Stories with Tag threads

• Tokio and Prctl = Nasty Bug

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  Posted: 2025-02-23 22:37:57

  Verbose tl;dr

  Combining Tokio's asynchronous runtime with prctl(PR_SET_PDEATHSIG) in a multi-threaded Rust application can lead to a subtle and difficult-to-debug issue. PR_SET_PDEATHSIG causes a signal to be sent to a child process when its parent terminates. If a thread in a Tokio runtime calls prctl to set this signal and then that thread's parent exits, the signal can be delivered to a different thread within the runtime, potentially one that is unprepared to handle it and is holding critical resources. This can result in resource leaks, deadlocks, or panics, as the unexpected signal disrupts the normal flow of the asynchronous operations. The blog post details a specific scenario where this occurred and provides guidance on avoiding such issues, emphasizing the importance of carefully considering signal handling when mixing Tokio with prctl.

  The blog post "Tokio and Prctl = Nasty Bug" details a subtle and difficult-to-debug issue encountered by the author when using the prctl system call within a Tokio runtime. Specifically, the problem arose when using prctl(PR_SET_NAME) to set the process name of a thread within a Tokio task. This seemingly innocuous operation resulted in sporadic and seemingly random panics within the Tokio runtime itself.

  The core of the problem lies in the interaction between how Tokio manages its worker threads and the underlying behavior of PR_SET_NAME. Tokio utilizes a thread pool, where worker threads are reused across multiple tasks. When a task uses prctl(PR_SET_NAME), it modifies the name of the underlying thread from the pool, not just the logical task. Consequently, if another task is subsequently scheduled on the same thread, it inherits the process name set by the previous task, even if this name is no longer relevant or accurate.

  This unexpected name inheritance became problematic because the author's application logic relied on retrieving the current process name using prctl(PR_GET_NAME) for logging and debugging purposes. Because the thread names were being overwritten unpredictably, the retrieved names were often incorrect and misleading. This led to confusion during debugging and made it difficult to track the actual execution flow of tasks.

  Further compounding the issue, the author's error handling logic inadvertently relied on these incorrect process names. In some cases, the corrupted names triggered unexpected code paths in the error handling, resulting in panics within the Tokio runtime. This manifested as seemingly random crashes that were difficult to reproduce and diagnose.

  The author's solution involved two key changes. First, they implemented a robust mechanism to restore the original thread name after each task completed. This ensured that subsequent tasks running on the same thread wouldn't inherit a stale name. Second, they removed the reliance on prctl(PR_GET_NAME) within the core application logic, particularly within error handling paths. By decoupling the error handling from the potentially volatile thread names, they eliminated the source of the panics.

  The post concludes with a reflection on the complexities of multithreaded programming and the importance of understanding the underlying behavior of system calls like prctl when working with asynchronous runtimes like Tokio. It emphasizes the need for careful consideration of shared resources like thread names in a multi-threaded environment to avoid unexpected interactions and difficult-to-debug issues.

  • Rust
  • Tokio
  • Prctl
  • Asynchronous Programming
  • concurrency
  • bugs
  • Debugging
  • System Programming
  • Linux
  • processes
  • threads
  • memory management

  Summary of Comments ( 59 )
  https://news.ycombinator.com/item?id=43153901

  Verbose tl;dr

  The Hacker News comments discuss the surprising interaction between Tokio and prctl(PR_SET_PDEATHSIG). Several commenters express surprise at the behavior, noting that it's non-intuitive and potentially dangerous for multi-threaded programs using Tokio. Some point out the complexities of signal handling in general, and the specific challenges when combined with asynchronous runtimes. One commenter highlights the importance of understanding the underlying system calls and their implications, especially when mixing different programming paradigms. The discussion also touches on the difficulty of debugging such issues and the lack of clear documentation or warnings about this particular interaction. A few commenters suggest potential workarounds or mitigations, including avoiding PR_SET_PDEATHSIG altogether in Tokio-based applications. Overall, the comments underscore the subtle complexities that can arise when combining asynchronous programming with low-level system calls.

  The Hacker News post "Tokio and Prctl = Nasty Bug" has generated several comments discussing the intricacies of the bug described in the linked article. The comments delve into the complexities of signal handling, particularly within the context of multi-threaded asynchronous Rust programs using Tokio.

  Several commenters express surprise at the interaction between prctl(PR_SET_PDEATHSIG, ...) and Tokio. They point out that this function, which sets a signal to be delivered to a process when its parent dies, isn't commonly used and its behavior within a multi-threaded, asynchronous environment like Tokio isn't immediately obvious. The core issue highlighted is that the signal, intended for the process, ends up being delivered to a specific thread within Tokio's runtime, disrupting its operation and potentially leading to deadlocks or crashes.

