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(for upgrading users, please see the notes at the bottom) Bazel brought a lot of nice things to the table, such as rebuilds based on content changes instead of modification times, caching of build products, detection of incorrect build rules via a sandbox, and so on. Rewriting the build in Bazel was also an opportunity to improve on the Makefile-based build we had prior, which was pretty poor: most dependencies were external or not pinned, and the build graph was poorly defined and mostly serialized. It was not uncommon for fresh checkouts to fail due to floating dependencies, or for things to break when trying to switch to an older commit. For day-to-day development, I think Bazel served us reasonably well - we could generally switch between branches while being confident that builds would be correct and reasonably fast, and not require full rebuilds (except on Windows, where the lack of a sandbox and the TS rules would cause build breakages when TS files were renamed/removed). Bazel achieves that reliability by defining rules for each programming language that define how source files should be turned into outputs. For the rules to work with Bazel's sandboxing approach, they often have to reimplement or partially bypass the standard tools that each programming language provides. The Rust rules call Rust's compiler directly for example, instead of using Cargo, and the Python rules extract each PyPi package into a separate folder that gets added to sys.path. These separate language rules allow proper declaration of inputs and outputs, and offer some advantages such as caching of build products and fine-grained dependency installation. But they also bring some downsides: - The rules don't always support use-cases/platforms that the standard language tools do, meaning they need to be patched to be used. I've had to contribute a number of patches to the Rust, Python and JS rules to unblock various issues. - The dependencies we use with each language sometimes make assumptions that do not hold in Bazel, meaning they either need to be pinned or patched, or the language rules need to be adjusted to accommodate them. I was hopeful that after the initial setup work, things would be relatively smooth-sailing. Unfortunately, that has not proved to be the case. Things frequently broke when dependencies or the language rules were updated, and I began to get frustrated at the amount of Anki development time I was instead spending on build system upkeep. It's now about 2 years since switching to Bazel, and I think it's time to cut losses, and switch to something else that's a better fit. The new build system is based on a small build tool called Ninja, and some custom Rust code in build/. This means that to build Anki, Bazel is no longer required, but Ninja and Rust need to be installed on your system. Python and Node toolchains are automatically downloaded like in Bazel. This new build system should result in faster builds in some cases: - Because we're using cargo to build now, Rust builds are able to take advantage of pipelining and incremental debug builds, which we didn't have with Bazel. It's also easier to override the default linker on Linux/macOS, which can further improve speeds. - External Rust crates are now built with opt=1, which improves performance of debug builds. - Esbuild is now used to transpile TypeScript, instead of invoking the TypeScript compiler. This results in faster builds, by deferring typechecking to test/check time, and by allowing more work to happen in parallel. As an example of the differences, when testing with the mold linker on Linux, adding a new message to tags.proto (which triggers a recompile of the bulk of the Rust and TypeScript code) results in a compile that goes from about 22s on Bazel to about 7s in the new system. With the standard linker, it's about 9s. Some other changes of note: - Our Rust workspace now uses cargo-hakari to ensure all packages agree on available features, preventing unnecessary rebuilds. - pylib/anki is now a PEP420 implicit namespace, avoiding the need to merge source files and generated files into a single folder for running. By telling VSCode about the extra search path, code completion now works with generated files without needing to symlink them into the source folder. - qt/aqt can't use PEP420 as it's difficult to get rid of aqt/__init__.py. Instead, the generated files are now placed in a separate _aqt package that's added to the path. - ts/lib is now exposed as @tslib, so the source code and generated code can be provided under the same namespace without a merging step. - MyPy and PyLint are now invoked once for the entire codebase. - dprint will be used to format TypeScript/json files in the future instead of the slower prettier (currently turned off to avoid causing conflicts). It can automatically defer to prettier when formatting Svelte files. - svelte-check is now used for typechecking our Svelte code, which revealed a few typing issues that went undetected with the old system. - The Jest unit tests now work on Windows as well. If you're upgrading from Bazel, updated usage instructions are in docs/development.md and docs/build.md. A summary of the changes: - please remove node_modules and .bazel - install rustup (https://rustup.rs/) - install rsync if not already installed (on windows, use pacman - see docs/windows.md) - install Ninja (unzip from https://github.com/ninja-build/ninja/releases/tag/v1.11.1 and place on your path, or from your distro/homebrew if it's 1.10+) - update .vscode/settings.json from .vscode.dist
123 lines
4.4 KiB
Markdown
123 lines
4.4 KiB
Markdown
# Protocol Buffers
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Anki uses [different implementations of Protocol Buffers](./architecture.md#protobuf)
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and each has its own peculiarities. This document highlights some aspects relevant
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to Anki and hopefully helps to avoid some common pitfalls.
