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The Ruby garbage collector is a highly configurable component of Ruby. However, it’s a black box to most Rails developers. In this talk, @shopify Senior Developer & member of the Ruby Core team Peter Zhu shows you how Ruby's garbage collector works, ways to collect garbage collector metrics, how Shopify sped up their highest traffic app, Storefront Renderer, by 13%, and finally, introduces Autotuner, a new tool designed to help you tune the garbage collector of your Rails app. Slides available at: https://blog.peterzhu.ca/assets/rails_world_2023_slides.pdf Links: https://rubyonrails.org/ https://railsatscale.com/2023-08-08-two-garbage-collection-improvements-made-our-storefronts-8-faster/ https://github.com/shopify/autotuner https://blog.peterzhu.ca/notes-on-ruby-gc/ https://shopify.engineering/adventures-in-garbage-collection #RailsWorld #RubyonRails #rails #rubygarbagecollection #autotuner
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In his talk at Rails World 2023, Peter Zhu, Senior Developer at Shopify and member of the Ruby Core team, explores how to optimize Ruby's garbage collector (GC) to enhance the performance of Rails applications. He begins by defining the garbage collector's function, emphasizing that it doesn’t just reclaim data but manages the entire lifecycle and memory allocation of objects. Zhu provides a detailed explanation of how Ruby's marking garbage collector operates, including its various phases: marking, sweeping, and compaction. Key points discussed include: - **Garbage Collector Mechanism**: Ruby uses a marking and sweeping technique to identify and reclaim unused objects. The compaction phase, though not enabled by default, helps reduce fragmentation and optimize memory usage. - **Generational Garbage Collection**: Ruby follows a generational hypothesis where objects are categorized based on their longevity. Young objects are monitored closely, while old objects, which have survived multiple GC cycles, are treated differently during collection cycles. - **Write Barriers**: These are mechanisms to ensure that the garbage collector is informed when references are created between old and young objects, helping to prevent issues like dangling references. - **Metric Collection**: Zhu discusses how to use `gc.stat` to monitor garbage collection metrics within Rails applications, providing insights that can highlight potential performance optimization areas. - **Tuning Strategies**: To enhance performance, he advises reducing object allocations, which subsequently minimizes the frequency of garbage collection cycles. Specific environment variables can be adjusted to control GC behavior. - **Case Study**: Zhu highlights the performance improvements from tuning the garbage collector in Shopify’s Storefront Renderer application, achieving a 13% improvement in average response times by reducing GC duration significantly. - **Autotuner Gem**: A new tool called Autotuner is introduced as a means for Rails developers to analyze their garbage collector settings and receive recommendations for optimizations. In conclusion, Zhu emphasizes that effective garbage collector tuning can lead to noticeable performance improvements in Rails applications. The session encourages developers to utilize tools and metrics to better manage memory and optimize application performance.
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