def resolve_environment_schema_or_error(graphene_info, selector, mode):
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.inst_param(selector, 'selector', ExecutionSelector)
dagster_pipeline = get_dagster_pipeline_from_selector(
graphene_info, selector)
if not dagster_pipeline.has_mode_definition(mode):
raise UserFacingGraphQLError(
graphene_info.schema.type_named('ModeNotFoundError')(
mode=mode, selector=selector))
return graphene_info.schema.type_named('EnvironmentSchema')(
dagster_pipeline=dagster_pipeline,
environment_schema=create_environment_schema(dagster_pipeline, mode),
)
def resolve_environment_schema_or_error(graphene_info, selector, mode):
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.inst_param(selector, 'selector', ExecutionSelector)
check.opt_str_param(mode, 'mode')
pipeline_def = get_pipeline_def_from_selector(graphene_info, selector)
if mode is None:
mode = pipeline_def.get_default_mode_name()
if not pipeline_def.has_mode_definition(mode):
raise UserFacingGraphQLError(
graphene_info.schema.type_named('ModeNotFoundError')(
mode=mode, selector=selector))
return graphene_info.schema.type_named('EnvironmentSchema')(
dagster_pipeline=pipeline_def,
environment_schema=create_environment_schema(pipeline_def, mode),
)
def _launch_pipeline_execution_for_created_run(graphene_info, run_id):
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.str_param(run_id, 'run_id')
# First retrieve the pipeline run
instance = graphene_info.context.instance
pipeline_run = instance.get_run_by_id(run_id)
if not pipeline_run:
return graphene_info.schema.type_named('PipelineRunNotFoundError')(run_id)
pipeline_def = get_pipeline_def_from_selector(graphene_info, pipeline_run.selector)
# Run config valudation
# If there are any config errors, then inject them into the event log
environment_schema = create_environment_schema(pipeline_def, pipeline_run.mode)
validated_config = validate_config(
environment_schema.environment_type, pipeline_run.environment_dict
)
if not validated_config.success:
# If the config is invalid, we construct a DagsterInvalidConfigError exception and
# insert it into the event log. We also return a PipelineConfigValidationInvalid user facing
# graphql error.
# We currently re-use the engine events machinery to add the error to the event log, but
# may need to create a new event type and instance method to handle these erros.
invalid_config_exception = DagsterInvalidConfigError(
'Error in config for pipeline {}'.format(pipeline_def.name),
validated_config.errors,
pipeline_run.environment_dict,
)
instance.report_engine_event(
str(invalid_config_exception.message),
pipeline_run,
EngineEventData.engine_error(
SerializableErrorInfo(
invalid_config_exception.message,
[],
DagsterInvalidConfigError.__class__.__name__,
None,
)
),
)
instance.report_run_failed(pipeline_run)
return DauphinPipelineConfigValidationInvalid.for_validation_errors(
pipeline_def.get_pipeline_index(), validated_config.errors
)
try:
pipeline_run = instance.launch_run(pipeline_run.run_id)
except DagsterLaunchFailedError:
error = serializable_error_info_from_exc_info(sys.exc_info())
instance.report_engine_event(
error.message, pipeline_run, EngineEventData.engine_error(error),
)
instance.report_run_failed(pipeline_run)
return graphene_info.schema.type_named('LaunchPipelineRunSuccess')(
run=graphene_info.schema.type_named('PipelineRun')(pipeline_run)
)
def _start_pipeline_execution_for_created_run(graphene_info, run_id):
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
instance = graphene_info.context.instance
execution_manager_settings = instance.dagit_settings.get(
'execution_manager')
if execution_manager_settings and execution_manager_settings.get(
'disabled'):
return graphene_info.schema.type_named(
'StartPipelineRunDisabledError')()
pipeline_run = instance.get_run_by_id(run_id)
if not pipeline_run:
return graphene_info.schema.type_named('PipelineRunNotFoundError')(
run_id)
pipeline_def = get_pipeline_def_from_selector(graphene_info,
pipeline_run.selector)
environment_schema = create_environment_schema(pipeline_def,
pipeline_run.mode)
validated_config = validate_config(environment_schema.environment_type,
pipeline_run.environment_dict)
if not validated_config.success:
# If the config is invalid, we construct a DagsterInvalidConfigError exception and
# insert it into the event log. We also return a PipelineConfigValidationInvalid user facing
# graphql error.
# We currently re-use the engine events machinery to add the error to the event log, but
# may need to create a new event type and instance method to handle these erros.
invalid_config_exception = DagsterInvalidConfigError(
'Error in config for pipeline {}'.format(pipeline_def.name),
validated_config.errors,
pipeline_run.environment_dict,
)
instance.report_engine_event(
str(invalid_config_exception.message),
pipeline_run,
EngineEventData.engine_error(
SerializableErrorInfo(
invalid_config_exception.message,
[],
DagsterInvalidConfigError.__class__.__name__,
None,
)),
)
instance.report_run_failed(pipeline_run)
return DauphinPipelineConfigValidationInvalid.for_validation_errors(
pipeline_def.get_pipeline_index(), validated_config.errors)
graphene_info.context.execution_manager.execute_pipeline(
graphene_info.context.get_handle(),
pipeline_def,
pipeline_run,
instance=instance,
)
return graphene_info.schema.type_named('StartPipelineRunSuccess')(
run=graphene_info.schema.type_named('PipelineRun')(pipeline_run))