Synthetic monitoring defined
Synthetic monitoring is a method to test and monitor applications and systems by simulating user interactions. It uses scripts designed to mimic a real user’s behavior, then uses the data to evaluate response times, error rates, and other performance metrics.
Considered a proactive monitoring solution, synthetic monitoring assesses how well an application will respond to user requests before a real user interacts with it. This allows developers to troubleshoot issues before their application is released and for IT operations teams to monitor critical user journeys in a consistent and repeatable way, once in production.
Unlike Real User Monitoring (RUM), a monitoring solution that requires human users, synthetic monitoring can be completely automated and run on a schedule. This allows you to detect issues, trigger alerts, and optimize system performance before real users run into issues. The result is a better end product and improved experience for users.
How does synthetic monitoring work?
Synthetic monitoring works by using a robot client (also known as an agent, or a probe) to send automated requests to your application, replicating what a potential user might do.
- First, you need to develop a script or a set of scripts that simulate potential user interactions. These scripts define what activities will be performed and in what sequence. Synthetic monitoring tools can be programmed to reproduce actions such as filling out forms, clicking buttons, logging in, making purchases, and browsing web pages.
- When the scripts are complete, you will need to configure your modeling parameters. Will the scripted transactions take place at regular intervals or on a predefined schedule? What devices or browsers should they simulate? What geographic locations should they execute from? What network conditions should they reproduce?
- Once the parameters are set, the testing can begin. Your monitoring tool executes the synthetic scripts according to your chosen configuration. It sends requests to the targeted system, emulating typical user actions and workflows.
- The synthetic tests collect performance metrics such as response times, page load times, error rates, and transaction completion rates. These metrics help evaluate the system's performance and identify areas that may need improvement.
- If any metric exceeds the set thresholds, it probably means there is a performance issue or other problem to address. When this happens, the monitoring tool will generate alerts to notify you and your team.
- Your synthetic monitoring tools will generate ongoing reports and dashboards that analyze performance trends. You and your team can look through the collected data to find patterns, spot performance bottlenecks, identify areas to troubleshoot, and plan for future scalability.
Importance of synthetic monitoring in modern applications
Synthetic monitoring is important in modern applications because it allows you to detect issues proactively, consistently monitor performance, and optimize the end experience for your users. It makes sure that your applications perform well under different scenarios so you can resolve issues before they impact real users. It optimizes app performance by supporting efficient incident management and capacity planning. By delivering high-quality applications, you can boost customer satisfaction and stay competitive in today’s digital landscape.
Benefits of synthetic monitoring
Synthetic monitoring can offer several benefits to your organization. This includes:
- Proactive monitoring: Synthetic monitoring allows organizations to spot performance issues and availability problems proactively. By simulating user interactions, it detects anomalies before they impact real users.
- Continuous monitoring: Synthetic monitoring can execute tests at regular intervals or on demand, ensuring that your application is being monitored consistently. This also helps you spot intermittent issues that ad hoc or manual testing may not catch.
- Simulated user experience: Synthetic monitoring lets your team simulate user interactions and transactions from various locations and devices. By replicating user behavior you can better understand your real users’ experiences.
- Validation of SLAs: Synthetic monitoring helps your team validate service level agreements (SLAs) with your service providers. It lets you monitor and measure the performance and availability of third-party services to make sure they meet your standards. This also helps you hold vendors accountable.
- Performance optimization: By collecting performance metrics and analyzing the results, synthetic monitoring helps identify areas for optimization. This helps your team to organize, prioritize, and resolve these issues.
- Capacity planning: By monitoring system performance under various load levels, synthetic monitoring can help you determine when you need to add additional resources or infrastructure upgrades to support the growing demands of your users.
- Real-time warnings: Through alerts and notifications, synthetic monitoring can immediately notify you when a performance threshold is breached or when it detects an anomaly.
- Retroactive performance analysis: Using historical data and trends, synthetic monitoring helps you analyze performance patterns over time and find recurring issues. You can also track the effectiveness of the fixes your team has implemented.
- Analysis of competitor performance: Synthetic monitoring can be used to monitor and benchmark the performance of competitor websites or applications. By simulating user interactions and measuring performance metrics, you can learn more about your competitors' user experience and compare your performance against them.
Challenges & limitations of synthetic monitoring
While synthetic monitoring offers many benefits, it is not without its challenges and limitations. Synthetic monitoring focuses on objective metrics such as response times, page load times, and error rates. It does not capture subjective metrics that matter just as much to some users — like user satisfaction and visual design appeal.
Synthetic monitoring primarily operates from predefined locations and devices which may not cover all the geographies or demographics of your users. This is particularly tricky for organizations with a very wide global reach.
Because synthetic monitoring simulates user interactions based on predefined scripts, it might miss certain edge cases, user-specific configurations, or network conditions that can impact actual user experiences. It may also be unable to provide granular visibility into rich media or video content on the website, such as the gaming experience. With a focus on the web experience, synthetic monitoring also may not provide insights into the performance of backend systems.
As your applications or websites evolve, your monitoring scripts need to be updated to reflect any changes in functionality or user flows. This can become a challenge, especially for complex systems with frequent updates.
