Super Useful Metrics to Track Your Server Expenses
Tags: webdev, cloud, beginners, programming
Published: 2022-11-01 12:47:31

Metrics for websites include the information you may use to track how well your website is performing. 

However, it's important to note that performance is not just about speed. Metrics may aid in understanding and enhancing everything from lead conversion to traffic volume.

Understanding the server's function becomes increasingly important as an application's user base grows in a production setting. It’s a best practice to collect performance measurements for the servers hosting your web applications to assess your applications' health.

In this article, you’ll learn about some super valuable metrics for tracking your server expenses.


## Server availability and uptime

When serving your app, your servers must be up and operating; thus, alerting in advance to availability can help you save the day before problems arise.

The duration of the server's uptime, which hosts your application, is measured. Customers may have a negative experience due to unreliable server system uptime metrics. 

There may be instances where the service operating on your servers hosting an app is unavailable, even if the servers themselves are up and functioning. 

Ideally, single points of failure should be eliminated to provide high availability.

However, uptime above 99% is acceptable, but it also means that it will cost more server expenses during the active or peak period.



## Application server monitoring

Standard server monitoring metrics can offer vital clues about how effectively your application performs.

The average response time shows how long a server typically needs to handle an application's request. [According to studies](https://www.nngroup.com/articles/response-times-3-important-limits/), response times should be kept to under one second to ensure user involvement. The longest response time in a given period is known as the peak response time. The value should be interpreted in light of the typical response time. 

A slow average response time can signal that the server's components aren't performing at their best and must be fixed.

For a particular kind of request, if the gap between the two is noticeably more significant, then it might point to a performance bottleneck. However, it might also be a temporary problem. 

On the other hand, consistently higher values for one or both of these metrics may point to underlying server performance issues, resulting in closer proximity of server response and increasing server busy time and expense.


## Server load sharing

These metrics inform you about how effectively your backend servers handle application load. Server load sharing is essential in high-availability designs, where user requests are handled by numerous servers protected by a load balancer.

How can one find out how many requests are being processed at any time? That is the [thread count](https://docs.oracle.com/cd/E19159-01/819-3681/abefn/index.html#:~:text=The%20Thread%20Count%20parameter%20specifies,drops%20below%20the%20maximum%20amount.). You can learn important details about a server's capacity here. 

Once the maximum threshold is reached, the requests are kept until there is adequate time to process them. The postponed requests will time out if they take too long. 

The maximum number of threads per process that a single server can manage is frequently throttled, and if the limit is surpassed, issues may result. 

As a result, it's crucial to monitor and manage the thread count by scaling requests to extra servers hidden behind a load balancer.


## Server capacity

Although guaranteeing your server's availability and uptime could increase the dependability of your application, if the server cannot handle the volume of requests, then your users may still have a bad experience.

The situation can be saved by implementing a scaling strategy based on this indicator's information. Before choosing a scaling procedure, it is essential to consider the server's average response time and the requests per second.

Data input and output, which gauge the request payload size received by the server and should be kept modest for better efficiency, are nevertheless crucial to take into account. 

The application might request more data than the server can handle if it has a large payload and receives many requests, which affects the server's expense.


## System-level performance

You receive no information about the health of your server through server performance monitoring metrics. In your list of server performance metrics to monitor, you should include server utilization metrics and operating system (OS) logs in addition to server availability monitoring and metrics.

OS logs provide details about any faults occurring in the environment that must be fixed. 

Additionally, you may develop alerts based on strict OS error codes to identify problems quickly. With so many operations linked to applications running simultaneously, it can be challenging to ascertain what is being written to or changed on the server operating systems. 

However, since several fundamental but significant metrics, such as CPU/memory utilization, disk I/O, and disk usage, are used to assess hardware utilization and significantly impact server performance, it may be beneficial to analyze these logs.

These parameters should be considered to ensure thorough performance monitoring and drastically lower server costs because any of them could be throttled.


## Tracking server expenses with Appwrite

With the help of the self-hosted backend-as-a-service platform [Appwrite](https://appwrite.io/?utm_source=hackmamba&utm_medium=hackmamba-blog), developers can create any application — it offers all the essential APIs. 

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## Conclusion

A large-scale infrastructure with numerous servers can be challenging to monitor. To start, you must carefully consider which server metrics are essential for your environment to track.

This post showed several helpful metrics for tracking your server costs.