class DauphinAsset(dauphin.ObjectType):
class Meta:
name = "Asset"
key = dauphin.NonNull("AssetKey")
assetMaterializations = dauphin.Field(
dauphin.non_null_list("AssetMaterialization"),
partitions=dauphin.List(dauphin.String),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
runs = dauphin.Field(
dauphin.non_null_list("PipelineRun"), cursor=dauphin.String(), limit=dauphin.Int(),
)
def resolve_assetMaterializations(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named("AssetMaterialization")(event=event)
for event in get_asset_events(
graphene_info,
self.key,
kwargs.get("partitions"),
kwargs.get("cursor"),
kwargs.get("limit"),
)
]
def resolve_runs(self, graphene_info, **kwargs):
cursor = kwargs.get("cursor")
limit = kwargs.get("limit")
run_ids = get_asset_run_ids(graphene_info, self.key)
if not run_ids:
return []
# for now, handle cursor/limit here instead of in the DB layer
if cursor:
try:
idx = run_ids.index(cursor)
run_ids = run_ids[idx:]
except ValueError:
return []
if limit:
run_ids = run_ids[:limit]
return [
graphene_info.schema.type_named("PipelineRun")(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter(run_ids=run_ids)
)
]
class DauphinPipeline(DauphinIPipelineSnapshotMixin, dauphin.ObjectType):
class Meta:
name = "Pipeline"
interfaces = (DauphinSolidContainer, DauphinIPipelineSnapshot)
id = dauphin.NonNull(dauphin.ID)
presets = dauphin.non_null_list("PipelinePreset")
runs = dauphin.Field(
dauphin.non_null_list("PipelineRun"), cursor=dauphin.String(), limit=dauphin.Int(),
)
def __init__(self, external_pipeline):
self._external_pipeline = check.inst_param(
external_pipeline, "external_pipeline", ExternalPipeline
)
def resolve_id(self, _graphene_info):
return self._external_pipeline.get_external_origin_id()
def get_represented_pipeline(self):
return self._external_pipeline
def resolve_presets(self, _graphene_info):
return [
DauphinPipelinePreset(preset, self._external_pipeline.name)
for preset in sorted(self._external_pipeline.active_presets, key=lambda item: item.name)
]
class DauphinPartitionSet(dauphin.ObjectType):
class Meta:
name = "PartitionSet"
id = dauphin.NonNull(dauphin.ID)
name = dauphin.NonNull(dauphin.String)
pipeline_name = dauphin.NonNull(dauphin.String)
solid_selection = dauphin.List(dauphin.NonNull(dauphin.String))
mode = dauphin.NonNull(dauphin.String)
partitionsOrError = dauphin.Field(
dauphin.NonNull("PartitionsOrError"),
cursor=dauphin.String(),
limit=dauphin.Int(),
reverse=dauphin.Boolean(),
)
partition = dauphin.Field("Partition",
partition_name=dauphin.NonNull(dauphin.String))
partitionStatusesOrError = dauphin.NonNull("PartitionStatusesOrError")
def __init__(self, external_repository_handle, external_partition_set):
self._external_repository_handle = check.inst_param(
external_repository_handle, "external_respository_handle",
RepositoryHandle)
self._external_partition_set = check.inst_param(
external_partition_set, "external_partition_set",
ExternalPartitionSet)
super(DauphinPartitionSet, self).__init__(
name=external_partition_set.name,
pipeline_name=external_partition_set.pipeline_name,
solid_selection=external_partition_set.solid_selection,
mode=external_partition_set.mode,
)
def resolve_id(self, _graphene_info):
return self._external_partition_set.get_external_origin_id()
def resolve_partitionsOrError(self, graphene_info, **kwargs):
return get_partitions(
graphene_info,
self._external_repository_handle,
self._external_partition_set,
cursor=kwargs.get("cursor"),
limit=kwargs.get("limit"),
reverse=kwargs.get("reverse"),
)
def resolve_partition(self, graphene_info, partition_name):
return get_partition_by_name(
graphene_info,
self._external_repository_handle,
self._external_partition_set,
partition_name,
)
def resolve_partitionStatusesOrError(self, graphene_info):
return get_partition_set_partition_statuses(
graphene_info, self._external_repository_handle,
self._external_partition_set.name)
class DauphinPartitionSet(dauphin.ObjectType):
class Meta(object):
name = 'PartitionSet'
name = dauphin.NonNull(dauphin.String)
pipeline_name = dauphin.NonNull(dauphin.String)
solid_selection = dauphin.List(dauphin.NonNull(dauphin.String))
mode = dauphin.NonNull(dauphin.String)
partitions = dauphin.Field(
dauphin.NonNull('Partitions'),
cursor=dauphin.String(),
limit=dauphin.Int(),
reverse=dauphin.Boolean(),
)
partition = dauphin.Field('Partition',
partition_name=dauphin.NonNull(dauphin.String))
def __init__(self, external_repository_handle, external_partition_set):
self._external_repository_handle = check.inst_param(
external_repository_handle, 'external_respository_handle',
RepositoryHandle)
self._external_partition_set = check.inst_param(
external_partition_set, 'external_partition_set',
ExternalPartitionSet)
super(DauphinPartitionSet, self).__init__(
name=external_partition_set.name,
pipeline_name=external_partition_set.pipeline_name,
solid_selection=external_partition_set.solid_selection,
mode=external_partition_set.mode,
)
def resolve_partitions(self, graphene_info, **kwargs):
return get_partitions(
graphene_info,
self._external_repository_handle,
self._external_partition_set,
cursor=kwargs.get("cursor"),
limit=kwargs.get("limit"),
reverse=kwargs.get('reverse'),
)
def resolve_partition(self, graphene_info, partition_name):
return get_partition_by_name(
graphene_info,
self._external_repository_handle,
self._external_partition_set,
partition_name,
)
class DauphinPipelineRuns(dauphin.ObjectType):
class Meta:
name = "PipelineRuns"
def __init__(self, filters, cursor, limit):
self._filters = filters
self._cursor = cursor
self._limit = limit
results = dauphin.non_null_list("PipelineRun")
count = dauphin.Int()
def resolve_results(self, graphene_info):
return get_runs(graphene_info, self._filters, self._cursor, self._limit)
def resolve_count(self, graphene_info):
return get_runs_count(graphene_info, self._filters)
class DauphinPartitionSet(dauphin.ObjectType):
class Meta(object):
name = 'PartitionSet'
name = dauphin.NonNull(dauphin.String)
pipeline_name = dauphin.NonNull(dauphin.String)
solid_subset = dauphin.List(dauphin.NonNull(dauphin.String))
mode = dauphin.NonNull(dauphin.String)
partitions = dauphin.Field(
dauphin.NonNull('Partitions'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
def __init__(self, partition_set):
self._partition_set = check.inst_param(partition_set, 'partition_set',
PartitionSetDefinition)
super(DauphinPartitionSet, self).__init__(
name=partition_set.name,
pipeline_name=partition_set.pipeline_name,
solid_subset=partition_set.solid_subset,
mode=partition_set.mode,
)
def resolve_partitions(self, graphene_info, **kwargs):
partitions = self._partition_set.get_partitions()
cursor = kwargs.get("cursor")
limit = kwargs.get("limit")
if cursor and limit:
index = next(
(idx for (idx, partition) in enumerate(partitions)
if partition.name == cursor),
None,
)
partitions = partitions[index:min(len(partitions), index + limit)]
elif limit:
partitions = partitions[:min(len(partitions), limit)]
return graphene_info.schema.type_named('Partitions')(results=[
graphene_info.schema.type_named('Partition')(
partition=partition, partition_set=self._partition_set)
for partition in partitions
])
class DauphinQuery(dauphin.ObjectType):
class Meta(object):
name = 'Query'
version = dauphin.NonNull(dauphin.String)
reloadSupported = dauphin.NonNull(dauphin.Boolean)
pipelineOrError = dauphin.Field(
dauphin.NonNull('PipelineOrError'),
params=dauphin.NonNull('ExecutionSelector'))
pipeline = dauphin.Field(dauphin.NonNull('Pipeline'),
params=dauphin.NonNull('ExecutionSelector'))
pipelinesOrError = dauphin.NonNull('PipelinesOrError')
pipelines = dauphin.Field(dauphin.NonNull('PipelineConnection'))
pipelineSnapshot = dauphin.Field(
dauphin.NonNull('PipelineSnapshot'),
snapshotId=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
pipelineSnapshotOrError = dauphin.Field(
dauphin.NonNull('PipelineSnapshotOrError'),
snapshotId=dauphin.String(),
activePipelineName=dauphin.String(),
)
scheduler = dauphin.Field(dauphin.NonNull('SchedulerOrError'))
scheduleOrError = dauphin.Field(
dauphin.NonNull('ScheduleOrError'),
schedule_name=dauphin.NonNull(dauphin.String),
limit=dauphin.Int(),
)
partitionSetsOrError = dauphin.Field(
dauphin.NonNull('PartitionSetsOrError'), pipelineName=dauphin.String())
partitionSetOrError = dauphin.Field(dauphin.NonNull('PartitionSetOrError'),
partitionSetName=dauphin.String())
pipelineRunsOrError = dauphin.Field(
dauphin.NonNull('PipelineRunsOrError'),
filter=dauphin.Argument('PipelineRunsFilter'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
pipelineRunOrError = dauphin.Field(dauphin.NonNull('PipelineRunOrError'),
runId=dauphin.NonNull(dauphin.ID))
pipelineRunTags = dauphin.non_null_list('PipelineTagAndValues')
usedSolids = dauphin.Field(dauphin.non_null_list('UsedSolid'))
usedSolid = dauphin.Field('UsedSolid',
name=dauphin.NonNull(dauphin.String))
isPipelineConfigValid = dauphin.Field(
dauphin.NonNull('PipelineConfigValidationResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
executionPlan = dauphin.Field(
dauphin.NonNull('ExecutionPlanResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
environmentSchemaOrError = dauphin.Field(
dauphin.NonNull('EnvironmentSchemaOrError'),
args={
'selector': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'mode': dauphin.Argument(dauphin.String),
},
description=
'''Fetch an environment schema given an execution selection and a mode.
