revng.model.__init__ would dynamically import the latest version of
the model when imported, however this would break IDE hints as the
classes would not be properly detected. Instead we now manually
specify the last version in the __init__.py file.
Add a specialized list class to be used in the model. This will do
runtime instance checking to make sure that list-like fields in the
model only contain object of the correct type.
Implement a constructor override for model classes that checks that the
constructor does not have positional arguments passed to it. This can
be dropped post-3.10 since it is implemented natively by kw_only.
A step's artifacts now include singleTargetFilename, which gives a
suggested filename to use when a single element is extracted from the
underlying container.
When requesting a produce for multiple targets the result will be a
dictionary mapping "<target>:<result>". This also changes the GraphQL
API where a json-serialized string is returned.
In typical scenarios most PipelineC calls block for too long, this is
debilitating to the GraphQL API since coroutines are run
cooperatively. This commit moves all PipelineC calls in a separate
thread.
`recalculateAllPossibleTarget` is now a private member of
PipelineManager This was done since all calls that can trigger a change
in the target list have been isolated and the call is done implicitly.
This removes rp_manager_recompute_all_available_targets from PipelineC,
since it was added as a stopgap until the above was implemented.
Add the possibility of passing a list of FIFOs to `revng daemon`. These
can be used to notify an external program when a non-reproducible change
(binary, context) has occurred.
Splits the module in 2:
* static_handlers: as the name suggests this is where handlers for
static endpoints (e.g. not dependent on the pipeline) are implemented
* schema_generator: this is where the schema is generated from a
manager's pipeline definition, together with the handler for the
autogenerated endpoint
This commit introduces some changes to how the revng pipeline handles
serializing to disk. Specificaly:
* Pipeline globals (specifically model.yml) are better handled if they
are in a subdirectory. They are now saved in the "context"
subdirectory.
* In python:revng.api the pipeline is serialized whenever there is a
non-reproducible change to the state (e.g. binary upload or model
change).
In the case of analyses this is done conservatively by checking that
the diff produced is not empty.
* The logic for computing a step's subdirectory has been moved to the
pipeline runner, consequently if a step is asked to serialize it
will not create any subdirectories.
* Functionality for saving a single step/context has been exposed in
Pipeline C.
* Finally, all path concatenations are now handled by
llvm::sys::path::append, for extra os-agnosticism.
This commit resolves a non-deterministic test failure in `revng daemon`.
When running daemon tests, `randint` was used for selecting the listen
port, this caused test failures when the same port number was used for
reused. This has been replaced with the use of port 0 that guarantees
the use of an unused port and psutil to find the port number once it has
been bound by the daemon process.
`revng.api` now autodetects files (libraries, pipeline yamls) for
initialization via the same mechanism used by `revng.cli`. This removes
the need to pass them via environment variables from `revng daemon`
and allows easy interaction with python's C API.
Remove code in revng.cli.model_dump and override_by_name that was used
when the model had tags. The code subclassed the default pyYAML loader
to ignore unknown tags to allow reading the model data easily. Since the
model no longer has tags this code is dead and can be removed.
Since PipelineC is not thread-safe, add a lock to all function calls to
it to avoid any thread safety-related issues.
At the same time, use a thread pool to run "expensive" PipelineC
functions in GraphQL (specifically `produce_target` and `run_analysis`)
to avoid problems due to the cooperative nature of Python coroutines.
This commit adds the newly implemented functionality in PipelineC both
in revng.api and the graphql api, allowing:
* retrieval of global variable names
* unwrapping of a single target
* execution of analyses
--production runs the server on all interfaces and with debug options
disabled
--hypercorn-args allows to pass extra options to hypercorn when running
revng daemon
In the future we will need to use GraphQL subscriptions. This is done
via websockets and is supported in Ariadne. However this support is
limited to ASGI frameworks, which Flask isn't a part of.
Startlette is a direct depencency of Ariadne, and all of Ariadne's
features are fully integrated with Starlette, so the switch allows to
drop some Flask integration cruft and streamline the revng.daemon
package.
Starlette does not have a built-in development server, instead
Hypercorn, which is an ASGI-compliant HTTP server, is used in place of
Flask's Werkzeug/Gunicorn for both the development and production
server roles.
Fix termination of the daemon process during test, previously it was the
daemon instances were not cleaned up properly and remained running after
the tests
Giacomo Vercesi