Enhancing Error Output For Better Debugging
Have you ever encountered cryptic error messages that leave you scratching your head? We've all been there, guys! In this article, we'll dive into a discussion about improving error output to make debugging a smoother experience. Specifically, we'll be focusing on a proposal to implement Rust-style diagnostic output, which aims to provide more context and clarity when errors occur.
The Current Challenge: Identifying Error Locations
Currently, the output of our CLI tool isn't the most user-friendly when it comes to pinpointing the exact location of an error. As noted by @dabrahams in a recent discussion, the tool only provides a path to the offending item. This means users have to manually sift through the file, line by line, to find the source of the problem. This can be a time-consuming and frustrating process, especially in large or complex files.
Improving error identification is crucial for a better user experience. Imagine spending hours trying to debug an issue simply because the error message didn't provide enough information. We want to empower developers to quickly and easily understand where things went wrong, so they can focus on fixing the problem rather than playing detective.
To further highlight the problem, consider this scenario: you're working on a configuration file with hundreds of lines, and the error message simply says "Invalid value in configuration." Where do you even begin? Without more specific information, you're essentially searching for a needle in a haystack. This is the pain point we're trying to address.
Clear and concise error messages are essential for efficient debugging. When an error occurs, developers need to know the following:
- What went wrong: A clear description of the error itself.
- Where it went wrong: The exact location of the error in the file.
- Why it went wrong: The underlying cause of the error, if possible.
Our current error output falls short in providing this level of detail. We need to go beyond simply stating that an error exists and provide the necessary context for developers to understand and resolve it.
The Proposal: Rust-Style Diagnostic Output
To address this challenge, the proposal is to implement Rust-style diagnostic output. Rust is known for its excellent error messages, which provide detailed information about the error, including the file name, line number, and even a snippet of the code where the error occurred. This level of detail makes debugging significantly easier.
Rust-style diagnostic output offers several key advantages:
- Clear and concise error messages: Rust's error messages are known for their clarity and readability.
- Precise error location: The output includes the file name, line number, and column number where the error occurred.
- Code snippets: Rust often includes a snippet of the code surrounding the error, providing valuable context.
- Helpful suggestions: In some cases, Rust can even provide suggestions for how to fix the error.
By adopting a similar approach, we can significantly improve the debugging experience for our users. Imagine an error message that not only tells you what went wrong but also shows you the exact line of code that caused the issue and even suggests a possible fix. That's the power of Rust-style diagnostic output.
The goal is to create error messages that are not only informative but also actionable. We want to guide developers towards the solution, rather than leaving them to fend for themselves. This is about empowering our users to debug effectively and efficiently.
This approach aligns with the principles of good software design, which emphasize the importance of providing clear and helpful feedback to the user. Error messages are a critical form of feedback, and we should strive to make them as informative and user-friendly as possible.
Prototype: A Glimpse of the Future
To demonstrate the potential of this approach, a prototype has been developed using saphyr and annotate_snippets. This prototype showcases how we can leverage these tools to produce Rust-style diagnostic reports.
The prototype utilizes saphyr
, which offers spanned YAML parsing, allowing us to track the precise location of elements within the YAML file. This is crucial for providing accurate error reporting.
annotate_snippets
, on the other hand, provides an ergonomic API for producing Rust-style diagnostic reports. It allows us to format the error messages in a clear and visually appealing way, including code snippets and annotations.
The image provided in the original discussion shows a snippet of the prototype's output. As you can see, the error message includes:
- The error title: A brief description of the error.
- The file path: The location of the file where the error occurred.
- The line number: The specific line number where the error occurred.
- A code snippet: A snippet of the code surrounding the error, with the error highlighted.
- An annotation: A label explaining the error in more detail.
This is a significant improvement over the current error output, which simply provides a path to the offending item. With this level of detail, developers can quickly and easily understand the error and its context.
The prototype is a proof of concept, and there's still work to be done to fully implement this approach. However, it demonstrates the feasibility and potential benefits of Rust-style diagnostic output. This is a glimpse of the future of error reporting in our tool.
Limitations: The Roadblocks We Face
While the prototype is promising, there are some limitations we need to address. The primary challenge lies in the fact that valico, our JSON schema validator, requires a serde_json::Value
as input. The problem is that serde_json::Value
is un-spanned, meaning it doesn't retain information about the location of elements within the original file.
This limitation creates a significant hurdle. To produce the desired diagnostic output, we need span information, which tells us the exact starting and ending positions of each element in the file. Without this information, we can't accurately highlight the code snippet where the error occurred.
To work around this, the prototype currently parses the file twice: once with serde_yaml
to produce errors via valico
, and again with saphyr
to retrieve span information. This double parsing approach has several downsides:
- Performance overhead: Parsing the file twice obviously takes more time than parsing it once.
- Increased binary size: Including both
serde_yaml
andsaphyr
increases the size of the compiled binary. - Increased surface area for bugs: Parsing the file twice increases the risk of inconsistencies or errors.
Ideally, we would like to avoid double parsing. It's not the most efficient solution, and it introduces potential complications. We need to find a way to validate the data and obtain span information without parsing the file multiple times. This is the core challenge we're trying to solve.
Another challenge is the lack of JSON schema validators that accept a parsed YAML type with span information as input. This means we can't simply switch to a different validator to solve the problem. We need to either find a way to modify our existing validator or explore alternative approaches.