See the descripton on EnvironmentSchema for more information.''',
)
instance = dauphin.NonNull('Instance')
def resolve_pipelineSnapshot(self, graphene_info, **kwargs):
return get_pipeline_snapshot(graphene_info, kwargs['snapshotId'])
def resolve_pipelineSnapshotOrError(self, graphene_info, **kwargs):
snapshot_id_arg = kwargs.get('snapshotId')
pipeline_name_arg = kwargs.get('activePipelineName')
check.invariant(
not (snapshot_id_arg and pipeline_name_arg),
'Cannot pass both snapshotId and activePipelineName',
)
check.invariant(snapshot_id_arg or pipeline_name_arg,
'Must set one of snapshotId or activePipelineName')
if pipeline_name_arg:
return get_pipeline_snapshot_or_error_from_pipeline_name(
graphene_info, pipeline_name_arg)
else:
return get_pipeline_snapshot_or_error_from_snapshot_id(
graphene_info, snapshot_id_arg)
def resolve_version(self, graphene_info):
return graphene_info.context.version
def resolve_reloadSupported(self, graphene_info):
if isinstance(graphene_info.context, DagsterSnapshotGraphQLContext):
return False
return graphene_info.context.reloader.is_reload_supported
def resolve_scheduler(self, graphene_info):
return get_scheduler_or_error(graphene_info)
def resolve_scheduleOrError(self, graphene_info, schedule_name):
return get_schedule_or_error(graphene_info, schedule_name)
def resolve_pipelineOrError(self, graphene_info, **kwargs):
return get_pipeline_or_error(graphene_info,
kwargs['params'].to_selector())
def resolve_pipeline(self, graphene_info, **kwargs):
return get_pipeline_or_raise(graphene_info,
kwargs['params'].to_selector())
def resolve_pipelinesOrError(self, graphene_info):
return get_pipelines_or_error(graphene_info)
def resolve_pipelines(self, graphene_info):
return get_pipelines_or_raise(graphene_info)
def resolve_pipelineRunsOrError(self, graphene_info, **kwargs):
filters = kwargs.get('filter')
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named('PipelineRuns')(
results=get_runs(graphene_info, filters, kwargs.get('cursor'),
kwargs.get('limit')))
def resolve_pipelineRunOrError(self, graphene_info, runId):
return get_run_by_id(graphene_info, runId)
def resolve_partitionSetsOrError(self, graphene_info, **kwargs):
pipeline_name = kwargs.get('pipelineName')
return get_partition_sets_or_error(graphene_info, pipeline_name)
def resolve_partitionSetOrError(self, graphene_info, partitionSetName):
return get_partition_set(graphene_info, partitionSetName)
def resolve_pipelineRunTags(self, graphene_info):
return get_run_tags(graphene_info)
def resolve_usedSolid(self, graphene_info, name):
return get_solid(graphene_info, name)
def resolve_usedSolids(self, graphene_info):
return get_solids(graphene_info)
def resolve_isPipelineConfigValid(self, graphene_info, pipeline, **kwargs):
return validate_pipeline_config(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_executionPlan(self, graphene_info, pipeline, **kwargs):
return get_execution_plan(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_environmentSchemaOrError(self, graphene_info, **kwargs):
return resolve_environment_schema_or_error(
graphene_info, kwargs['selector'].to_selector(),
kwargs.get('mode'))
def resolve_instance(self, graphene_info):
return graphene_info.schema.type_named('Instance')(
graphene_info.context.instance)
class DauphinRunningSchedule(dauphin.ObjectType):
class Meta(object):
name = 'RunningSchedule'
schedule_definition = dauphin.NonNull('ScheduleDefinition')
python_path = dauphin.Field(dauphin.String)
repository_path = dauphin.Field(dauphin.String)
status = dauphin.NonNull('ScheduleStatus')
runs = dauphin.Field(dauphin.non_null_list('PipelineRun'),
limit=dauphin.Int())
runs_count = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(dauphin.non_null_list('ScheduleTick'),
limit=dauphin.Int())
ticks_count = dauphin.NonNull(dauphin.Int)
stats = dauphin.NonNull('ScheduleTickStatsSnapshot')
# TODO: Delete attempts and attempts_count in 0.8.0 release
# https://github.com/dagster-io/dagster/issues/228
attempts = dauphin.Field(dauphin.non_null_list('ScheduleAttempt'),
limit=dauphin.Int())
attempts_count = dauphin.NonNull(dauphin.Int)
logs_path = dauphin.NonNull(dauphin.String)
def __init__(self, graphene_info, schedule):
self._schedule = check.inst_param(schedule, 'schedule', Schedule)
super(DauphinRunningSchedule, self).__init__(
schedule_definition=graphene_info.schema.type_named(
'ScheduleDefinition')(
graphene_info=graphene_info,
schedule_def=get_dagster_schedule_def(
graphene_info, schedule.name),
),
status=schedule.status,
python_path=schedule.python_path,
repository_path=schedule.repository_path,
)
# TODO: Delete in 0.8.0 release
# https://github.com/dagster-io/dagster/issues/228
def resolve_attempts(self, graphene_info, **kwargs):
limit = kwargs.get('limit')
results = get_schedule_attempt_filenames(graphene_info,
self._schedule.name)
if limit is None:
limit = len(results)
latest_results = heapq.nlargest(limit, results, key=os.path.getctime)
attempts = []
for result_path in latest_results:
with open(result_path, 'r') as f:
line = f.readline()
if not line:
continue # File is empty
start_scheduled_execution_response = json.loads(line)
run = None
if 'errors' in start_scheduled_execution_response:
status = DauphinScheduleAttemptStatus.ERROR
json_result = start_scheduled_execution_response['errors']
else:
json_result = start_scheduled_execution_response['data'][
'startScheduledExecution']
typename = json_result['__typename']
if (typename == 'StartPipelineRunSuccess'
or typename == 'LaunchPipelineRunSuccess'):
status = DauphinScheduleAttemptStatus.SUCCESS
run_id = json_result['run']['runId']
if graphene_info.context.instance.has_run(run_id):
run = graphene_info.schema.type_named(
'PipelineRun')(graphene_info.context.instance.
get_run_by_id(run_id))
elif typename == 'ScheduledExecutionBlocked':
status = DauphinScheduleAttemptStatus.SKIPPED
else:
status = DauphinScheduleAttemptStatus.ERROR
attempts.append(
graphene_info.schema.type_named('ScheduleAttempt')(
time=os.path.getctime(result_path),
json_result=json.dumps(json_result),
status=status,
run=run,
))
return attempts
# TODO: Delete in 0.8.0 release
# https://github.com/dagster-io/dagster/issues/228
def resolve_attempts_count(self, graphene_info):
attempt_files = get_schedule_attempt_filenames(graphene_info,
self._schedule.name)
return len(attempt_files)
# TODO: Delete in 0.8.0 release
# https://github.com/dagster-io/dagster/issues/228
def resolve_logs_path(self, graphene_info):
instance = graphene_info.context.instance
repository = graphene_info.context.get_repository_definition()
return instance.log_path_for_schedule(repository, self._schedule.name)
def resolve_stats(self, graphene_info):
repository = graphene_info.context.get_repository_definition()
stats = graphene_info.context.instance.get_schedule_tick_stats_by_schedule(
repository, self._schedule.name)
return graphene_info.schema.type_named('ScheduleTickStatsSnapshot')(
stats)
def resolve_ticks(self, graphene_info, limit=None):
repository = graphene_info.context.get_repository_definition()
# TODO: Add cursor limit argument to get_schedule_ticks_by_schedule
# https://github.com/dagster-io/dagster/issues/2291
ticks = graphene_info.context.instance.get_schedule_ticks_by_schedule(
repository, self._schedule.name)
if not limit:
tick_subset = ticks
else:
tick_subset = ticks[:limit]
return [
graphene_info.schema.type_named('ScheduleTick')(
tick_id=tick.tick_id,
status=tick.status,
timestamp=tick.timestamp,
tick_specific_data=tick_specific_data_from_dagster_tick(
graphene_info, tick),
) for tick in tick_subset
]
def resolve_ticks_count(self, graphene_info):
repository = graphene_info.context.get_repository_definition()
ticks = graphene_info.context.instance.get_schedule_ticks_by_schedule(
repository, self._schedule.name)
return len(ticks)
def resolve_runs(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named('PipelineRun')(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter.for_schedule(self._schedule),
limit=kwargs.get('limit'),
)
]
def resolve_runs_count(self, graphene_info):
return graphene_info.context.instance.get_runs_count(
filters=PipelineRunsFilter.for_schedule(self._schedule))
class DauphinScheduleState(dauphin.ObjectType):
class Meta(object):
name = 'ScheduleState'
schedule_origin_id = dauphin.NonNull(dauphin.String)
schedule_name = dauphin.NonNull(dauphin.String)
cron_schedule = dauphin.NonNull(dauphin.String)
status = dauphin.NonNull('ScheduleStatus')
runs = dauphin.Field(dauphin.non_null_list('PipelineRun'),
limit=dauphin.Int())
runs_count = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(dauphin.non_null_list('ScheduleTick'),
limit=dauphin.Int())
ticks_count = dauphin.NonNull(dauphin.Int)
stats = dauphin.NonNull('ScheduleTickStatsSnapshot')
logs_path = dauphin.NonNull(dauphin.String)
running_schedule_count = dauphin.NonNull(dauphin.Int)
repository_origin_id = dauphin.NonNull(dauphin.String)
id = dauphin.NonNull(dauphin.ID)
def __init__(self, _graphene_info, schedule_state):
self._schedule_state = check.inst_param(schedule_state, 'schedule',
