw data (logs, metrics and traces) that describe system performance, and performance engineering teams use this same data to track the effects of their fixes. Performance engineers also use various other tools for application performance management and monitoring, stress testing, browser auditing and benchmarking to gain the clearest possible images of their systems.

Performance engineering is the broader, end-to-end discipline of optimizing IT systems to meet predetermined benchmarks. Application performance management (APM) and performance testing are two of the activities involved in that overall process.

Application performance management is a practice that uses software tools, data analysis and application management processes to help organizations optimize the performance, availability and user experience of business applications. While performance engineering spans the entire development process, APM focuses on detecting and fixing problems in live applications.

Similarly, performance testing is the specific activity of testing a network or application’s performance under various conditions through load testing, stress testing, endurance testing and other tests. Like APM, performance testing is just one activity within the broader practice of performance engineering.

Performance engineering is accomplished through a flexible but comprehensive sequence that includes setting benchmarks, testing and prioritizing, optimization, planning and performance monitoring.

First, the organization identifies the level of performance required for its systems and applications to meet business goals. Then, performance engineers test current performance to establish reference points and determine how to meet benchmarks.

Common benchmark metrics include latency, throughput, resource utilization and error rate. Development teams can measure these metrics at the micro-level (within one particular server or service) or on a larger scale across an entire application or network.

Benchmarking often involves specific questions about performance requirements and the development environment. For example, instead of attempting to set a general threshold for CPU utilization, engineers might ask whether less than 60% of CPU is used when 5,000 users are concurrently using an application.

Using performance testing tools, performance engineers validate test data against established benchmarks to identify where, and which, changes should be made to meet required service levels.

Common forms of performance testing include:

After identifying the system’s limits and shortcomings, the process of optimization begins.

Depending on the nature of the performance bottleneck in question, performance engineers might use optimization strategies such as:

• Algorithm optimization, where the algorithms that power system functions are tweaked to improve efficiency.
  
  
• Database optimization, where underlying issues with system data are fixed through processes such as query optimization (the process of determining the most efficient way for a database to execute a query) or index optimization, which aims to ensure that the system uses the fastest possible retrieval paths.
  
  
• Caching, which stores the results of demanding computing operations so they can be more quickly reproduced and retrieved.
  
  
• Memory optimization, where engineers reduce memory leaks and speed up garbage collection by reusing objects in memory, reorganizing data or storing some data offsite.
  
  
• Network optimization, which involves bandwidth compression, load balancing, the reuse of HTTP connections and other methods to speed up network performance.

After the system has been optimized, performance engineers continually monitor for divergence from the new baseline and plan for future growth and activity.

Observability enables performance engineers to determine whether their system is performing as planned. By collecting and analyzing logs, metrics and traces, observability tools allow IT teams to automate problem identification and resolution in real time. The more observable a system, the more quickly and accurately IT teams can shift from an identified performance issue to its root cause without extra testing or coding.

Capacity planning allows performance engineers to stay one step ahead of business need by anticipating future IT infrastructure requirements. Capacity planning involves analyzing current demand and available capacity, comparing it to the organization’s capabilities and resources. Organizations then develop an adjustable strategy that enables them to efficiently scale resources and production.

The benefits of performance engineering include an improved user experience, a more scalable IT infrastructure, more efficient problem resolution and improved capacity planning.

Performance engineering improves the user experience by fixing performance issues such as high latency, which can drive users away from a service. By optimizing the process of software engineering and its outputs, performance engineering can help build trust with users and drive repeat business.

Performance engineering offers a clear picture of the issues within a system. This picture makes it easier to avoid bottlenecks when expanding that system (either horizontally, by adding new services, or vertically, by using more network capacity).

Performance engineering helps ensure that engineers are equipped with the tools and knowledge they need to produce systems that meet established benchmarks. Engineers can solve performance problems more quickly, reducing mean time to repair (MTTR), and at less cost, because problems are caught before they have the opportunity to significantly disrupt network performance.

Performance engineering can help improve the effectiveness of capacity planning by improving engineers’ understanding of how systems behave. Through the benchmarking process and ongoing observability practices, engineers gain greater insight into what their networks need. This insight helps engineers make better decisions about capacity, reducing the risk of both overspending and underspending on server capacity.

Challenges of performance engineering include the complexity of modern systems, identifying root causes of problems, accounting for “long-tail” problems and building the necessary toolsets and expertise.

Modern IT environments are dominated by microservices that can number in the thousands, often hosted in complex hybrid cloud environments. Collecting, analyzing and acting on insights across these distributed systems can be a resource-intensive process with sometimes unpredictable workflows.

Complexity also makes it more difficult to identify the true root causes of network issues. If an API is responding slowly, it might be the result of a poorly indexed database, a memory leak or a configuration issue. Performance engineers might need to perform root cause analysis to identify the actual opportunity for optimization.

Long-tail issues are poor network conditions experienced by a small minority of users. They are often caused by idiosyncratic, hard-to-detect problems that evade normal observability practices. In the context of performance engineering, these issues pose a challenge because they threaten overall network conditions but their root causes are difficult to uncover through normal performance testing.

The practice of performance engineering requires staff expertise and sophisticated platform capabilities. Performance testing requires expensive, large-scale simulations of network conditions. Teams must understand systems well enough to turn a large amount of telemetry data into actionable insights. The flexible, iterative nature of performance engineering requires an institutional structure that can handle rapid change.