This is the second post in a series I’m writing on Azure’s Application Insights (AI) service. In the previous post we looked at how to instrument our application code for monitoring.
Here in this post we will walk through how to create application monitoring dashboards directly in Azure using the Azure Dashboards feature and leveraging data from Application Insights and Azure resource metrics.
This is the first post in a new series I’m writing on Azure’s Application Insights (AI) service. The goal of the series is to walk through some of the basics for monitoring your Azure hosted services with Application Insights. We will cover topics like instrumentation, monitoring dashboards, and paging alerts.
In this post we have a look at code instrumentation: What is it? What are SLIs? How do we use the Application Insights client libraries? What are some instrumentation best practices?
Why Azure WebJobs?
Azure WebJobs are a great resource included in Azure App Service plans. They provide a way for you to run binaries or scripts that perform scheduled or event-triggered job processing for work that isn’t suited to be done during a normal web request. Have a monthly cleanup task for your website? Sending a big batch of emails? Need to process some user uploaded files? These are all great examples of things you can throw into a WebJob.
.NET developers should be familiar with the classic web.config file used for storing web site options, application settings, and connection strings. When you deploy a .NET Web Application to Azure this file can still be used, however a new additional settings layer in Azure is provided.
About Deployment Slots
Azure web app service has a fantastic feature called “Deployment Slots”, available on standard or premium mode pricing tiers. This feature allows you to spin up multiple slots (full instances) of your web application for test/development and staging uses.