For example, in the Dedicated tier, with the Premium V2 and V3 plans, you can deploy 30 VM instances, whereas in the Isolated tier, you can scale out to https://pin-upapp.in/en-in/ 100 VM instances. Scaling in is the reverse of these scenarios; you might scale in to reduce costs or unused capacity. In contrast, scaling down by changing plans removes or reduces the feature set, depending on which plan you choose. This feature enables you to capture the performance of the application and obtain detailed traces to identify hot code paths.
Always On is an App Service feature that ensures that VM instances are kept alive, even if there are no ingress requests or traffic to the VM instances for more than 20 minutes. This can help prevent the instance from going offline due to an idle timeout, thereby creating a cold-start situation that leads to delayed response times. Always On is disabled by default, but you easily enable it using the Azure Portal (see Figure 3-35), Azure PowerShell, or Azure CLI. Follow a similar approach to point a new build to staging to avoid unintended code activations.
When creating a deployment slot, decide whether to clone the app configuration (from production or another slot) or start fresh. Generally, swappable settings include general settings, WebJob contents, app settings, path mappings, hybrid connections, connection strings, service endpoints, handler mappings, and Azure CDN configurations. It is highly recommended to use deployment slots when deploying new code to staging or production because it makes it easier to swap the previous version of the code in case of any issues. This can reduce downtime while also automating the entire DevOps pipeline workflow to build, test, and validate new code before rolling it out into production.
Select an appropriate deployment source
If the app has only a single instance, traffic will not be blocked, even if that instance is deemed unhealthy, as there is no alternative to failover. App Service has a Health Check feature that monitors the health status of running instances and automatically removes unhealthy instances from the load balancer to avoid disruption in service. This feature is disabled by default; however, it is recommended that you enable it.
Please refer this MSdoc for better understanding of the above environment variable setting. From there click on “Check Swap Operations” which will open a page showing all the swaps performed on the webapp and their results. It will include possible root causes for the failures and recommendations on how to fix them.
Azure App Service, deployment slots (with configuration !) and Terraform
After a successful swap, both production and development slots display the updated builds. For example, refreshing the production site will show the updated “build 1.0” dashboard, while the development slot reverts to its previous state. We notice that when we deploy a certain deployment slot, not all instances serve the same version Azure App Service, since 4-8 weeks, we notice that when we deploy a certain deployment slot, not all instances serve the same version. I know I can “solve” this by restarting the instances, but that’s a work-a-round.
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After the selected deployment mechanism copies the code to the web app, every VM instance running the app receives a notification regarding the availability of the new files. Each instance syncs the new files from the shared location in the web app’s / directory. The recommended approach for any enterprise environment is to identify a centralized source so that all the code repository structures can be built and maintained centrally, and code backups can be performed on a regular basis. For example, App Service has built-in integration with GitHub, which can be easily set up when the GitHub environment is online. Each of these is used differently in every environment, as each environment has unique use cases and development needs.
Is there anything I can do to actually solve the problem (other than just delete and recreate slot)? The only side effect this setting has is that it may cause problems when used with some Windows Communication Foundation (WCF) application. The following sections step you through the process of setting up scaling for your web app using the Azure Portal, Azure PowerShell, and Azure CLI. Per-app scaling is enabled at the App Service plan level and can be configured thereafter independently for each app. Using this feature well can help you achieve the highest level of app density per App Service plan.
Note that if you use separate branches (e.g., development vs. production), the production App Service remains on the older version until a merge or slot swap occurs. As I almost always do I have prepared a GitHub repository with a demo consisting of a simple ASP.NET web app, Terraform code and GitHub Actions for deployment. You can fork this repo and run the demo by yourself by following the instructions in the README file. When you scale out, it applies to all apps running on the App Service plan on all new instances. It can provide both performance benefits and cost savings—which can be maximized if the scaling occurs automatically. If CPU or memory utilization reaches or exceeds 90%, you should bring additional VM instances online so the overall load goes down.
