@AustinSchuh's `rules_cc` patches have all been merged upstream. This
updates `rules_cc` to include the relevant patches, removing the need to
patch it here.
This also squashes this warning on every build:
> WARNING: For repository 'rules_cc', the root module requires module
version rules_cc@0.2.13, but got rules_cc@0.2.14 in the resolved
dependency graph. Please update the version in your MODULE.bazel or set
--check_direct_dependencies=off
This pulls in the `mrclib` maven repository as shared libraries, as a
prereq for #8858.
Alternative to #8869, which avoids the unnecessary lockfile entry. This
should be a one-to-one replacement for that PR.
Closes#8869
---------
Co-authored-by: PJ Reiniger <pj.reiniger@gmail.com>
Since we currently have both a Bazel build and Gradle build, we need to
keep the Doxygen versions in sync between the two.
40188d9cc6/docs/build.gradle (L71)
It's awkward that these are in very disjoint parts of the repo. This
puts them in the same directory so it's more obvious it should be kept
in sync.
Commands v3 had a few changes due to the upgrade:
- Java 24 removed the Pinned: MONITOR IllegalStateException when
yielding in a synchronized block, so we no longer need to special case
for it
- Lambda method name generation was tweaked, requiring tests to be
updated
- Bazel java_rules needed to be bumped to support Java 25
Closes#8425
Jackson is a very heavy library; it supports loads of features that we
don't need, and historically has caused issues due to long class loading
times (a little over 2 seconds to load AprilTagFieldLayout). This often
manifests as a help request in the form of "my robot disables when I do
X, but doesn't disable when doing X in subsequent attempts until code
restart." While SC has brought down Jackson loading times significantly,
with AprilTagFieldLayout loads taking only 330 milliseconds, that's
still a rather long delay, and while libraries should handle any JSON
loading ahead of time to prevent delays in auto/teleop, it would still
be good to make the worst case better to reduce user frustration.
Benchmarks indicate using [Avaje
Jsonb](https://github.com/avaje/avaje-jsonb) to load AprilTagFieldLayout
only takes ~70 ms, a fair chunk of which isn't actually in Avaje Jsonb
(~4 ms is spent on using getResourceAsStream to retrieve the JSON file,
~8 ms is spent on just loading the AprilTag class and its dependencies).
Note that all times listed are end-to-end, meaning nothing else was done
except for the operation being benchmarked, and doing arithmetic on them
can be flawed due to some classes being loaded twice, i.e.,
getResourceAsStream and `new AprilTag()` likely load some of the same
JDK classes and so subtracting both from the Avaje Jsonb load time is
likely slightly incorrect because class loading is being double counted.
For our purposes, it's likely accurate enough and is mostly just for
contextualization.
Benchmarks were run on a Raspberry Pi CM5 with 2 GB of RAM. Source code
for the
[results](https://github.com/user-attachments/files/26471452/benchmark.txt)
can be found in the "Fastjson2" commit
(2456d15ca8ebd17635e607cd40bf8816e77869a1).
Avaje Jsonb uses code generation via annotation processors to generate
the classes needed to do JSON serde and uses service providers to find
them, which will require downstream changes in robot projects, as the
different service providers in each library must be merged together for
Avaje Jsonb to function. We will use the Gradle shadow plugin, as its
already used by the installer and therefore adds zero additional
dependencies.
This hooks up the bazel build to the robotpyExamples. It can use the
(formly pyfrc or whatever) automatic unit tests for an example, as well
as exposing the ability to run the example in simulation, with or
without `halsim_gui` with a command such as `bazel run
//robotpyExamples:AddressableLED-sim`
This required building and using wheels instead of just a normal
`py_library`, so that things like `ENTRY_POINTS` can be used. I took a
bare bones approach to building and naming the wheels (for example the
native ones don't have the OS info or python version in them, so they
wouldn't be suitable publish to pypi, but that can always be updated
later.
Robotpy dropped support for 3.10 recently, so this updates to the next
lowest version. This will be helpful when the examples get merged into
`mostrobotpy`
There's not really a specific reason why I just jumped to 3.11 instead
of a newer version. If anybody suggests otherwise I will gladly bump it
higher.
