Stories with Tag cgo

• Building Statically Linked Go Executables with CGO and Zig

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  Posted: 2025-03-28 14:11:07

  Verbose tl;dr

  This blog post details how to create a statically linked Go executable that utilizes C code, overcoming the challenges typically associated with CGO and external dependencies. The author leverages Zig as a build system and cross-compiler, using its ability to compile C code and link it directly into a Go-compatible archive. This approach eliminates the need for a system C toolchain on the target machine during deployment, producing a truly self-contained binary. The post provides a practical example, guiding the reader through the necessary Zig build script configuration and explaining the underlying principles. This allows for simplified deployment, particularly useful for environments like scratch Docker containers, and offers a more robust and reproducible build process.

  This blog post details a method for creating statically linked Go executables that incorporate C code via cgo, utilizing the Zig build system and its powerful linker. The author emphasizes that while Go's built-in support for cgo allows linking against C libraries, it doesn't inherently facilitate fully static linking, particularly on platforms like Linux where the resulting binary often retains dynamic dependencies on system libraries like glibc. This can create deployment challenges, as the target system must have compatible versions of these libraries.

  The proposed solution leverages Zig's cc object, which offers a simplified interface to clang, and its flexible linking capabilities. The process starts with compiling the C code into a static library (.a) using Zig's cc.linkLibCStatic() method. This ensures the C code is compiled without relying on dynamically linked libraries.

  Next, the Go code, which uses cgo to interact with the C code, is compiled. Crucially, the linking stage is handled by Zig, not Go's default linker. The blog post meticulously outlines the required build steps, including setting the CGO_LDFLAGS environment variable to instruct Go to use Zig's linker (specifically, zig cc) and specifying the path to the previously created static C library. Additional linker flags are passed to ensure the Go runtime is also statically linked, a critical step for achieving a truly self-contained executable. Specifically, -static and -extldflags "-static" flags are used to enforce static linking of both the C and Go components.

  The post further explains the importance of linking against libgcc_static and libstdc++_static, which are often required by statically linked C++ code, even if the user's code is written in C. This is because certain C standard library functions might internally depend on C++ code. Including these libraries preemptively avoids potential runtime errors.

  Finally, the author provides a complete, runnable example demonstrating the entire process with a straightforward C function and a corresponding Go program that calls it. The example illustrates how to structure the Zig build file and how to invoke the build process. This allows readers to replicate the approach and adapt it to their own projects. The resulting binary, produced through this method, is then truly statically linked, devoid of any dynamic library dependencies, making it highly portable across systems with compatible architectures. The post concludes by highlighting the benefits of this approach for producing self-contained and easily deployable Go applications.

  • Go
  • Golang
  • cgo
  • Zig
  • Static Linking
  • Executables
  • Cross Compilation
  • C
  • Build Systems
  • programming
  • Software Development
  • native code

  Summary of Comments ( 2 )
  https://news.ycombinator.com/item?id=43505646

  Verbose tl;dr

  Hacker News users discuss the clever use of Zig as a build tool to statically link C dependencies for Go programs, effectively bypassing the complexities of cgo and resulting in self-contained binaries. Several commenters praise the approach for its elegance and practicality, particularly for cross-compilation scenarios. Some express concern about the potential fragility of relying on undocumented Go internals, while others highlight the ongoing efforts within the Go community to address static linking natively. A few users suggest alternative solutions like using Docker for consistent build environments or exploring fully statically-linked C libraries. The overall sentiment is positive, with many appreciating the ingenuity and potential of this Zig-based workaround.

  The Hacker News post discussing the blog post "Building Statically Linked Go Executables with CGO and Zig" has generated a moderate number of comments, focusing primarily on the complexities and nuances of linking C code with Go, especially when static linking is desired.

  Several commenters point out that the core issue stems from Go's reliance on libc even when using CGO, making fully static linking challenging. One commenter highlights the historical context, explaining how Go's initial design prioritized cross-compilation and dynamic linking, leading to this current situation. They also suggest that the current approach of relying on system libraries and then attempting to statically link them later is somewhat backwards and inherently problematic.

