The pace of change has never been this fast for Performance Engineering. Before we even realize, Performance Testing transformed from independent and traditional practice to a broader and deeper arena – Performance Engineering. The rate of change further accelerated with the rise in competition and consumers getting spoiled with countless choices.
Amid the array of diversity, the responsibility of tackling competition and living up to the consumers' expectations fell upon the Performance Engineering team. Therefore now, more than ever, is the time to adopt best practices and track trends to ensure your product or service is optimized.
Since Performance Engineering is the hook that will keep your customers anchored to the product, it’s crucial to stay updated with the developments in the market. Here are the top trends in Performance Engineering that will keep you ahead and seal your market position.
Performance Engineering Top Trends
Testing the performance and analyzing the results towards the end level of product development is too risky. The technical debt that comes with it is huge. To build a sturdy, fail-proof product, Performance Engineering needs to be baked into the software from the very beginning. The practice of developers writing performant code from the start is becoming an SOP and gaining acceptance.
Since Performance Engineering is about understanding how all the parts of the system fit together, it’s important to know the performance quality metrics from the first design. Therefore, going back or testing later is dangerous as it easily becomes ‘too little, too late.’
Simply put, the practice implies that organizations should leave behind the simple “record/playback” testing (that happens late in the product cycle) and move towards a more robust engineering approach that starts early in the cycle and takes place continuously.
Focus on DevOps
One of the core objectives of Performance Engineering is to deliver fast, efficient, and responsive systems. DevOps helps the Performance Engineering team meet the purpose in time and remove hindrances in product delivery or updating the system. Earlier, the organization's structure around the SDLC acted as a blockade to faster release. A product had to go back and forth through various levels before it could be marked fit for the market. The process allowed organizations to remove risks and improve quality. However, that also meant a delay in the release of the product in a highly competitive world. DevOps combines various processes, smoothens stages, and integrates tools to deliver speed without compromising quality.
In Performance Engineering it’s important to gauge human perception. Because in the LIVE environment, people interact with actual products and not with networking protocols. “The days of just recording load tests through a browser and playing it back are coming to an end, if not already over,” said Leandro Melendez, consulting performance tester of Qualitest. So, it’s important to run scripts in a space that stimulates user behavior. That’s the best way to measure actual, real-world performance that users will experience. In the end, it’s the users who will first flag the issue with a web browser or mobile application.
There are several benefits of introducing auto-scaling into the Performance Engineering lifecycle. First and foremost, it saves the company from the embarrassment it may face due to performance degradation or system crash. The method, associated with cloud computing, automatically adjusts the number of resources to meet the traffic requirement. Moreover, it saves costs by reducing resources when not required. With the auto-scaling infrastructure in place, management associated with scaling becomes a cakewalk. However, auto-scaling comes with its cons, like increased development complexity and regional limitations. One should understand them before implementing auto-scaling.
Developing a Performance Engineering Culture
Performance Engineering is widening and deepening in scope and scale. Businesses are recognizing Performance Engineering’s contribution to winning clients with a blink-of-an-eye digital experience and preventing users from negative exposure. With the rising significance, experts are seeing the development of a culture where product performance responsibility falls past the QA team.
Everyone in the business, from developer to product owner, is taking their fair share of responsibility to address the evolving needs of end-users. The collaborations smoothen the process as the Performance Engineers can easily coordinate between teams, tools, and processes for maintaining the continuous feedback loop.
In order to bring the Performance Engineering culture, it’s important to push the agile team to think about the Performance as early as possible. It helps companies deliver value at a rapid pace as compared to traditional set-ups.
Performance Engineering Is Everyone's Job
Why Blue-Green Deployment?
- Helps achieve zero downtime
- Minimize the impact of a bug
- Supports agile methodology
- Improves CI/CD
The technique greatly improves error detection and reduces downtime and risk by maintaining two clones of production, called Blue and Green. Blue-Green deploys are a popular technique where the user traffic is gradually routed from a previous version of an app or microservice to a similar environment, and a load balancer switches traffic from the older version to the newer version.
AI, Machine Learning, and Sentiment Analysis
To obtain accurate trend assessment, performance engineers are required to work with the right data. Artificial intelligence makes the rendering of reliable data easier and faster. Furthermore, Machine learning algorithms are applied to predict user patterns, curate high-quality data, and filter the information as per the business requirements.
Sentiment Analysis is an underrated practice where customers’ tickets and feedback are examined to understand user perception. Sentiment Analysis tells companies what the user definition of “slow” is and allows them to set the SLA at an appropriate level so they don’t end up spending time on unnecessary amendments.
Overall, AI-powered techniques enhance the quality of performance monitoring and testing and spare humans from tedious tasks.
Future Of Performance Engineering
Performance Engineering is becoming mainstream in the world of technology. Organizations need it to maintain high-performance levels for all systems and ensure end-to-end system optimization. The practitioners, eventually, are understanding that writing bug-free code is not enough, and they are expected to create a holistic product that is user-friendly, safe, and scalable. Hence, it’s important to embed Performance Engineering across all phases of SDLC, along with a continuous assessment of performance metrics.
Performance Engineering, as the framework, is helping organizations fix responsibility on everyone, from designing, and development, to QA testing teams, to ensure high performance under all circumstances. Given the significance it holds, Performance Engineering helps companies fine-tune their systems with advanced technologies and give rise to highly available and scalable products, even in events of failure. Therefore, the demand for Performance Engineers is destined to boom in the coming future.
Besides, organizations become more complex as they grow. To deal with growing complexities, the industry will need people with expertise to fix them. This also essentially means that the role of performance engineers is bound to evolve. And to stay relevant, it’s extremely important to up-skill in the architectural and data-science space.
In a nutshell, to address the ever-changing technological landscape, it’s certainly important to match foot with future trends of Performance Engineering, and adopt the mindset of an ‘enabler,’ rather than that of ‘deliverers.’
If you want to get expert advice on specific performance issues of your application, drop an email sometime. We’d love to brainstorm with you. email@example.com