When working in a DevOps team, you must often schedule a DevOps pipeline, automated processes, or tools that enable developers and operations experts to collaborate on developing and deploying code to production environments. Building a successful DevOps pipeline allows businesses to create, test, and deliver new code continuously. One of the primary goals of a DevOps pipeline is to automate the software delivery process, removing the need for manual adjustments at every stage. The advantage of an Azure DevOps Scheduled pipeline is setting a specific time to run pipelines according to your requirement.
In this article, let us understand how to create Azure DevOps Scheduled pipelines.
Table of Contents
Prerequisites
- Basic Knowledge of software development.
What is a DevOps Pipeline?
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A DevOps Pipeline is a series of steps used by development and operations teams to quickly design, test, and deploy software. A pipeline’s primary function is to keep the software development process orderly and focused. However, the term “pipeline” may be confusing because software development is continuous rather than linear. For example, you must first write the code before releasing an application or a new feature to users. Then double-check that it doesn’t result in any fatal mistakes, which could cause the app to crash. To avoid a situation like this, perform a series of tests to find any flaws, typos, or errors.
Several DevOps methods must be implemented to guarantee that code flows smoothly from one stage to the next. Continuous Integration and Continuous Delivery (CI/CD) is the most significant part of the DevOps Pipeline.
Continuous Integration (CI) is a technique for integrating small bits of code from many developers as frequently as possible into a shared code repository. Instead of waiting for other team members to contribute their code, you may automatically test the code for faults with a CI technique.
On the other hand, Continuous Delivery (CD) extends continuous integration (CI). It entails encouraging developers to deploy code to production in small pieces to speed up the release process. Although continuous delivery and deployment are similar in many aspects, they have significant distinctions.
Continuous Deployment is about automating the complete release cycle, whereas Continuous Delivery allows development teams to publish software, features, and code upgrades manually.
During the continuous deployment stage, code updates are delivered to end-users automatically and without manual involvement. Implementing an automatic release approach can be risky. Bad code will be deployed to production if it fails to mitigate defects found along the route. The application may become unusable, or users may encounter downtime.
Continuous Testing is the practice of performing tests as frequently as possible during the development process to detect flaws before reaching the production environment. A Continuous Testing strategy quickly assesses the business risks associated with individual release candidates in the delivery pipeline.
Both functional and non-functional tests should be included in the testing scope. This comprises executing unit, system, integration, and performance tests on an application and server infrastructure’s security and performance.
What is Azure Pipelines?
Azure Pipelines develops and tests code projects automatically before making them available to others. It can be used with almost any language or project type. Azure Pipelines integrates Continuous Integration (CI) and Continuous Delivery (CD) to test and build your code and ship it to any target.
Continuous Integration (CI) is a development process that automates the merging and testing of code. Implementing CI allows you to catch bugs early in the development cycle, saving you money on bug fixes. To verify quality, automated tests are run as part of the CI process. To enable frequent deployments, artifacts from CI systems are fed into release procedures. The Build service in Azure DevOps Server assists you in setting up and managing continuous integration (CI) for your apps.
Code is produced, tested, and deployed to one or more test and production environments using the Continuous Delivery (CD) method. Quality is improved by deploying and testing in many settings. Infrastructure and applications are examples of deployable assets produced by CI systems. Automated release processes use these artifacts to provide new versions and fix existing systems. Monitoring and alerting systems run in the background to provide continuous visibility into the CD process.
Continuous Testing (CT) uses automated build-deploy-test workflows with various tools and frameworks to test your changes continuously in a quick, scalable, and efficient manner, whether on-premises or in the cloud.
Having your source code in a version control system is the starting point for configuring CI and CD for your applications. Azure DevOps supports version control in two ways: GitHub and Azure Repos. Any modifications to your version control repository will be built and evaluated automatically.
What are Triggers?
Triggers can be used to run a pipeline automatically. Fortunately, Azure Pipelines supports many types of triggers. Based on your pipeline’s type, you can select the appropriate Azure DevOps Scheduled Trigger
Let us talk about the various types of triggers:
- Scheduled Triggers are not related to a repository and allow you to run a pipeline at a set time.
- Comment Triggers are supported only by GitHub repositories.
- Pipeline Triggers in Azure DevOps YAML schedule pipelines and Build Completion Triggers in traditional build pipelines allow you to start one pipeline after another has finished.
- Azure Pipelines offers a variety of triggers for configuring your pipeline. Scheduled Triggers start your pipeline regularly, like the trigger can be activated every night. On the other hand, Event-Based Triggers kick off your pipeline in reaction to specific actions, such as submitting a pull request or pushing to a branch.
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How to create Azure DevOps Scheduled Triggers?
You provide a trigger’s schedule (start date, recurrence, end date, etc.) and correlate it with a pipeline while building a Azure DevOps Scheduled trigger. There is a many-to-many link between Azure DevOps Scheduled pipelines and triggers. Many triggers can start a single pipeline, and a single trigger can create multiple pipelines.
Before you get started, make sure you:
- Create a GitHub account to create a repository.
- Create an Azure DevOps organization that you can make for free.
- You can run Pipelines on Microsoft-hosted agents.
