Convert the `RuntimeError`s emitted by the pipebox (e.g. when checking
preconditions or running analyses) into `PypelineError`s and have the
pypeline cli infrastructure handle them gracefully, without emitting
stacktraces.
Add infrastructure to pypeline that allows containers to be notified
when they are being used last, this allows two things:
* `Pipe`s eagerly clearing those containers once they are done reading
their contents
* `ScheduledTask`s clearing those out at the end of their execution in
case the pipe did not do it
This overall should improve memory usage as container no longer take up
memory if they are no longer used as part of a `Schedule`.
Inline the body of `PipelineNode.run` into `ScheduledTask.run`, making
`PipelineNode` a pure data structure. This also allows eliding all the
arguments since all of them as properties of `ScheduledTask`.
The function `Request.check` did not work properly due to
`Container.contains_all` performing the check with the operands swapped.
Fix the call and improve the body of `ObjectSet.issubset`.
Also improve the error message, highlighting the missing objects.
When setting the variable names for the analysis' configuration,
sanitize the analysis' name, as it might contain dashes and other
undesirable characters.
When running `invalidate`, use a temporary table to store the
invalidated model paths that need to be used. This saves to template
them into the query and allows them to be recycled later when the
`dependency` table needs to be cleaned up.
Now commands 'pype project artifact' and 'pype pipeline run_pipe' now
accepts --format to specify in which format to serialize the resulting
container, and have --tar and --yaml shortcuts. Moreover, these commands
and 'pipe pipeline run_analysis' can automatically figure out the format
of containers so the user won't have to specify them most of the time.
Now containers can be dump and loaded in both yaml and tar. Also the
container can automatically try to figure out the format of the
container. Moreover, now containers have to_bytes and from_bytes.
Optimize the `run` function to skip objects that have just been
serialized when deserializing objects for outgoing requests. For
example, previously if a schedule had for a savepoint:
```yaml
incoming: ["/binary"]
outgoing: ["/binary"]
```
then the `Savepoint.run` function would have first serialized the object
into storage and then deserialized it right after, instead with the new
function the object is only serialized.
Overload the `get` function of `Requests` so that, in addition to the
usual `Mapping` behavior, will return an empty `ObjectSet` if no default
is specified.
Improve the `schedule` in the `Pipeline` class by introducing two
optimizations:
* Prune all the `ContainerDeclarations` that are not actually used.
* Skip all tasks that have no outgoing elements that would have been
written by them.
Allow the `configuration` field of a pipe in a pipeline yaml file to be
of any type. When constructing the pipe object, the object will be
re-serialized via `yaml.dump` before being passed to the constructor.
Move the epoch inside the storage_provider, this allows us to make the
storage_provider_factory to return an async context manager.
This way we can simplify the locking for the daemon, but forces the
CLI project commands to deal with async code.
Now the pype command has a `--verbose` argument that to enable debug
logging in the pypelien code.
Adapted the codebase to use this new logging format but replacing
`logging` with `revng.pypeline.utils.logger`, this is done in
preparation of debug-log.
Add implementation of the generic pype daemon.
It's implementation is split into `daemon.py` where we implement an
http framework agnostic interface, and `app.py` that uses it to serve
them using starlette.
Now pype has an `autocomplete` command usable to enable autocompletion
and revng2 now is based on `pype`, modifying its defalults and
injecting new commands.
Now the model has a mime_type that we expect to return to the UI, and
model_name so the name of the model on disk can be impl-specific.
Moreover, now Model and Container use the same logic to detect if
a mimetype is textual.
Change the signature of `Container.serialize` and
`Container.deserialize` to use `Buffer` instead of `bytes`, as this
makes the functions compatible with the `revng::pypeline::Buffer`
classes.
The `serialize` function of `revng::pypeline::Model` erroneously
returned a buffer instead of `bytes`, fix the function so that `bytes`
are returned instead.
Giacomo Vercesi