Server monitoring starts with collecting data (such as error rates or CPU usage), and analyzing this data can help you determine the health and performance of your IT services. Your DevOps team can analyze these metrics to shift from reactive to proactive monitoring. Instead of waiting for a problem to occur, you can create alerts to warn you when an anomaly is detected, helping you prevent problems before they occur.
Overall, the main goal of server monitoring is to reduce the number of server outages or failures. But another goal is to reduce the time needed to resolve problems. No organization manages to prevent every possible issue, but server monitoring provides the data needed to quickly identify and fix the root cause of most problems.
Server monitoring becomes more complex when the complexity of the IT infrastructure increases. A more dispersed or denser IT infrastructure is much harder to monitor. A good example of this distributed architecture is the microservices movement. Most services perform just a single task, and each of these services require monitoring to determine their performance and health.
Because of the microservices movement, there’s an even bigger need to retrieve insights from your services through active monitoring and data analysis. This article discusses the importance of server monitoring and the most important metrics to keep your IT services healthy. Additionally, it’s important to understand you can measure both server- and application-related metrics.
Importance of Server Monitoring
As the number of IT services your organization provides grows, you need better and better monitoring capabilities, so using automated solutions designed to capture and analyze metrics is almost unavoidable. Automated monitoring tools such as SolarWinds® AppOptics™ help save your team members time and resources because they don’t have to focus as much attention on resolving detectable or avoidable server issues.
Automated server monitoring helps you shift from reactive to proactive monitoring. A traditional software team only acts when a server issue pops up. However, this is a bad practice—when issues occur, customers can’t use your IT service and your organization can miss out on sales. Automated server monitoring lets you shift to proactive monitoring, so you can detect problems before they occur. In other words, you aim to resolve problems before they become bigger problems and cause a server failure.
Nowadays, customer experience matters a lot. According to Hotjar’s research on customer experience, the number one customer frustration when using digital products is long wait times. This includes the unavailability of a service, with over 20% of respondents naming this as their top frustration. For this reason, proactive monitoring is essential to improving customer experience.
Monitoring not only establishes important statistics about your application, network, and server but allows software teams to resolve problems much faster. By monitoring metrics such as disk utilization, CPU usage, and memory allocation, it becomes much easier to identify the root cause of a problem. Imagine a service experiences a failure. By looking at the memory allocation, you notice a pattern indicating a data leak has occurred. The faster you can identify the root cause of a problem, the faster you can fix it.
Top Server Monitoring Metrics to Consider
Requests per Second
The number of requests your server handles per second (also referred to as throughput) gives you a good overview of its usage. If your server doesn’t scale properly, a spike or high load can cause it to crash. Therefore, requests per second is a good metric to measure to identify potential scaling issues, especially when your organization and service usage is growing.
When you measure the number of requests per second, you should also measure the average response time for a request.
Average Response Time
Measuring the average response time tells you how long it takes for your server to handle a request and send a reply. Ideally, you want to keep your average response time as low as possible. A study by Nielsen Norman Group suggests keeping the average response time below one second to avoid interrupting the user’s flow of thought.
However, don’t stare blindly at the average response time. If you measure only the average response time, you might not notice issues like performance bottlenecks. Make sure to also measure the outer edges; most importantly, measure the slowest responses. Some developers like to refer to this metric as the peak response time (PRT). If you often see high response times for certain requests, it’s a clear indication of performance bottlenecks or other anomalies for particular requests.
I’ve grouped metrics such as CPU usage, memory allocation, disk space, and disk utilization under the category of hardware utilization. These metrics contribute heavily to the overall performance of your server, and it’s essential to track them.
Moreover, monitoring these metrics helps you easily detect performance or resource bottlenecks. Why does this matter? If your server doesn’t have enough CPU power to operate smoothly, CPU usage can lead to a resource bottleneck, causing the whole system’s performance to degrade. It’s important to measure all the hardware metrics because a single component can cause a resource bottleneck.
The uptime reflects the overall health of the server. An IT service in good health typically has a high server uptime. Many organizations aim for a server uptime of at least 99%, and many companies push it even further to 99.5% or even 99.9% uptime.
Server uptime is important for improving customer experience.
HTTP Server Error Rate
The HTTP server error rate also contributes greatly to customer experience. This metric tells you how often users see an internal HTTP error code or experience an internal server error. Obviously, you want to keep this rate as low as possible; a high error rate undermines the trustworthiness of your service.
You should also track HTTP 5xx error codes because they represent internal errors. These types of errors often have the biggest impact on user experience.
It’s also worth tracking other error codes, such as 404 error codes. When a user sees a 404 error code, it means a page doesn’t exist or the server can’t find the page. Best practices recommend you create alerts to warn you when a user sees a 404 page. It’s one of the easiest errors to track and one of the easiest to fix.
I hope you now understand the top metrics to consider when implementing server monitoring. You should use a software tool designed to enable automated server monitoring and real-time data visualizations—AppOptics, for example, offers a free trial of their platform, which supports your automated server monitoring endeavors.
This article covered five important metrics, but you can also measure many other advanced metrics. For example, you can keep track of the thread count for your application. But even though advanced metrics can help you, I suggest starting with the five metrics above. You can easily track these metrics, and they provide you with important information about the performance and health of your server. In other words, try to maximize the return on investment for the metrics you monitor.
Do you want to learn more about server monitoring? Check out this overview of different server monitoring software solutions.
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. He also loves learning about marketing, UX psychology, and entrepreneurship. When he’s not wr