  One commenter suggests that the behavior, while surprising, is technically correct according to POSIX standards. They elaborate that signals are delivered to a single thread within a process, and the specific thread chosen can be unpredictable. In this case, Tokio's worker thread happened to receive the signal, leading to the observed problems. This emphasizes the importance of careful signal handling design in complex multi-threaded applications.

  The discussion extends to the challenges of debugging such issues, with commenters noting the difficulty of tracing the root cause of the problem back to the prctl call. The asynchronous nature of Tokio and the subtle interaction with signals make it difficult to pinpoint the source of the failure.

  Some commenters offer potential solutions or workarounds. One suggests masking the signal in threads where it's not expected or desired. Another mentions the possibility of using a dedicated signal handling thread to manage these situations more effectively.

  The overall sentiment seems to be one of caution when using less common system calls like prctl in conjunction with complex runtime environments like Tokio. The comments underscore the importance of understanding the implications of signal handling within multi-threaded asynchronous programs and the need for robust error handling strategies to mitigate potential issues.

• Ruby “Thread Contention” Is Simply GVL Queuing

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  Posted: 2025-02-03 08:43:13

  Verbose tl;dr

  The post argues that the term "thread contention" is misused in the context of Ruby's Global VM Lock (GVL). True thread contention involves multiple threads attempting to modify the same shared resource simultaneously. However, in Ruby with the GVL, only one thread can execute Ruby code at any given time. What appears as "contention" is actually just queuing: threads waiting their turn to acquire the GVL. The post emphasizes that understanding this distinction is crucial for profiling and optimizing Ruby applications. Instead of focusing on eliminating "contention," developers should concentrate on reducing the time threads hold the GVL, minimizing the queueing time and improving overall performance.

  The blog post "Ruby “Thread Contention” Is Simply GVL Queuing" by Peter Cooper argues that the common phrase "thread contention" is often misused in the context of Ruby's multi-threading performance limitations, leading to confusion and misdiagnosis of performance issues. Instead of actual contention, where multiple threads are actively competing for the same shared resource simultaneously, the performance bottleneck in Ruby's multi-threaded applications typically stems from the Global Virtual Machine Lock (GVL).

  Cooper elaborates that the GVL, a core mechanism in the standard implementation of Ruby known as CRuby, serializes execution of Ruby code. While multiple operating system (OS) threads can exist within a Ruby process, only one Ruby thread can execute Ruby bytecode at any given instant due to the GVL. Other threads wishing to execute Ruby code are placed in a queue managed by the GVL. This queuing mechanism is what frequently gets misinterpreted as "thread contention."

  The author meticulously distinguishes between true contention and GVL queuing. True contention occurs when multiple threads attempt to access and modify the same shared resource, such as a shared memory location or a file, simultaneously. This leads to race conditions and requires synchronization primitives like mutexes or semaphores to ensure data integrity and prevent unexpected behavior. However, in Ruby, the GVL inherently prevents true contention within Ruby code itself because it restricts execution to a single thread at a time.

  Therefore, what appears as "contention" in Ruby profiling tools is actually the overhead of the GVL's queuing mechanism. Threads wait their turn to acquire the GVL, creating a queue of waiting threads. This queuing and dequeuing process, along with the context switching between threads managed by the operating system, contributes to the perceived performance bottleneck. Cooper emphasizes that this is not true contention for resources, but rather a queuing delay imposed by the GVL's serialization of Ruby code execution.

  The post further clarifies that true contention can still occur in Ruby in scenarios involving non-Ruby code, particularly when interacting with external libraries or system calls. For example, if multiple Ruby threads simultaneously attempt to write to the same file descriptor, true contention can occur at the operating system level, outside the scope of the GVL.

  In summary, Cooper advocates for more precise language when discussing Ruby's multi-threading performance. He argues that using the term "GVL queuing" instead of "thread contention" provides a more accurate description of the performance bottleneck in CRuby, highlighting the role of the GVL and its queuing mechanism as the primary source of performance limitations in multi-threaded Ruby applications and facilitating more effective diagnosis and optimization of such applications.