ScheduleState)
self._external_schedule_origin_id = self._schedule_state.schedule_origin_id
super(DauphinScheduleState, self).__init__(
schedule_origin_id=schedule_state.schedule_origin_id,
schedule_name=schedule_state.name,
cron_schedule=schedule_state.cron_schedule,
status=schedule_state.status,
)
def resolve_id(self, _graphene_info):
return self._external_schedule_origin_id
def resolve_running_schedule_count(self, graphene_info):
running_schedule_count = graphene_info.context.instance.running_schedule_count(
self._external_schedule_origin_id)
return running_schedule_count
def resolve_stats(self, graphene_info):
stats = graphene_info.context.instance.get_schedule_tick_stats(
self._external_schedule_origin_id)
return graphene_info.schema.type_named('ScheduleTickStatsSnapshot')(
stats)
def resolve_ticks(self, graphene_info, limit=None):
# TODO: Add cursor limit argument to get_schedule_ticks_by_schedule
# https://github.com/dagster-io/dagster/issues/2291
ticks = graphene_info.context.instance.get_schedule_ticks(
self._external_schedule_origin_id)
if not limit:
tick_subset = ticks
else:
tick_subset = ticks[:limit]
return [
graphene_info.schema.type_named('ScheduleTick')(
tick_id=tick.tick_id,
status=tick.status,
timestamp=tick.timestamp,
tick_specific_data=tick_specific_data_from_dagster_tick(
graphene_info, tick),
) for tick in tick_subset
]
def resolve_ticks_count(self, graphene_info):
ticks = graphene_info.context.instance.get_schedule_ticks(
self._external_schedule_origin_id)
return len(ticks)
def resolve_runs(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named('PipelineRun')(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter.for_schedule(self._schedule_state),
limit=kwargs.get('limit'),
)
]
def resolve_runs_count(self, graphene_info):
return graphene_info.context.instance.get_runs_count(
filters=PipelineRunsFilter.for_schedule(self._schedule_state))
def resolve_repository_origin_id(self, _graphene_info):
return self._schedule_state.repository_origin_id
class DauphinRunningSchedule(dauphin.ObjectType):
class Meta(object):
name = 'RunningSchedule'
id = dauphin.NonNull(dauphin.String)
schedule_definition = dauphin.NonNull('ScheduleDefinition')
python_path = dauphin.Field(dauphin.String)
repository_path = dauphin.Field(dauphin.String)
status = dauphin.NonNull('ScheduleStatus')
runs = dauphin.Field(dauphin.non_null_list('PipelineRun'), limit=dauphin.Int())
runs_count = dauphin.NonNull(dauphin.Int)
attempts = dauphin.Field(dauphin.non_null_list('ScheduleAttempt'), limit=dauphin.Int())
attempts_count = dauphin.NonNull(dauphin.Int)
logs_path = dauphin.NonNull(dauphin.String)
def __init__(self, graphene_info, schedule):
self._schedule = check.inst_param(schedule, 'schedule', Schedule)
super(DauphinRunningSchedule, self).__init__(
id=schedule.schedule_id,
schedule_definition=graphene_info.schema.type_named('ScheduleDefinition')(
graphene_info=graphene_info,
schedule_def=get_dagster_schedule_def(graphene_info, schedule.name),
),
status=schedule.status,
python_path=schedule.python_path,
repository_path=schedule.repository_path,
)
def resolve_attempts(self, graphene_info, **kwargs):
limit = kwargs.get('limit')
results = get_schedule_attempt_filenames(graphene_info, self._schedule.name)
if limit is None:
limit = len(results)
latest_results = heapq.nlargest(limit, results, key=os.path.getctime)
attempts = []
for result_path in latest_results:
with open(result_path, 'r') as f:
line = f.readline()
if not line:
continue # File is empty
start_scheduled_execution_response = json.loads(line)
run = None
if 'errors' in start_scheduled_execution_response:
status = DauphinScheduleAttemptStatus.ERROR
json_result = start_scheduled_execution_response['errors']
else:
json_result = start_scheduled_execution_response['data'][
'startScheduledExecution'
]
typename = json_result['__typename']
if typename == 'StartPipelineExecutionSuccess':
status = DauphinScheduleAttemptStatus.SUCCESS
run_id = json_result['run']['runId']
run = graphene_info.schema.type_named('PipelineRun')(
graphene_info.context.instance.get_run_by_id(run_id)
)
elif typename == 'ScheduleExecutionBlocked':
status = DauphinScheduleAttemptStatus.SKIPPED
else:
status = DauphinScheduleAttemptStatus.ERROR
attempts.append(
graphene_info.schema.type_named('ScheduleAttempt')(
time=os.path.getctime(result_path),
json_result=json.dumps(json_result),
status=status,
run=run,
)
)
return attempts
def resolve_attempts_count(self, graphene_info):
attempt_files = get_schedule_attempt_filenames(graphene_info, self._schedule.name)
return len(attempt_files)
def resolve_logs_path(self, graphene_info):
scheduler = graphene_info.context.get_scheduler()
return scheduler.log_path_for_schedule(self._schedule.name)
def resolve_runs(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named('PipelineRun')(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter(
tags={'dagster/schedule_id': self._schedule.schedule_id}
),
limit=kwargs.get('limit'),
)
]
def resolve_runs_count(self, graphene_info):
return graphene_info.context.instance.get_runs_count(
filter=PipelineRunsFilter(tags=[("dagster/schedule_id", self._schedule.schedule_id)])
)
class DauphinRunningSchedule(dauphin.ObjectType):
class Meta(object):
name = 'RunningSchedule'
schedule_definition = dauphin.NonNull('ScheduleDefinition')
python_path = dauphin.Field(dauphin.String)
repository_path = dauphin.Field(dauphin.String)
status = dauphin.NonNull('ScheduleStatus')
runs = dauphin.Field(dauphin.non_null_list('PipelineRun'), limit=dauphin.Int())
runs_count = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(dauphin.non_null_list('ScheduleTick'), limit=dauphin.Int())
ticks_count = dauphin.NonNull(dauphin.Int)
stats = dauphin.NonNull('ScheduleTickStatsSnapshot')
logs_path = dauphin.NonNull(dauphin.String)
running_schedule_count = dauphin.NonNull(dauphin.Int)
def __init__(self, graphene_info, schedule):
self._schedule = check.inst_param(schedule, 'schedule', Schedule)
external_repository = graphene_info.context.legacy_external_repository
external_schedule = external_repository.get_external_schedule(schedule.name)
super(DauphinRunningSchedule, self).__init__(
schedule_definition=graphene_info.schema.type_named('ScheduleDefinition')(
schedule_def=get_dagster_schedule_def(graphene_info, schedule.name),
external_schedule=external_schedule,
),
status=schedule.status,
python_path=schedule.python_path,
repository_path=schedule.repository_path,
)
def resolve_running_schedule_count(self, graphene_info):
external_repository = graphene_info.context.legacy_external_repository
running_schedule_count = graphene_info.context.instance.running_schedule_count(
external_repository.name, self._schedule.name
)
return running_schedule_count
def resolve_stats(self, graphene_info):
external_repository = graphene_info.context.legacy_external_repository
stats = graphene_info.context.instance.get_schedule_tick_stats_by_schedule(
external_repository.name, self._schedule.name
)
return graphene_info.schema.type_named('ScheduleTickStatsSnapshot')(stats)
def resolve_ticks(self, graphene_info, limit=None):
external_repository = graphene_info.context.legacy_external_repository
# TODO: Add cursor limit argument to get_schedule_ticks_by_schedule
# https://github.com/dagster-io/dagster/issues/2291
ticks = graphene_info.context.instance.get_schedule_ticks_by_schedule(
external_repository.name, self._schedule.name
)
if not limit:
tick_subset = ticks
else:
tick_subset = ticks[:limit]
return [
graphene_info.schema.type_named('ScheduleTick')(
tick_id=tick.tick_id,
status=tick.status,
timestamp=tick.timestamp,
tick_specific_data=tick_specific_data_from_dagster_tick(graphene_info, tick),
)
for tick in tick_subset
]
def resolve_ticks_count(self, graphene_info):
external_repository = graphene_info.context.legacy_external_repository
ticks = graphene_info.context.instance.get_schedule_ticks_by_schedule(
external_repository.name, self._schedule.name
)
return len(ticks)
def resolve_runs(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named('PipelineRun')(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter.for_schedule(self._schedule), limit=kwargs.get('limit'),
)
]
def resolve_runs_count(self, graphene_info):
return graphene_info.context.instance.get_runs_count(
filters=PipelineRunsFilter.for_schedule(self._schedule)
)
class DauphinJobState(dauphin.ObjectType):
class Meta:
name = "JobState"
id = dauphin.NonNull(dauphin.ID)
name = dauphin.NonNull(dauphin.String)
jobType = dauphin.NonNull("JobType")
status = dauphin.NonNull("JobStatus")
repositoryOrigin = dauphin.NonNull("RepositoryOrigin")
jobSpecificData = dauphin.Field("JobSpecificData")
runs = dauphin.Field(dauphin.non_null_list("PipelineRun"),
limit=dauphin.Int())
runsCount = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(
dauphin.non_null_list("JobTick"),
dayRange=dauphin.Int(),
dayOffset=dauphin.Int(),
limit=dauphin.Int(),
)
nextTick = dauphin.Field("FutureJobTick")
runningCount = dauphin.NonNull(dauphin.Int) # remove with cron scheduler
def __init__(self, job_state):
