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Hello everyone! It's great to see you all here.
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I'm Noah Gibbs, and today we're going to talk about improving memory configurations for Ruby applications.
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Specifically, we'll focus on how to optimize Ruby's memory usage to prevent performance issues.
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I also want to take a moment to express my gratitude to AppFolio, where I'm a Ruby fellow.
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They support my efforts in writing blog posts and running extensive benchmarks.
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Now, let's dive into the topic at hand.
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You're all here to learn how to improve the performance of Ruby applications facing memory problems.
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Before we start, let me share a bit about my life.
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I recently welcomed a three-month-old baby who is incredibly cute, which explains why I may look a bit tired.
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Life is busy with two other kids and activities like visiting the City Museum.
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If you're not familiar with it, I highly recommend checking out the beautiful hand-painted maps.
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Now, let's focus on Ruby and its memory system.
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To understand how to improve Ruby's performance, it's important to comprehend how its memory system functions.
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We will explore the concepts of tiny, small, and big objects in Ruby.
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Tiny objects are the smallest in Ruby and fit entirely inside their references.
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Ruby maintains 64-bit references, generally pointing to something, but tiny objects fit in that reference.
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These objects can include integers from about negative and positive one billion, symbols, floating-point numbers, and special values.
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The significance of this is that tiny objects are efficient and fast.
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When we discuss symbols being faster than strings, it refers to how symbols fit within the reference itself.
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Conversely, let's now examine small objects.
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Small objects occupy 40-byte slots, allowing for short strings and arrays, as well as small big decimal numbers.
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Class instances with only a few instance variables also fit into these slots.
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Allocating memory slots for small objects is economical since the memory allocator is called incrementally.
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If small objects exceed their size limitations, they become big objects.
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Big objects have more expensive allocation calls whenever they are created or resized.
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Consequently, they are crucial to track down when diagnosing memory issues in your Ruby applications.
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Now let's discuss garbage collection.
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Garbage collection is the main aspect to address when dealing with memory in Ruby.
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Ruby utilizes a generational mark-sweep garbage collector.
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While it may not be the top garbage collector, it works well.
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Generational garbage collection checks newer objects more frequently than older ones.
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Objects that remain for a longer time are less frequently marked for collection since older objects might still be in use.
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The mark phase examines all available objects to identify those no longer in use.
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Once identified, the sweep phase collects and clears memory from those marked objects.
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While Ruby's garbage collector is efficient, alternatives like those in the JVM are even superior.
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Generational garbage collection works through two main types: major and minor.
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The major garbage collection evaluates all objects, while minor garbage collection focuses specifically on newer ones.
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Major collections occur when certain thresholds are crossed, prompting a complete check.
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You can also manually trigger major garbage collection by calling `GC.start`, which initiates a thorough sweep.
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During an application's startup phase, Ruby starts with a modest memory allocation.
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As applications run, they accumulate more memory through a cycle of growth and garbage collection.
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This cycle involves growing memory usage, performing garbage collection, and expanding available slots as needed.
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The expansion phase results from running out of allocated memory slots.
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Although most applications eventually stabilize to a certain size, monitoring growth patterns is important.
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The overall goal is to recognize how your application behaves during its lifecycle and identify possible areas for optimization.
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Now, let's discuss how to tackle and resolve memory issues.
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The most effective way to improve garbage collection performance is to reduce object creation.
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Less garbage generated leads to more efficient and faster garbage collection.
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Utilizing more efficient algorithms, caching, or pre-calculating values can help minimize garbage.
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Additionally, when dealing with large objects, consider combining multiple smaller objects into a single entity.
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You can also take advantage of destructive operations to reuse data efficiently.
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For instance, methods that modify strings and arrays in place reduce the creation of new objects.
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This is critical for situations where low memory usage is imperative.
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Further, using tools like `GC.stat` will provide insights into your memory configuration.
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This tool can help identify available slots and how often garbage collections occur.
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After gathering this information, adjusting environment variables can lead to improvements.
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You can utilize options like `RUBY_GC_HEAP_FREE_SLOTS` to manage memory allocation effectively.
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Proper allocation means starting your application with an optimal number of slots.
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Maintaining a healthy ratio of free slots will also keep performance steady.
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For instance, the `RUBY_GC_HEAP_GROWTH_MAX_SLOTS` variable can help expand available memory limits.
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Considering all these aspects contributes greatly to effective memory management in Ruby.
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Now, I want to introduce a tool I've been developing called `mmm`.
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This tool aims to simplify the process of tuning memory configurations for Ruby applications.
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By setting appropriate environment variables, it allows for optimal allocation from the start.
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After running your application, you can use `mmm` to determine the suitable allocation sizes.
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This can significantly improve startup times for large applications.
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It's important to consider the impact of memory management on your application's performance.
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Examples of real-world applications effectively using this concept showcase decreased warm-up times.
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Therefore, if you're encountering performance hiccups during startup, this tool could be beneficial.
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Next, I'd like to touch on memory allocators and their importance.
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Most operating systems come with a memory allocator by default, like `malloc`.
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However, there are alternative implementations, such as `jemalloc`, that offer significant advantages.
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Increased efficiency in allocating memory leads to faster application performance.
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For Ruby, integrating with `jemalloc` has been a game changer for several applications.
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Testing has shown marked improvements, especially when running under load.
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Implementing these optimizations could yield a 10-12% increase in overall speed.
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Another allocator, `tcmalloc`, may also provide slight performance boosts.
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It's crucial to evaluate the performance implications of your application and the allocator you choose.
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To round out this discussion, always prioritize being on the latest version of Ruby.
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This optimizes garbage collection and can lead to performance improvements.
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As Ruby evolves, it brings enhanced features to memory management.
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Lastly, addressing potential fragmentation issues and common pitfalls can yield better performance.
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Make sure to regularly profile your application to identify areas needing improvement.
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Utilizing tools like `GC::Profiler` can provide valuable insights.
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Monitor garbage collection statistics for duration and frequency of pauses.
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This will help determine if memory management is causing noticeable slowdowns.
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As a practical exercise, compile Ruby with additional options to enhance profiling.
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After extracting data, analyze it for any abnormalities.
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Keep experimenting with different configurations to find an optimal setup.
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The understanding of memory management will make a significant difference in Ruby applications.
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As we conclude, feel free to ask any questions regarding memory, garbage collection, or anything we've discussed.
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I'm here to assist and clarify anything that may be unclear.
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Understanding and managing memory effectively can greatly improve performance.
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Thank you for your attention!