These limitations highlight the complexity of the problem. There's no easy solution, and we need to carefully consider the trade-offs involved in each approach. However, the potential benefits of improved error output make it worth the effort.
Possible Solutions: Charting a Course Forward
To overcome these limitations, we've considered several possible solutions, each with its own set of pros and cons. Let's explore these options in detail.
1. Accept Defeat: The Pragmatic Approach
One option is to simply accept the downsides of double parsing, at least temporarily. We could view it as an acceptable cost for significantly improving the ergonomics of the tool. This would allow us to deliver the benefits of Rust-style diagnostic output relatively quickly.
Pros:
- Easy implementation: The existing prototype demonstrates that this approach is relatively simple to implement.
- No performance overhead if no errors: Double parsing only occurs if there are errors, so users who don't encounter errors wouldn't experience the performance penalty.
Cons:
- Performance overhead: Double parsing adds a performance overhead when errors occur.
- Increased binary size: Including both
serde_yaml
andsaphyr
increases the binary size. - Increased surface area for bugs: Parsing the file twice increases the risk of inconsistencies or errors.
This approach is a pragmatic one, but it's not ideal. It's a trade-off between immediate improvement and long-term efficiency. We need to weigh the benefits of improved error output against the costs of double parsing.
2. Contribute a saphyr
Feature Flag to valico
: A Collaborative Solution
Another option is to contribute a feature flag to valico
that would allow it to accept saphyr
's parsed YAML type as input. This would eliminate the need for double parsing, as valico
could directly access the span information provided by saphyr
.
Pros:
- No double parsing: This would eliminate the performance overhead and potential inconsistencies associated with double parsing.
- No performance overhead: There would be no performance penalty for users who don't encounter errors.
Cons:
- Increased complexity for
valico
: Supporting two separate formats would add complexity tovalico
's codebase. - Requires upstream maintainer approval: This approach relies on the maintainers of
valico
being willing to accept the change. If they're not, we would need to fork the library, which adds maintenance overhead.
This approach is more elegant than double parsing, but it requires collaboration with the valico
maintainers. It's a long-term solution that would benefit the entire ecosystem, but it's also the most uncertain.
3. Implement Conversion from saphyr
to serde_json::Value
: A Middle Ground
We could also implement a conversion function that transforms saphyr
's parsed YAML type into a serde_json::Value
. This would allow us to pass the converted value to valico
for validation without double parsing.
Pros:
- No double parsing: This eliminates the performance overhead and potential inconsistencies associated with double parsing.
- Relatively simple implementation: A proof of concept for this conversion already exists.
Cons:
- Requires cloning the entire AST: The conversion process would require cloning the entire abstract syntax tree (AST), which could be performance-intensive.
- Some performance impact due to conversion: The conversion process itself would add some overhead.
- Runtime overhead even if no errors: The conversion would occur regardless of whether there are errors, adding overhead even in successful validations.
This approach is a middle ground between double parsing and contributing to valico
. It avoids double parsing but introduces a performance overhead due to the conversion process. We need to carefully measure this overhead to determine if it's acceptable.
4. Implement Spanned Parsing for serde_yaml
/ serde_json
/ serde
: The Ambitious Approach
A more ambitious approach would be to add support for span information directly to our existing dependencies, such as serde_yaml
and serde_json
. This would involve modifying the underlying serialization framework, serde
, to support span information.
Pros:
- Able to use existing dependencies: This would avoid the need for double parsing or additional dependencies.
- Addresses all limitations: This would address all the limitations of the current approach.
Cons:
- Likely not feasible: Adding span information to
serde
is a complex undertaking, and it's not clear if it's even possible within the current architecture.
This approach is the most elegant and comprehensive solution, but it's also the most challenging. It would require significant effort and expertise, and it's not guaranteed to succeed. We need to carefully assess the feasibility of this approach before committing to it.
5. Implement a "Lingua Franca" for Data Formats: The Long-Term Vision
A more visionary approach would be to develop a common interface for working with different data formats. This interface would provide a way to access the structure and content of data without being tied to a specific format, such as JSON or YAML.
Pros:
- No double parsing: This eliminates the performance overhead and potential inconsistencies associated with double parsing.
- No performance overhead: There would be no performance penalty for users who don't encounter errors.
- No increase in binary size: This approach wouldn't require adding new dependencies.
- No hacky glue code required in
action-validator
: This would provide a clean and elegant solution.
Cons:
- Outside the scope of this project: This is a large undertaking that would require collaboration across multiple projects.
- Relies on upstream maintainer acceptance: This approach would require the maintainers of validation libraries, such as
valico
, to adopt the new interface.
This approach is the most ambitious and forward-thinking. It would solve not only our current problem but also a broader issue of ecosystem fragmentation. However, it's a long-term vision that requires significant effort and collaboration.
Conclusion: Moving Forward Together
We've explored the challenges of providing clear and informative error output, and we've discussed several possible solutions. Each approach has its own trade-offs, and we need to carefully consider which path to take.
The goal is to improve the debugging experience for our users, making it easier to identify and resolve errors. This is crucial for the usability and adoption of our tool.
I'm eager to hear your thoughts on this topic. Which approach do you think is the most promising? Are there other solutions we should consider? Let's discuss this together and chart a course forward. If there's sufficient interest in this feature, I'm happy to dedicate my time and effort to bring it to fruition. Let's make error messages less of a headache and more of a helpful guide!