def resolve_nodes(self, graphene_info):
pipeline = get_pipeline_reference_or_raise(graphene_info,
self._pipeline_run.selector)
if isinstance(pipeline, DauphinPipeline):
pipeline_def = get_pipeline_def_from_selector(
graphene_info, self._pipeline_run.selector)
execution_plan = create_execution_plan(
pipeline_def,
self._pipeline_run.environment_dict,
mode=self._pipeline_run.mode,
step_keys_to_execute=self._pipeline_run.step_keys_to_execute,
)
execution_plan_index = ExecutionPlanIndex.from_plan_and_index(
execution_plan, pipeline_def.get_pipeline_index())
else:
pipeline = None
execution_plan = None
execution_plan_index = None
return [
from_event_record(graphene_info, log, pipeline,
execution_plan_index) for log in
graphene_info.context.instance.all_logs(self._pipeline_run.run_id)
]
def resolve_executionPlan(self, graphene_info):
if (self._pipeline_run.execution_plan_snapshot_id
and self._pipeline_run.pipeline_snapshot_id):
from .execution import DauphinExecutionPlan
instance = graphene_info.context.instance
execution_plan_snapshot = instance.get_execution_plan_snapshot(
self._pipeline_run.execution_plan_snapshot_id)
pipeline_snapshot = instance.get_pipeline_snapshot(
self._pipeline_run.pipeline_snapshot_id)
return (DauphinExecutionPlan(
ExecutionPlanIndex(
execution_plan_snapshot=execution_plan_snapshot,
pipeline_index=PipelineIndex(pipeline_snapshot),
))
# check this in case fetches fail
if execution_plan_snapshot and pipeline_snapshot else None)
else:
# "legacy" code path for runs created before pipeline and
# execution plan snapshots.
pipeline = self.resolve_pipeline(graphene_info)
if isinstance(pipeline, DauphinPipeline):
selector = self._pipeline_run.selector
environment_dict = self._pipeline_run.environment_dict
mode = self._pipeline_run.mode
pipeline_def = get_pipeline_def_from_selector(
graphene_info, selector)
if is_config_valid(pipeline_def, environment_dict, mode):
return get_execution_plan(graphene_info, selector,
environment_dict, mode)
return None
def resolve_executionPlan(self, graphene_info):
pipeline = self.resolve_pipeline(graphene_info)
if isinstance(pipeline, DauphinPipeline):
execution_plan = create_execution_plan(
get_pipeline_def_from_selector(graphene_info, self._pipeline_run.selector),
self._pipeline_run.environment_dict,
RunConfig(mode=self._pipeline_run.mode),
)
return graphene_info.schema.type_named('ExecutionPlan')(pipeline, execution_plan)
else:
return None
def resolve_executionPlan(self, graphene_info):
pipeline = self.resolve_pipeline(graphene_info)
if isinstance(pipeline, DauphinPipeline):
selector = self._pipeline_run.selector
environment_dict = self._pipeline_run.environment_dict
mode = self._pipeline_run.mode
pipeline_def = get_pipeline_def_from_selector(
graphene_info, selector)
if is_config_valid(pipeline_def, environment_dict, mode):
return get_execution_plan(graphene_info, selector,
environment_dict, mode)
return None
def resolve_nodes(self, graphene_info):
pipeline = get_pipeline_reference_or_raise(graphene_info, self._pipeline_run.selector)
if isinstance(pipeline, DauphinPipeline):
execution_plan = create_execution_plan(
get_pipeline_def_from_selector(graphene_info, self._pipeline_run.selector),
self._pipeline_run.environment_dict,
RunConfig(mode=self._pipeline_run.mode),
)
else:
pipeline = None
execution_plan = None
return [
from_event_record(graphene_info, log, pipeline, execution_plan)
for log in graphene_info.context.instance.all_logs(self._pipeline_run.run_id)
]
def create_execution_params(graphene_info, graphql_execution_params):
preset_name = graphql_execution_params.get('preset')
if preset_name:
check.invariant(
not graphql_execution_params.get('environmentConfigData'),
'Invalid ExecutionParams. Cannot define environment_dict when using preset',
)
check.invariant(
not graphql_execution_params.get('mode'),
'Invalid ExecutionParams. Cannot define mode when using preset',
)
selector = graphql_execution_params['selector'].to_selector()
check.invariant(
not selector.solid_subset,
'Invalid ExecutionParams. Cannot define selector.solid_subset when using preset',
)
pipeline_def = get_pipeline_def_from_selector(graphene_info, selector)
if not pipeline_def.has_preset(preset_name):
raise UserFacingGraphQLError(
graphene_info.schema.type_named('PresetNotFoundError')(
preset=preset_name, selector=selector))
preset = pipeline_def.get_preset(preset_name)
return ExecutionParams(
selector=ExecutionSelector(selector.name, preset.solid_subset),
environment_dict=preset.environment_dict,
mode=preset.mode,
execution_metadata=create_execution_metadata(
graphql_execution_params.get('executionMetadata')),
step_keys=graphql_execution_params.get('stepKeys'),
previous_run_id=graphql_execution_params.get('retryRunId'),
)
return execution_params_from_graphql(graphql_execution_params)