self._job_state = check.inst_param(job_state, "job_state", JobState)
super(DauphinJobState, self).__init__(
id=job_state.job_origin_id,
name=job_state.name,
jobType=job_state.job_type,
status=job_state.status,
)
def resolve_repositoryOrigin(self, graphene_info):
origin = self._job_state.origin.external_repository_origin
return graphene_info.schema.type_named("RepositoryOrigin")(origin)
def resolve_jobSpecificData(self, graphene_info):
if not self._job_state.job_specific_data:
return None
if self._job_state.job_type == JobType.SENSOR:
return graphene_info.schema.type_named("SensorJobData")(
self._job_state.job_specific_data)
if self._job_state.job_type == JobType.SCHEDULE:
return graphene_info.schema.type_named("ScheduleJobData")(
self._job_state.job_specific_data)
return None
def resolve_runs(self, graphene_info, **kwargs):
if self._job_state.job_type == JobType.SENSOR:
filters = PipelineRunsFilter.for_sensor(self._job_state)
else:
filters = PipelineRunsFilter.for_schedule(self._job_state)
return [
graphene_info.schema.type_named("PipelineRun")(r)
for r in graphene_info.context.instance.get_runs(
filters=filters,
limit=kwargs.get("limit"),
)
]
def resolve_runsCount(self, graphene_info):
if self._job_state.job_type == JobType.SENSOR:
filters = PipelineRunsFilter.for_sensor(self._job_state)
else:
filters = PipelineRunsFilter.for_schedule(self._job_state)
return graphene_info.context.instance.get_runs_count(filters=filters)
def resolve_ticks(self,
graphene_info,
dayRange=None,
dayOffset=None,
limit=None):
before = pendulum.now("UTC").subtract(
days=dayOffset).timestamp() if dayOffset else None
after = (pendulum.now("UTC").subtract(
days=dayRange +
(dayOffset or 0)).timestamp() if dayRange else None)
return [
graphene_info.schema.type_named("JobTick")(graphene_info, tick)
for tick in graphene_info.context.instance.get_job_ticks(
self._job_state.job_origin_id,
before=before,
after=after,
limit=limit)
]
def resolve_nextTick(self, graphene_info):
# sensor
if self._job_state.job_type == JobType.SENSOR:
return get_sensor_next_tick(graphene_info, self._job_state)
else:
return get_schedule_next_tick(graphene_info, self._job_state)
def resolve_runningCount(self, graphene_info):
if self._job_state.job_type == JobType.SENSOR:
return 1 if self._job_state.status == JobStatus.RUNNING else 0
else:
return graphene_info.context.instance.running_schedule_count(
self._job_state.job_origin_id)
class DauphinQuery(dauphin.ObjectType):
class Meta:
name = 'Query'
version = dauphin.NonNull(dauphin.String)
reloadSupported = dauphin.NonNull(dauphin.Boolean)
pipelineOrError = dauphin.Field(
dauphin.NonNull('PipelineOrError'), params=dauphin.NonNull('ExecutionSelector')
)
pipeline = dauphin.Field(
dauphin.NonNull('Pipeline'), params=dauphin.NonNull('ExecutionSelector')
)
pipelinesOrError = dauphin.NonNull('PipelinesOrError')
pipelines = dauphin.Field(dauphin.NonNull('PipelineConnection'))
configTypeOrError = dauphin.Field(
dauphin.NonNull('ConfigTypeOrError'),
pipelineName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
configTypeName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
mode=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
runtimeTypeOrError = dauphin.Field(
dauphin.NonNull('RuntimeTypeOrError'),
pipelineName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
runtimeTypeName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
scheduler = dauphin.Field(dauphin.NonNull('SchedulerOrError'))
pipelineRunsOrError = dauphin.Field(
dauphin.NonNull('PipelineRunsOrError'),
filter=dauphin.Argument(dauphin.NonNull('PipelineRunsFilter')),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
pipelineRunOrError = dauphin.Field(
dauphin.NonNull('PipelineRunOrError'), runId=dauphin.NonNull(dauphin.ID)
)
pipelineRunTags = dauphin.non_null_list('PipelineTagAndValues')
isPipelineConfigValid = dauphin.Field(
dauphin.NonNull('PipelineConfigValidationResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
executionPlan = dauphin.Field(
dauphin.NonNull('ExecutionPlanResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
environmentSchemaOrError = dauphin.Field(
dauphin.NonNull('EnvironmentSchemaOrError'),
args={
'selector': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'mode': dauphin.Argument(dauphin.String),
},
description='''Fetch an environment schema given an execution selection and a mode.
See the descripton on EnvironmentSchema for more information.''',
)
instance = dauphin.NonNull('Instance')
def resolve_configTypeOrError(self, graphene_info, **kwargs):
return get_config_type(
graphene_info, kwargs['pipelineName'], kwargs['configTypeName'], kwargs.get('mode')
)
def resolve_runtimeTypeOrError(self, graphene_info, **kwargs):
return get_runtime_type(graphene_info, kwargs['pipelineName'], kwargs['runtimeTypeName'])
def resolve_version(self, graphene_info):
return graphene_info.context.version
def resolve_reloadSupported(self, graphene_info):
return graphene_info.context.reloader.is_reload_supported
def resolve_scheduler(self, graphene_info):
return get_scheduler_or_error(graphene_info)
def resolve_pipelineOrError(self, graphene_info, **kwargs):
return get_pipeline_or_error(graphene_info, kwargs['params'].to_selector())
def resolve_pipeline(self, graphene_info, **kwargs):
return get_pipeline_or_raise(graphene_info, kwargs['params'].to_selector())
def resolve_pipelinesOrError(self, graphene_info):
return get_pipelines_or_error(graphene_info)
def resolve_pipelines(self, graphene_info):
return get_pipelines_or_raise(graphene_info)
def resolve_pipelineRunsOrError(self, graphene_info, **kwargs):
filters = kwargs['filter'].to_selector()
provided = [
i for i in [filters.run_id, filters.pipeline, filters.tag_key, filters.status] if i
]
if len(provided) > 1:
return graphene_info.schema.type_named('InvalidPipelineRunsFilterError')(
message="You may only provide one of the filter options."
)
return graphene_info.schema.type_named('PipelineRuns')(
results=get_runs(graphene_info, filters, kwargs.get('cursor'), kwargs.get('limit'))
)
def resolve_pipelineRunOrError(self, graphene_info, runId):
return get_run(graphene_info, runId)
def resolve_pipelineRunTags(self, graphene_info):
return get_run_tags(graphene_info)
def resolve_isPipelineConfigValid(self, graphene_info, pipeline, **kwargs):
return validate_pipeline_config(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_executionPlan(self, graphene_info, pipeline, **kwargs):
return get_execution_plan(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_environmentSchemaOrError(self, graphene_info, **kwargs):
return resolve_environment_schema_or_error(
graphene_info, kwargs['selector'].to_selector(), kwargs.get('mode')
)
def resolve_instance(self, graphene_info):
return graphene_info.schema.type_named('Instance')(graphene_info.context.instance)
class DauphinIPipelineSnapshotMixin(object):
# Mixin this class to implement IPipelineSnapshot
#
# Graphene has some strange properties that make it so that you cannot
# implement ABCs nor use properties in an overridable way. So the way
# the mixin works is that the target classes have to have a method
# get_represented_pipeline()
#
def get_represented_pipeline(self):
raise NotImplementedError()
name = dauphin.NonNull(dauphin.String)
description = dauphin.String()
pipeline_snapshot_id = dauphin.NonNull(dauphin.String)
dagster_types = dauphin.non_null_list('DagsterType')
dagster_type_or_error = dauphin.Field(
dauphin.NonNull('DagsterTypeOrError'),
dagsterTypeName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
solids = dauphin.non_null_list('Solid')
modes = dauphin.non_null_list('Mode')
solid_handles = dauphin.Field(dauphin.non_null_list('SolidHandle'),
parentHandleID=dauphin.String())
solid_handle = dauphin.Field(
'SolidHandle',
handleID=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
tags = dauphin.non_null_list('PipelineTag')
runs = dauphin.Field(
dauphin.non_null_list('PipelineRun'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
schedules = dauphin.non_null_list('RunningSchedule')
def resolve_pipeline_snapshot_id(self, _):
return self.get_represented_pipeline().identifying_pipeline_snapshot_id
def resolve_name(self, _):
return self.get_represented_pipeline().name
def resolve_description(self, _):
return self.get_represented_pipeline().description
def resolve_dagster_types(self, _graphene_info):
represented_pipeline = self.get_represented_pipeline()
return sorted(
list(
map(
lambda dt: to_dauphin_dagster_type(
represented_pipeline.pipeline_snapshot, dt.key),
[
t for t in represented_pipeline.dagster_type_snaps
if t.name
],
)),
key=lambda dagster_type: dagster_type.name,
)
@capture_dauphin_error
def resolve_dagster_type_or_error(self, _, **kwargs):
type_name = kwargs['dagsterTypeName']
represented_pipeline = self.get_represented_pipeline()
if not represented_pipeline.has_dagster_type_named(type_name):
from .errors import DauphinDagsterTypeNotFoundError
raise UserFacingGraphQLError(
DauphinDagsterTypeNotFoundError(dagster_type_name=type_name))
return to_dauphin_dagster_type(
