def resolve_runs(self, graphene_info):
runs_filter = PipelineRunsFilter(
tags={
'dagster/partition_set': self._external_partition_set.name,
'dagster/partition': self._partition_name,
})
return get_runs(graphene_info, runs_filter)
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_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_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_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 = RunsFilter(
run_ids=filters.run_ids,
pipeline_name=filters.job_name,
statuses=filters.statuses,
tags=merge_dicts(filters.tags, partition_tags),
)
else:
runs_filter = RunsFilter(tags=partition_tags)
return get_runs(
graphene_info, runs_filter, cursor=kwargs.get("cursor"), limit=kwargs.get("limit")
)
def resolve_runs(self, graphene_info, **kwargs):
filters = kwargs.get("filter")
partition_tags = {
"dagster/partition_set": self._external_partition_set.name,
"dagster/partition": 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,
status=filters.status,
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"))
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_pipelineRuns(self, graphene_info):
return get_runs(graphene_info)
