In the ever-evolving landscape of programming languages, few have captured the imagination of developers as swiftly as Zig. Emerging as a systems programming language designed to rival C and C++, Zig promised simplicity, performance, and safety without the baggage of legacy systems. Its tagline, “a general-purpose programming language and toolchain for maintaining robust, optimal, and reusable software,” resonated with developers tired of wrestling with the complexities of modern C++ or the garbage collection overhead of Rust.

Zig’s design philosophy is refreshingly straightforward: no hidden control flow, no hidden memory allocations, and a focus on explicitness. It offered a compelling alternative for low-level programming, with features like compile-time code execution, a powerful build system, and a small, focused standard library. For many, Zig felt like the future — a language that could deliver C-like performance with modern ergonomics.

Our team was among those captivated. We embarked on a project to build a high-performance, real-time data processing system, and Zig seemed like the perfect fit. Its ability to produce small, fast binaries and its seamless integration with C libraries made it an attractive choice. We dove in, eager to harness Zig’s potential.

But as we pushed deeper into our project, we encountered a stumbling block that shook our confidence: multithreading. What began as a promising journey turned into a sobering lesson about the gap between Zig’s ambitions and its current reality. This article chronicles our experience, exploring why Zig initially felt like the future and how its multithreading limitations forced us to reconsider.

Why Zig Felt Like the Future

To understand our enthusiasm, let’s first unpack what makes Zig so appealing. Zig was created by Andrew Kelley with a clear goal: to build a language that prioritizes simplicity and performance while avoiding the pitfalls of its predecessors. Unlike Rust, which leans heavily on a borrow checker to enforce memory safety, or C++, which carries decades of accumulated complexity, Zig takes a minimalist approach.

Key Features That Won Us Over

1. Explicitness and Simplicity: Zig’s syntax is clean and intuitive, with no operator overloading or macros to obscure intent. Memory management is manual but explicit, giving developers full control without the risk of hidden allocations.
2. Compile-Time Superpowers: Zig’s compile-time code execution allows for metaprogramming without the need for a separate preprocessor. This feature enabled us to generate optimized code tailored to our data structures, reducing runtime overhead.
3. Interoperability with C: Zig’s seamless integration with C libraries meant we could leverage existing ecosystems without writing glue code. For our project, this was a game-changer, as we relied on several C-based libraries for data processing.
4. No Standard Library Bloat: Zig’s standard library is lean, focusing on essentials. This kept our binaries small and our builds fast, which was critical for our real-time application.
5. Error Handling Done Right: Zig’s error union system forces developers to handle errors explicitly, reducing the likelihood of unhandled edge cases. Compared to C’s error codes or C++’s exceptions, this felt like a breath of fresh air.

Our initial experiments with Zig were exhilarating. We built a single-threaded prototype of our data processing pipeline, and the results were impressive. The code was readable, the performance was on par with C, and the build process was a breeze. We felt like we were on the cusp of a breakthrough, ready to scale our system to handle massive datasets in real time.

The Multithreading Dream

Our project required processing streams of data from multiple sources concurrently, with low latency and high throughput. To achieve this, we needed to distribute workloads across multiple threads, leveraging modern multi-core CPUs. Multithreading seemed like a natural next step, and we assumed Zig would handle it with the same elegance it brought to other areas.

In systems programming, multithreading is a cornerstone of performance. Whether it’s a web server handling thousands of requests or a game engine rendering frames in real time, the ability to parallelize tasks is non-negotiable. We envisioned a system where each data stream would be processed in its own thread, with a coordinator thread managing synchronization and aggregation. With Zig’s promise of low-level control, we were confident we could implement this efficiently.

The Reality Check: Zig’s Multithreading Limitations

As we began implementing our multithreaded architecture, cracks in Zig’s foundation started to appear. While Zig excels in many areas, its support for multithreading — at least at the time of our project — was surprisingly underdeveloped. Here’s what we encountered:

1. Lack of a Mature Standard Library for Concurrency

Zig’s standard library provides basic threading primitives, such as std.Thread for spawning threads and mutexes for synchronization. However, these primitives are minimal, lacking the higher-level abstractions found in languages like Rust or Go. For example, there’s no equivalent to Rust’s Arc (atomic reference counting) or Go’s channels for safe, idiomatic concurrent programming.

In our project, we needed to share data between threads safely. In Rust, we could have used Arc<Mutex<T>> to share mutable state, but in Zig, we had to roll our own synchronization mechanisms using raw mutexes and manual memory management. This was error-prone and time-consuming, as we had to ensure thread safety without the guardrails provided by more mature languages.

2. Async/Await: A Work in Progress

Zig’s asynchronous programming model, based on its async and await keywords, is one of its most ambitious features. It aims to provide lightweight concurrency without the overhead of traditional threads. However, during our project, we found that Zig’s async system was still experimental and poorly documented.