represented_pipeline.pipeline_snapshot,
represented_pipeline.get_dagster_type_by_name(type_name).key,
)
def resolve_solids(self, _graphene_info):
represented_pipeline = self.get_represented_pipeline()
return build_dauphin_solids(
represented_pipeline,
represented_pipeline.dep_structure_index,
)
def resolve_modes(self, _):
represented_pipeline = self.get_represented_pipeline()
return [
DauphinMode(represented_pipeline.config_schema_snapshot,
mode_def_snap)
for mode_def_snap in sorted(represented_pipeline.mode_def_snaps,
key=lambda item: item.name)
]
def resolve_solid_handle(self, _graphene_info, handleID):
return _get_solid_handles(
self.get_represented_pipeline()).get(handleID)
def resolve_solid_handles(self, _graphene_info, **kwargs):
handles = _get_solid_handles(self.get_represented_pipeline())
parentHandleID = kwargs.get('parentHandleID')
if parentHandleID == "":
handles = {
key: handle
for key, handle in handles.items() if not handle.parent
}
elif parentHandleID is not None:
handles = {
key: handle
for key, handle in handles.items() if handle.parent
and handle.parent.handleID.to_string() == parentHandleID
}
return [handles[key] for key in sorted(handles)]
def resolve_tags(self, graphene_info):
represented_pipeline = self.get_represented_pipeline()
return [
graphene_info.schema.type_named('PipelineTag')(key=key,
value=value) for
key, value in represented_pipeline.pipeline_snapshot.tags.items()
]
def resolve_solidSubset(self, _graphene_info):
return self.get_represented_pipeline().solid_subset
def resolve_runs(self, graphene_info, **kwargs):
runs_filter = PipelineRunsFilter(
pipeline_name=self.get_represented_pipeline().name)
return get_runs(graphene_info, runs_filter, kwargs.get('cursor'),
kwargs.get('limit'))
def resolve_schedules(self, graphene_info):
external_repository = graphene_info.context.legacy_external_repository
schedules = graphene_info.context.instance.all_schedules(
external_repository.name)
return [
graphene_info.schema.type_named('RunningSchedule')(
graphene_info, schedule=schedule) for schedule in schedules
if get_dagster_schedule_def(graphene_info, schedule.name).name ==
self.get_represented_pipeline().name
]
class DauphinJobState(dauphin.ObjectType):
class Meta:
name = "JobState"
id = dauphin.NonNull(dauphin.ID)
name = dauphin.NonNull(dauphin.String)
jobType = dauphin.NonNull("JobType")
status = dauphin.NonNull("JobStatus")
repositoryOrigin = dauphin.NonNull("RepositoryOrigin")
jobSpecificData = dauphin.Field("JobSpecificData")
runs = dauphin.Field(dauphin.non_null_list("PipelineRun"), limit=dauphin.Int())
runsCount = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(dauphin.non_null_list("JobTick"), limit=dauphin.Int())
runningCount = dauphin.NonNull(dauphin.Int) # remove with cron scheduler
def __init__(self, job_state):
self._job_state = check.inst_param(job_state, "job_state", JobState)
super(DauphinJobState, self).__init__(
id=job_state.job_origin_id,
name=job_state.name,
jobType=job_state.job_type,
status=job_state.status,
)
def resolve_repositoryOrigin(self, graphene_info):
origin = self._job_state.origin.external_repository_origin
return graphene_info.schema.type_named("RepositoryOrigin")(origin)
def resolve_jobSpecificData(self, graphene_info):
if not self._job_state.job_specific_data:
return None
if self._job_state.job_type == JobType.SENSOR:
return graphene_info.schema.type_named("SensorJobData")(
self._job_state.job_specific_data
)
if self._job_state.job_type == JobType.SCHEDULE:
return graphene_info.schema.type_named("ScheduleJobData")(
self._job_state.job_specific_data
)
return None
def resolve_runs(self, graphene_info, **kwargs):
if self._job_state.job_type == JobType.SENSOR:
filters = PipelineRunsFilter.for_sensor(self._job_state)
else:
filters = PipelineRunsFilter.for_schedule(self._job_state)
return [
graphene_info.schema.type_named("PipelineRun")(r)
for r in graphene_info.context.instance.get_runs(
filters=filters, limit=kwargs.get("limit"),
)
]
def resolve_runsCount(self, graphene_info):
if self._job_state.job_type == JobType.SENSOR:
filters = PipelineRunsFilter.for_sensor(self._job_state)
else:
filters = PipelineRunsFilter.for_schedule(self._job_state)
return graphene_info.context.instance.get_runs_count(filters=filters)
def resolve_ticks(self, graphene_info, limit=None):
ticks = graphene_info.context.instance.get_job_ticks(self._job_state.job_origin_id)
if limit:
ticks = ticks[:limit]
return [graphene_info.schema.type_named("JobTick")(graphene_info, tick) for tick in ticks]
def resolve_runningCount(self, graphene_info):
if self._job_state.job_type == JobType.SENSOR:
return 1 if self._job_state.status == JobStatus.RUNNING else 0
else:
return graphene_info.context.instance.running_schedule_count(
self._job_state.job_origin_id
)
class DauphinSensor(dauphin.ObjectType):
class Meta:
name = "Sensor"
id = dauphin.NonNull(dauphin.ID)
name = dauphin.NonNull(dauphin.String)
pipelineName = dauphin.NonNull(dauphin.String)
solidSelection = dauphin.List(dauphin.String)
mode = dauphin.NonNull(dauphin.String)
status = dauphin.NonNull("JobStatus")
runs = dauphin.Field(dauphin.non_null_list("PipelineRun"),
limit=dauphin.Int())
runsCount = dauphin.NonNull(dauphin.Int)
ticks = dauphin.Field(dauphin.non_null_list("JobTick"),
limit=dauphin.Int())
def resolve_id(self, _):
return "%s:%s" % (self.name, self.pipelineName)
def __init__(self, graphene_info, external_sensor):
self._external_sensor = check.inst_param(external_sensor,
"external_sensor",
ExternalSensor)
self._sensor_state = graphene_info.context.instance.get_job_state(
self._external_sensor.get_external_origin_id())
if not self._sensor_state:
# Also include a SensorState for a stopped sensor that may not
# have a stored database row yet
self._sensor_state = self._external_sensor.get_default_job_state()
super(DauphinSensor, self).__init__(
name=external_sensor.name,
pipelineName=external_sensor.pipeline_name,
solidSelection=external_sensor.solid_selection,
mode=external_sensor.mode,
)
def resolve_status(self, _graphene_info):
return self._sensor_state.status
def resolve_runs(self, graphene_info, **kwargs):
return [
graphene_info.schema.type_named("PipelineRun")(r)
for r in graphene_info.context.instance.get_runs(
filters=PipelineRunsFilter.for_sensor(self._external_sensor),
limit=kwargs.get("limit"),
)
]
def resolve_runsCount(self, graphene_info):
return graphene_info.context.instance.get_runs_count(
filters=PipelineRunsFilter.for_sensor(self._external_sensor))
def resolve_ticks(self, graphene_info, limit=None):
ticks = graphene_info.context.instance.get_job_ticks(
self._external_sensor.get_external_origin_id())
if limit:
ticks = ticks[:limit]
return [
graphene_info.schema.type_named("JobTick")(graphene_info, tick)
for tick in ticks
]
class DauphinQuery(dauphin.ObjectType):
class Meta(object):
name = 'Query'
version = dauphin.NonNull(dauphin.String)
reloadSupported = dauphin.NonNull(dauphin.Boolean)
pipelineOrError = dauphin.Field(
dauphin.NonNull('PipelineOrError'),
params=dauphin.NonNull('ExecutionSelector'))
pipeline = dauphin.Field(dauphin.NonNull('Pipeline'),
params=dauphin.NonNull('ExecutionSelector'))
pipelinesOrError = dauphin.NonNull('PipelinesOrError')
pipelines = dauphin.Field(dauphin.NonNull('PipelineConnection'))
runtimeTypeOrError = dauphin.Field(
dauphin.NonNull('RuntimeTypeOrError'),
pipelineName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
runtimeTypeName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
scheduler = dauphin.Field(dauphin.NonNull('SchedulerOrError'))
scheduleOrError = dauphin.Field(
dauphin.NonNull('ScheduleOrError'),
schedule_name=dauphin.NonNull(dauphin.String),
limit=dauphin.Int(),
)
partitionSetsOrError = dauphin.Field(
dauphin.NonNull('PartitionSetsOrError'), pipelineName=dauphin.String())
partitionSetOrError = dauphin.Field(dauphin.NonNull('PartitionSetOrError'),
partitionSetName=dauphin.String())
pipelineRunsOrError = dauphin.Field(
dauphin.NonNull('PipelineRunsOrError'),
filter=dauphin.Argument('PipelineRunsFilter'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
pipelineRunOrError = dauphin.Field(dauphin.NonNull('PipelineRunOrError'),
runId=dauphin.NonNull(dauphin.ID))
pipelineRunTags = dauphin.non_null_list('PipelineTagAndValues')
usedSolids = dauphin.Field(dauphin.non_null_list('UsedSolid'))
usedSolid = dauphin.Field('UsedSolid',
name=dauphin.NonNull(dauphin.String))
isPipelineConfigValid = dauphin.Field(
dauphin.NonNull('PipelineConfigValidationResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
executionPlan = dauphin.Field(
dauphin.NonNull('ExecutionPlanResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'environmentConfigData': dauphin.Argument('EnvironmentConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
environmentSchemaOrError = dauphin.Field(
dauphin.NonNull('EnvironmentSchemaOrError'),
args={
'selector': dauphin.Argument(dauphin.NonNull('ExecutionSelector')),
'mode': dauphin.Argument(dauphin.String),
},
description=
'''Fetch an environment schema given an execution selection and a mode.