Depending on how frequently you test and how complex your monitoring setup is, synthetic monitoring can have budget implications related to the cost of the monitoring tools and people required to manage your data.
Synthetic monitoring use cases
Synthetic monitoring has a variety of use cases across different industries. Financial services can use it to monitor online banking systems, payment gateways, and trading platforms. Healthcare technology can use it to monitor electronic health record systems, patient portals, and telehealth platforms. In the ecommerce sector, synthetic monitoring is used to observe online retail platforms such as inventory systems and shopping carts. It can be applied to monitor media streaming platforms, video-on-demand services, and online gaming systems. Public sector organizations can use it to monitor citizen-facing portals, online services, and government websites. Systems reliability engineers (SREs) across industries can rely on a robust synthetic monitoring system to be a single source of truth for a system’s health and performance.
The technology industry in particular can utilize synthetic monitoring to optimize its various applications and platforms. Here are some tech-centered use cases:
- Software as a Service (SaaS) platforms can identify issues like slow response times, API failures, or integration problems with third-party services using various performance metrics.
- Cloud infrastructure services can use synthetic monitoring to monitor response times for serverless functions and data storage APIs.
- DevOps and continuous delivery practices apply it to validate deployments, track performance changes (known as performance testing), and ensure the stability of application releases.
Synthetic monitoring in DevOps allows for proactive monitoring, which is sweeping for issues before they occur. For example, uptime monitoring is an automated process to check that applications are running. While application monitoring measures application performance using a network of synthetic monitoring tools for performance optimization.
- IoT applications and devices can use synthetic monitoring to measure response times for data ingestion and processing as well as validate real-time interactions between IoT devices and applications.
- Synthetic tests can also be used in load testing, which simulates demand to ensure application and website reliability.
- When monitoring big data and analytics platforms, synthetic monitoring can measure response times for data ingestion, query processing, and data visualization.
- In mobile application development, synthetic monitoring can identify issues like slow response times, crashes, and compatibility problems specific to mobile environments. Mobile app developers can use synthetic testing to fix potential bugs before the apps go live.
How to analyze synthetic monitoring data
Analyzing synthetic monitoring data involves examining performance metrics you have collected to gain insights into your application's behavior. First, you will review metrics such as response times, page load times, and error rates to assess overall performance. Visualizations, such as charts and graphs, will help you understand trends and spot anomalies. Then you can compare the data against the thresholds you have previously defined to identify any deviations or problems.
Next, you can conduct trend analysis by examining historical data to find patterns or recurring performance issues. This will help you make decisions about optimization and capacity planning. If you find any performance problems, you can investigate further by conducting root cause analysis. This involves examining specific transactions, network conditions, or server configurations to surface the underlying factors causing the problems.
Based on this analysis, you will generate reports summarizing your findings and provide a list of items for your teams to focus on improving. This could include recommendations for optimizing code, infrastructure changes, or performance optimizations.
Synthetic monitoring vs. real user monitoring
Synthetic monitoring involves simulating user interactions using predefined scripts to monitor application performance and availability. It helps identify issues proactively by measuring metrics based on simulated user actions.
In contrast, real user monitoring (RUM) gathers data from actual users interacting with your app in real time. It collects data on user actions, page views, and performance metrics from the users' devices and browsers. Real user monitoring gives you insights into the experience of your users and the real-world issues they face, such as slow page load times or errors.
Synthetic monitoring proactively identifies performance issues before users encounter them, whereas real user monitoring lets you observe the real user experience. You do not have to choose between the two — in fact, using both approaches will provide you with a comprehensive view of your application’s performance.
Synthetic monitoring best practices
Synthetic monitoring best practices are important to follow to get the best insights from your data. Some of them include:
- Defining relevant monitoring scenarios and customer journeys that mimic real user interactions.
- Choosing appropriate monitoring locations geographically that represent your user base.
- Setting realistic monitoring intervals to maintain regular checks without overloading the system.
- Establishing baseline performance metrics to establish benchmarks for comparison.
- Testing across different devices and browsers to ensure compatibility and optimal performance.
- Monitoring third-party dependencies to spot potential issues.
- Configuring alert thresholds to get notifications when performance metrics exceed your predefined limits.
- Regularly updating monitoring scripts to reflect changes in user workflows or application functionality.
- Utilizing visualizations and reports to analyze and communicate monitoring data effectively.
- Integrating synthetic monitoring with other monitoring techniques for a comprehensive view of your application's performance.
Synthetic monitoring with Elastic
With Elastic's synthetic monitoring, you will gain the insights you need to drive better business outcomes. You will also benefit from additional features such as automatic scaling, built-in security, and seamless integration with Elastic Observability.
Use Elastic's synthetic monitoring to:
- Test, track, and visualize the performance of your critical user journeys
- Conduct lightweight and full browser tests with Elastic's global, managed testing infrastructure
- Streamline the testing process from dev to production with GitOps
- Get complete visibility and find issues with your website performance and availability before your customers do
Monitor your applications, infrastructure, and your users all within a single solution: Elastic Observability.