  • ruby
  • threads
  • GVL
  • Global Interpreter Lock
  • concurrency
  • Parallelism
  • performance
  • Queuing
  • programming
  • Software Development
  • CRuby
  • MRI
  • Multithreading

  Summary of Comments ( 38 )
  https://news.ycombinator.com/item?id=42916203

  Verbose tl;dr

  HN commenters generally agree with the author's premise that Ruby's "thread contention" is largely a misunderstanding of the GVL (Global VM Lock). Several pointed out that true contention can occur in Ruby, specifically around I/O operations and interactions with native extensions/C code that release the GVL. One commenter shared a detailed example of contention in a Rails app due to database connection pooling. Others highlighted that the article might undersell the performance impact of the GVL, particularly for CPU-bound tasks, where true parallelism is impossible. The real takeaway, according to the comments, is to understand the GVL's limitations and choose the right concurrency model (e.g., processes, async I/O) for the specific task, rather than blindly reaching for threads. Finally, a few commenters discussed the complexities of truly removing the GVL from Ruby, citing the challenges and potential breakage of existing code.

  The Hacker News post titled "Ruby “Thread Contention” Is Simply GVL Queuing" has generated several comments discussing the nuances of Ruby's Global VM Lock (GVL) and its impact on concurrency.

  One commenter points out the distinction between "true contention" and mere queuing for the GVL. They argue that while multiple threads might appear to be contending for resources, the actual bottleneck is often the serialized execution enforced by the GVL. This commenter further emphasizes that profiling tools might misrepresent this queuing as contention, leading developers to misdiagnose performance issues. They suggest that a more accurate term would be "GVL contention" or "GVL queuing" to reflect the underlying mechanism.

  Another commenter concurs, adding that while the GVL doesn't eliminate all forms of contention (e.g., contention for shared memory), it does significantly influence how threads interact with resources. They highlight the importance of understanding this distinction when optimizing Ruby code for multi-threaded environments.

  A further comment delves into the complexities of the GVL's implementation, noting that its behavior can vary across different Ruby interpreters (e.g., MRI, JRuby, TruffleRuby). This commenter emphasizes the need to consider the specific interpreter when analyzing GVL-related performance characteristics. They also mention the potential benefits and drawbacks of using alternative concurrency models, such as fibers and actors, in Ruby.

  Another discussion thread focuses on the practical implications of the GVL for Ruby developers. Commenters share their experiences with debugging and optimizing multi-threaded Ruby applications, offering advice on how to mitigate the performance limitations imposed by the GVL. Specific techniques, such as using asynchronous I/O operations and carefully managing shared resources, are discussed.

  One commenter offers a contrasting perspective, arguing that the term "thread contention" is still relevant in the context of the GVL. They explain that even though the GVL serializes execution, threads are still competing for the opportunity to acquire the lock. This competition, they contend, can still be considered a form of contention, albeit one mediated by the GVL.

  Overall, the comments on the Hacker News post provide a rich discussion on the intricacies of the GVL in Ruby. They highlight the importance of understanding the GVL's impact on concurrency, the potential for misinterpreting profiling data, and the strategies developers can employ to optimize their multi-threaded Ruby code. The comments also reveal the ongoing debate about the appropriate terminology for describing the GVL's effects on thread behavior.

• The Mythical IO-Bound Rails App

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  Posted: 2025-01-25 08:47:31

  Verbose tl;dr

  The article "The Mythical IO-Bound Rails App" argues that the common belief that Rails applications are primarily I/O-bound, and thus not significantly impacted by CPU performance, is a misconception. While database queries and external API calls contribute to I/O wait times, a substantial portion of a request's lifecycle is spent on CPU-bound activities within the Rails application itself. This includes things like serialization/deserialization, template rendering, and application logic. Optimizing these CPU-bound operations can significantly improve performance, even in applications perceived as I/O-bound. The author demonstrates this through profiling and benchmarking, showing that seemingly small optimizations in code can lead to substantial performance gains. Therefore, focusing solely on database or I/O optimization can be a suboptimal strategy; CPU profiling and optimization should also be a priority for achieving optimal Rails application performance.

  The blog post "The Mythical IO-Bound Rails App" by Jean Boussier explores the common misconception that Ruby on Rails applications are inherently I/O-bound, meaning their performance is primarily limited by waiting for input/output operations like database queries or external API calls. Boussier argues that while many Rails applications appear I/O-bound due to profiling tools predominantly highlighting time spent in database interactions or external service calls, a significant portion of the perceived I/O wait time is actually attributable to Ruby's Global Virtual Machine Lock (GVL).

  The GVL allows only one Ruby thread to execute Ruby code at any given time, even on multi-core processors. This means that even if multiple threads are initiated to handle concurrent requests, they still end up queuing and waiting for the GVL, making the application behave like a single-threaded application. This queuing and context switching introduces latency that gets mistakenly attributed to I/O wait time, as profilers often measure wall-clock time spent within I/O-related functions, including the time spent waiting for the GVL.