See the descripton on EnvironmentSchema for more information.''',
)
instance = dauphin.NonNull('Instance')
def resolve_runtimeTypeOrError(self, graphene_info, **kwargs):
return get_dagster_type(graphene_info, kwargs['pipelineName'],
kwargs['runtimeTypeName'])
def resolve_version(self, graphene_info):
return graphene_info.context.version
def resolve_reloadSupported(self, graphene_info):
return graphene_info.context.reloader.is_reload_supported
def resolve_scheduler(self, graphene_info):
return get_scheduler_or_error(graphene_info)
def resolve_scheduleOrError(self, graphene_info, schedule_name):
return get_schedule_or_error(graphene_info, schedule_name)
def resolve_pipelineOrError(self, graphene_info, **kwargs):
return get_pipeline_or_error(graphene_info,
kwargs['params'].to_selector())
def resolve_pipeline(self, graphene_info, **kwargs):
return get_pipeline_or_raise(graphene_info,
kwargs['params'].to_selector())
def resolve_pipelinesOrError(self, graphene_info):
return get_pipelines_or_error(graphene_info)
def resolve_pipelines(self, graphene_info):
return get_pipelines_or_raise(graphene_info)
def resolve_pipelineRunsOrError(self, graphene_info, **kwargs):
filters = kwargs.get('filter')
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named('PipelineRuns')(
results=get_runs(graphene_info, filters, kwargs.get('cursor'),
kwargs.get('limit')))
def resolve_pipelineRunOrError(self, graphene_info, runId):
return get_run(graphene_info, runId)
def resolve_partitionSetsOrError(self, graphene_info, **kwargs):
pipeline_name = kwargs.get('pipelineName')
return get_partition_sets_or_error(graphene_info, pipeline_name)
def resolve_partitionSetOrError(self, graphene_info, partitionSetName):
return get_partition_set(graphene_info, partitionSetName)
def resolve_pipelineRunTags(self, graphene_info):
return get_run_tags(graphene_info)
def resolve_usedSolid(self, graphene_info, name):
repository = graphene_info.context.repository_definition
invocations = []
definition = None
for pipeline in repository.get_all_pipelines():
for handle in build_dauphin_solid_handles(
pipeline.get_pipeline_snapshot(), pipeline):
if handle.handleID.definition_name == name:
if definition is None:
definition = handle.solid.resolve_definition(
graphene_info)
invocations.append(
DauphinSolidInvocationSite(pipeline=pipeline,
solidHandle=handle))
return DauphinUsedSolid(definition=definition, invocations=invocations)
def resolve_usedSolids(self, graphene_info):
repository = graphene_info.context.repository_definition
inv_by_def_name = defaultdict(list)
definitions = []
for pipeline in repository.get_all_pipelines():
for handle in build_dauphin_solid_handles(
pipeline.get_pipeline_snapshot(), pipeline):
definition = handle.solid.resolve_definition(graphene_info)
if definition.name not in inv_by_def_name:
definitions.append(definition)
inv_by_def_name[definition.name].append(
DauphinSolidInvocationSite(pipeline=pipeline,
solidHandle=handle))
return map(
lambda d: DauphinUsedSolid(
definition=d,
invocations=sorted(inv_by_def_name[d.name],
key=lambda i: i.solidHandle.handleID),
),
sorted(definitions, key=lambda d: d.name),
)
def resolve_isPipelineConfigValid(self, graphene_info, pipeline, **kwargs):
return validate_pipeline_config(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_executionPlan(self, graphene_info, pipeline, **kwargs):
return get_execution_plan(
graphene_info,
pipeline.to_selector(),
kwargs.get('environmentConfigData'),
kwargs.get('mode'),
)
def resolve_environmentSchemaOrError(self, graphene_info, **kwargs):
return resolve_environment_schema_or_error(
graphene_info, kwargs['selector'].to_selector(),
kwargs.get('mode'))
def resolve_instance(self, graphene_info):
return graphene_info.schema.type_named('Instance')(
graphene_info.context.instance)
class DauphinQuery(dauphin.ObjectType):
class Meta(object):
name = "Query"
version = dauphin.NonNull(dauphin.String)
repositoriesOrError = dauphin.NonNull("RepositoriesOrError")
repositoryOrError = dauphin.Field(
dauphin.NonNull("RepositoryOrError"),
repositorySelector=dauphin.NonNull("RepositorySelector"),
)
pipelineOrError = dauphin.Field(dauphin.NonNull("PipelineOrError"),
params=dauphin.NonNull("PipelineSelector"))
pipelineSnapshotOrError = dauphin.Field(
dauphin.NonNull("PipelineSnapshotOrError"),
snapshotId=dauphin.String(),
activePipelineSelector=dauphin.Argument("PipelineSelector"),
)
scheduler = dauphin.Field(dauphin.NonNull("SchedulerOrError"))
scheduleDefinitionOrError = dauphin.Field(
dauphin.NonNull("ScheduleDefinitionOrError"),
schedule_selector=dauphin.NonNull("ScheduleSelector"),
)
scheduleDefinitionsOrError = dauphin.Field(
dauphin.NonNull("ScheduleDefinitionsOrError"),
repositorySelector=dauphin.NonNull("RepositorySelector"),
)
scheduleStatesOrError = dauphin.Field(
dauphin.NonNull("ScheduleStatesOrError"),
repositorySelector=dauphin.Argument("RepositorySelector"),
withNoScheduleDefinition=dauphin.Boolean(),
)
partitionSetsOrError = dauphin.Field(
dauphin.NonNull("PartitionSetsOrError"),
repositorySelector=dauphin.NonNull("RepositorySelector"),
pipelineName=dauphin.NonNull(dauphin.String),
)
partitionSetOrError = dauphin.Field(
dauphin.NonNull("PartitionSetOrError"),
repositorySelector=dauphin.NonNull("RepositorySelector"),
partitionSetName=dauphin.String(),
)
pipelineRunsOrError = dauphin.Field(
dauphin.NonNull("PipelineRunsOrError"),
filter=dauphin.Argument("PipelineRunsFilter"),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
pipelineRunOrError = dauphin.Field(dauphin.NonNull("PipelineRunOrError"),
runId=dauphin.NonNull(dauphin.ID))
pipelineRunTags = dauphin.non_null_list("PipelineTagAndValues")
runGroupOrError = dauphin.Field(dauphin.NonNull("RunGroupOrError"),
runId=dauphin.NonNull(dauphin.ID))
runGroupsOrError = dauphin.Field(
dauphin.NonNull("RunGroupsOrError"),
filter=dauphin.Argument("PipelineRunsFilter"),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
isPipelineConfigValid = dauphin.Field(
dauphin.NonNull("PipelineConfigValidationResult"),
args={
"pipeline": dauphin.Argument(dauphin.NonNull("PipelineSelector")),
"runConfigData": dauphin.Argument("RunConfigData"),
"mode": dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
executionPlanOrError = dauphin.Field(
dauphin.NonNull("ExecutionPlanOrError"),
args={
"pipeline": dauphin.Argument(dauphin.NonNull("PipelineSelector")),
"runConfigData": dauphin.Argument("RunConfigData"),
"mode": dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
runConfigSchemaOrError = dauphin.Field(
dauphin.NonNull("RunConfigSchemaOrError"),
args={
"selector": dauphin.Argument(dauphin.NonNull("PipelineSelector")),
"mode": dauphin.Argument(dauphin.String),
},
description=
"""Fetch an environment schema given an execution selection and a mode.
See the descripton on RunConfigSchema for more information.""",
)
instance = dauphin.NonNull("Instance")
assetsOrError = dauphin.Field(dauphin.NonNull("AssetsOrError"))
assetOrError = dauphin.Field(
dauphin.NonNull("AssetOrError"),
assetKey=dauphin.Argument(dauphin.NonNull("AssetKeyInput")),
)
def resolve_repositoriesOrError(self, graphene_info):
return fetch_repositories(graphene_info)
def resolve_repositoryOrError(self, graphene_info, **kwargs):
return fetch_repository(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get("repositorySelector")),
)
def resolve_pipelineSnapshotOrError(self, graphene_info, **kwargs):
snapshot_id_arg = kwargs.get("snapshotId")
pipeline_selector_arg = kwargs.get("activePipelineSelector")
check.invariant(
not (snapshot_id_arg and pipeline_selector_arg),
"Must only pass one of snapshotId or activePipelineSelector",
)
check.invariant(
snapshot_id_arg or pipeline_selector_arg,
"Must set one of snapshotId or activePipelineSelector",
)
if pipeline_selector_arg:
pipeline_selector = pipeline_selector_from_graphql(
graphene_info.context, kwargs["activePipelineSelector"])
return get_pipeline_snapshot_or_error_from_pipeline_selector(
graphene_info, pipeline_selector)
else:
return get_pipeline_snapshot_or_error_from_snapshot_id(
graphene_info, snapshot_id_arg)
def resolve_version(self, graphene_info):
return graphene_info.context.version
def resolve_scheduler(self, graphene_info):
return get_scheduler_or_error(graphene_info)
def resolve_scheduleDefinitionOrError(self, graphene_info,
schedule_selector):
return get_schedule_definition_or_error(
graphene_info,
ScheduleSelector.from_graphql_input(schedule_selector))
def resolve_scheduleDefinitionsOrError(self, graphene_info, **kwargs):
return get_schedule_definitions_or_error(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get("repositorySelector")))
def resolve_scheduleStatesOrError(self, graphene_info, **kwargs):
return get_schedule_states_or_error(
graphene_info,
RepositorySelector.from_graphql_input(kwargs["repositorySelector"])
if kwargs.get("repositorySelector") else None,
kwargs.get("withNoScheduleDefinition"),
)
def resolve_pipelineOrError(self, graphene_info, **kwargs):
return get_pipeline_or_error(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context,
kwargs["params"]),
)
def resolve_pipelineRunsOrError(self, graphene_info, **kwargs):
filters = kwargs.get("filter")
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named("PipelineRuns")(
results=get_runs(graphene_info, filters, kwargs.get("cursor"),
kwargs.get("limit")))
def resolve_pipelineRunOrError(self, graphene_info, runId):
return get_run_by_id(graphene_info, runId)
def resolve_runGroupsOrError(self, graphene_info, **kwargs):
filters = kwargs.get("filter")
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named("RunGroupsOrError")(
results=get_run_groups(graphene_info, filters, kwargs.get(
"cursor"), kwargs.get("limit")))
def resolve_partitionSetsOrError(self, graphene_info, **kwargs):
return get_partition_sets_or_error(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get("repositorySelector")),
kwargs.get("pipelineName"),
)
def resolve_partitionSetOrError(self, graphene_info, **kwargs):
return get_partition_set(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get("repositorySelector")),
kwargs.get("partitionSetName"),
)
def resolve_pipelineRunTags(self, graphene_info):
return get_run_tags(graphene_info)
def resolve_runGroupOrError(self, graphene_info, runId):
