rust-analyzer · bors · Nov 15, 2018 · Nov 6, 2018 · Nov 6, 2018 · killercup
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+# Rust Analyzer Roadmap 01
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+Written on 2018-11-06, extends approximately to February 2019.
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+After that, we should coordinate with the compiler/rls developers to align goals and share code and experience.
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+# Overall Goals
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+The mission is:
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+  * Provide an excellent "code analyzed as you type" IDE experience for the Rust language,
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+  * Implement the bulk of the features in Rust itself.
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+High-level architecture constraints:
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+  * Long-term, replace the current rustc frontend.
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+    It's *obvious* that the code should be shared, but OTOH, all great IDEs started as from-scratch rewrites.
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+  * Don't hard-code a particular protocol or mode of operation.
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+    Produce a library which could be used for implementing an LSP server, or for in-process embedding.
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+  * As long as possible, stick with stable Rust (NB: we currently use beta for 2018 edition and salsa).
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+# Current Goals
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+Ideally, we would be coordinating with the compiler/rls teams, but they are busy working on making Rust 2018 at the moment.
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+The sync-up point will happen some time after the edition, probably early 2019.
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+In the meantime, the goal is to **experiment**, specifically, to figure out how a from-scratch written RLS might look like.
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+## Data Storage and Protocol implementation
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+The fundamental part of any architecture is who owns which data, how the data is mutated and how the data is exposed to user.
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+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.
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+Modification to source files is mostly driven by the language client, but we also should support watching the file system. The current
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+file watching implementation is a stub.
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+**Action Item:** implement reliable file watching service.
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+We also should extract LSP bits as a reusable library. There's already `gen_lsp_server`, but it is pretty limited.
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+**Action Item:** try using `gen_lsp_server` in more than one language server, for example for TOML and Nix.
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+The ideal architecture for `gen_lsp_server` is still unclear. I'd rather avoid futures: they bring significant runtime complexity
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+(call stacks become insane) and the performance benefits are negligible for our use case (one thread per request is perfectly OK given
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+the low amount of requests a language server receives). The current interface is based on crossbeam-channel, but it's not clear
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+if that is the best choice.
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+## Low-effort, high payoff features
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+Implementing 20% of type inference will give use 80% of completion.
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+Thus it makes sense to partially implement name resolution, type inference and trait matching, even though there is a chance that
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+this code is replaced later on when we integrate with the compiler
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+Specifically, we need to:
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+* **Action Item:** implement path resolution, so that we get completion in imports and such.
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+* **Action Item:** implement simple type inference, so that we get completion for inherent methods.
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+* **Action Item:** implement nicer completion infrastructure, so that we have icons, snippets, doc comments, after insert callbacks, ...
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+## Dragons to kill
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+To make experiments most effective, we should try to prototype solutions for the hardest problems.
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+In the case of Rust, the two hardest problems are:
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+  * Conditional compilation and source/model mismatch.
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+    A single source file might correspond to several entities in the semantic model.
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+    For example, different cfg flags produce effectively different crates from the same source.
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+  * Macros are intertwined with name resolution in a single fix-point iteration algorithm.
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+    This is just plain hard to implement, but also interacts poorly with on-demand.
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+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.
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+The **action item** here is basically "write code, see what works, keep high-level picture in mind".
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+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.
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+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.