Merge #1000

1000: Clean up some documentation debt r=Xanewok a=matklad



Co-authored-by: Aleksey Kladov <[email protected]>
Co-authored-by: bjorn3 <[email protected]>

1 files changed, 0 insertions, 77 deletions

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-# Rust Analyzer Roadmap 01
-
-Written on 2018-11-06, extends approximately to February 2019.
-After that, we should coordinate with the compiler/rls developers to align goals and share code and experience.
-
-
-# Overall Goals
-
-The mission is:
-  * Provide an excellent "code analyzed as you type" IDE experience for the Rust language,
-  * Implement the bulk of the features in Rust itself.
-
-
-High-level architecture constraints:
-  * Long-term, replace the current rustc frontend.
-    It's *obvious* that the code should be shared, but OTOH, all great IDEs started as from-scratch rewrites.
-  * Don't hard-code a particular protocol or mode of operation.
-    Produce a library which could be used for implementing an LSP server, or for in-process embedding.
-  * As long as possible, stick with stable Rust.
-
-
-# Current Goals
-
-Ideally, we would be coordinating with the compiler/rls teams, but they are busy working on making Rust 2018 at the moment.
-The sync-up point will happen some time after the edition, probably early 2019.
-In the meantime, the goal is to **experiment**, specifically, to figure out how a from-scratch written RLS might look like.
-
-
-## Data Storage and Protocol implementation
-
-The fundamental part of any architecture is who owns which data, how the data is mutated and how the data is exposed to user.
-For storage we use the [salsa](http://github.com/salsa-rs/salsa) library, which provides a solid model that seems to be the way to go.
-
-Modification to source files is mostly driven by the language client, but we also should support watching the file system. The current
-file watching implementation is a stub.
-
-**Action Item:** implement reliable file watching service.
-
-We also should extract LSP bits as a reusable library. There's already `gen_lsp_server`, but it is pretty limited.
-
-**Action Item:** try using `gen_lsp_server` in more than one language server, for example for TOML and Nix.
-
-The ideal architecture for `gen_lsp_server` is still unclear. I'd rather avoid futures: they bring significant runtime complexity
-(call stacks become insane) and the performance benefits are negligible for our use case (one thread per request is perfectly OK given
-the low amount of requests a language server receives). The current interface is based on crossbeam-channel, but it's not clear
-if that is the best choice.
-
-
-## Low-effort, high payoff features
-
-Implementing 20% of type inference will give use 80% of completion.
-Thus it makes sense to partially implement name resolution, type inference and trait matching, even though there is a chance that
-this code is replaced later on when we integrate with the compiler
-
-Specifically, we need to:
-
-* **Action Item:** implement path resolution, so that we get completion in imports and such.
-* **Action Item:** implement simple type inference, so that we get completion for inherent methods.
-* **Action Item:** implement nicer completion infrastructure, so that we have icons, snippets, doc comments, after insert callbacks, ...
-
-
-## Dragons to kill
-
-To make experiments most effective, we should try to prototype solutions for the hardest problems.
-In the case of Rust, the two hardest problems are:
-  * Conditional compilation and source/model mismatch.
-    A single source file might correspond to several entities in the semantic model.
-    For example, different cfg flags produce effectively different crates from the same source.
-  * Macros are intertwined with name resolution in a single fix-point iteration algorithm.
-    This is just plain hard to implement, but also interacts poorly with on-demand.
-
-
-For the first bullet point, we need to design descriptors infra and explicit mapping step between sources and semantic model, which is intentionally fuzzy in one direction.
-The **action item** here is basically "write code, see what works, keep high-level picture in mind".
-
-For the second bullet point, there's hope that salsa with its deep memoization will result in a fast enough solution even without being fully on-demand.
-Again, the **action item** is to write the code and see what works. Salsa itself uses macros heavily, so it should be a great test.