**Note**: This PR also contains content of previous Grid View PR. We decided to discard the previous, because this one did some refactoring of old one, and it's not a big addition.
Added a scrollable::GridView component, which just embeds the GridView in ScrollArea. Also, re-worked the idea of text layers.
https://user-images.githubusercontent.com/3919101/179020359-512ee127-c333-4f86-bff5-f1cb4154e03c.mp4
This PR contains all work for finishing integration of first Component List Panel in the IDE:
* It adds a stub for the whole Component Browser View. The documentation panel is re-used from the old searcher.
* It has the presenter implementation, integrating the view with Hierarchical Component List from the controller.
* It extends the View API, so the integration is possible, making use of Component Group Set wrapper.
* The selection integration was also merged into this PR, because it depended on the API extension mentioned above. However, we should avoid such practice in the future.
https://user-images.githubusercontent.com/3919101/177816427-8c4285b4-8941-4048-a400-52f4acf77a9f.mp4
# Important Notes
There are some known issues, to-be-fixed in the future.
* The performance is bad. It should be improved with new text::Area, and the decent one shall come with [GridView inside component browser](https://www.pivotaltracker.com/story/show/182561072)
* There is no keyboard navigation. It should also be delivered with [GridView](https://www.pivotaltracker.com/story/show/182561072).
* The Favorites section is not [filtered out by node source type](https://www.pivotaltracker.com/story/show/182661634).
Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust.
### New Libraries
#### `enso-reflect`
Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs).
#### `enso-metamodel`
Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term).
The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`.
The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process.
### Important Notes
- Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases.
- Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code.
- `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants.
- Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.)
### Binaries
The `enso-parser-generate-java` crate defines several binaries:
- `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script.
- `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing.
- `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations.
Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
[Task link](https://www.pivotaltracker.com/story/show/182194574).
[ci no changelog needed]
This PR implements a new selection box that will replace an old (not really working) one in the component browser. The old selection box wasn't working well with the headers of the component groups, so we were forced to make a much harder implementation.
The new implementation duplicates some visual components and places them in a separate layer. Then, a rectangular mask cuts off everything that is not "selected". This way:
- We have more control over what the selected entries should look like.
- We can easily support the multi-layer structure of the component groups with headers.
- We avoid problems with nested masks that our renderer doesn't support at the moment.
To be more precise, we duplicate the following:
- Background of the component group becomes the "fill" of the selection.
- Entries text and icons - we can alter them easily.
- Header background and header text. By placing them in separate scene layers we ensure correct rendering order.
https://user-images.githubusercontent.com/6566674/173657899-1067f538-4329-44f9-9dc2-78c8a4708b5a.mp4
# Important Notes
- This PR implements the base of our future selection mechanism, selecting entries with a mouse and keyboard still has several issues that will be fixed in the future tasks.
- The scrolling behavior will also be improved in future tasks. Right we only restrict the selection box position so that it never leaves the borders of the component group.
- I added a new function to `add` shapes to new layers in a non-exclusive way (we had only `add_exclusive`) before. I have no idea how we didn't use this feature before even though we mention it a lot in the docs.
- The demo scene restricts the position of the selection box for one-column component groups but does not for the wide component group.
### Pull Request Description
Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`):
- `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`.
- `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental.
(See key in first chart for the settings defining these profiles.)
We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes:
- `release`: A profile that supports fast developer iteration, while offering realistic performance.
- `production`: A maximally-optimized profile, for nightly builds and actual releases.
Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags.
### Performance details
As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster.
<details>
<summary>Methodology (click to show)</summary>
I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data:
Build the application with profiling:
`./run.sh ide build --profiling-level=debug`
Run the `open_project` workflow repeatedly:
`for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done`
For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV:
`echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do`
(Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...)
The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data.
</details>
#### Build times
In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds.
(To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
Define some workflows for batch-mode profiling.
Implemented:
- collapse nodes
- create node
- enter collapsed node
- new project
- open visualization
They can currently be built and run with a command like:
`./run.sh ide build --profiling-level=debug && dist/ide/linux-unpacked/enso --entry-point profile --workflow create_node --save-profile out.json`
And the data can be displayed with:
`dist/ide/linux-unpacked/enso --entry-point profiling_run_graph --load-profile out.json`
Demo of recording and viewing a profile with a command-line one-liner:
https://user-images.githubusercontent.com/1047859/169954795-2d9520ca-84f9-45d2-b83a-5063ebe6f718.mp4
See: https://www.pivotaltracker.com/story/show/182195399.
# Important Notes
- When defining workflows, two helpers are enough to allow us to tell when the action is really done: `Fixture::compile_new_shaders`, and `Fixture::backend_execution`. Often, it is appropriate to await both, but it depends on the task.
- The shader compiler is now driven by a `Controller`; while the `Compiler` is reset if context is lost, the `Controller`'s state survives context loss.
- A new `--load-profile` option supports specifying a profile by path when running `profiling_run_graph`.
- Drop the `with_same_start` profiler interface; we ended up preferring a child profiler convention, and this interface was not implemented compatibly with the stricter data model we've had since the introduction of `profiler::data`.
- Fix the noisy `rustfmt` output.
* The bash entry point was renamed `run.sh` -> `run`. Thanks to that `./run` works both on Linux and Windows with PowerShell (sadly not on CMD).
* Everyone's favorite checks for WASM size and program versions are back. These can be disabled through `--wasm-size-limit=0` and `--skip-version-check` respectively. WASM size limit is stored in `build-config.yaml`.
* Improved diagnostics for case when downloaded CI run artifact archive cannot be extracted.
* Added GH API authentication to the build script calls on CI. This should fix the macOS build failures that were occurring from time to time. (Actually they were due to runner being GitHub-hosted, not really an OS-specific issue by itself.)
* If the GH API Personal Access Token is provided, it will be validated. Later on it is difficult to say, whether fail was caused by wrong PAT or other issue.
* Renamed `clean` to `git-clean` as per suggestion to reduce risk of user accidently deleting unstaged work.
* Whitelisting dependabot from changelog checks, so PRs created by it are mergeable.
* Fixing issue where wasm-pack-action (third party) randomly failed to recognize the latest version of wasm-pack (macOS runners), leading to failed builds.
* Build logs can be filtered using `ENSO_BUILD_LOG` environment variable. See https://docs.rs/tracing-subscriber/0.3.11/tracing_subscriber/struct.EnvFilter.html#directives for the supported syntax.
* Improve help for ci-run source, to make clear that PAT token is required and what scope is expected there.
Also, JS parts were updated with some cleanups and fixes following the changes made when introducing the build script.
