How to keep up with CI CD best practices GitLab

Multiple developers work on the same codebase simultaneously and make frequent commits to the code repository. Build frequency can be daily or even several times per day at some points in the project’s lifecycle. These small, frequent builds enable easy and low-risk experimentation, as well as the ability to easily roll back or abandon undesirable outcomes. The monolithic all-or-nothing paradigm of traditional waterfall software development has largely been replaced by rapid iterative techniques that support development and release. These techniques go by several names, including continuous integration, continuous delivery and continuous deployment. A pipeline is just another way of characterizing the series of steps involved in deploying a new version of software.

ci cd pipeline monitoring

CD likewise relies heavily on tools and automation to take a build through advanced testing, including functional, user acceptance, configuration and load testing. These validate that the build meets requirements and is ready for use in a production environment. Again, small incremental iterations ensure that any problems revealed in testing are identified and remediated quickly and less expensively than traditional software development approaches. Continuous integration (CI) focuses on the early stages of a software development pipeline where the code is built and undergoes initial testing.

Observability for CI/CD administratorsedit

In this lesson, we will be hands-on and fully implement CI/CD pipelines that build in complexity using GitHub Actions. In assessing the maturity of a monitoring solution, you will often refer to terms such as “reactive” and “proactive” in order to evaluate them. It’s rather a matter of determining the degree of complexity they require in order to implement them. If you aim for a highly effective solution, you should use a combination of both approaches by selecting their best features. The trace data is similar to structured log data, and you can create alerts on it just as easily, in many cases it can be achieved based on Elasticsearch API, and in the case of Logz.io it’s the same alerting mechanism. Once the data is stored in Prometheus, it’s easy to build Grafana style dashboards on top of it (or any other visualization you prefer on top of Prometheus).

Configuring even the simplest pipeline with one job that runs on every code change will save a team many headaches in the future. Failure during the test stage exposes problems in code that developers didn’t foresee when writing the code. It’s essential for this stage to produce feedback to developers quickly, while the problem space is still fresh in their minds and they can maintain the state of flow. Failure to pass the build stage is an indicator of a fundamental problem in a project’s configuration, and it’s best to address it immediately.

Continuous monitoring and observability in CI/CD

Test Stage includes the execution of automated tests to validate the correctness of code and the behaviour of the software. Understand the intended benefits, such as faster code building or lower error/rework rates, and then implement metrics to measure those criteria. Compare the metrics against pre-pipeline performance and track those metrics as the pipeline evolves. This makes it easier to see the pipeline’s value, spot problems over time, and invest in ways to build and enhance the CI/CD pipeline.

ci cd pipeline monitoring

Identifying an environment problem can save us wasting time looking for bugs in the released code. We’ve started addressing that in our Kibana dashboard above by monitoring failure rate per machine in an aggregated fashion. Many times, pipeline runs fail not because of bugs in the released code but because of problematic machines.

Monitoring a Kubernetes CI/CD Pipeline

It was originally developed as a fork of the Hudson project and has since become one of the most widely used automation servers in the world. CI/CD pipeline introduces automation and continuous monitoring throughout the lifecycle of a software product. It involves from the integration and testing phase to delivery and deployment. It builds code, runs tests, and helps you to safely deploy a new version of the software. CI/CD pipeline reduces manual errors, provides feedback to developers, and allows fast product iterations.

ci cd pipeline monitoring

Synthetic monitoring is one of a few methods of monitoring for CI/CD pipelines. In a nutshell, synthetic monitoring is a monitoring technique in which engineers run scripts that simulate user transactions. Then, they monitor and analyze the transactions to determine how the application would respond if a real user initiated the same transaction. If you spot a slow or failing
build and need to understand what’s happening, you can drill into the trace view of the build to look
for the high duration jobs or jobs with errors.

Application Metrics

Jenkins can be run on a variety of operating systems, including Windows, Mac OS X, and Linux, and it can be deployed on-premises or in the cloud. Its user interface is web-based, and it provides a rich set of features for managing jobs, nodes, and builds. Continuous Integration refers to the practice of frequently integrating code changes made by developers into a shared repository. This ensures that code changes are continuously tested and integrated with the existing codebase, which helps identify and resolve any issues early on. On the other hand, Continuous Delivery/Deployment refers to the practice of automatically building, testing, and deploying code changes to production as soon as they are approved. This reduces the time and effort required to release new features and bug fixes and allows for faster feedback from users.

  • Even the most wildly optimistic deployment candidates are rarely committed to production without reservation.
  • CI/CD is a solution to the problems integrating new code can cause for development and operations teams (AKA “integration hell”).
  • A typical example is a middleware around an HTTP request that measures the time that has been spent producing a response as well as the information on both the request and response, such as status code and payloads.
  • Once the data is in Elasticsearch, define reports and alerts on top of that data to automate as much as possible.
  • The pipeline summary shows a breakdown of duration and
    failure rates across the pipeline’s individual builds and jobs to spot slowdowns or failures.
  • The outcome of this practice is that merging any pull request becomes a non-event.
  • A fast and reliable CI/CD pipeline is crucial for doing that sustainably over time.

A CI/CD pipeline can’t be reliable if a pipeline run modifies the next pipeline’s environment. Each workflow should start from the same, ci cd pipeline monitoring clean, and isolated environment. Context propagation is supported; therefore, you can benefit from the integrations described above.

Understanding pipeline metrics

If you also exhausted the built-in observability capabilities of your CI/CD tool, it’s time to set up proper observability – just like you have for your Production environment, with a dedicated monitoring and observability setup. In this article I’ll show how to achieve observability into your CI/CD pipeline in four steps. I’ll use Jenkins as the reference tool, as many know this popular open source project, and as in my company we’ve used it extensively. But even if you’re using other tools, you’ll find much of that largely applicable. Observability for the CI/CD pipelines is the step-child, with a less established practice.

ci cd pipeline monitoring

The test traces help you understand test execution,
detect bottlenecks, and compare test executions across time to detect misbehavior and issues. This integration feeds, out of the box, the Service Map with all the services that are connected to the Ansible Playbook. All of these features can help you quickly and visually assess your services used in your provisioning and Continuous Deployment. To learn more, see the integration of Maven builds with Elastic Observability. The visualization of CI/CD pipelines as distributed traces in Elastic Observability provides
documentation and health indicators of all your pipelines. Integrating with many popular CI/CD and DevOps tools like Maven or Ansible using OpenTelemetry, Elastic Observability
solves these problems by providing deep insights into the execution of CI/CD pipelines.

What is continuous deployment?

You can also use synthetic monitoring to compare how applications perform with and without the use of CDNs, which will also help you anticipate different types of user experiences. The Maven OpenTelemetry extension integration provides comprehensive visibility into all of your Maven builds. The extension generates traces for each build and performance metrics to help you understand which Maven goals
or Maven submodules are run the most, how often they fail, and how long they take to complete. The Service page provides more granular insights into your CI/CD workflows by breaking down health
and performance metrics by pipeline. To quickly view which pipelines experience the most errors, are the
most frequently executed, or are the slowest, you can sort and filter the list. Development teams need to continuously optimize their ever-changing CI/CD pipelines to improve
their reliability while chasing faster pipelines.

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