rp_shutdown must be called after all the owning pointers given by
PipelineC are freed, in order to guarantee this easily, the python
ApiWrapper will use an atomic counter, which when shutdown has been
signaled and counter reaches zero will automatically call rp_shutdown
Rework both classes in schema_generator.py, main changes:
* Drop manager.pipeline_artifact_structure as it was used exclusively
by SchemaGenerator and is no longer needed
* Rename SchemaGen and BindableGen to SchemaGenerator and
DynamicBindableGenerator to better express their role, add
docstrings that explain what they do
* Decouple Artifacts and Analyses discovery, since a step can have
analyses and no artifacts (and vice-versa)
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.
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.
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
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.
This commit introduces the `revng.daemon` Python module, a Flask-powered
web application that exposes the functionality from `revng.api` across a
GraphQL API
Antonio Frighetto