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)