Synthetic monitoring data

Synthetic monitoring data refers to information collected from simulated user interactions with applications, websites, or services. It is a proactive monitoring approach where scripted tests mimic typical user behaviors to assess performance, availability, and functionality before real users are impacted. This data helps identify potential issues, optimize system performance, and trigger alerts for problems like slow load times or downtime.

Unlike Real User Monitoring (RUM), which relies on actual user traffic, synthetic monitoring does not require real users and can run continuously, even for applications not yet in production. The data generated includes key performance metrics such as webpage load times, response times, transaction success rates, uptime, and error rates.

Examples of synthetic monitoring data include:

• E-commerce user journey simulation: Synthetic monitoring data can be generated by scripting a complete user journey on an e-commerce site, from browsing products and adding items to a shopping cart to completing the checkout process. The data collected would include the time taken for each step, any errors encountered during payment processing, or issues with applying discount codes. This helps detect and fix problems before they affect real customers.
• API performance and functionality checks: Data is collected by simulating requests to an application's APIs to ensure they are responding correctly and within acceptable timeframes. For instance, a script might repeatedly call a third-party payment gateway API to verify its reliability and response time, providing data on API latency and success rates.
• Website uptime and load time monitoring: Basic synthetic tests can continuously ping a website or attempt to load its homepage from various global locations. The data generated shows the website's uptime percentage, average page load times, and identify if the site is experiencing downtime or slow performance in specific regions.
• Critical business process validation: For a banking application, synthetic monitoring can simulate a user logging in, checking their account balance, and transferring funds. The data would indicate whether these critical transactions complete successfully, their response times, and if any errors occur at specific stages of the process.
• Core web vitals tracking: Synthetic monitoring tools can simulate browser interactions to collect data on Core Web Vitals metrics, such as Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), and First Input Delay (FID). This data helps developers optimize the visual stability and responsiveness of web pages.
• User-specific experience testing: Data can be generated by simulating interactions from the perspective of specific user types, such as users with accessibility needs or those accessing the application from a particular geographic region or device. This provides insights into how different user segments experience the application's performance.