In the last five to six years, I had a chance to work in a few applications which are into CRM and financial closure domains. While working as a QA Lead and the Scrum Master, I was able to work as the performance tester as well. Fortunately, in these applications, our customers are willing to spend some time on performance engineering and monitoring through different Application Performance Management Systems which targeting application performance and infrastructure monitoring. Other than the APM systems, I was able to setup CI environments and dashboards.
From this article, I would like to share my experience with the application performance monitoring and how it is connected with Performance Engineering. First of all, I will start with the main performance approaches and talk about how we can connect tools, etc. Then all can clearly understand what I am going to be discussed.
Three Performance Engineering Approaches:
In Performance Engineering, we have 3 approaches which we can present the entire life cycle. Those are Reactive, Proactive and Pridictive.
First: Reactive approach
When we are dealing with software applications, there a lot of sudden performance concerns due to various issues such as the requirements are not identified properly (SLA), customer database related issues, problems in the user groups or user personas and issues when the user load getting increased unexpectedly. This approach is needed, but not encourage to categorize as the best way of ensuring the product performance. Hence we are looking for the second approach.
Second: Proactive approach
In this, we are targeting a sustainable way of ensuring the product performance. Nowadays, when we are going to start a software product implementation, we need to think about the non-functional aspects more than ever. Specially performance engineering aspects as it will trigger issues such as we may need to change the entire application architecture at the last stages. Hence, starting the performance assessments in the early stages of the life cycle is a must. In an agile environment, we can say it should be started from Sprint 0. Not only that, it should go throughout all the sprints.
Furthermore, we need to focus more on continuous performance assessments and monitoring (Unit and functional level) rather than doing manual assessments by developers or testers. By using continues approach, we can eliminate the poor cost of quality and its remedies more effectively and efficiently. Using a monitoring dashboard, we can see the release or commit wise performance figures to detect issues ASAP. And it will increase the visibility for the customers who bother about the application performance and improvements. Say for an example, if we can establish a CI environment which triggers in the nightly build, we can find the performance issues easily by spotting the trend graphs rather than waiting till hardening sprint or system tests at the end of the release. As depicted in the following images, we can display figures on the dashboard:
Following is the figure I took from the daily performance monitoring dashboard which configured in Jenkins with JMeter plugin. Every morning, it will be triggered and pass all the data to the dashboard plugin.
Some of the useful links to configure the dashboard as follows:
Third: Predictive approach
Now I am going to talk about the approach that most of the project teams, customers and stakeholders are neglected. However, if you noticed the topic of the third approach, it will give you the whole idea behind this. When our application is in the production, it’s not enough to deal with the development and continues CI environments. Hence, we must work on the production figures and trends to analyze the current user behaviors to identify the usage patterns. In other words, we should be able to predict or forecast the future loads and the impact (Ex: To indentify seasons or dates such as Black Friday, etc).
As I told you in the first paragraph, there are a lot of APM (Application Performance Management) systems in the market to get these valuable data and deliver great digital customer experience. Therefore real user relations and critical transactions can be monitored and analyzed down to code level. So we can categorize those as standalone applications and systems that coming with the cloud platforms.
Following are some standalone APM systems that we can use on the market (Only a few).
I had a chance to work with New Relic tool and it has most of the features which intended to have in such application. Very good visualization and data analysis, including find the most cumbersome SQL queries, etc. Following is a sample dashboard which used New Relic insights:
If we are using cloud platforms, then we can use Application Insights with Azure and CloudWatch with AWS.
Both Azure and AWS have a built in solutions for keeping data and tracking of metrics with the online alerting system. These tools are there to see the big picture of what is going on with your platform as a service environment, like standing on the Mount Everest and watching everything below. If we talk about Azure application insights, we can make its own Dashboard or show the data through the PowerBI.
The first thing you see after you log in to the Azure portal is the dashboard. Here you can bring together the charts (Server responce Time, page view Load Time, Errors, etc) that are most important to you and to your stakeholders.
*Following are the areas of the dashboard:
- Navigate to specific resources such as your app in Application Insights: Use the left bar.
- Return to the current dashboard, or switch to other recent views: Use the drop-down menu at top left.
- Switch dashboards: Use the drop-down menu on the dashboard title
- Create, edit, and share dashboards in the dashboard toolbar.
- Edit the dashboard: Hover over a tile and then use its top bar to move, customize, or remove it.
If the Azure dashboard is not good enough for you, as I told you earlier, we can use PowerBI tool easily.
Power BI is a suite of business analytics tools to analyze and share data. Its dashboards provide a wideangle view for business users with their most important metrics in one place.
Some of the useful links to configure PBI:
Not only the PowerBI standard reports (adapter), but also we can get the data via Export Analytics queries and Azure Stream Analytics. In this way, we can write any query you want to use and export it to Power BI. In our current project, we are basically working with Azure Stream Analytics as it has good control of data.
Hope you understood the difference between these approaches and what are the places which we can use monitoring systems and dashboards to upgrade the customer visibility, application performance and product quality.