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I saw a green build on a Friday afternoon. I knew I need to push it to production before the weekend. My gut told me it was a trap. I had already stayed late to revert a broken deploy. I knew the risk. In the middle of a service extraction project, we decided to migrate from REST to GraphQL and optimize API usage. My deploy was a part of this radical change. Why was I deploying so late? How did we measure the migration effects? And why was I testing on production? I'll tell you a tale of small steps, monitoring, and old tricks in a new setting. Hope, despair, and broken production included.
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In his talk at RailsConf 2021, speaker Maciek Rzasa discusses the complexities of API optimization within the context of migrating from REST to GraphQL while extracting a billing service from a monolithic Rails application. The presentation unfolds with Maciek reflecting on a near mishap during a Friday afternoon deployment. He highlights the importance of deploying safely, especially when many engineers are actively working on the same project. The platform in question is a large Rails application with over a million lines of code, requiring careful handling to ensure stability while implementing changes. Key points discussed include: - **Deployment Timing and Risks**: Maciek explains his initial hesitation to deploy on a Friday due to his previous experience of breaking production. Despite this, he recognized the necessity of the deployment before the weekend. - **Incremental Service Extraction**: The process of extracting billing logic was detailed. The approach began with a rails engine and evolved to include networking capabilities with safe deployment practices, such as feature flags and fallbacks to maintain user experience during errors. - **Monitoring Enhancements**: To effectively measure impacts post-migration, they enhanced monitoring setups, identifying slow calls and performance bottlenecks, which informed the prioritization of their migration efforts. - **Optimization Efforts**: Several optimization techniques were employed, such as preloading, caching, and improving API queries through intelligent data handling to reduce unnecessary load on services. - **Learning from Mistakes**: Maciek shares anecdotes about production errors caused by over-sanitizing parameters during deployment and the importance of testing changes thoroughly before relying solely on CI/CD pipelines. Through these points, Maciek illustrates not only the technical challenges faced during the project's transition but also emphasizes organizational learnings and the significance of cultivating a safe environment for deployment. He concludes with the take-home message that successful deployment strategies rely on robust monitoring, safe rollback procedures, and incremental improvements, encouraging engineers to share their journeys to foster community learning.
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