The Top 15 Application Performance Metrics

Throughput

Throughput is used to refer to the amount of requests an application can handle within a given period of time, often per second. This is often measured as requests per second (RPS) or transactions per second (TPS). By monitoring throughput it is possible to ascertain an application's capacity and ability to scale, for example, if throughput is consistently observed to be reaching its maximum limit, this can often be an indicator that the infrastructure may need to be scaled up. This may lead to the purchasing of additional servers or research and development conducted to find replacement solutions that are less resource intensive to the system.

Error Rate

By its namesake, the error rate indicates the amount of failed requests when compared to the total number of requests as represented by a percentage. A high error rate would likely be an indicator of bugs, connection issues or system overload. It is essential for analysts to look into the root cause of why these issues may be occurring in order to resolve these errors in a timely manner.

CPU Usage

Observing the metrics associated with CPU usage allows users to observe which resources are being utilised by the application as it is running. When a high CPU threshold is reached this can often align with sluggish and slow performance as well as the eventual unresponsiveness of the application entirely.

Memory Usage

The metric that represents the amount of RAM being used for storage and memory is the memory usage metric. It is vital to be able to observe memory usage statistics as this is often the first point of call in diagnosing memory leaks, crashes and other issues which can drastically affect performance.

Disk I/O

If users wish to refer to the rate of input and output operations that are occurring on the disk, they will want to observe disk I/O metrics. A high disk I/O rate can usually be an indicator of an application that is frequently reading and writing data to the disk. This can prove to be a drawback in terms of slowing response times and the performance of the application overall.

Network Latency

To identify the time it takes to send requests between the software application and dependencies such as APIs, databases or other external services, users will wish to measure network latency to provide clarity in their network performance. The drawbacks that occur when high network latency occurs include delays in data retrieval which can easily affect the application's performance overall.

Concurrency

Concurrency metrics are useful to monitor to visualise the number of users and processes that are occurring at the same time in order to measure the load an application can handle without encountering a significant detriment to the performance of the software.

Garbage Collection

Analysing the impact of garbage collection can provide insights into resource usage and necessary feedback on application responsiveness. For example, if an application is running garbage collection too frequently, this can lead to temporary freezes of the software which would obviously be detrimental to user experience as a whole.

Database Queries

To see how long it takes for the database to execute queries and observe the performance of the database generally, database query metrics can detail these key insights for further observation. As a worst-case scenario, slow database queries which are not identified can significantly affect the response time of your application when left unoptimised.

Dependency Health

As applications often have necessary dependencies in order to run, such as APIs or other services that they make calls to, measuring the status of dependency health can often be key to maintaining the uptime of your application. Dependencies that encounter failures can easily disrupt the flow of an application and when left unidentifed can make troubleshooting a lot more tedious and time-consuming.

HTTP Status Codes

In close association with the previously cited error rate, identifying and visualising the various different HTTP status codes as they occur can greatly assist with troubleshooting common errors around server performance and broken resources. In line with this metric, common status codes you may witness include; 200, 404, 500 and 301 status codes.

Thread Count

Thread count is another key metric in addition to concurrency which can illuminate the amount of processing activity currently happening within an application. Thread count can be a valuable metric when it comes to identifying bottlenecks associated with thread handling in addition to the classification of thread leaks.

Error Logs

Error log analysis provides users with a view of recurring errors affecting the application's health, when used proactively, errors can be resolved before they have a chance to become show-stopping issues. Error logs can be viewed easily when a log viewer is utilised as part of an extensive application monitoring analytics dashboard, such as the one offered by Logit.io.

User Experience

UX metrics include a number of common measurements including click-through rates, bounce rate, and page load time. These all provide key insights on how users are responding to various elements of the application or service. Dropoffs between key steps and features can serve to alert developers where holistic improvements may need to be made to improve the user experience.

If you want to find out more about how monitoring application performance metrics can assist in improving your application’s performance then why not sign up for a free trial of Logit.io’s application performance for 14 days to see how we can help you?

If you found this article informative then why not read more about how CMMC affects small businesses or find out about cloud monitoring tools next?