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RubyConf 2019 - Statistically Optimal API Timeouts by Daniel Ackerman Have you ever written code that retries an API request? How did you pick the amount of time to wait before retrying? If you resorted to making an 'educated best guess' then this talk is for you. You will walk away from this talk with: 1. Knowledge of when a timeout is optimal & how important this optimality is! 2. An explanation of the algorithms necessary to calculate optimal timeouts! 3. An overview of a new open source Ruby library to calculate optimal timeouts using an open source CAS! By the end: you will be equipped to optimize your timeouts, using your favorite language, Ruby. #confreaks #rubyconf2019
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The video titled "Statistically Optimal API Timeouts," presented by Daniel Ackerman at RubyConf 2019, delves into the best practices surrounding API timeout strategies. It specifically addresses how to determine optimal retry times for API requests, moving beyond arbitrary guesses to data-driven decisions. The talk begins with Ackerman explaining the importance of timeouts and their correct implementation within Ruby applications. His goal is to equip developers with the knowledge to improve their API interactions by accurately assessing and optimizing timeout values. Key points of the talk include: - **Understanding Timeouts**: Ackerman provides an overview of timeout mechanisms and the circumstances under which they are necessary, emphasizing that proper timeout policies can lead to improved API efficiency. - **Historical Context**: He references how timeout values are often chosen randomly, challenging the audience to consider how these decisions might be better informed by mathematical analysis. - **Statistical Insights**: The speaker illustrates the concept of statistical distributions through a visual representation of cumulative distribution functions (CDF), highlighting how different distributions impact response times. Two scenarios are presented where the 80th and 95th percentiles of response times differ significantly, resulting in the conclusion that timeout strategies could be optimized based on these metrics. - **Practical Application**: Ackerman emphasizes that adjusting timeout settings is a straightforward process, often only requiring updates to configuration files instead of extensive code changes. This highlights the accessibility of implementing optimal timeout strategies in existing projects. - **Ruby Library**: The presentation wraps up with a discussion about an open-source Ruby library that Ackerman developed to facilitate the calculation of optimal API timeouts, presenting a practical tool for attendees to employ in their projects. In conclusion, Ackerman's talk provides insights into the statistical analysis of API timeout policies, offering a framework to improve how developers approach these crucial design decisions. By the end of the presentation, viewers will have a deeper understanding of the mathematical basis behind timeout configuration and actionable strategies to optimize their API interactions.
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