- Next, fork this repository into your GitHub account to get started.
Now follow the steps to create your first Azure pipeline:
- After logging in to your Azure DevOps organization, click the Projects option.
- Select New Pipeline from the Pipelines menu.
- Select GitHub as the location where your source code repository is present.
Then you will be redirected to the GitHub sign-in page. If that’s the case, log in with your GitHub credentials.
- Select your required repository from the list of repositories on GitHub.
- To install the Azure Pipelines app, you may be routed to GitHub. If that’s the case, click Approve and Install.
- Then you might be redirected to GitHub again to install the Azure Pipelines app.
- If you approve this request, the Azure pipelines app will examine your repository and suggest a Python package pipeline template; you can then approve this template.
- Your new pipeline with a YAML file will be created. You can now look at the YAML to see how it works. Select Save and Run when you’re finished.
- You’re suggested to commit a new YAML file called azure-pipelines.yml. After you’re comfortable with the message, select Save and Run again.
- Select the “build job” option to see your pipeline in action.
Because your code looked to be a suitable fit for the Python package template, the code you forked from GitHub automatically constructed and executed a pipeline for you.
You now have a functional YAML pipeline in your repository (azure-pipelines.yml) that you can edit and make changes to whenever required.
- Select your pipeline on the Pipelines icon, then Edit the azure-pipelines.yml file when you’re ready to make any changes.
If you want the build to run anytime someone updates the code, choose to Enable continuous integration on the Triggers tab to enable this trigger.
For example, if you want a trigger to be instigated whenever there is a change in the master branch and some feature branches, you can specify it in the “include” or “exclude” option.
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How to Schedule Triggers?
Now, let us understand how to schedule triggers based on the chosen days and hours you wish to perform the build using the classic editor.
Under the Artifacts section in the Pipeline option, choose the Schedule icon. Select your release schedule by toggling the Enabled/Disabled button. Several schedules can trigger a release.
When you are scheduling your pipeline, you can also set the timing under the Trigger tab. If this is set, this will overwrite any schedule specifications in your YAML file.
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If you wish to use wildcard characters, specify the branch specification (for example, features/modules/*) and then press Enter. You can use an exact name or a wildcard to identify a branch or tag. “*” matches zero or more characters, while “?” matches a single character in wildcard patterns.
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How to view Azure DevOps Scheduled Triggers?
Choose the ‘Scheduled Runs’ option that is available from the main menu on the pipeline information page to see a preview of future Azure DevOps Scheduled builds.
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You may use this view to validate your Azure DevOps Scheduled triggers once you’ve created or updated them.
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Also, If no code modifications have been made after the last successful scheduled run, your pipeline will not operate as planned. Consider the following scenario: you’ve scheduled a pipeline to run every night at 9:00 P.M. You make numerous modifications to your code over the week. The pipeline is running according to plan. You do not make any modifications to your code on weekends. The pipeline does not operate as planned over the weekend if no code modifications have been made since Friday’s scheduled run.
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Unfortunately, the classic editor has a drawback that the number of times you may schedule a pipeline to operate is limited. These restrictions were implemented to prevent the abuse of Azure DevOps Scheduled Pipelines resources, specifically the Microsoft-hosted agents. This limit is approximately 1000 pipeline runs each week.
The classic editor scheduled trigger has two entries, in this case, resulting in the builds below.
Let us look at an example of Building branches that fit the features/India/* branch filter criteria every Monday through Friday at 3:00 AM (UTC + 5:30 time zone).
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The cron syntax (mm HH DD MM DW) for the first schedule, M-F 3:00 AM (UTC + 5:30) India daily build, is 30 21 * * Sun-Thu.
Build branches that fulfill the features/nc/* branch filter criteria every Monday through Friday at 3:00 AM (UTC – 5:00 time zone).
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The cron syntax for the second schedule, M-F 3:00 AM (UTC – 5) NC daily build, is 0 8 * * Mon-Fri.
You can use the Azure DevOps YAML Schedule trigger file as given below to schedule a trigger, unlike the classic editor.
schedules:
- cron: "30 21 * * Sun-Thu"
displayName: M-F 3:00 AM (UTC + 5:30) India daily build
branches:
include:
- /features/india/*
- cron: "0 8 * * Mon-Fri"
displayName: M-F 3:00 AM (UTC - 5) NC daily build
branches:
include:
- /features/nc/*
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Conclusion
Many companies use Microsoft Azure Data Factory to orchestrate business activities and build data pipelines. Organizations may use ADF to transport data from on-premises and cloud source data stores to a centralized data store for analysis.
In this article, you learned about utilizing the Azure Data Factory UI to configure Azure DevOps Scheduled Triggers and run pre-built Data Pipelines. Azure DevOps Scheduled Triggers may also be created and configured using Azure PowerShell, Azure CLI, Azure Resource Manager Template, .NET SDK, and Python SDK. However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.
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Share your experience of learning about Azure DevOps Scheduled Pipelines. Tell us in the comments below!
Kavya is passionate about writing on data science who loves in-depth research on complex topics and loves to simplify complex topics related to data integration and analysis through detailed content.