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Server-Side TelemetryĮach component of an application that is required to generate server-side telemetry at the very least needs to consume one of the Application Insights SDKs as a NuGet. The MegaStore application contains a helper class ( ) to pass environment variables to calling code. An ASP.NET Core application component will automatically recognise APPINSIGHTS_INSTRUMENTATIONKEY-in other components it may need to be set manually. When working with containers probably the easiest way to make an instrumentation key available to applications is via an environment variable named APPINSIGHTS_INSTRUMENTATIONKEY. The Terraform configuration developed in a previous post created three Application Insights resource instances for each of the environments the MegaStore application runs in: Each stage of the deployment pipeline is then configured to make the appropriate instrumentation key available and the application running in that stage of the pipeline sends telemetry back using that key. The principal technique to avoid this is to have separate Application Insights resource instances which each have their own instrumentation key.
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When using Application Insights with an application that is deployed to different environments it's important to take steps to ensure that telemetry from different environments is not mixed up together. I describe an overview below, but everything is implemented in the sample application here. When using it in conjunction with an application as we are here there are several configuration options to address. Application Insightsįrom the docs: Application Insights, a feature of Azure Monitor, is an extensible Application Performance Management (APM) service for developers and DevOps professionals. In order to capture sufficient retrospective data to be useful requires the services of a dedicated tool. Of course, pipeline environments only really tell you what's going on at that moment in time (or maybe for the previous few minutes depending on how busy the logs are). This is the log from the message-queue-deployment pod: It gets better though because you can drill in to the pods and view the log for each pod. For example, this is what's displayed for the MegaStore.SaveSaleHandler deployment and pods: It turns out that these are great for quickly taking a peek at the health of the components deployed to a cluster.
You may apply a new custom channel group series#
If you are following along with this series you may recall that in the last post we configured an Azure DevOps Pipeline Environment for the Kubernetes cluster. If you want to follow along you can clone / fork my repo here, and if you haven't already done so please take a look at the first post to understand the background, what this series hopes to cover and the tools mentioned in this post.
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Whilst there are lots of third party offerings in the telemetry and diagnostics space in this post I take a look at what's available for those wanting to stick with the Microsoft experience. So while instrumenting your application for telemetry and diagnostic information should be fairly high on your to do list anyway, this is even more so when running application is containers. One of the problems with running applications in containers in an orchestration system such as Kubernetes is that it can be harder to understand what is happening when things go wrong.
You may apply a new custom channel group how to#
This is the sixth post in a series where I'm taking a fresh look at how to deploy a dockerized application to Azure Kubernetes Service (AKS) using Azure Pipelines after having previously blogged about this in 2018. Deploy a Dockerized Application to Azure Kubernetes Service using Azure YAML Pipelines 6 – Telemetry and Diagnostics Posted by Graham Smith on No Comments (click here to comment)