In some organizations, its a common practice to put everything related to one project in one single git repository. Over the time, as the project goes on, more and more files keep getting added and it may reach a large size over the time. In such a case, you would like to check only a particular path, so that you can reduce the checkout time. It also make sense to checkout only selected paths, when you are running a continuous integration build, so that you can reduce overall build time. Even though git is very fast, but small improvements can really add up to be significant.
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Since in last post, we discussed on how to run Azure Pipelines agents as docker containers and configure them accordingly, the next step would be to run them on the Kubernetes platform. This kubernetes cluster can be on-premise and/or cloud and could be self managed or managed by the cloud service provider itself.
One of the reasons you may want to run them on Kubernetes is because you want better utilization of your kubernetes cluster. Another reason might be to leverage your existing knowledge of the kubernetes platform and work on it. Another reason would be to not use Microsoft hosted agents, as by default you would get only 1800 minutes of agent time to utilize, for free accounts.
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To run the build or deployment jobs in Azure DevOps or Azure Pipelines (formerly known as TFS and VSTS respectively), an agent is required. Microsoft provides the different types of the agents and they are hosted and managed by Microsoft only. However, it is advisable to host your own private agent for various reasons other than the cost. Microsoft provides the facility of installing agent on various OS’es like Windows, Linux, Mac OS etc. They have done a good job in terms of documentation, however you still need to perform few steps in order to set it up correctly.
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It is easy to create variables in the Azure Pipelines and they make the pipelines more generic in nature. Therefore, we can customize the release steps as per the context of the stage used. Same goes for the build definitions. Now sometimes, it may happen that the variables are common across multiple build and release definitions. In such a case, instead of defining them again and again, we can use a variable group. A variable group allows us to store values that we want to make available across multiple build and release pipelines. It also prevent duplication of values, making it easier to update all occurrences as one operation.
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It has been long since I have written blog post about using Nuget Package feeds in VSTS, which can be found here. I have always wanted to write a follow up blog post about how to use feeds further but I was occupied by other priorities and then it fell off my mind. Since then VSTS has been renamed to Azure DevOps and Package feeds are now known as artifacts feeds. However, other than this, most of the things and functionality has been more or less intact in terms of this feature.
For this who are not aware of the Nuget, it is a technology which works on the principal of the package management and very helpful for code sharing in .NET framework and .NET Core based applications. Read More »
Few days back, we learned about how to publish Azure Container Instances where-in we can deploy either a container or group of containers and use the same. Azure Web App for Containers allows you to not only run your containers but it also brings forth the PaaS innovations for the Web App. So it brings best of the both worlds together. It also allows you to not worry about the maintaining an container orchestrator mechanism. You can prefer to package their code and dependencies into containers using various CI/CD systems like Jenkins, Maven, Travis CI or VSTS, alongside setting up continuous deployment web hooks with App Service.
In this blog post we’ll learn more about how to deploy .NETCore application packaged as docker container and using CI/CD in Azure Pipelines (Formerly VSTS).Read More »
Using SonarQube extesions from Marketplace for Azure DevOps provides much of the integration functionality between Azure DevOps and SonarQube. Once the build pipeline completes, you can login in SonarQube server and view the code analysis results. Based on the code analysis results against the Quality threshold set or default Quality Gate threshold, it will be assigned a rating. However, there is no way to stop check-in of code, if it fails to passes the Quality Gate criteria. However, we can use some PowerShell and SonarQube Web APIs to do this part for us. In this blog post, we’ll learn steps to do the same.Read More »
After upgrading our SonarQube Community version to the newly released version, our build pipelines hosted in the Azure DevOps (formerly VSTS) started failing with below errors:
“02:14:02.317 ERROR: Caused by: Validation of project reactor failed:
o To use the property “sonar.branch.name”, the branch plugin is required but not installed. See the documentation of branch support: https://redirect.sonarsource.com/doc/branches.html”
We were using the same release branches that we were using earlier and there was no changes made to the build configuration. Read More »