Anti-patterns for advanced deployment strategies

Deploying directly to production: Deploying changes directly to production without first testing in pre-production environments risks unforeseen errors, bugs, or performance issues that can lead to service disruptions or downtime. Pre-production testing in environments that mimic production as closely as possible is crucial to verify the functionality and compatibility of changes under realistic conditions.

Ignoring rollbacks and data compatibility: The absence of an automatic rollback strategy and a lack of consideration for data compatibility can lead to prolonged service disruptions and compatibility issues. An automatic rollback mechanism can reduce downtime and maintain system reliability as it ensures a quick return to a stable state in the event of a fault. Maintaining backward compatibility in data stores and schemas can prevent disruptions to existing functionalities and integration pipelines. Changes should be designed to coexist with previous data structures and contracts, allowing both old and new versions to operate concurrently.

Monolithic deployment model: Deploying all changes simultaneously and treating the entire system as a single unit increases the risk of errors that could impact the entire system and limits scalability. To mitigate these risks, adopt staggered deployments and consider cell-based architectures. Staggering deployments through wave, one-box, or rolling deployments allows for easier issue detection and rollback which reduces negative impact of failed deployments. A cell-based architecture enhances fault isolation, granular control, and operational resilience, making it a preferred strategy for complex, distributed systems.

Abrupt feature release: Releasing new features to all users at once without incremental deployment or testing can result in widespread disruptions if the feature fails or impacts the system negatively. Techniques like dark launching, two-phase deployments, feature flags, and canary releases reduce this risk by providing control and facilitating monitoring of the feature's impact in real-world conditions.