Refactoring EqualsFilter For Open-mSupply With Generics

Hey guys, let's dive into a cool optimization for the open-msupply project. We're talking about revamping the EqualsFilter to fully harness the power of generics. This change aims to make our lives easier, our code cleaner, and our filtering capabilities more flexible. This refactor is not just about making the code work; it's about making it sing. We'll explore the current pain points, the proposed solution, and why it's a win-win for everyone involved.

The Core Problem: Lack of Generic Implementation

So, what's the deal? Well, currently, when you use EqualFilter<T>::equal_to, it often defaults to T: String because there isn't a generic implementation ready to go. This can be a real headache, especially when you're trying to filter data based on things like type or status, which are super common in tables. This situation can lead to directly instantiating the generic struct definition, which, while it works, isn't always the cleanest or most intuitive approach. The essence of the problem lies in the limited use of generics, forcing developers to write repetitive code for different data types. This is exactly what we want to avoid! We're aiming to create a system that's more adaptable, easier to maintain, and less prone to errors.

Imagine you're trying to filter a table of products by their status. Currently, you might have to write separate implementations of EqualFilter for each possible status type (e.g., String, i32). This is a classic example of where generics can shine. By creating a single, generic EqualFilter, we can handle any data type without the need for redundant code. This approach not only streamlines the development process but also improves code readability and maintainability. When your code is DRY (Don't Repeat Yourself), you're not just saving time; you're also making it easier for others (and your future self) to understand and modify your work. This is the heart of what we are trying to achieve.

The Proposed Solution: Embrace Generics Fully

The solution is pretty straightforward: let's implement generic functions and ditch the specific type implementations in filter_store_pagination.rs that become redundant. This will let the compiler do the heavy lifting for us. Think of it as giving the compiler superpowers! The aim is to create a more flexible and efficient system. Here's a quick peek at the suggested generic implementation:

impl<T> EqualFilter<T> {
    pub fn equal_to(value: T) -> Self {
        EqualFilter {
            equal_to: Some(value),
            not_equal_to: None,
            equal_any: None,
            not_equal_all: None,
            is_null: None,
        }
    }
}

This simple change allows us to use EqualFilter with any data type T without the need for separate implementations. This is a game-changer because it eliminates the need for writing repetitive code. The existing specific implementations can be replaced with this generic one, significantly reducing code duplication and making the system more flexible. By leveraging generics, we are making the code base more adaptable to future changes and new data types.

Example Use Case: filter_store_pagination.rs

Let's consider filter_store_pagination.rs. Currently, it has specific EqualFilter implementations for various types. With the generic approach, we can replace all of those with a single implementation. This not only reduces the amount of code but also makes the code more consistent. This allows you to filter your data in a more unified manner. For instance, imagine filtering products by a specific ID (integer) or by a specific name (string). With generics, you can use the same EqualFilter for both, making your code cleaner and more efficient.

By unifying these implementations, we enhance code readability. Anyone looking at the code can quickly understand how filtering works, regardless of the data type being filtered. This is particularly valuable in a collaborative environment where multiple developers may be working on the same codebase. The goal is to make it easy for anyone to contribute and understand the system.

Why Invest Time in This? Boosting Productivity and Happiness

Why bother? Because the current situation is, frankly, a bit frustrating. Whenever you try something beyond the specific implementations we've already set up for primitive types, things get complicated. These specific implementations are basically boilerplate code that the compiler could generate for us automatically. By adopting a generic approach, we're making the compiler work for us, leading to a more streamlined and efficient development process. This approach is all about boosting productivity and improving developer satisfaction. Who wouldn't want to spend less time writing repetitive code and more time building awesome features?

Imagine the team's delight when they no longer have to worry about creating new filter implementations for every data type. Instead, they can focus on the core logic and features of the application. This translates to happier developers and faster project timelines. By removing these roadblocks, we're fostering an environment where innovation thrives, and developers can enjoy their work more. The benefits extend beyond just individual developers; the entire team benefits from the increased efficiency and reduced errors.

Risks and Effort: A Smooth Transition

Are there any major risks? Honestly, not really. This is a fairly safe change with minimal potential for breaking things. As for effort, it should take a couple of hours, including some testing and code review. This project represents a low-risk, high-reward opportunity to significantly improve the code base. The main risk is the possibility of minor bugs, which can be easily identified and fixed through thorough testing. The benefits of this refactor outweigh any potential risks, making it an ideal choice for improving the project.

We anticipate a smooth transition. The proposed changes are relatively straightforward to implement, and the impact will be positive. This project is a testament to the power of thoughtful refactoring and its ability to make a positive impact on the team and the project.

Agreed Solution: Implementation and Code Cleanup

To recap, the agreed solution involves implementing the generic functions as shown in the example and removing the redundant code. This will streamline our filtering process, reduce boilerplate, and make our codebase more adaptable. The next steps will involve diving into the code, implementing the generic functions, and deleting the now-unnecessary specific implementations. This means we'll be working directly in filter_store_pagination.rs to replace the existing implementations. After that, we'll perform thorough testing to ensure everything works as expected.

This refactoring effort is a step towards a more robust and flexible open-msupply. By embracing generics, we're not just improving the code; we're also setting the stage for future enhancements and making the project more accessible to contributors. It's a win-win for everyone involved. Ready to get started? Let's make some magic!