For example, if your App Service plan supports 20 instances, but one of your apps requires only five instances to handle peak loads, you can use per-app scaling to set the scaling limit to five for that app. This enables you to make more appropriate use of the resources available in the App Service plan by assigning more resources only to the apps that require this. Application Initialization is a great feature to warm up a new application instance before it is made available to users. You can query a single URL path or multiple URL paths in the application to warm them up or trigger a script to preload cache. If not set, it can result in application instances being put into service at the time of any restart or scale-out operation, without being ready to service clients. As discussed, there are various App Service tiers available for deployment, and each tier offers specific hardware SKUs to meet different requirements.
Application Initialization
After deployment, the App Service plan can be scaled down based on the new average resource requirements. If you are deploying a new workload using an existing App Service plan, you should check the CPU and memory usage to make sure there is enough spare capacity to handle this deployment spike. If you build an app using .NET, it is highly recommended that you set up Profiler to obtain these deep insights. Application Insights is an Azure Monitor feature to detect and analyze performance anomalies, debug code errors, and provide a better understanding of user behavior when using the app. If multiple URL paths are defined, App Service will wait for all the paths to confirm their status (success or failure) before the instance is made live.
For enterprise environments in which DevOps processes are in place, this would generally be the minimum plan selected. Each deployment slot has its own unique URL, which you can use to access the code in that slot for testing at no additional cost. However, when you try doing that, the result is the data appears from the ‘self’ deployment slot and not from the slot named ‘pre-production. Swapping pushes the new code from the “dev” slot into production while moving the old production code into the “dev” slot. You can configure separate deployment slots for different branches to facilitate parallel development. For example, create a “dev” branch in Azure Repos containing the same content as the main branch, and then add a corresponding deployment slot in your App Service.
Working with Branches and Slots
You can use this to load code for testing; then, when testing is complete, you can swap the slot so the code moves to staging or production. This guide demonstrated setting up CI/CD with Azure App Service and using deployment slots to efficiently manage multiple environments. These techniques not only streamline deployments but also ensure seamless testing and safe rollbacks to maintain production stability. If you choose not to create a pull request, you can swap the deployment slots directly. Swapping exchanges the contents of the “dev” and production slots, enabling a quick rollback if needed. Running a production app with one VM instance creates a single point-of-failure.
- You should employ GitHub Actions if you are using GitHub as a deployment source to help automate code deployments with proper controls and change tracking.
- Each of these is used differently in every environment, as each environment has unique use cases and development needs.
- A multi-region design can also help in routing requests to the closest datacenter based on the user’s region.
Setting up continuous deployment for production slots can result in code going live without proper controls. Deployment slots enable you to create multiple environments (such as staging, QA, UAT, or development) within a single Azure App Service. Testing new code in a staging slot prior to a production swap minimizes downtime and avoids performance issues like cold starts. Getting to the end of this post has been quite a ride, the writing went pretty smooth but once again I have probably spent way too much time on this demo.
You should also set up production branch integrations against a dummy slot in the App Service. After the code is deployed in the slot, performing a swap to make the code live is a better approach, as it provides more control over the deployment and allows easy rollback in case of issues. You should set up the continuous deployment of code only for staging or testing slots.
- For example, in the Dedicated tier, with the Premium V2 and V3 plans, you can deploy 30 VM instances, whereas in the Isolated tier, you can scale out to 100 VM instances.
- It also allows you to target development or troubleshooting efforts more appropriately.
- Each instance syncs the new files from the shared location in the web app’s / directory.
- I have learned a ton doing this, as I had barely touched GitHub Actions before.
- We notice that when we deploy a certain deployment slot, not all instances serve the same version
Planning deployment and operations
If the App Service plan is being used for a production application deployment, it is advisable to select hardware in the Production tier. (See Figure 3-37.) This ensures that the hardware resources available to the application are consistent and can be scaled automatically as required. For production applications, it is highly recommended that when you design and code the app, you keep in mind that multiple instances of the application will need to run simultaneously. This might require you to code the app to handle multiple VM instances running the app without sync issues when reading or writing data. If you scale a deployment slot up or out, all the slots on the App Service will be scaled accordingly, as they all share the same App Service plan. After you created an App Service you only have one deployment slot and having one deployment slot means that by default 100% of the traffic will flow throught it.
