def test_roundtrip_run():
run = PipelineRun(
pipeline_name='pipey_mcpipeface',
run_id='8675309',
environment_dict={'good': True},
mode='default',
selector=ExecutionSelector('pipey_mcpipeface'),
step_keys_to_execute=['step_1', 'step_2', 'step_3'],
tags={'tag_it': 'bag_it'},
status=PipelineRunStatus.NOT_STARTED,
previous_run_id='previousID',
)
for field in run:
# ensure we have a test value to round trip for each field
assert field
exec_params = execution_params_from_pipeline_run(run)
assert run == pipeline_run_from_execution_params(exec_params)
exec_params_gql = execution_params_from_graphql(
exec_params.to_graphql_input())
assert exec_params_gql == exec_params
assert run == pipeline_run_from_execution_params(exec_params_gql)
empty_run = PipelineRun.create_empty_run('foo', 'bar')
exec_params = execution_params_from_pipeline_run(empty_run)
assert empty_run == pipeline_run_from_execution_params(exec_params)
exec_params_gql = execution_params_from_graphql(
exec_params.to_graphql_input())
assert exec_params_gql == exec_params
assert empty_run == pipeline_run_from_execution_params(exec_params_gql)
def test_roundtrip_run():
run = PipelineRun(
pipeline_name='pipey_mcpipeface',
run_id='8675309',
environment_dict={'good': True},
mode='default',
selector=ExecutionSelector('pipey_mcpipeface'),
step_keys_to_execute=['step_1', 'step_2', 'step_3'],
tags={'tag_it': 'bag_it'},
status=PipelineRunStatus.NOT_STARTED,
)
assert run == pipeline_run_from_execution_params(
execution_params_from_pipeline_run(run))
empty_run = PipelineRun.create_empty_run('foo', 'bar')
assert empty_run == pipeline_run_from_execution_params(
execution_params_from_pipeline_run(empty_run))
def test_roundtrip_run():
run_with_snapshot = PipelineRun(
pipeline_name='pipey_mcpipeface',
run_id='8675309',
environment_dict={'good': True},
mode='default',
selector=ExecutionSelector('pipey_mcpipeface'),
step_keys_to_execute=['step_1', 'step_2', 'step_3'],
tags={'tag_it': 'bag_it'},
status=PipelineRunStatus.NOT_STARTED,
root_run_id='previousID',
parent_run_id='previousID',
previous_run_id='previousID',
pipeline_snapshot_id='pipey_mcpipeface_snapshot_id',
)
for field in run_with_snapshot:
# ensure we have a test value to round trip for each field
assert field
# The invariant that all the execution parameter structs
# pipeline run can be constructed from each other is no longer
# true. Clients of the GraphQL API cannot know the value of the
# pipeline_snapshot_id prior to execution, because it is
# constructed on the server. Hence these roundtrip tests
# do not include snapshot_id
run = run_with_snapshot._replace(pipeline_snapshot_id=None)
exec_params = execution_params_from_pipeline_run(run)
assert run == pipeline_run_from_execution_params(exec_params)
exec_params_gql = execution_params_from_graphql(exec_params.to_graphql_input())
assert exec_params_gql == exec_params
assert run == pipeline_run_from_execution_params(exec_params_gql)
empty_run = PipelineRun.create_empty_run('foo', 'bar')
exec_params = execution_params_from_pipeline_run(empty_run)
assert empty_run == pipeline_run_from_execution_params(exec_params)
exec_params_gql = execution_params_from_graphql(exec_params.to_graphql_input())
assert exec_params_gql == exec_params
assert empty_run == pipeline_run_from_execution_params(exec_params_gql)
def _create_pipeline_run(instance, pipeline, execution_params):
step_keys_to_execute = execution_params.step_keys
if not execution_params.step_keys and execution_params.previous_run_id:
execution_plan = create_execution_plan(
pipeline,
execution_params.environment_dict,
run_config=RunConfig(
mode=execution_params.mode, previous_run_id=execution_params.previous_run_id
),
)
step_keys_to_execute = get_retry_steps_from_execution_plan(instance, execution_plan)
return pipeline_run_from_execution_params(execution_params, step_keys_to_execute)
def start_pipeline_execution(graphene_info, execution_params, reexecution_config):
check.inst_param(graphene_info, 'graphene_info', ResolveInfo)
check.inst_param(execution_params, 'execution_params', ExecutionParams)
check.opt_inst_param(reexecution_config, 'reexecution_config', ReexecutionConfig)
instance = graphene_info.context.instance
dauphin_pipeline = get_dauphin_pipeline_from_selector_or_raise(
graphene_info, execution_params.selector
)
get_validated_config(
graphene_info,
dauphin_pipeline,
environment_dict=execution_params.environment_dict,
mode=execution_params.mode,
)
execution_plan = create_execution_plan(
dauphin_pipeline.get_dagster_pipeline(),
execution_params.environment_dict,
run_config=RunConfig(mode=execution_params.mode),
)
_check_start_pipeline_execution_errors(
graphene_info, execution_params, execution_plan, reexecution_config
)
run = instance.create_run(
pipeline_run_from_execution_params(execution_params, reexecution_config)
)
graphene_info.context.execution_manager.execute_pipeline(
graphene_info.context.get_handle(),
dauphin_pipeline.get_dagster_pipeline(),
run,
instance=instance,
)
return graphene_info.schema.type_named('StartPipelineExecutionSuccess')(
run=graphene_info.schema.type_named('PipelineRun')(run)
)