  Another commenter discusses the difficulties of statically linking libc itself, emphasizing the various assumptions and dependencies that libc has on the target system. They suggest that truly static linking requires a "freestanding" environment, devoid of any operating system dependencies, which is difficult to achieve in practice, especially with Go's current architecture.

  The complexity of the proposed Zig/CGO approach is also a topic of discussion. One commenter expresses skepticism about its practicality, noting that it introduces another layer of tooling and build complexity. They also question the long-term maintainability of such a solution. Another user agrees, suggesting that the proposed workaround might be more trouble than it's worth, particularly for simpler projects.

  The conversation also touches upon alternative solutions, such as using a completely different language for parts requiring C libraries or exploring other approaches for interacting with C code from Go. A commenter suggests that sometimes the best solution is to re-evaluate the need for the C library in the first place and explore if a pure Go alternative exists.

  The comments don't offer a definitive solution to the static linking challenges with CGO but rather highlight the inherent complexities involved and spark a discussion about various workarounds and their trade-offs. The general sentiment seems to be that while the Zig approach presented in the blog post is interesting, it's not a silver bullet and might not be the most practical solution in many cases. There's a clear desire for a more streamlined and robust solution for static linking within the Go ecosystem, but the comments acknowledge the significant challenges involved in achieving this.

• ML in Go with a Python Sidecar

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  Posted: 2024-11-11 17:44:42

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  This blog post explores using Go's strengths for web service development while leveraging Python's rich machine learning ecosystem. The author details a "sidecar" approach, where a Go web service communicates with a separate Python process responsible for ML tasks. This allows the Go service to handle routing, request processing, and other web-related functionalities, while the Python sidecar focuses solely on model inference. Communication between the two is achieved via gRPC, chosen for its performance and cross-language compatibility. The article walks through the process of setting up the gRPC connection, preparing a simple ML model in Python using scikit-learn, and implementing the corresponding Go service. This architectural pattern isolates the complexity of the ML component and allows for independent scaling and development of both the Go and Python parts of the application.

  Eli Bendersky's blog post, "ML in Go with a Python Sidecar," explores a practical approach to integrating machine learning (ML) models, typically developed and trained in Python, into applications written in Go. Bendersky acknowledges the strengths of Go for building robust and performant backend systems while simultaneously recognizing Python's dominance in the ML ecosystem, particularly with libraries like TensorFlow, PyTorch, and scikit-learn. Instead of attempting to replicate the extensive ML capabilities of Python within Go, which could prove complex and less efficient, he advocates for a "sidecar" architecture.

  This architecture involves running a separate Python process alongside the main Go application. The Go application interacts with the Python ML service through inter-process communication (IPC), specifically using gRPC. This allows the Go application to leverage the strengths of both languages: Go handles the core application logic, networking, and other backend tasks, while Python focuses solely on executing the ML model.

  Bendersky meticulously details the implementation of this sidecar pattern. He provides comprehensive code examples demonstrating how to define the gRPC service in Protocol Buffers, implement the Python server utilizing TensorFlow to load and execute a pre-trained model, and create the corresponding Go client to communicate with the Python server. The example focuses on a simple image classification task, where the Go application sends an image to the Python sidecar, which then returns the predicted classification label.

  The post highlights several advantages of this approach. Firstly, it enables clear separation of concerns. The Go and Python components remain independent, simplifying development, testing, and deployment. Secondly, it allows leveraging existing Python ML code and expertise without requiring extensive Go ML libraries. Thirdly, it provides flexibility for scaling the ML component independently from the main application. For example, the Python sidecar could be deployed on separate hardware optimized for ML tasks.

  Bendersky also discusses the performance implications of this architecture, acknowledging the overhead introduced by IPC. He mentions potential optimizations, like batching requests to the Python sidecar to minimize communication overhead. He also suggests exploring alternative IPC mechanisms besides gRPC if performance becomes a critical bottleneck.