For instance, we attempted to use async functions to handle I/O-bound tasks, such as reading from multiple data streams concurrently. But the lack of a standard event loop or runtime meant we had to build our own, which was a significant undertaking. The documentation was sparse, and examples were scarce, leaving us to experiment through trial and error. In contrast, languages like Go or JavaScript provide robust runtime support for async programming, making concurrency more accessible.

3. Thread Safety and Memory Management

Zig’s manual memory management, while powerful, becomes a double-edged sword in multithreaded contexts. Ensuring thread safety requires careful coordination to avoid data races, and Zig’s lack of built-in tools for concurrent memory management made this challenging.

In one instance, we encountered a subtle bug where two threads accessed a shared buffer simultaneously, leading to data corruption. Debugging this was a nightmare, as Zig’s tooling for concurrency-related issues was limited. We eventually resorted to using C libraries like pthreads for better control, but this undermined Zig’s promise of being a self-contained solution.

4. Limited Ecosystem Support

Zig’s ecosystem is still young, and this was particularly evident in the concurrency space. While Zig can interoperate with C libraries, finding concurrency-focused libraries that worked seamlessly with Zig was difficult. Many C libraries required additional wrappers or modifications to integrate with Zig’s threading model, adding complexity to our codebase.

In contrast, Rust’s ecosystem offers crates like tokio and rayon, which provide battle-tested solutions for asynchronous and parallel programming. Zig’s lack of equivalent tools forced us to reinvent the wheel, which was neither practical nor scalable for our project.

5. Documentation and Community Gaps

Zig’s documentation, while improving, is still a work in progress. When we hit roadblocks with multithreading, we turned to the community for help. The Zig community is passionate and supportive, but it’s small compared to those of Rust or C++. Finding detailed guides or tutorials on multithreading in Zig was nearly impossible, and the official documentation offered little beyond basic examples.

We spent hours scouring forums, GitHub issues, and Discord channels for insights, but the answers were often incomplete or outdated. This lack of resources slowed our progress and highlighted Zig’s immaturity in handling complex, real-world scenarios.

The Breaking Point

The culmination of these challenges came during a critical phase of our project. We had implemented a multithreaded pipeline, but performance was inconsistent, and crashes were frequent. Profiling revealed that our custom synchronization logic was introducing bottlenecks, and the lack of high-level concurrency primitives made optimization difficult.

After weeks of struggling, we made the difficult decision to pivot. We rewrote our core processing logic in Rust, which offered robust concurrency tools and a mature ecosystem. The transition was painful, as we had invested heavily in Zig, but the results were undeniable: Rust’s tokio runtime and rayon library allowed us to achieve the performance and reliability we needed with less effort.

Reflections: What Zig Got Right and Where It Fell Short

Our experience with Zig was a rollercoaster of highs and lows. On one hand, Zig’s simplicity, performance, and interoperability make it a formidable contender in systems programming. For single-threaded applications or projects with minimal concurrency requirements, Zig is a joy to work with. Its compile-time features and explicit design are genuinely innovative, and we still believe it has the potential to shape the future of low-level programming.

On the other hand, Zig’s multithreading story is a significant blind spot. Concurrency is a fundamental requirement for modern systems, and Zig’s current limitations in this area make it unsuitable for certain classes of applications. The lack of high-level abstractions, mature async support, and comprehensive documentation forced us to grapple with low-level details that other languages handle more gracefully.

What Zig Needs to Succeed

To realize its full potential, Zig must address its concurrency shortcomings. Here are a few areas where improvements could make a big difference:

1. Rich Concurrency Primitives: Zig’s standard library should include higher-level concurrency abstractions, such as thread pools, channels, or atomic reference counting, to simplify safe multithreading.
2. Mature Async Runtime: A standard event loop and robust async/await support would make Zig more competitive for I/O-bound applications.
3. Better Tooling: Enhanced debugging and profiling tools for concurrency issues would help developers identify and fix problems more effectively.
4. Comprehensive Documentation: Detailed guides and tutorials on multithreading and async programming would empower developers to use Zig’s features confidently.
5. Ecosystem Growth: Encouraging the development of concurrency-focused libraries and frameworks would reduce the need for custom solutions.

Conclusion: A Bright Future, But Not Yet

Zig remains a language to watch. Its bold vision and innovative features have earned it a dedicated following, and its potential to disrupt systems programming is undeniable. For projects that don’t require heavy multithreading, Zig is a fantastic choice, offering a rare blend of simplicity and power.

However, for applications where concurrency is critical, Zig’s current limitations are hard to ignore. Our journey taught us that while Zig looked like the future, it’s not ready to tackle every challenge — yet. As the language matures and its concurrency story improves, we’re optimistic that Zig will live up to its promise.

For now, we’ve moved on to Rust, but we’re keeping a close eye on Zig. The lessons we learned will stay with us, and we’re rooting for Zig to bridge the gap. Until then, it’s a reminder that no language is a silver bullet, and the future of programming is as much about evolution as it is about revolution.

If you’re a developer experimenting with Zig or facing similar challenges, share your story in the comments! Let’s keep the conversation going.