  Boussier explains that when a thread performs an I/O operation, it releases the GVL, allowing another thread to acquire it and execute. However, upon completion of the I/O operation, the original thread must reacquire the GVL to process the results. This contention for the GVL introduces delays that are often miscategorized as part of the I/O wait time. Consequently, developers might misinterpret the performance bottleneck as being external to the application, leading them to focus on optimizing database queries or network requests, while the actual bottleneck lies within the Ruby interpreter's GVL contention.

  To illustrate this, the author presents a scenario where a Rails application makes multiple database queries. While these queries might be relatively fast individually, the cumulative time spent waiting for the GVL during the execution of these queries, and the context switching overhead, can significantly inflate the overall response time. This creates the illusion of an I/O-bound application, when in reality, the GVL contention is a major contributor to the perceived slowness.

  The author emphasizes that understanding the impact of the GVL is crucial for accurately diagnosing performance issues in Rails applications. Simply observing that a large percentage of time is spent in database calls doesn't necessarily imply that optimizing the database is the optimal solution. Instead, developers should carefully analyze the application's behavior and consider strategies to mitigate GVL contention, such as reducing the number of threads or utilizing alternative concurrency models offered by Ruby, like fibers or using multiple processes. By addressing the GVL-related bottlenecks, developers can unlock substantial performance improvements in their Rails applications and achieve true I/O-bound performance if the application logic genuinely demands extensive I/O operations.

  • ruby
  • rails
  • performance
  • IO-bound
  • myth
  • optimization
  • Profiling
  • web application
  • Database
  • disk IO
  • network IO
  • concurrency
  • threads
  • processes
  • Asynchronous Programming
  • blocking operations

  Summary of Comments ( 13 )
  https://news.ycombinator.com/item?id=42820419

  Verbose tl;dr

  Hacker News users generally agreed with the article's premise that Rails apps are often CPU-bound rather than I/O-bound, with many sharing anecdotes from their own experiences. Several commenters highlighted the impact of ActiveRecord and Ruby's object allocation overhead on performance. Some discussed the benefits of using tools like rack-mini-profiler and flamegraphs for identifying performance bottlenecks. Others mentioned alternative approaches like using different Ruby implementations (e.g., JRuby) or exploring other frameworks. A recurring theme was the importance of profiling and measuring before optimizing, with skepticism expressed towards premature optimization for perceived I/O bottlenecks. Some users questioned the representativeness of the author's benchmarks, particularly the use of SQLite, while others emphasized that the article's message remains valuable regardless of the specific examples.

  The Hacker News post titled "The Mythical IO-Bound Rails App" generated a modest discussion with several insightful comments. Many of the comments revolve around the complexities of profiling and optimizing Rails applications, agreeing with the author's premise that pure I/O-bound Rails apps are rare.

  One commenter points out the often overlooked cost of ActiveRecord instantiations, suggesting that even when database queries are fast, the overhead of creating Ruby objects from the results can be substantial. This echoes a sentiment expressed by another user who highlights the tendency of Rails developers to fetch entire database rows when only a few columns are necessary, further contributing to object creation overhead.

  Another commenter discusses the impact of garbage collection, particularly in Ruby, and how it can be mistakenly perceived as I/O wait time. This reinforces the article's point about the importance of accurate profiling to identify true bottlenecks.

  Several users share their experiences with profiling tools and techniques. One recommends using tools like stackprof and rbspy for more granular profiling data beyond what traditional tools might offer. They emphasize the value of understanding what the CPU is actually doing during suspected I/O wait times. Another commenter mentions using flame graphs to visualize performance bottlenecks and identify unexpected hot spots.

  The discussion also touches on the role of caching in mitigating performance issues. A commenter suggests that effective caching strategies can significantly reduce database load and improve overall performance. However, another commenter cautions against premature optimization and emphasizes the importance of identifying genuine bottlenecks before implementing caching.

  A few commenters share anecdotes about their experiences optimizing Rails applications. One describes a scenario where a seemingly I/O-bound issue was actually caused by inefficient N+1 queries. Another recounts an instance where optimizing database indexes dramatically improved performance. These anecdotes serve to illustrate the diverse range of potential performance bottlenecks in Rails applications.

  Finally, one commenter offers a more general perspective, suggesting that while true I/O-bound situations might be rare, focusing on efficient database interactions is still crucial for Rails performance. They emphasize the importance of writing efficient queries and minimizing unnecessary data retrieval.

  Overall, the comments on the Hacker News post provide valuable insights into the complexities of Rails performance optimization. They reinforce the article's central argument that I/O-bound Rails apps are less common than assumed and highlight the importance of careful profiling and understanding the nuances of Ruby and Rails internals.