How do I achieve zero-downtime deployments for Developer experience (DX)?

Achieving zero-downtime deployments is essential for enhancing the Developer Experience (DX). To implement this, various strategies can be adopted, such as blue-green deployments, canary releases, and using load balancers. These methods allow for seamless updates while maintaining service availability. Below, we illustrate a basic example of a blue-green deployment approach using PHP and a web server configuration.

<?php // Example of a blue-green deployment strategy function deploy($version) { // Assume we have two versions of our application (blue and green) $currentVersion = 'blue'; $newVersion = $currentVersion === 'blue' ? 'green' : 'blue'; // Deploy the new version echo "Deploying the new version: " . $newVersion; // Switch traffic to the new version switchTraffic($newVersion); } function switchTraffic($newVersion) { // Update the load balancer to point to the new version echo "Switching traffic to " . $newVersion . " version."; } deploy('1.0'); ?>

Zero-Downtime Deployment Developer Experience Blue-Green Deployment Canary Release Load Balancer

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?