return get_run_group(graphene_info, runId)
def resolve_isPipelineConfigValid(self, graphene_info, pipeline, **kwargs):
return validate_pipeline_config(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context, pipeline),
kwargs.get("runConfigData"),
kwargs.get("mode"),
)
def resolve_executionPlanOrError(self, graphene_info, pipeline, **kwargs):
return get_execution_plan(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context, pipeline),
kwargs.get("runConfigData"),
kwargs.get("mode"),
)
def resolve_runConfigSchemaOrError(self, graphene_info, **kwargs):
return resolve_run_config_schema_or_error(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context,
kwargs["selector"]),
kwargs.get("mode"),
)
def resolve_instance(self, graphene_info):
return graphene_info.schema.type_named("Instance")(
graphene_info.context.instance)
def resolve_assetsOrError(self, graphene_info):
return get_assets(graphene_info)
def resolve_assetOrError(self, graphene_info, **kwargs):
return get_asset(graphene_info,
AssetKey.from_graphql_input(kwargs["assetKey"]))
class DauphinPartitionSet(dauphin.ObjectType):
class Meta(object):
name = 'PartitionSet'
name = dauphin.NonNull(dauphin.String)
pipeline_name = dauphin.NonNull(dauphin.String)
solid_selection = dauphin.List(dauphin.NonNull(dauphin.String))
mode = dauphin.NonNull(dauphin.String)
partitions = dauphin.Field(
dauphin.NonNull('Partitions'),
cursor=dauphin.String(),
limit=dauphin.Int(),
reverse=dauphin.Boolean(),
)
def __init__(self, partition_set_def, external_partition_set):
self._partition_set_def = check.inst_param(partition_set_def,
'partition_set_def',
PartitionSetDefinition)
self._external_partition_set = check.inst_param(
external_partition_set, 'external_partition_set',
ExternalPartitionSet)
super(DauphinPartitionSet, self).__init__(
name=external_partition_set.name,
pipeline_name=external_partition_set.pipeline_name,
solid_selection=external_partition_set.solid_selection,
mode=external_partition_set.mode,
)
def resolve_partitions(self, graphene_info, **kwargs):
partition_names = self._external_partition_set.partition_names
cursor = kwargs.get("cursor")
limit = kwargs.get("limit")
reverse = kwargs.get('reverse')
start = 0
end = len(partition_names)
index = 0
if cursor:
index = next(
(idx for (idx, partition_name) in enumerate(partition_names)
if partition_name == cursor),
None,
)
if reverse:
end = index
else:
start = index + 1
if limit:
if reverse:
start = end - limit
else:
end = start + limit
partition_names = partition_names[start:end]
return graphene_info.schema.type_named('Partitions')(results=[
graphene_info.schema.type_named('Partition')(
partition_name=partition_name,
partition_set_def=self._partition_set_def,
external_partition_set=self._external_partition_set,
) for partition_name in partition_names
])
class DauphinPartition(dauphin.ObjectType):
class Meta:
name = "Partition"
name = dauphin.NonNull(dauphin.String)
partition_set_name = dauphin.NonNull(dauphin.String)
solid_selection = dauphin.List(dauphin.NonNull(dauphin.String))
mode = dauphin.NonNull(dauphin.String)
runConfigOrError = dauphin.NonNull("PartitionRunConfigOrError")
tagsOrError = dauphin.NonNull("PartitionTagsOrError")
runs = dauphin.Field(
dauphin.non_null_list("PipelineRun"),
filter=dauphin.Argument("PipelineRunsFilter"),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
status = dauphin.Field("PipelineRunStatus")
def __init__(self, external_repository_handle, external_partition_set,
partition_name):
self._external_repository_handle = check.inst_param(
external_repository_handle, "external_respository_handle",
RepositoryHandle)
self._external_partition_set = check.inst_param(
external_partition_set, "external_partition_set",
ExternalPartitionSet)
self._partition_name = check.str_param(partition_name,
"partition_name")
super(DauphinPartition, self).__init__(
name=partition_name,
partition_set_name=external_partition_set.name,
solid_selection=external_partition_set.solid_selection,
mode=external_partition_set.mode,
)
def resolve_runConfigOrError(self, graphene_info):
return get_partition_config(
graphene_info,
self._external_repository_handle,
self._external_partition_set.name,
self._partition_name,
)
def resolve_tagsOrError(self, graphene_info):
return get_partition_tags(
graphene_info,
self._external_repository_handle,
self._external_partition_set.name,
self._partition_name,
)
def resolve_runs(self, graphene_info, **kwargs):
filters = kwargs.get("filter")
partition_tags = {
PARTITION_SET_TAG: self._external_partition_set.name,
PARTITION_NAME_TAG: self._partition_name,
}
if filters is not None:
filters = filters.to_selector()
runs_filter = PipelineRunsFilter(
run_ids=filters.run_ids,
pipeline_name=filters.pipeline_name,
statuses=filters.statuses,
tags=merge_dicts(filters.tags, partition_tags),
)
else:
runs_filter = PipelineRunsFilter(tags=partition_tags)
return get_runs(graphene_info,
runs_filter,
cursor=kwargs.get("cursor"),
limit=kwargs.get("limit"))
class DauphinIPipelineSnapshotMixin:
# Mixin this class to implement IPipelineSnapshot
#
# Graphene has some strange properties that make it so that you cannot
# implement ABCs nor use properties in an overridable way. So the way
# the mixin works is that the target classes have to have a method
# get_represented_pipeline()
#
def get_represented_pipeline(self):
raise NotImplementedError()
name = dauphin.NonNull(dauphin.String)
description = dauphin.String()
id = dauphin.NonNull(dauphin.ID)
pipeline_snapshot_id = dauphin.NonNull(dauphin.String)
dagster_types = dauphin.non_null_list("DagsterType")
dagster_type_or_error = dauphin.Field(
dauphin.NonNull("DagsterTypeOrError"),
dagsterTypeName=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
solids = dauphin.non_null_list("Solid")
modes = dauphin.non_null_list("Mode")
solid_handles = dauphin.Field(dauphin.non_null_list("SolidHandle"),
parentHandleID=dauphin.String())
solid_handle = dauphin.Field(
"SolidHandle",
handleID=dauphin.Argument(dauphin.NonNull(dauphin.String)),
)
tags = dauphin.non_null_list("PipelineTag")
runs = dauphin.Field(
dauphin.non_null_list("PipelineRun"),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
schedules = dauphin.non_null_list("ScheduleDefinition")
parent_snapshot_id = dauphin.String()
def resolve_pipeline_snapshot_id(self, _):
return self.get_represented_pipeline().identifying_pipeline_snapshot_id
def resolve_id(self, _):
return self.get_represented_pipeline().identifying_pipeline_snapshot_id
def resolve_name(self, _):
return self.get_represented_pipeline().name
def resolve_description(self, _):
return self.get_represented_pipeline().description
def resolve_dagster_types(self, _graphene_info):
represented_pipeline = self.get_represented_pipeline()
return sorted(
list(
map(
lambda dt: to_dauphin_dagster_type(
represented_pipeline.pipeline_snapshot, dt.key),
[
t for t in represented_pipeline.dagster_type_snaps
if t.name
],
)),
key=lambda dagster_type: dagster_type.name,
)
@capture_dauphin_error
def resolve_dagster_type_or_error(self, _, **kwargs):
type_name = kwargs["dagsterTypeName"]
represented_pipeline = self.get_represented_pipeline()
if not represented_pipeline.has_dagster_type_named(type_name):
from .errors import DauphinDagsterTypeNotFoundError
raise UserFacingGraphQLError(
DauphinDagsterTypeNotFoundError(dagster_type_name=type_name))
return to_dauphin_dagster_type(
represented_pipeline.pipeline_snapshot,
represented_pipeline.get_dagster_type_by_name(type_name).key,
)
def resolve_solids(self, _graphene_info):
represented_pipeline = self.get_represented_pipeline()
return build_dauphin_solids(
represented_pipeline,
represented_pipeline.dep_structure_index,
)
def resolve_modes(self, _):
represented_pipeline = self.get_represented_pipeline()
return [
DauphinMode(represented_pipeline.config_schema_snapshot,
mode_def_snap)
for mode_def_snap in sorted(represented_pipeline.mode_def_snaps,
key=lambda item: item.name)
]
def resolve_solid_handle(self, _graphene_info, handleID):
return _get_solid_handles(
self.get_represented_pipeline()).get(handleID)
def resolve_solid_handles(self, _graphene_info, **kwargs):
handles = _get_solid_handles(self.get_represented_pipeline())
parentHandleID = kwargs.get("parentHandleID")
if parentHandleID == "":
handles = {
key: handle
for key, handle in handles.items() if not handle.parent
}
elif parentHandleID is not None:
handles = {
key: handle
for key, handle in handles.items() if handle.parent
and handle.parent.handleID.to_string() == parentHandleID
}
return [handles[key] for key in sorted(handles)]
def resolve_tags(self, graphene_info):
represented_pipeline = self.get_represented_pipeline()
return [
graphene_info.schema.type_named("PipelineTag")(key=key,
value=value) for
key, value in represented_pipeline.pipeline_snapshot.tags.items()
]
def resolve_solidSelection(self, _graphene_info):
return self.get_represented_pipeline().solid_selection
def resolve_runs(self, graphene_info, **kwargs):
runs_filter = PipelineRunsFilter(
pipeline_name=self.get_represented_pipeline().name)
return get_runs(graphene_info, runs_filter, kwargs.get("cursor"),
kwargs.get("limit"))
def resolve_schedules(self, graphene_info):
represented_pipeline = self.get_represented_pipeline()
if not isinstance(represented_pipeline, ExternalPipeline):
# this is an historical pipeline snapshot, so there are not any associated running
# schedules
return []
pipeline_selector = represented_pipeline.handle.to_selector()
schedules = get_schedule_definitions_for_pipeline(
graphene_info, pipeline_selector)
return schedules
def resolve_parent_snapshot_id(self, _graphene_info):
lineage_snapshot = self.get_represented_pipeline(
).pipeline_snapshot.lineage_snapshot
if lineage_snapshot:
return lineage_snapshot.parent_snapshot_id
else:
return None
class DauphinRetriesPreviousAttempts(dauphin.InputObjectType):
class Meta(object):
name = 'RetriesPreviousAttempts'
key = dauphin.String()
count = dauphin.Int()
class DauphinSchedule(dauphin.ObjectType):
class Meta:
name = "Schedule"
id = dauphin.NonNull(dauphin.ID)
name = dauphin.NonNull(dauphin.String)
cron_schedule = dauphin.NonNull(dauphin.String)
pipeline_name = dauphin.NonNull(dauphin.String)
