We are joined by Anand Naidu, our resident development expert, to discuss the recent Wasmer 7 release. This update represents a significant leap forward in making Python a first-class citizen in the WebAssembly world, addressing long-standing challenges around performance, compatibility, and debugging. Our conversation will explore how a new experimental async API is finally unlocking powerful libraries that were previously out of reach. We’ll also delve into the technical breakthrough of dynamic linking in WASIX, which opens the door to native modules, and examine the clever compiler optimizations that have slashed build times. Finally, we’ll touch on the engineering behind a unified exception-handling system and the strategic expansion of hardware support.
The new experimental async API unlocks powerful Python libraries like SQLAlchemy. Could you explain the technical approach behind this API and walk us through a practical example of how a developer might now run a previously incompatible async package?
The core of this new API is providing genuine, first-class support for asynchronous functions directly within the runtime. Previously, Python’s async/await model just didn’t have a bridge to the WebAssembly environment, which caused anything reliant on it to fail. We’ve now built that bridge across all our major back ends—Singlepass, Cranelift, and LLVM. Imagine a developer with a data-heavy application using SQLAlchemy for its object-relational mapping. That library is fundamentally asynchronous. Before, trying to run it in Wasmer was a non-starter. Now, that same developer can take their application, compile it to WebAssembly, and the runtime will correctly handle all those asynchronous database calls, allowing the entire package to function as intended without modification.
Before this update, native Python modules like Numpy were largely unsupported. How does adding dynamic linking support to WASIX technically solve this limitation, and what were the biggest challenges your team overcame to implement it effectively?
The limitation was quite fundamental. Python in Wasmer was previously just the core interpreter. Any package that relied on a native C extension, which is a massive part of the ecosystem including libraries like Numpy or Pydantic, simply couldn’t be used because the interpreter couldn’t load those shared object files. By enabling dynamic linking in WASIX, our extension to the WebAssembly System Interface, we’ve given the runtime the ability to load and link these native modules at runtime, just like a standard operating system would. The main challenge was designing a stable and secure mechanism within the sandboxed WASI environment to handle this linking, ensuring that memory and system calls from these dynamically loaded libraries remained properly contained and managed. It was a delicate balance between providing the necessary functionality and upholding the security promises of WebAssembly.
Wasmer now uses a unified unwinding mechanism by integrating the Cranelift compiler with libunwind. Could you detail the engineering reasons behind this choice and describe the concrete benefits this provides for developers when debugging WebAssembly exceptions?
The primary engineering driver was consistency and robustness. The Cranelift compiler has its own unwinding implementation, but other compilers don’t. This meant we could have different, potentially incompatible, exception-handling behaviors depending on the compiler used. By integrating Cranelift with the standard libunwind library, we’ve created a single, unified unwinding mechanism that works the same way for all compilers that support WebAssembly exceptions. For a developer, this is a huge win. When an exception occurs, they get a consistent and reliable stack trace, regardless of the underlying compiler. It makes debugging far more predictable and straightforward, removing a layer of frustrating inconsistency that can really slow down development.
Python build times have reportedly dropped from 90 seconds to just 10. Can you elaborate on the “selectively disabled optimizations” technique used to achieve this? Please explain the trade-offs and how you identify which large functions are candidates for this approach.
This was a fascinating optimization problem. We noticed that when compiling very large packages like Python or PHP with a powerful compiler like LLVM, a disproportionate amount of time was spent trying to optimize a few, extremely large functions. The compiler would spend ages on these, for often diminishing returns in runtime performance. The technique involves identifying these massive functions during the build process and telling the compiler to essentially skip its most aggressive optimization passes for just those specific functions. The trade-off is that those few functions might not be as maximally optimized as they could be, but the overall build time plummets from around 90 seconds down to just 10. The result is a dramatic improvement in the developer feedback loop with a negligible impact on the final application’s performance.
With the addition of Singlepass support for RISC-V, you’ve broadened architectural compatibility. What specific use cases or market demands drove this enhancement, and what new possibilities does this open up for developers deploying applications on RISC-V hardware?
The push for better RISC-V support is directly driven by its growing adoption in the embedded systems and edge computing space. While we already had support through the more complex LLVM and Cranelift compilers, adding the Singlepass compiler is a game-changer for these environments. Singlepass is incredibly fast and lightweight, which is ideal for resource-constrained devices where quick startup times and a low memory footprint are critical. This enhancement opens up new possibilities for deploying Python applications on a wide range of RISC-V hardware, from smart sensors to IoT gateways. Developers can now leverage Python’s simplicity for tasks that were previously the domain of lower-level languages, all while benefiting from WebAssembly’s security and portability.
What is your forecast for the role of WebAssembly in the Python ecosystem over the next few years?
I believe WebAssembly is poised to become a standard deployment target for a significant portion of the Python ecosystem. As we continue to break down barriers like native module support and async functionality, the line between running Python natively and running it in a Wasm runtime will blur. I foresee a future where developers can write Python for AI/ML, data science, or web backends and deploy it universally—from cloud servers to edge devices and even browsers—without changing their code. The security, portability, and performance benefits are too compelling to ignore, and it will unlock use cases for Python in environments where it was previously considered impractical or insecure.
