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Only try to record real exceptions
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@jenshnielsen
jenshnielsen committed Jul 27, 2023
1 parent dccb463 commit 8d00f27
Showing 1 changed file with 94 additions and 89 deletions.
183 changes: 94 additions & 89 deletions qcodes/dataset/measurements.py
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Original file line number Diff line number Diff line change
Expand Up @@ -549,106 +549,110 @@ def _calculate_write_period(
return float(write_period)

def __enter__(self) -> DataSaver:
# We want to enter the opentelemetry span here
# and end it in the `__exit__` of this context manger
# so here we capture it in a exitstack that we keep around.
self._span = TRACER.start_span("qcodes.dataset.Measurement.run")
with ExitStack() as stack:
stack.enter_context(trace.use_span(self._span, end_on_exit=True))

self._exit_stack = stack.pop_all()
# TODO: should user actions really precede the dataset?
# first do whatever bootstrapping the user specified
for func, args in self.enteractions:
func(*args)

# next set up the "datasaver"
if self.experiment is not None:
exp_id: int | None = self.experiment.exp_id
path_to_db: str | None = self.experiment.path_to_db
conn: ConnectionPlus | None = self.experiment.conn
else:
exp_id = None
path_to_db = None
conn = None

if self._dataset_class is DataSetType.DataSet:
self.ds = DataSet(
name=self.name,
exp_id=exp_id,
conn=conn,
in_memory_cache=self._in_memory_cache,
)
elif self._dataset_class is DataSetType.DataSetInMem:
if self._in_memory_cache is False:
raise RuntimeError(
"Cannot disable the in memory cache for a "
"dataset that is only in memory."
)
self.ds = DataSetInMem._create_new_run(
name=self.name,
exp_id=exp_id,
path_to_db=path_to_db,
# TODO: should user actions really precede the dataset?
# first do whatever bootstrapping the user specified
for func, args in self.enteractions:
func(*args)

# next set up the "datasaver"
if self.experiment is not None:
exp_id: int | None = self.experiment.exp_id
path_to_db: str | None = self.experiment.path_to_db
conn: ConnectionPlus | None = self.experiment.conn
else:
exp_id = None
path_to_db = None
conn = None

if self._dataset_class is DataSetType.DataSet:
self.ds = DataSet(
name=self.name,
exp_id=exp_id,
conn=conn,
in_memory_cache=self._in_memory_cache,
)
elif self._dataset_class is DataSetType.DataSetInMem:
if self._in_memory_cache is False:
raise RuntimeError(
"Cannot disable the in memory cache for a "
"dataset that is only in memory."
)
else:
raise RuntimeError("Does not support any other dataset classes")
self.ds = DataSetInMem._create_new_run(
name=self.name,
exp_id=exp_id,
path_to_db=path_to_db,
)
else:
raise RuntimeError("Does not support any other dataset classes")

# .. and give the dataset a snapshot as metadata
if self.station is None:
station = Station.default
else:
station = self.station
# .. and give the dataset a snapshot as metadata
if self.station is None:
station = Station.default
else:
station = self.station

if station is not None:
snapshot = station.snapshot()
else:
snapshot = {}

self.ds.prepare(
snapshot=snapshot,
interdeps=self._interdependencies,
write_in_background=self._write_in_background,
shapes=self._shapes,
parent_datasets=self._parent_datasets,
)
if station is not None:
snapshot = station.snapshot()
else:
snapshot = {}

# register all subscribers
if isinstance(self.ds, DataSet):
for callble, state in self.subscribers:
# We register with minimal waiting time.
# That should make all subscribers be called when data is flushed
# to the database
log.debug(f"Subscribing callable {callble} with state {state}")
self.ds.subscribe(callble, min_wait=0, min_count=1, state=state)
self._span.set_attributes(
{
"guid": self.ds.guid,
"run_id": self.ds.run_id,
"captured_run_id": self.ds.captured_run_id,
"exp_name": self.ds.exp_name,
"sample_name": self.ds.sample_name,
"ds_name": self.ds.name,
"write_in_background": self._write_in_background,
"extra_log_info": self._extra_log_info,
}
)
print(
f"Starting experimental run with id: {self.ds.captured_run_id}."
f" {self._extra_log_info}"
)
log.info(
f"Starting measurement with guid: {self.ds.guid}, "
f'sample_name: "{self.ds.sample_name}", '
f'exp_name: "{self.ds.exp_name}", '
f'ds_name: "{self.ds.name}". '
f"{self._extra_log_info}"
)
log.info(f"Using background writing: {self._write_in_background}")
self.ds.prepare(
snapshot=snapshot,
interdeps=self._interdependencies,
write_in_background=self._write_in_background,
shapes=self._shapes,
parent_datasets=self._parent_datasets,
)

self.datasaver = DataSaver(
dataset=self.ds,
write_period=self.write_period,
interdeps=self._interdependencies,
)
# register all subscribers
if isinstance(self.ds, DataSet):
for callble, state in self.subscribers:
# We register with minimal waiting time.
# That should make all subscribers be called when data is flushed
# to the database
log.debug(f"Subscribing callable {callble} with state {state}")
self.ds.subscribe(callble, min_wait=0, min_count=1, state=state)
self._span.set_attributes(
{
"guid": self.ds.guid,
"run_id": self.ds.run_id,
"captured_run_id": self.ds.captured_run_id,
"exp_name": self.ds.exp_name,
"sample_name": self.ds.sample_name,
"ds_name": self.ds.name,
"write_in_background": self._write_in_background,
"extra_log_info": self._extra_log_info,
}
)
print(
f"Starting experimental run with id: {self.ds.captured_run_id}."
f" {self._extra_log_info}"
)
log.info(
f"Starting measurement with guid: {self.ds.guid}, "
f'sample_name: "{self.ds.sample_name}", '
f'exp_name: "{self.ds.exp_name}", '
f'ds_name: "{self.ds.name}". '
f"{self._extra_log_info}"
)
log.info(f"Using background writing: {self._write_in_background}")

self.datasaver = DataSaver(
dataset=self.ds,
write_period=self.write_period,
interdeps=self._interdependencies,
)

return self.datasaver
return self.datasaver

def __exit__(
self,
Expand Down Expand Up @@ -677,7 +681,8 @@ def __exit__(
f"{self.ds.guid};\nTraceback:\n{exception_string}"
)
self._span.set_status(trace.Status(trace.StatusCode.ERROR))
self._span.record_exception(exception_value)
if isinstance(exception_value, Exception):
self._span.record_exception(exception_value)
self.ds.add_metadata("measurement_exception", exception_string)

# and finally mark the dataset as closed, thus
Expand Down

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