Synthetic Monitoring is a technique that continuously simulates user interactions with a system or application to test its performance and availability. It is also known as proactive monitoring. Unlike Real User Monitoring (RUM), which tracks actual user behavior, synthetic monitoring operates in a controlled environment, generating traffic at regular intervals to identify issues before end-users experience them. Let’s understand how different monitoring methods differ from each other.
A single-line difference between SM, RUM, and APM (Refer to APM vs RUM):
- Synthetic Monitoring (SM): It simulates user interactions to test application performance proactively.
- Real User Monitoring (RUM) captures performance data from actual user interactions in real time.
- Application Performance Monitoring (APM) tracks and analyzes backend application performance and dependencies to detect issues.
Why is it important?
Synthetic monitoring provides the benefit of:
- Early Detection: Issues are discovered before real users encounter them.
- Benchmarking: Establishes baseline performance levels.
- Regression Monitoring: Tracks performance changes post-deployment.
- Global Visibility: Simulates user behavior from different geographical regions.
- 24/7 Monitoring: Provides continuous checks, even during off-hours.
How does it work?
Synthetic monitoring uses scripted transactions to mimic user journeys. These scripts run at scheduled intervals from various monitoring locations. The data collected is then analyzed for response time, error rates, and availability.
It answers vital questions such as:
- Is the application up and running?
- Are critical transactions completing successfully?
- Are response times within acceptable limits?
Key Components
- Monitoring Scripts: Automate real user actions like login, search, add to cart etc.
- Execution Locations: Cloud-based agents that run the scripts.
- Alerts & Reporting: Notifications when performance degrades or errors occur.
Example
Consider an e-commerce website. To ensure the shopping cart functionality works correctly around the clock, they set up synthetic scripts that:
- Navigate to the homepage.
- Search for a product.
- Add it to the cart.
- Proceed to checkout.
If any step fails or takes longer than the defined SLA (e.g., 3 seconds for add-to-cart), an alert is triggered.
This type of synthetic test helps:
- Validate core functionality.
- Measure the system’s response under typical and edge-case scenarios.
- Track uptime and performance during non-peak hours or after code deployments.
Popular Tools
Here are some widely used synthetic monitoring tools:
| Tool Name | Key Features |
|---|---|
| Dynatrace | AI-driven monitoring, synthetic scripts, global test locations |
| New Relic Synthetics | Simple script management, public and private locations |
| Pingdom | Website uptime monitoring, HTTP checks |
| Catchpoint | Advanced global testing, network-level metrics |
| Uptrends | Multi-browser monitoring, waterfall analysis |
Best Practices for Synthetic Monitoring
- Simulate Critical Paths: Focus on core business transactions like login, search, and checkout.
- Use Realistic Data: Mimic real user behavior with valid input data.
- Geo-Distributed Monitoring: Test from multiple regions to catch latency issues.
- Monitor Post-Deployment: Run synthetic tests after each release to catch regressions.
- Combine with RUM: Use synthetic monitoring alongside RUM for complete visibility.
Conclusion
Synthetic Monitoring is a crucial pillar in any performance testing strategy. For performance testers and engineers, it enables proactive detection of issues, offers insights into application reliability, and provides performance benchmarks over time.
By combining synthetic monitoring with real user data and continuous performance testing, teams can ensure a smooth, uninterrupted digital experience for their users, everywhere, all the time.