Being a programmer is about much more than merely writing bug-free code. As highly distributed applications become more complex, developers must make their systems as user-friendly, secure, and scalable as possible. Application performance engineering is a necessary practice for any DevOps team, enabling developers across departments to stay agile and efficient.
As performance engineering is gaining ground in software development, it is crucial that companies – even smaller ones – pay attention to the differences between performance engineering and performance testing. Organizations must take steps to implement a performance plan that will deliver results.
With proper integration, teams can identify potential performance issues in their applications much earlier in the development process and create consistent, high-quality fixes. Everything from automated network systems to an evolving cloud infrastructure to collecting and analyzing more UX data requires your teams to integrate reliable testing processes into application development.
When it comes time to start implementing your performance plan, you may be wondering what the difference is between performance testing and performance engineering. As you review your research, you may come across various performance definitions. Meanings may vary by organization, but be aware that there is no standardized approach to performance.
Performance Engineering vs. Performance Testing
Performance testing and performance engineering may not look different apparently at first as the two overlap. It can be said that performance testing falls under the scope of performance engineering as some of the practices are used to ensure high-performance systems. Performance engineering goes a step further by developing and implementing a series of strategies to ensure your application is built for performance right from the start. Still, drawing the final line can be difficult.
To better understand whether your organization is performing performance testing or performance engineering, consider the following descriptions:
Performance testing
Performance testing is a group of practices where the team simulates realistic end-user workload and access patterns in controlled environments to determine system scalability, speed, and stability. Often the results of performance tests are weighted against a set of metrics that allow engineers to identify and remove potential bottlenecks.
Performance testing measures include:
- Transaction response time
- Simultaneous user loading supported
- Server throughput
- Browser performance
- Code performance
- Use of server resources
Performance engineering
Developers think of performance engineering as hardware and software components such as bandwidth, response time, and overall utilization. But, performance engineering is just the technique used to ensure network components achieve their intended mission.
Distributed applications are built of many complex modules and must offer users different and dynamic response times depending on the necessary functions. Performance engineers have to run tests and determine the stability of individual solutions. This allows both designers and developers to find specific system flaws to test and improve potential solutions.
As application performance engineering grows, development teams will need to create processes throughout the system lifecycle. This gives teams greater flexibility, raw data, and better opportunities to automate processes and efficiently configure potentially disruptive components.
Importance of performance engineering
Application performance can significantly affect the financial performance of an organization. A failure of even a few minutes can cost you thousands or millions of dollars – while finding the source of a bug in an increasingly complex system can take time. This means that the user experience and managed performance of an application must be considered throughout the application lifecycle, not just when it is first launched.
With the growing number of DevOps teams continuously deploying applications, performance engineers must regularly test as well as on-demand to ensure the quality and stability of each additional integration.
Now that we’ve determined the importance of performance engineering and why people should analyze data, here are some best practices:
Identify level-based engineering transactions.
The engineering scripts contain a single transaction that targets a specific deployment tier. Monitoring frontend key performance indicators for engineering transactions (TPS (transactions per second), response times) will drastically save time in identifying the root causes of bottlenecks. A demotion in a particular engineering transaction will help you identify the level of implementation on which to focus your efforts.
Take your time and extract the transactions based on the tier, as they will help you in the analysis phase. If you’re unsure which trades hit which tiers, ask your infrastructure development or support team. Collaboration is key.
It is recommended that each of these engineering transactions be its own script. Its own TPS and response time values can be plotted independently of all other business transactions. Also, pause these engineering scripts to separate the runtime intervals and create a constant sampling rate.
Define KPIs
Developers need to be clear about how their application’s success looks to their business. Is it faster loading or faster transactions? This requires teams to meticulously gather data and identify the root causes of performance issues. But the data doesn’t say everything, especially if your organization has unique SLAs or unusual architecture.
Hit rates and free resources are two very informative KPIs for each “server” that can tell us performance stories. That’s why we want to monitor them.
The hit ratio will change with the load. As the load increases during the increasing load test, the hit ratio also increases. These resources are monitored by APM (monitoring solutions).
Manual analysis
In addition to automatic data collection, there is a need for manual control. Analyzing performance test results, separating relevant data points, developing efficient solutions, and detecting trends requires a human being at the fore. However, developers should use their time wisely and be able to create repeatable results when testing and modifying application software.
Now that we move into the analysis phase, we intend to greatly reduce the number of trades we plot and actually use for analysis. The reason for this is that there are probably 25, 50, maybe 100 flagged business transactions. It is too much to analyze effectively.
All of these business transactions use shared deployment resources. So you’re going to pick a few to avoid analytical paralysis. Which? It is recommended to choose a transaction based on your unique application.
From the forthcoming single user load test results, select the landing page, log in, the business transaction with the highest response time, and the transaction with the fastest response time.
To make sure an application meets its business goals, developers should look for performance engineering tools that span multiple technologies, gather data flexibility, and integrate development tools. Application performance management (APM) tools are designed specifically for developers to identify potential application problems through direct code analysis.
As with selecting any engineering tool, teams need to determine their unique business performance metrics and what measurable solutions will work best for their budget. And if you are an organization looking to improve performance you can visit https://www.saventech.com/ and check out the performance improvement services on offer.
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