  In summary, the blog post presents a pragmatic solution for incorporating ML models into Go applications by leveraging a Python sidecar. The provided code examples and detailed explanations offer a valuable starting point for developers seeking to implement a similar architecture in their own projects. While acknowledging the inherent performance trade-offs of IPC, the post emphasizes the significant benefits of this approach in terms of development simplicity, flexibility, and the ability to leverage the strengths of both Go and Python.

  • Go
  • Golang
  • Python
  • machine learning
  • ML
  • Sidecar
  • Microservices
  • Interoperability
  • Language Interoperability
  • Foreign Function Interface
  • FFI
  • cgo
  • Data Science
  • Software Architecture
  • performance
  • Efficiency
  • Programming Languages

  Summary of Comments ( 16 )
  https://news.ycombinator.com/item?id=42108933

  Verbose tl;dr

  HN commenters discuss the practicality and performance implications of the Python sidecar approach for ML in Go. Some express skepticism about the added complexity and overhead, suggesting gRPC or REST might be overkill for simple tasks and questioning the performance benefits compared to pure Python or using GoML libraries directly. Others appreciate the author's exploration of different approaches and the detailed benchmarks provided. The discussion also touches on alternative solutions like using shared memory or embedding Python in Go, as well as the broader topic of language interoperability for ML tasks. A few comments mention specific Go ML libraries like gorgonia/tensor as potential alternatives to the sidecar approach. Overall, the consensus seems to be that while interesting, the sidecar approach may not be the most efficient solution in many cases, but could be valuable in specific circumstances where existing Go ML libraries are insufficient.

  The Hacker News post titled "ML in Go with a Python Sidecar" (https://news.ycombinator.com/item?id=42108933) elicited a modest number of comments, generally focusing on the practicality and trade-offs of the proposed approach of using Python for machine learning tasks within a Go application.

  One commenter highlighted the potential benefits of this approach, especially for computationally intensive ML tasks where Go's performance might be a bottleneck. They acknowledged the convenience and rich ecosystem of Python's ML libraries, suggesting that leveraging them while keeping the core application logic in Go could be a sensible compromise. This allows for utilizing the strengths of both languages: Go for its performance and concurrency in handling application logic, and Python for its mature ML ecosystem.

  Another commenter questioned the performance implications of the inter-process communication between Go and the Python sidecar, particularly for real-time applications. They raised concerns about the overhead introduced by serialization and deserialization of data being passed between the two processes. This raises the question of whether the benefits of using Python for ML outweigh the performance cost of this communication overhead.

  One comment suggested exploring alternatives like using shared memory for communication between Go and Python, as a potential way to mitigate the performance overhead mentioned earlier. This alternative approach aims to optimize the data exchange by avoiding the serialization/deserialization steps, leading to potentially faster processing.

  A further comment expanded on the shared memory idea, specifically mentioning Apache Arrow as a suitable technology for this purpose. They argued that Apache Arrow’s columnar data format could further enhance the performance and efficiency of data exchange between the Go and Python processes, specifically highlighting zero-copy reads for improved efficiency.

  The discussion also touched upon the complexity introduced by managing two separate processes and the potential challenges in debugging and deployment. One commenter briefly discussed potential deployment complexities with two processes and debugging. This contributes to a more holistic view of the proposed architecture, considering not only its performance characteristics but also the operational aspects.

  Another commenter pointed out the maturity and performance improvements in Go's own machine learning libraries, suggesting they might be a viable alternative in some cases, obviating the need for a Python sidecar altogether. This introduces the consideration of whether the proposed approach is necessary in all scenarios, or if native Go libraries are sufficient for certain ML tasks.

  Finally, one commenter shared an anecdotal experience, confirming the practicality of the Python sidecar approach. They mentioned successfully using a similar setup in production, lending credibility to the article's proposal. This real-world example provides some validation for the discussed approach and suggests it's not just a theoretical concept but a practical solution.