Synthetic Monitoring is an essential part of Digital Experience Monitoring, where organizations can detect their service outages or performance degrade proactively by periodically running the tests.
RealLoad provides an easy way of configuring synthetic tests with an outcome of accurate metrics, alerting and SLA details.
RealLoad Synthetic Monitoring Features
- Due to the universal architecture, RealLoad Tests of any kind can be run as a monitoring job (HTTP Test Wizard tests, JUnit tests, Selenium tests, etc.).
- Synthetic monitoring jobs can also run with many virtual users.
- Support for performance warning thresholds.
- Support for delayed alarm notifications.
- Supported alert devices: Email, SMS and WebHooks (including Pagerduty and Slack).
Below illustrates Synthetic Tests configurations, Alerts configurations, Real-Time Dashboard, Statistics with SLA measured.
Define a Test
You have first to define a RealLoad ‘Test’ before you can add it to Synthetic Monitoring. The following 3 examples show how a test can be defined:
- Execution of a Simple HTTP/S Test
- Converting a Selenium IDE Test to a RealLoad Test
- Developing a JUnit Monitoring Test
After you can see all the tests you have defined like below under Tests page
Configure Synthetic Monitoring
Next step is to create Monitoring Groups and add & define tests under each group as jobs like below, Agents will be available for selection based on how many agents are already configured on different geographical locations, please refer here for more details
Synthetic Real-Time Dashboard
Now the created monitoring groups with its monitoring jobs will be available under the Real-Time Dashboard like below with the important metrics and also have the option to drill down into test results
Custom dashboards can be created on third party products like Grafana, using our WebSocket API
Next explains how to configure the Alerting, below illustrates how to configure alerting for created Monitoring Groups or Jobs
Statistics with Performance & Availability
Next explains about the statistics data, which will be the source for many KPIs , for example to calculate the defined SLA for an application or an API service