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5 Tips to Achieve Performance Engineering at Scale

The expertise needed to tackle an enterprise’s performance engineering is not always readily available. Here's how organizations can leverage technology to help.
Mar 30th, 2023 10:00am by
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Performance testing enterprise applications requires a high level of expertise as today’s applications are complex and require numerous supporting applications and systems. With every release, testers and developers need to ensure each outperforms the last and performance doesn’t degrade. But only a handful of experts within an organization can reliably measure the performance of a system as complex as the modern application stack.

And in today’s environment, the expertise needed to tackle an enterprise’s performance engineering is not always readily available. As a result, enterprises are finding quality assurance to be a bottleneck as they struggle to keep pace with release cycles. Or worse, experience outages that impact their business.

This predicament requires enterprises to consider how they are going to scale applications released daily with limited resources. Since personal expertise is difficult to scale, organizations can leverage technology to help, supporting the requirements for different types of teams, applications and deployment models. This approach not only empowers DevOps teams but fortifies and simplifies repeatability for project success.

What exactly does this approach look like? Where should enterprises start? And are there any larger considerations for implementation? Let’s answer these questions with five key aspects of a successful implementation for performance engineering at scale.

1: Promote Collaboration Between Experts and Non-Experts

Enterprise-wide performance engineering is a team sport. A highly collaborative approach fosters efficiency and effectiveness and enables performance expertise to scale without the need for additional experts. Developers, performance engineers, business analysts, and others working as a team make it easy to design tests with agreed-upon service level objectives (SLOs) that define measurable performance metrics. This ensures that everyone is measuring performance consistently and getting consistent results most efficiently. With consistent reporting, aspects such as root cause analysis and trend reporting are easier to generate across the board.

When operating effectively, performance engineering experts take on more of an enabler role. Instead of assuming responsibility for all testing operations themselves, they create the strategy and building blocks that allow non-experts to test at speed and scale. They can structure the test environment, implement quality-control guardrails, enhance delivery pipelines, and embed best practices into performance engineering processes that empower decentralized teams.

2: Automate Performance Testing in Continuous Integration (CI) Pipelines

Given today’s rapid development cycles, it’s impossible for performance engineers to manually build, run and analyze daily performance tests for hundreds of code pushes. These skilled engineers should instead focus on more strategic tasks. By integrating automated performance tests into CI pipelines as part of the pass/fail criteria, teams of developers can start testing APIs, microservices, and components at the beginning of the development process and continue testing throughout the evolution of the application.

3: Utilize No-Code/Low-Code Tooling

If different teams need to tailor their performance testing approach for their unique needs, testing must be user-friendly. Widespread adoption of testing tools correlates with ease of use and a shorter learning curve. Teams should seek tools utilizing no-code/low-code approaches that leverage intuitive drag-and-drop and point-and-click functionality to maximize adoption and ensure high quality.

In addition to testing through a codeless GUI, the platform should specifically enable DevOps teams to run tests in the command line interface (CLI) or within their day-to-day integrated development environment (IDE). It’s important to utilize no-code/low-code tooling to test fast, and release applications even faster. However, how quickly tests can be created is directly related to how easy the testing tool is to use, hence the need for tools with an easier interface.

4: Consider a Cloud Native Approach

Today, enterprises must ensure their approach to performance engineering is cloud-ready as more applications, processes and tools move to the cloud. An enterprise’s approach to performance engineering in the cloud should anticipate several complexities.

First, performance engineering tools should be vendor neutral so that performance and scalability can be measured across various cloud providers such as Azure, Amazon Web Services and Google. If one cloud provider has an issue, organizations need to have their applications ready to shift from one cloud provider to another quickly to prevent their users from noticing a change. While this is mainly for larger businesses, it is critical to understand your cloud service provider vendor lock-in impacts to ensure your business success.

Additionally, if a business is working on a migration initiative, whether that is to lift and shift to the cloud, re-platform, or refactor, organizations need to baseline performance before and then validate after the migration occurs. Despite its complexity, performance testing is crucial to perform alongside any significant changes.

5: Choose a Solution That Covers All Bases

In adopting a single performance engineering approach, an enterprise should first standardize on a performance testing solution that’s designed to support a wide range of enterprise testing requirements. A typical enterprise will employ a gamut of technologies and methodologies, carried out by either centralized teams of internal and external experts, autonomous development teams, or a combination of both. A standardized platform must work well for each of these groups equally. In addition, the chosen platform needs to test everything from enterprise-grade legacy monolithic core systems to dynamic microservices-based applications and packaged applications to ensure the migrations or transitions don’t have performance degradation.

When aligning on the solution, there should be a similarly wide technology coverage, from the newest frameworks to the tried-and-true technologies. Enterprises should be able to use the same solution to test individual APIs at the component level and the performance of all apps end-to-end. A standardized platform must work equally well for each scenario.

Finally, the chosen platform should not limit enterprises to a single deployment option. Virtually every organization’s environment is a combination of network types: on-premises and multiple public cloud providers. As enterprises increasingly modernize their business, they need a solution that can test performance for complex migrations. For example, moving SAP to an S/4 HANA implementation.

Today’s enterprises not only demand but expect the highest level of confidence in the quality of their software releases. While there are simply not enough expert DevOps professionals to keep up with the rapid pace of development, an agile performance engineering approach enables organizations to empower non-expert performance engineers to give enterprises the predictability, validation, scalability, and assurance they require. When companies create high levels of collaboration, utilize no-code/low-code tooling and automation, think cloud natively, and select solutions that can be utilized in all environments, they will ensure their business and critical applications are scalable for years to come.

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