solid_selection = dauphin.List(dauphin.String)
mode = dauphin.NonNull(dauphin.String)
execution_timezone = dauphin.Field(dauphin.String)
scheduleState = dauphin.NonNull("JobState")
partition_set = dauphin.Field("PartitionSet")
futureTicks = dauphin.NonNull("FutureJobTicks",
cursor=dauphin.Float(),
limit=dauphin.Int())
futureTick = dauphin.NonNull("FutureJobTick",
tick_timestamp=dauphin.NonNull(dauphin.Int))
def resolve_id(self, _):
return "%s:%s" % (self.name, self.pipeline_name)
def resolve_partition_set(self, graphene_info):
if self._external_schedule.partition_set_name is None:
return None
repository = graphene_info.context.get_repository_location(
self._external_schedule.handle.location_name).get_repository(
self._external_schedule.handle.repository_name)
external_partition_set = repository.get_external_partition_set(
self._external_schedule.partition_set_name)
return graphene_info.schema.type_named("PartitionSet")(
external_repository_handle=repository.handle,
external_partition_set=external_partition_set,
)
def resolve_futureTicks(self, graphene_info, **kwargs):
cursor = kwargs.get(
"cursor",
get_timestamp_from_utc_datetime(get_current_datetime_in_utc()))
limit = kwargs.get("limit", 10)
tick_times = []
time_iter = self._external_schedule.execution_time_iterator(cursor)
for _ in range(limit):
tick_times.append(next(time_iter).timestamp())
future_ticks = [
graphene_info.schema.type_named("FutureJobTick")(
self._schedule_state, tick_time) for tick_time in tick_times
]
return graphene_info.schema.type_named("FutureJobTicks")(
results=future_ticks, cursor=tick_times[-1] + 1)
def resolve_futureTick(self, graphene_info, tick_timestamp):
return graphene_info.schema.type_named("FutureJobTick")(
self._schedule_state, float(tick_timestamp))
def __init__(self, graphene_info, external_schedule):
self._external_schedule = check.inst_param(external_schedule,
"external_schedule",
ExternalSchedule)
self._schedule_state = graphene_info.context.instance.get_job_state(
self._external_schedule.get_external_origin_id())
if not self._schedule_state:
# Also include a ScheduleState for a stopped schedule that may not
# have a stored database row yet
self._schedule_state = self._external_schedule.get_default_job_state(
graphene_info.context.instance)
super(DauphinSchedule, self).__init__(
name=external_schedule.name,
cron_schedule=external_schedule.cron_schedule,
pipeline_name=external_schedule.pipeline_name,
solid_selection=external_schedule.solid_selection,
mode=external_schedule.mode,
scheduleState=graphene_info.schema.type_named("JobState")(
self._schedule_state),
execution_timezone=(self._external_schedule.execution_timezone
if self._external_schedule.execution_timezone
else pendulum.now().timezone.name),
)
class DauphinQuery(dauphin.ObjectType):
class Meta(object):
name = 'Query'
version = dauphin.NonNull(dauphin.String)
repositoriesOrError = dauphin.NonNull('RepositoriesOrError')
repositoryOrError = dauphin.Field(
dauphin.NonNull('RepositoryOrError'),
repositorySelector=dauphin.NonNull('RepositorySelector'),
)
pipelineOrError = dauphin.Field(dauphin.NonNull('PipelineOrError'),
params=dauphin.NonNull('PipelineSelector'))
pipelineSnapshotOrError = dauphin.Field(
dauphin.NonNull('PipelineSnapshotOrError'),
snapshotId=dauphin.String(),
activePipelineSelector=dauphin.Argument('PipelineSelector'),
)
scheduler = dauphin.Field(dauphin.NonNull('SchedulerOrError'))
scheduleDefinitionOrError = dauphin.Field(
dauphin.NonNull('ScheduleDefinitionOrError'),
schedule_selector=dauphin.NonNull('ScheduleSelector'),
)
scheduleDefinitionsOrError = dauphin.Field(
dauphin.NonNull('ScheduleDefinitionsOrError'),
repositorySelector=dauphin.NonNull('RepositorySelector'),
)
scheduleStatesOrError = dauphin.Field(
dauphin.NonNull('ScheduleStatesOrError'),
repositorySelector=dauphin.NonNull('RepositorySelector'),
withNoScheduleDefinition=dauphin.Boolean(),
)
partitionSetsOrError = dauphin.Field(
dauphin.NonNull('PartitionSetsOrError'),
repositorySelector=dauphin.NonNull('RepositorySelector'),
pipelineName=dauphin.NonNull(dauphin.String),
)
partitionSetOrError = dauphin.Field(
dauphin.NonNull('PartitionSetOrError'),
repositorySelector=dauphin.NonNull('RepositorySelector'),
partitionSetName=dauphin.String(),
)
pipelineRunsOrError = dauphin.Field(
dauphin.NonNull('PipelineRunsOrError'),
filter=dauphin.Argument('PipelineRunsFilter'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
pipelineRunOrError = dauphin.Field(dauphin.NonNull('PipelineRunOrError'),
runId=dauphin.NonNull(dauphin.ID))
pipelineRunTags = dauphin.non_null_list('PipelineTagAndValues')
runGroupOrError = dauphin.Field(dauphin.NonNull('RunGroupOrError'),
runId=dauphin.NonNull(dauphin.ID))
runGroupsOrError = dauphin.Field(
dauphin.NonNull('RunGroupsOrError'),
filter=dauphin.Argument('PipelineRunsFilter'),
cursor=dauphin.String(),
limit=dauphin.Int(),
)
isPipelineConfigValid = dauphin.Field(
dauphin.NonNull('PipelineConfigValidationResult'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('PipelineSelector')),
'runConfigData': dauphin.Argument('RunConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
executionPlanOrError = dauphin.Field(
dauphin.NonNull('ExecutionPlanOrError'),
args={
'pipeline': dauphin.Argument(dauphin.NonNull('PipelineSelector')),
'runConfigData': dauphin.Argument('RunConfigData'),
'mode': dauphin.Argument(dauphin.NonNull(dauphin.String)),
},
)
runConfigSchemaOrError = dauphin.Field(
dauphin.NonNull('RunConfigSchemaOrError'),
args={
'selector': dauphin.Argument(dauphin.NonNull('PipelineSelector')),
'mode': dauphin.Argument(dauphin.String),
},
description=
'''Fetch an environment schema given an execution selection and a mode.
See the descripton on RunConfigSchema for more information.''',
)
instance = dauphin.NonNull('Instance')
assetsOrError = dauphin.Field(dauphin.NonNull('AssetsOrError'))
assetOrError = dauphin.Field(
dauphin.NonNull('AssetOrError'),
assetKey=dauphin.Argument(dauphin.NonNull('AssetKeyInput')),
)
def resolve_repositoriesOrError(self, graphene_info):
return fetch_repositories(graphene_info)
def resolve_repositoryOrError(self, graphene_info, **kwargs):
return fetch_repository(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get('repositorySelector')),
)
def resolve_pipelineSnapshotOrError(self, graphene_info, **kwargs):
snapshot_id_arg = kwargs.get('snapshotId')
pipeline_selector_arg = kwargs.get('activePipelineSelector')
check.invariant(
not (snapshot_id_arg and pipeline_selector_arg),
'Must only pass one of snapshotId or activePipelineSelector',
)
check.invariant(
snapshot_id_arg or pipeline_selector_arg,
'Must set one of snapshotId or activePipelineSelector',
)
if pipeline_selector_arg:
pipeline_selector = pipeline_selector_from_graphql(
graphene_info.context, kwargs['activePipelineSelector'])
return get_pipeline_snapshot_or_error_from_pipeline_selector(
graphene_info, pipeline_selector)
else:
return get_pipeline_snapshot_or_error_from_snapshot_id(
graphene_info, snapshot_id_arg)
def resolve_version(self, graphene_info):
return graphene_info.context.version
def resolve_scheduler(self, graphene_info):
return get_scheduler_or_error(graphene_info)
def resolve_scheduleDefinitionOrError(self, graphene_info,
schedule_selector):
return get_schedule_definition_or_error(
graphene_info,
ScheduleSelector.from_graphql_input(schedule_selector))
def resolve_scheduleDefinitionsOrError(self, graphene_info, **kwargs):
return get_schedule_definitions_or_error(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get('repositorySelector')))
def resolve_scheduleStatesOrError(self, graphene_info, **kwargs):
return get_schedule_states_or_error(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get('repositorySelector')),
kwargs.get('withNoScheduleDefinition'),
)
def resolve_pipelineOrError(self, graphene_info, **kwargs):
return get_pipeline_or_error(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context,
kwargs['params']),
)
def resolve_pipelineRunsOrError(self, graphene_info, **kwargs):
filters = kwargs.get('filter')
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named('PipelineRuns')(
results=get_runs(graphene_info, filters, kwargs.get('cursor'),
kwargs.get('limit')))
def resolve_pipelineRunOrError(self, graphene_info, runId):
return get_run_by_id(graphene_info, runId)
def resolve_runGroupsOrError(self, graphene_info, **kwargs):
filters = kwargs.get('filter')
if filters is not None:
filters = filters.to_selector()
return graphene_info.schema.type_named('RunGroupsOrError')(
results=get_run_groups(graphene_info, filters, kwargs.get(
'cursor'), kwargs.get('limit')))
def resolve_partitionSetsOrError(self, graphene_info, **kwargs):
return get_partition_sets_or_error(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get('repositorySelector')),
kwargs.get('pipelineName'),
)
def resolve_partitionSetOrError(self, graphene_info, **kwargs):
return get_partition_set(
graphene_info,
RepositorySelector.from_graphql_input(
kwargs.get('repositorySelector')),
kwargs.get('partitionSetName'),
)
def resolve_pipelineRunTags(self, graphene_info):
return get_run_tags(graphene_info)
def resolve_runGroupOrError(self, graphene_info, runId):
return get_run_group(graphene_info, runId)
def resolve_isPipelineConfigValid(self, graphene_info, pipeline, **kwargs):
return validate_pipeline_config(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context, pipeline),
kwargs.get('runConfigData'),
kwargs.get('mode'),
)
def resolve_executionPlanOrError(self, graphene_info, pipeline, **kwargs):
return get_execution_plan(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context, pipeline),
kwargs.get('runConfigData'),
kwargs.get('mode'),
)
def resolve_runConfigSchemaOrError(self, graphene_info, **kwargs):
return resolve_run_config_schema_or_error(
graphene_info,
pipeline_selector_from_graphql(graphene_info.context,
kwargs['selector']),
kwargs.get('mode'),
)
def resolve_instance(self, graphene_info):
return graphene_info.schema.type_named('Instance')(
graphene_info.context.instance)
def resolve_assetsOrError(self, graphene_info):
return get_assets(graphene_info)
def resolve_assetOrError(self, graphene_info, **kwargs):
return get_asset(graphene_info,
AssetKey.from_graphql_input(kwargs['assetKey']))
