Indexing With AST: A Safer Approach Than PSI

Hey guys! Let's dive into a critical aspect of indexing, focusing on how we can make the process safer and more efficient. We're going to explore why avoiding the use of PSI (Program Structure Interface) during indexing is crucial and how shifting to Abstract Syntax Tree (AST) parsing can significantly improve the stability and reliability of your projects. This is super important because the PSI isn't thread-safe, which means it can cause some serious problems if you're not careful, especially when it comes to indexing. Let's break down why this is a big deal and how you can make sure your indexing processes are as smooth as possible.

The Perils of PSI in Indexing

Okay, so what's the deal with PSI, and why is it such a headache in the context of indexing? Simply put, using the PSI API for indexing is generally a no-go because it's not thread-safe. Imagine trying to do a bunch of things at once in a kitchen: if you don't have a good system in place, things can get messy, right? PSI is like that – it's not designed to handle multiple operations simultaneously without risking conflicts and errors. Indexing, by its very nature, often involves parallel processing to speed things up. You want to analyze multiple files or parts of your code at the same time, right?

When you try to use PSI in a multithreaded environment, you run the risk of race conditions, data corruption, and even crashes. It's like trying to bake a cake with five chefs all using the same bowl at the same time without any coordination – it’s a recipe for disaster! PSI's internal state isn't protected against concurrent access, so different threads could potentially modify the same data simultaneously, leading to unpredictable behavior. This can manifest as incorrect search results, broken code completion, or even the entire indexing process grinding to a halt. Also, think about the resources used by PSI. Because it builds a complete representation of the code's structure, it can be quite resource-intensive, especially for large projects. Doing this in multiple threads can quickly overload your system, causing further performance degradation and instability. So, to ensure a safe and efficient indexing process, it's often best to steer clear of PSI during the initial indexing phase. We need a better way to extract the critical information required for indexing without putting our projects at risk.

To further emphasize this point, consider the implications for the user experience. Imagine your IDE constantly freezing or returning incomplete search results due to indexing issues. It's a frustrating experience that can seriously impact productivity and user satisfaction. By avoiding the pitfalls of PSI and embracing a thread-safe approach, we can ensure a more reliable and responsive development environment for everyone involved. The first step is to look for a better, safer way to extract the needed information for indexing. This is where AST parsing comes into play, offering a more robust solution.

Embracing AST Parsing for Safer Indexing

Alright, so if PSI is a no-go, what’s the alternative? The answer lies in AST parsing. AST, or Abstract Syntax Tree, is a tree representation of the abstract syntactic structure of source code written in a programming language. Essentially, it's a simplified, more manageable version of your code’s structure. Unlike PSI, which builds a complete and detailed representation, AST parsing allows you to extract only the necessary information required for indexing. This selective approach is key to improving safety and efficiency. AST parsing focuses on the essential elements, such as class names, function definitions, and variable declarations, rather than building a full and complex representation.

This makes it less resource-intensive and, more importantly, inherently more thread-safe. Because you're not dealing with a complete model of the code, the chances of conflicts and race conditions are significantly reduced. The basic idea is to build only the absolutely necessary relevant information during the indexing phase. This minimal approach means you're only processing the parts of the code you actually need, reducing the risk of thread safety issues. You can extract specific data, such as function signatures or class hierarchies, without building the entire PSI structure. It's like extracting only the ingredients you need for a recipe instead of prepping the whole kitchen. When the AST parsing is complete, you can create the index and add the data to it. And you can do this without fear of crashing the program.

Implementing AST parsing requires a bit more work upfront. You'll need to write code to traverse the AST and extract the relevant information. The benefits are well worth the effort. The code for this can be more complex to handle than just using PSI, but the safety and efficiency gains are substantial. The code is more streamlined, using fewer resources and preventing common errors from occurring. This is a more resilient approach, particularly in the context of concurrent operations. Also, consider the benefit of caching. Because you're processing a smaller, well-defined set of data, caching becomes much easier to implement. You can cache the extracted information and reuse it across multiple indexing operations, further improving performance. With AST parsing and caching, your indexing will be smoother and quicker, providing a more positive experience for everyone.

Referencing PSI Safely: ReadAction and Caching

So, does this mean PSI is completely out of the picture? Not necessarily. There are still cases where you might need to access PSI, but it must be done in a safe and controlled manner. The key is to reference PSI only from a ReadAction-safe context. A ReadAction is a special type of action provided by IDEs that ensures thread safety when reading code structures. It allows you to safely read data from the PSI without the risk of conflicts. The trick is to build the essential information with AST parsing first and then use the ReadAction to work with PSI for any complex tasks. For instance, you could use AST parsing to identify all class definitions and then, within a ReadAction, use PSI to get detailed information about each class, like its inheritance hierarchy or method signatures.

This approach minimizes the time spent within the PSI context, reducing the risk of thread-related issues. By keeping the PSI interaction brief and within a controlled context, you can still leverage its capabilities when necessary. Now, think about optimization. This is where caching comes in handy. As you build your index, you can cache the results of the AST parsing and the data fetched via PSI within ReadAction. Caching these results will improve performance. For example, you might want to cache the information about the class hierarchy so that you don’t have to re-parse the code every time you need the information. Caching is your secret weapon here! It allows you to avoid redundant operations and dramatically improve the performance of your indexing process. This is particularly important when you need to provide real-time code completion or other IDE features. The right amount of caching can create a fast and responsive user experience, and that makes everybody happy.

To effectively use caching, consider the following tips:

• Choose the right caching strategy: You can use different types of caching, such as in-memory caching, disk caching, or a combination of both. Choose the method that best suits your needs, considering the size and frequency of data access.
• Implement cache invalidation: Make sure to have a strategy to invalidate the cache when the underlying code changes. This will prevent your index from becoming outdated.
• Monitor cache performance: Track cache hit rates and cache size to optimize your caching strategy. A well-managed cache can significantly improve the performance of your IDE features.

Putting It All Together: A Practical Guide

Let's walk through how you can put these ideas into practice. First, your initial indexing phase should use AST parsing to extract the necessary information. For example, identify class definitions, method signatures, and variable declarations. Then, build your index with this information. Second, use ReadAction only when you need to reference PSI. For example, to get detailed information about a specific class, you could use a ReadAction within a loop over the results of your AST parsing. This keeps the PSI interactions brief and controlled. Third, implement caching to optimize performance. Cache the results of AST parsing, and the data retrieved via PSI within your ReadAction. Make sure your cache invalidates whenever the code changes.

This means you'll want to use a library that can parse the programming language you are working with and convert the code into an AST. Then, write code that visits the AST and extracts specific pieces of information. This is the core of the indexing process. The code will be a bit more complex, but it will provide a more thread-safe solution. As an example, consider an IDE extension that provides code completion for a specific programming language. The indexing process could use AST parsing to identify all available classes, functions, and variables. The code completion logic would then use this information to provide suggestions to the user. By using AST parsing for indexing and ReadAction for retrieving PSI data, the extension can provide a reliable and responsive code completion experience.

Let's be honest: managing all of this stuff can be tricky, but following these guidelines will significantly improve the safety and performance of your indexing. Remember: prioritize AST parsing during indexing, reference PSI in a ReadAction-safe context, and implement caching whenever possible. By taking these steps, you'll build a more stable, efficient, and user-friendly IDE experience. That’s all for now. Thanks for reading, and happy coding, guys!