In order to gain insights into the actual user experience, RUM (Real User Monitoring) works by gathering information directly from the browsers or devices of the end users.
In the beginning, a monitoring script or tag is uploaded to the website. As the user engages with it, performance data is gathered. The monitoring script gathers information on page load times, network latency, user activities, and other performance indicators as the user navigates the website. This is the data collection stage.
After being delivered to a data aggregation server, the data is processed and reviewed. Data that is frequently stored in a database is used to build performance reports and dashboards. This is the data aggregation stage.
After that, using a variety of tools and approaches, the combined data is viewed and examined. Developers and operations teams can use this to find performance problems and monitor performance trends over time. This is the stage of analysis and visualization.
Based on the insights, RUM gives developers the ability to spot and resolve problems that could arise from aspects of the user experience that are unrelated to the program, like network latency or device performance. It is a reliable method of assessing performance in the present without using additional tools or technology. RUM gives vital information on how users interact with an application by gathering data from real user traffic, enabling developers to adjust their program for the optimum user experience.
Synthetic monitoring is an active monitoring technique that creates a controlled and reliable testing environment by simulating user interactions and gathering data from such interactions.
Initial user interactions with the website are simulated by a set of scripts. The majority of the time, these scripts are written by programmers using specific tools or frameworks that let them capture and replay user interactions. This is the script creation stage.
The tests run periodically, for instance every hour or five minutes. To ensure that performance is uniform across different regions, the tests are also set up to run from several locations worldwide. This is the test configuration stage.
Follow up tests are run automatically at the configured intervals. They simulate user interactions with the website and collect data on performance metrics such as page load times, network latency, and error rates. This is the test execution stage.
The collected data is analyzed to identify performance issues and trends. This analysis may involve comparing performance data from different test runs or analyzing performance data over time to identify trends or anomalies. This is the data analysis stage.
Based on the analysis of the collected data, alerts, and reports are generated to notify developers and operations teams of performance issues. These alerts and reports may include details on the specific performance metrics causing the issue and recommendations for how to address it. This is the alerting and reporting stage.
Synthetic monitoring is a crucial part of any effective monitoring plan since it has the added benefit of being a proactive tool that can identify problems before they influence actual users. Synthetic Monitoring offers thorough insight of the program’s performance by simulating user interactions, enabling quick and effective corrective action.
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