Why end-to-end synthetic monitoring?
Historically, teams tended to leverage synthetic monitoring most often as part of their pre-deployment testing routine. They used automated testing frameworks like Selenium to script simulated transactions within their applications, then analyzed the data to validate that their release performed as required prior to deploying a new release into production. Once the release was in production, they would shift to RUM and rely on monitoring data from actual transactions.
That made sense when monitoring needs were straightforward and application hosting environments were relatively simple.
If you’re deploying a monolithic web application hosted in a virtual machine, for instance, the variables at play in each transaction are relatively few. Each transaction will involve the same application frontend and backend, and the same server. Under these circumstances, RUM may be enough to achieve accurate observability into production environments.
Synthetic monitoring in a cloud-native world
In today’s cloud-native world, however, applications tend not to be so simple. They are more likely to be composed of a dozen or more microservices, each hosted in a different set of container instances. The containers may be distributed across a large cluster of servers. A user accessing the app may interact with a variety of different services during each engagement.
In this type of environment, transactions come in a variety of constantly changing forms. User requests could be routed in a multitude of ways across the sprawling microservices environment. The set of services and service integrations that are triggered by each request are likely to vary from one user to another. And because environment conditions constantly change as container instances spin up and down and individual services are updated, data collected from one transaction may not necessarily be representative of another transaction, even if the request is identical in both cases.
RUM plays an important role in tracking these complex transactions. But so does synthetic monitoring, which reinforces your team’s ability to analyze the reliability and performance of each service not just pre-deployment, but also in production. By using synthetic monitoring in production to evaluate the behavior of every microservice under every likely condition within the environment, your team is in a much stronger position to identify outliers or unexpected problems that may not be evident from real-user transactions until it’s too late.
Building an end-to-end synthetic monitoring strategy
Getting the most from synthetic monitoring in modern environments requires more than simply deploying synthetic monitoring tools at all stages of the application delivery pipeline. Teams must take additional steps to ensure that they optimize the visibility that synthetic monitoring provides as part of their broader performance management strategy.
Use application-agnostic monitoring tools
A basic first step toward end-to-end synthetic monitoring is to ensure that the monitoring tools you use can support any type of application or service that you need to monitor.
Today’s applications come in an array of forms, from web apps, to native mobile and server applications, to hybrid applications. They can be built using an assortment of different cloud and on-premises services, and they can be deployed using bare-metal servers, virtual machines, containers, serverless functions, or (as is common in modern environments) a combination of these various technologies.
For this reason, monitoring solutions that work with only one kind of application or only one cloud platform are not enough for guaranteeing holistic visibility via synthetic monitoring. Look instead for tools that are compatible with whatever you throw at them. Even if you don’t need to monitor a certain type of application or service today, you may in the future as your applications evolve.
Monitor for uptime and performance
Delighting users means ensuring not just that applications and services remain available, but also that they perform adequately. An unacceptably slow transaction is just as bad as a transaction that doesn’t work at all because a service is down.
Monitor interactions, not individual services
When you set up synthetic monitoring routines, you typically collect data from all of the applications or services you need to monitor. In order to make full use of that data, it’s critical to analyze how an event in one service impacts the availability or performance of another service.
Holistic visibility into service interactions is crucial for microservices environments where the user experience may be poor, even if each service is performing adequately on an individual basis. Data that takes too long to move from one service to another, for example, or problems associated with service discovery and orchestration during certain types of transactions could lead to major disruptions for users. Synthetic monitoring operations can surface such problems before they impact actual users.
Leverage synthetic monitoring as part of a broader toolset
Synthetic monitoring is one powerful tool in the modern reliability engineer’s toolbox, but it’s not the only one. To deliver the greatest value, synthetic monitoring should be paired with other monitoring and observability techniques, including not just RUM, but also log analysis, metrics collection and tracing.
Focus on leveraging synthetic monitoring to gain insights that these other techniques can’t deliver with the same efficacy, such as application behavior under unusual variables that are occasionally but not frequently triggered by real users.
Be sure to aggregate and analyze all of the data produced by your various monitoring systems and routines in a single place. It’s only by integrating and correlating all of the information at your disposal that you can gain complete contextual insight into the state of your systems and total visibility into problems.
The complexity and scale of today’s application environments and user requests demand a monitoring strategy that offers more precision and broader coverage than ever before. End-to-end synthetic monitoring that delivers visibility into all applications and services, both pre- and post-deployment, is an essential component of such a strategy. By allowing you to gain insights that other monitoring techniques don’t provide, end-to-end synthetic monitoring delivers vital context that your team needs when observing its systems as a whole.
Splunk offers a complete digital experiencing monitoring platform for integrating end-to-end synthetic monitoring into your broader reliability engineering and performance management operations. Using Splunk Synthetic Monitoring, you can simulate transactions via Selenium to test all of the conditions and variables that you need to evaluate, in any application or service, at all stages of the application lifecycle. You can then correlate and analyze your synthetic monitoring data easily by integrating with Splunk Observability Cloud. Watch this demo and start your free trial of Splunk Observability Cloud today.