Speed Up Ash: Reducing Compilation Time

Introduction

Hey guys! Let's dive into a critical initiative aimed at reducing compilation time in the Ash project. Currently, Ash has a whopping 187 compilation cycles, which, let's be honest, can be a real drag. This high number not only slows down the overall development process but also means that even the smallest code tweaks can trigger a full recompile. Nobody wants to wait around forever for their code to build, right? This issue is here to keep track of our progress and to spark discussions about the changes we'll be making. Think of this as our central hub for all things related to speeding up Ash!

The main goal here is to refactor the modules within Ash to significantly reduce the number of compilation cycles. This means we'll be digging deep into the codebase, identifying bottlenecks, and streamlining the structure. Imagine the time savings and increased productivity we'll achieve! But, there are some key constraints we need to keep in mind. First and foremost, we can't break anything! The public APIs must remain untouched, ensuring that existing users of Ash aren't affected by our changes. Secondly, the core functionality of Ash needs to stay exactly the same. We're aiming for speed, not a rewrite. Finally, we'll only be shifting things from compile-time to runtime if it's absolutely necessary and if runtime performance isn't significantly impacted. We want a faster Ash, but not at the cost of its efficiency.

So, why is this so important? Well, compilation time directly affects developer productivity. The longer you wait for your code to compile, the less time you have for actual coding. This can lead to frustration, decreased focus, and ultimately, slower development cycles. By reducing the number of compilation cycles, we're not just making Ash faster; we're making the entire development experience smoother and more enjoyable. Imagine being able to quickly iterate on your code, test new features, and fix bugs without the constant interruption of long compilation times. It's a game-changer! Plus, faster compilation times mean faster feedback loops. You can quickly see the results of your changes, identify issues, and refine your code more efficiently. This leads to higher quality code and a more robust final product. This initiative is all about making Ash a better tool for everyone involved.

Problem Statement: The Drag of Long Compilation Times

Okay, so let's really break down the problem. Ash's current state with 187 compilation cycles is, to put it mildly, a bottleneck. Imagine you're in the middle of coding a new feature, you make a small change, and then... you wait. And wait. And wait some more. That's the reality we're facing with Ash right now. These long compilation times disrupt the flow of development, making it harder to stay focused and maintain momentum. It's like trying to run a race with lead weights attached to your ankles – you can still move, but it's way harder than it needs to be.

The root of the problem lies in the way Ash's modules are structured and how they interact with each other during the compilation process. Each compilation cycle represents a stage where the compiler processes a specific part of the code. The more cycles there are, the more overhead there is in terms of time and resources. This is especially noticeable when making small changes. Even a minor tweak can trigger a cascade of recompilations across multiple modules, leading to significant delays. It's like a domino effect – one small change can knock over a whole chain of modules, forcing them to recompile.

Furthermore, these long compilation times can have a ripple effect across the entire project. They can slow down testing, making it harder to identify and fix bugs quickly. They can also impact collaboration, as developers spend more time waiting for builds and less time actually working together. In the worst-case scenario, long compilation times can even discourage experimentation and innovation. If it takes too long to see the results of your changes, you might be less likely to try out new ideas or explore different approaches. This can stifle creativity and ultimately hinder the progress of the project. So, it's clear that addressing this issue is crucial for the long-term health and success of Ash.

Proposed Solution: Refactoring for Speed

Alright, let's talk solutions! Our main strategy is to refactor Ash's modules to drastically cut down on those pesky compilation cycles. Think of it as giving Ash a serious organizational makeover. We're going to be diving deep into the codebase, untangling dependencies, and streamlining the structure. The goal is to create a more efficient system where changes in one part of the code have less of a ripple effect on other parts. This means fewer recompilations and much faster build times. Imagine the possibilities – quicker feedback loops, faster iteration, and a much smoother development experience overall.

But, we're not just going to start hacking away at the code willy-nilly. We have some important constraints to keep in mind. First and foremost, we need to ensure that our changes don't break anything. The public APIs of Ash are sacrosanct. We can't introduce any breaking changes that would affect existing users of the library. This means we'll need to be extra careful when refactoring and thoroughly test our changes to make sure everything still works as expected. It's like performing surgery – we need to be precise and avoid any unintended consequences.

Secondly, we want to maintain the core functionality of Ash. This isn't about adding new features or changing the way things work. It's about making Ash faster and more efficient. We're focused on optimizing the existing codebase, not rewriting it. Finally, we'll only be moving things from compile-time to runtime if it's absolutely necessary and if runtime performance isn't significantly impacted. There's a trade-off between compile-time and runtime performance, and we need to strike the right balance. We don't want to make Ash faster to compile at the expense of making it slower to run. It's like tuning a car – we want to optimize performance without sacrificing reliability.

Constraints: Maintaining Stability and Performance

Let's drill down a bit more on those constraints, because they're super important for the success of this initiative. As I mentioned before, we're committed to not breaking anything. This means no changes to the public APIs. Think of the public API as the contract that Ash makes with its users. It's a promise that certain functions and interfaces will work in a specific way. We can't just go changing things willy-nilly, or we risk breaking existing code that relies on those APIs. It's like changing the rules of a game halfway through – nobody wants that!

So, how do we ensure we don't break the public API? Well, careful planning and rigorous testing are key. We'll need to thoroughly analyze the existing codebase, identify the public APIs, and make sure any changes we make don't affect them. This might involve creating new internal APIs or refactoring existing code in a way that doesn't impact the public interface. We'll also need to write a comprehensive suite of tests to verify that everything still works as expected after our changes. It's like having a safety net – the tests will catch any unintended consequences and prevent us from releasing a broken version of Ash.

Another crucial constraint is maintaining the core functionality of Ash. We're not trying to reinvent the wheel here. We're focused on making Ash faster and more efficient, not on changing its fundamental purpose. This means we need to be careful about the changes we make and ensure they don't alter the behavior of Ash in any unexpected ways. It's like tuning an engine – we want to improve its performance without changing the way it runs. Finally, we need to be mindful of the trade-off between compile-time and runtime performance. Moving code from compile-time to runtime can sometimes improve compilation times, but it can also impact runtime performance. We need to carefully weigh the pros and cons of each change and only make the switch if it's absolutely necessary and if runtime performance won't be significantly affected. It's like balancing a budget – we need to make sure we're not sacrificing long-term stability for short-term gains.

Alternatives Considered: Why Refactoring Is the Best Path

Okay, so you might be wondering,