Why container monitoring is critical for modern cloud environments
Modern cloud application environments are complex, running across hundreds or even thousands of compute instances. Because of this complexity, modern applications require container monitoring to continuously collect metrics, track potential failures, and gather granular insights into container behavior. So, it’s not a question of whether or not to implement monitoring. Monitoring is an ongoing fact of life for every online business and its IT personnel.
Types of monitoring
There are two categories of online monitoring, Real User Monitoring (RUM) and Synthetic Monitoring. RUM is a passive monitoring technology that provides insight into an application’s front-end performance from the perspective of real users. For example, Software as a Service (SaaS) and Application Service Providers (ASPs) use RUM to monitor and manage service quality delivered to their clients.
Instead of tracking the online activities of live users, Synthetic Monitoring is a method to monitor applications by simulating users’ actions. It mimics what a typical user might do by issuing automated, simulated transactions from a robot client to an application. This type of “directed monitoring” provides information about critical business transactions’ uptime and performance capabilities and reveals the most commonly used paths in the application.
Synthetic Monitoring focuses on how an application responds to user interaction. In this way, you get a steady, solid baseline to monitor server and application performance 24/7, even during periods of low user engagement.
Challenges of Synthetic Monitoring
One of the challenges of Synthetic Monitoring is that synthetic tests are difficult to create and maintain at scale. Scripting synthetic tests requires coding skills, and running those programs requires a dedicated infrastructure. Another limitation is that synthetic monitoring does not specify what actual users are experiencing. In some cases, you may believe the application is performing well, but there may be users affected by poor networking.
Synthetic monitoring does not replace the importance of RUM. There are no programs that can replicate the unpredictability of software glitches and human nature. In reality, both types of monitoring are needed because RUM is used to track and identify long-term performance trends, and Synthetic Monitoring helps to diagnose and solve short-term issues needing immediate attention.
What is Datadog Synthetic Monitoring?
Datadog Synthetic Monitoring saves valuable engineering time because it provides code-free synthetic monitoring and user-friendly interfaces. It enables you to create tests that proactively simulate user transactions on an application and monitor network endpoints and business workflows across various layers of systems.
Datadog Synthetic Monitoring helps ensure uptime, identify regional issues, track application performance, and manage your Service-Level Agreements (SLAs) and Service-Level Objectives (SLOs). By unifying Synthetic Monitoring with the rest of your metrics, traces, and logs, you can observe how all your systems are performing as experienced by your users.
Browser tests are the scenarios that Datadog Synthetic Monitoring executes on a web application like automated user-experience monitoring. These tests ensure that users can complete basic actions like signing up for an account or adding items to their cart. Such tests can be configured to run at periodic intervals from multiple locations with multiple devices and processes, and they can be executed for CI/CD pipelines. These tests verify that users can perform key business transactions on the application and that the most recent code changes will not negatively impact them. Datadog machine learning detects changes to the application and automatically updates the tests accordingly.
With end-to-end testing in production and CI environments, development teams can proactively ensure that no defective code makes it to production. Datadog browser tests provide end-to-end visibility for troubleshooting issues. An alert from a synthetic test can point you to the exact application, endpoint, or region that is experiencing issues. For example, when a browser test fails because of front-end or back-end application issues, Datadog provides the context to troubleshoot the issues. Screenshots from the text show what users are seeing before the element disappears from the page.
A slow API endpoint or an unexpected timeout in processing a request can significantly affect the user experience. API tests determine if your applications receive and respond to requests efficiently. A single API test launches requests on different layers of systems like HTTP, SSL, DNS, TCP and ICMP. Multistep API tests enable you to run HTTP tests in sequence so that you can monitor the uptime of the journey at the API levels.
Datadog Synthetic Monitoring tracks how efficiently your API endpoints handle traffic at every step to ensure that they are processing incoming requests as expected. It monitors the performance of endpoints and the overall health of applications so that the most critical servers are available at all times and in every location.
How to use Datadog Synthetic Monitoring
In the following short video tutorial, I will show you how to use Datadog Synthetic Monitoring to run browser and API tests.
Need help with Site Reliability Engineering (SRE), 24/7 Support, or DevOps on AWS? The nClouds team is here to help with these and all your AWS infrastructure requirements.
Want more tips on using Datadog? Check out these related resources:
Blog post: How to Use Slack Slash Commands to Perform Actions on Datadog
Webinar (On-demand): How DevOps Teams Use SRE to Innovate Faster with Reliability