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For information about Protobuf's types and syntax, please see the official [language guide](https://developers.google.com/protocol-buffers/docs/proto3).
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## General Notes
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### Names
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Generated code follows the naming conventions of the targeted language. So to access
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the message field `foo_bar` you need to use `fooBar` in Typescript and the
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namespace created by the message `FooBar` is called `foo_bar` in Rust.
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### Optional Values
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In Python and Typescript, unset optional values will contain the type's default
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value rather than `None`, `null` or `undefined`. Here's an example:
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```protobuf
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message Foo {
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optional string name = 1;
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optional int32 number = 2;
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}
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```
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```python
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message = Foo()
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assert message.number == 0
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assert message name == ""
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```
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In Python, we can use the message's `HasField()` method to check whether a field is
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actually set:
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```python
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message = Foo(name="")
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assert message.HasField("name")
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assert not message.HasField("number")
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```
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In Typescript, this is even less ergonomic and it can be easier to avoid using
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the default values in active fields. E.g. the `CsvMetadata` message uses 1-based
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indices instead of optional 0-based ones to avoid ambiguity when an index is `0`.
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### Oneofs
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All fields in a oneof are implicitly optional, so the caveats [above](#optional-values)
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apply just as much to a message like this:
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```protobuf
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message Foo {
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oneof bar {
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string name = 1;
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int32 number = 2;
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}
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}
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```
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In addition to `HasField()`, `WhichOneof()` can be used to get the name of the set
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field:
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```python
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message = Foo(name="")
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assert message.WhichOneof("bar") == "name"
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```
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### Backwards Compatibility
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The official [language guide](https://developers.google.com/protocol-buffers/docs/proto3)
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makes a lot of notes about backwards compatibility, but as Anki usually doesn't
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use Protobuf to communicate between different clients, things like shuffling around
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field numbers are usually not a concern.
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However, there are some messages, like `Deck`, which get stored in the database.
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If these are modified in an incompatible way, this can lead to serious issues if
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clients with a different protocol try to read them. Such modifications are only
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safe to make as part of a schema upgrade, because schema 11 (the targeted schema
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when choosing _Downgrade_), does not make use of Protobuf messages.
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### Field Numbers
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Field numbers larger than 15 need an additional byte to encode, so `repeated` fields
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should preferably be assigned a number between 1 and 15. If a message contains
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`reserved` fields, this is usually to accommodate potential future `repeated` fields.
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## Implementation-Specific Notes
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### Python
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Protobuf has an official Python implementation with an extensive [reference](https://developers.google.com/protocol-buffers/docs/reference/python-generated).
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- Every message used in aqt or pylib must be added to the respective `.pylintrc`
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to avoid failing type checks. The unqualified protobuf message's name must be
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used, not an alias from `collection.py` for example. This should be taken into
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account when choosing a message name in order to prevent skipping typechecking
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a Python class of the same name.
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### Typescript
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Anki uses [protobuf.js](https://protobufjs.github.io/protobuf.js/), which offers
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some documentation.
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- If using a message `Foo` as a type, make sure not to use the generated interface
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`IFoo` instead. Their definitions are very similar, but the interface requires
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null checks for every field.
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### Rust
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Anki uses the [prost crate](https://docs.rs/prost/latest/prost/).
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Its documentation has some useful hints, but for working with the generated code,
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there is a better option: From within `anki/rslib` run `cargo doc --open --document-private-items`.
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Inside the `pb` module you will find all generated Rust types and their implementations.
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- Given an enum field `Foo foo = 1;`, `message.foo` is an `i32`. Use the accessor
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`message.foo()` instead to avoid having to manually convert to a `Foo`.
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- Protobuf does not guarantee any oneof field to be set or an enum field to contain
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a valid variant, so the Rust code needs to deal with a lot of `Option`s. As we
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don't expect other parts of Anki to send invalid messages, using an `InvalidInput`
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error or `unwrap_or_default()` is usually fine.
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