def start_scheduled_execution(graphene_info, schedule_name):
from dagster_graphql.schema.roots import create_execution_metadata
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.str_param(schedule_name, 'schedule_name')
schedule = get_dagster_schedule(graphene_info, schedule_name)
schedule_def = get_dagster_schedule_def(graphene_info, schedule_name)
# Run should_execute and halt if it returns False
should_execute = schedule_def.should_execute
if should_execute() != True:
return graphene_info.schema.type_named('ScheduledExecutionBlocked')(
message='Schedule {schedule_name} did not run because the should_execute did not return '
'True'
)
# Add dagster/schedule_id tag to executionMetadata
execution_params = merge_dicts(
{'executionMetadata': {'tags': []}}, schedule_def.execution_params
)
# Check that the dagster/schedule_id tag is not already set
check.invariant(
not any(
tag['key'] == 'dagster/schedule_id'
for tag in execution_params['executionMetadata']['tags']
),
"Tag dagster/schedule_id tag is already defined in executionMetadata.tags",
)
# Check that the dagster/schedule_name tag is not already set
check.invariant(
not any(
tag['key'] == 'dagster/schedule_name'
for tag in execution_params['executionMetadata']['tags']
),
"Tag dagster/schedule_name tag is already defined in executionMetadata.tags",
)
execution_params['executionMetadata']['tags'].append(
{'key': 'dagster/schedule_id', 'value': schedule.schedule_id}
)
execution_params['executionMetadata']['tags'].append(
{'key': 'dagster/schedule_name', 'value': schedule.name}
)
selector = execution_params['selector']
execution_params = ExecutionParams(
selector=ExecutionSelector(selector['name'], selector.get('solidSubset')),
environment_dict=execution_params.get('environmentConfigData'),
mode=execution_params.get('mode'),
execution_metadata=create_execution_metadata(execution_params.get('executionMetadata')),
step_keys=execution_params.get('stepKeys'),
previous_run_id=None,
)
return start_pipeline_execution(graphene_info, execution_params)
def start_scheduled_execution(graphene_info, schedule_name):
from dagster_graphql.schema.roots import create_execution_metadata
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.str_param(schedule_name, 'schedule_name')
schedule = get_dagster_schedule(graphene_info, schedule_name)
schedule_def = get_dagster_schedule_def(graphene_info, schedule_name)
schedule_context = ScheduleExecutionContext(graphene_info.context.instance)
# Run should_execute and halt if it returns False
if not schedule_def.should_execute(schedule_context):
return graphene_info.schema.type_named(
'ScheduledExecutionBlocked'
)(message=
'Schedule {schedule_name} did not run because the should_execute did not return'
' True'.format(schedule_name=schedule_name))
# Get environment_dict
environment_dict = schedule_def.get_environment_dict(schedule_context)
tags = schedule_def.get_tags(schedule_context)
check.invariant('dagster/schedule_id' not in tags)
tags['dagster/schedule_id'] = schedule.schedule_id
check.invariant('dagster/schedule_name' not in tags)
tags['dagster/schedule_name'] = schedule_def.name
execution_metadata_tags = [{
'key': key,
'value': value
} for key, value in tags.items()]
execution_params = merge_dicts(
schedule_def.execution_params,
{'executionMetadata': {
'tags': execution_metadata_tags
}})
selector = ExecutionSelector(
execution_params['selector']['name'],
execution_params['selector'].get('solidSubset'))
execution_params = ExecutionParams(
selector=selector,
environment_dict=environment_dict,
mode=execution_params.get('mode'),
execution_metadata=create_execution_metadata(
execution_params.get('executionMetadata')),
step_keys=execution_params.get('stepKeys'),
previous_run_id=None,
)
# Launch run if run launcher is defined
run_launcher = graphene_info.context.instance.run_launcher
if run_launcher:
return launch_pipeline_execution(graphene_info, execution_params)
return start_pipeline_execution(graphene_info, execution_params)