February 2018 Summaries

The announcement highlights the introduction of hosted Helm repositories to the Codefresh platform, complementing its existing features such as CI/CD pipelines, a built-in Docker registry, a Kubernetes Dashboard, and on-demand environments. This addition underscores Codefresh's commitment to open source by building these repositories on the Chart Museum project and contributing to its development. While Codefresh already supports integration with external Chart repositories, the integration of built-in repositories aims to enhance user experience by deepening its existing connections with Helm and Kubernetes.

Codefresh has enhanced its pipeline automation capabilities by introducing support for event-driven execution, initially focusing on Docker Hub push events. This allows users to trigger Continuous Delivery (CD) pipelines not only from code pushes but also from external events such as when a new Docker image is pushed to Docker Hub. The system supports a variety of DevOps tasks, including build, test, analyze, deploy, configure, and release, by leveraging container-based pipelines. Users can initiate pipeline execution manually or through the Codefresh CLI tool, and the platform is expanding to include more external triggers beyond Docker Hub. Codefresh provides functionality to link specific pipelines to trigger events, where detailed instructions and endpoint URLs are provided for setting up webhooks. When a pipeline is triggered by a Docker Hub event, relevant environment variables are automatically generated to facilitate the process, making it easier for developers to manage and automate their DevOps workflows.

Canary releases, a modern software delivery technique, facilitate stress-free rollouts of new application versions by deploying a new release alongside an existing version and directing a small fraction of production traffic to it. This method allows for incremental updates if the new version proves stable. Utilizing the Codefresh-Kubernetes integration, this process involves building application and test images, deploying the canary using Kubernetes deployment tools, and conducting end-to-end integration tests to verify the canary's health. If successful, the canary is exposed to 10% of production traffic, and upon passing quality gates, a Codefresh pipeline is triggered to update all production pods. The example provided focuses on deploying an ornithology data management service using Python and a MongoDB backend, employing trunk-based development and delivery patterns with two Codefresh pipelines. The deployment strategy emphasizes automated verification processes, traffic distribution, and a rolling update mechanism for production pods, highlighting the importance of defining quality gates and integrating observability platforms for effective monitoring and decision-making during the release process.