Critical Metrics and Alerts in the Continuous Delivery Process
What is Continuous Delivery?
Continuous delivery is a software development approach in which code changes are automatically staged for production release.
A foundation for modern application development, continuous delivery extends continuous integration by automatically deploying code changes to test and production environments after the build phase. When properly implemented, developers have deployable build artifacts that have passed a standardized testing process and can be deployed to environments as needed.
Continuous delivery allows developers to go beyond unit testing and functional testing, to verify all aspects of application updates before deploying them to production. These tests may include UI tests, load tests, integration tests, API stability tests, and more. This allows developers to more thoroughly validate updates and proactively identify issues. The cloud makes it easy and cost-effective to automate the creation and cloning of multiple test environments, which was traditionally difficult to achieve on-premises.
Benefits of Continuous Delivery
Reduced Risk Associated With Releases
A continuous delivery framework applies a repeatable and automated templating process. This reduces the risks associated with software distribution and makes the process easier. Developers can confidently push updates as needed at any time, reducing the possibility of unexpected production errors.
Better Quality Software
By automating the delivery process, engineers and developers can focus on what they do best: writing code. Developers can rigorously enforce security, testing, and performance checks and address errors early in the deployment pipeline. This not only saves time and ensures the quality of software updates, but also improves overall product quality.
Improved Developer Satisfaction
By implementing an automated CD workflow, organizations can remove developer pain points and let them focus on strategy and optimization. The speed of the CD pipeline also means that engineers’ code is deployed faster, allowing engineers to see the impact of their work and how it helps customers achieve their goals.
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Deploying updates has traditionally been an arduous process. However, investing in an automated deployment pipeline can significantly reduce the cost of delivering updates throughout the entire product lifecycle, in particular reducing cloud costs for dev/test environments.
Key Metrics for Continuous Delivery
These metrics can help you understand if continuous delivery pipelines are working properly and providing value.
Lead Time to Production
When you go into production, the clock starts ticking. Track cycle times only when lead times are excessive. This is because when teams first embark on a continuous delivery journey, lead times to production are often measured in months, and as cycles become shorter, it becomes easier to get feedback on new updates.
CD reduces production lead times – you can quantify this by measuring the time from when a project task begins to when that task meets the team’s definition of “done.”
Defect Resolution Time
High-quality code is a prerequisite for successful continuous delivery. Along with your team’s commitment to quality, one way to measure quality is by tracking the bug lifecycle. After a team builds a bug list page by page and measures the oldest bug by year, “truly successful teams fix bugs as they arise.
Regression Test Duration
It is important to keep track of how long it takes to complete a full regression test. This metric is measured in weeks or months for teams that test manually, and in minutes or hours for teams that rely heavily on automated testing. This is an important metric because regression testing helps reduce the risk of a failed deployment, and keeping this cycle as short as possible increases the chances of continuous delivery success.
Number of Code Branches
Continuous delivery advocates keeping everything in the same pipeline. Tracking how many branches you have in your code will give you an idea of where you are ready to start continuous delivery. Advanced practices such as continuous integration and continuous delivery cannot be achieved without version control to manage and track code, and without transitioning to a main branch strategy.
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Production Downtime During Deployment
Some applications, such as batch processes, may not require a zero-downtime deployment. By contrast, downtime for customer-facing or revenue-generating applications is critical. In these cases, the goal should be no downtime during deployment. Measuring actual downtime for production applications over time can determine the effectiveness of a continuous delivery process.
How to Implement Proactive Failure Notification
Pre-failure notification alerts you that a system has failed or its performance is degrading. This is done by generating alerts when a monitored value approaches a known failure threshold. Here is how to achieve proactive failure notification:
- Use alert rules: Alert rules define the conditions under which alerts are generated and define notification channels for those alerts. While email and collaboration channels, such as slack and Microsoft Teams are popular, their inability to prevent critical alerts from going unnoticed calls for a better tool. Incident alerting tools, such as OnPage, are used by modern software teams to manage and elevate critical alerts. Using API or email-based integration, pre-failure notification alerts surface as loud, audible alerts that also override the silent switch, ensuring that errors are addressed almost instantly. In the context of alert orchestration, the system ensures that alerts are delivered to the on-call code owners based on pre-defined schedules.
- Use a threshold: Alert rules should use thresholds for monitored metrics that represent real problems. Define thresholds that trigger alert rules, generating notifications for relevant teams. Meaningful alerts are critical for effective incident response.
- Choose your threshold carefully: Select a threshold to generate an alert only if that threshold actually predicts a problem. In general, you should determine the level of a value that users start to be affected by, and trigger an alert notification at that point.
- Post-mortem analysis of the incident: When performing post-event analysis, determine which metrics predicted the event and fine-tune metrics accordingly. Conversely, false-positive alerts triggered from a metric necessitates alert sensitivity.
- Plan your notification strategy: If the notification requires no action or requires the same action each time, the response should be automatic. Event notification volume should also be considered. Too many notifications during an event can be unhelpful and distracting, and can lead to alert fatigue – which leads to slow response times or missed alerts.
In conclusion, the continuous delivery process is essential for delivering high-quality software to users quickly and safely.
By implementing key metrics and alerts, teams can monitor the performance of their applications and proactively identify and respond to potential problems. This can help to improve the reliability and efficiency of the deployment process, as well as reduce the risk of failures and other issues that can impact the user experience. By leveraging the power of continuous delivery, teams can stay ahead of the competition and deliver value to their users faster and more effectively.
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.