def test_check_valid_name():
assert check_valid_name("a") == "a"
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name("has a space")
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name("")
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name("context")
def test_check_valid_name():
assert check_valid_name('a') == 'a'
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name('has a space')
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name('')
with pytest.raises(DagsterInvalidDefinitionError):
assert check_valid_name('context')
def to_job(
self,
name: Optional[str] = None,
description: Optional[str] = None,
resource_defs: Optional[Dict[str, ResourceDefinition]] = None,
config: Union[ConfigMapping, Dict[str, Any],
"PartitionedConfig"] = None,
tags: Optional[Dict[str, Any]] = None,
logger_defs: Optional[Dict[str, LoggerDefinition]] = None,
executor_def: Optional["ExecutorDefinition"] = None,
hooks: Optional[AbstractSet[HookDefinition]] = None,
op_retry_policy: Optional[RetryPolicy] = None,
version_strategy: Optional[VersionStrategy] = None,
op_selection: Optional[List[str]] = None,
partitions_def: Optional["PartitionsDefinition"] = None,
) -> "JobDefinition":
"""
Make this graph in to an executable Job by providing remaining components required for execution.
Args:
name (Optional[str]):
The name for the Job. Defaults to the name of the this graph.
resource_defs (Optional[Dict[str, ResourceDefinition]]):
Resources that are required by this graph for execution.
If not defined, `io_manager` will default to filesystem.
config:
Describes how the job is parameterized at runtime.
If no value is provided, then the schema for the job's run config is a standard
format based on its solids and resources.
If a dictionary is provided, then it must conform to the standard config schema, and
it will be used as the job's run config for the job whenever the job is executed.
The values provided will be viewable and editable in the Dagit playground, so be
careful with secrets.
If a :py:class:`ConfigMapping` object is provided, then the schema for the job's run config is
determined by the config mapping, and the ConfigMapping, which should return
configuration in the standard format to configure the job.
If a :py:class:`PartitionedConfig` object is provided, then it defines a discrete set of config
values that can parameterize the job, as well as a function for mapping those
values to the base config. The values provided will be viewable and editable in the
Dagit playground, so be careful with secrets.
tags (Optional[Dict[str, Any]]):
Arbitrary metadata for any execution of the Job.
Values that are not strings will be json encoded and must meet the criteria that
`json.loads(json.dumps(value)) == value`. These tag values may be overwritten by tag
values provided at invocation time.
logger_defs (Optional[Dict[str, LoggerDefinition]]):
A dictionary of string logger identifiers to their implementations.
executor_def (Optional[ExecutorDefinition]):
How this Job will be executed. Defaults to :py:class:`multi_or_in_process_executor`,
which can be switched between multi-process and in-process modes of execution. The
default mode of execution is multi-process.
op_retry_policy (Optional[RetryPolicy]): The default retry policy for all ops in this job.
Only used if retry policy is not defined on the op definition or op invocation.
version_strategy (Optional[VersionStrategy]):
Defines how each solid (and optionally, resource) in the job can be versioned. If
provided, memoizaton will be enabled for this job.
partitions_def (Optional[PartitionsDefinition]): Defines a discrete set of partition
keys that can parameterize the job. If this argument is supplied, the config
argument can't also be supplied.
Returns:
JobDefinition
"""
from .job_definition import JobDefinition
from .partition import PartitionedConfig, PartitionsDefinition
from .executor_definition import ExecutorDefinition, multi_or_in_process_executor
job_name = check_valid_name(name or self.name)
tags = check.opt_dict_param(tags, "tags", key_type=str)
executor_def = check.opt_inst_param(
executor_def,
"executor_def",
ExecutorDefinition,
default=multi_or_in_process_executor)
if resource_defs and "io_manager" in resource_defs:
resource_defs_with_defaults = resource_defs
else:
resource_defs_with_defaults = merge_dicts(
{"io_manager": default_job_io_manager}, resource_defs or {})
hooks = check.opt_set_param(hooks, "hooks", of_type=HookDefinition)
op_retry_policy = check.opt_inst_param(op_retry_policy,
"op_retry_policy", RetryPolicy)
op_selection = check.opt_list_param(op_selection,
"op_selection",
of_type=str)
presets = []
config_mapping = None
partitioned_config = None
if partitions_def:
check.inst_param(partitions_def, "partitions_def",
PartitionsDefinition)
check.invariant(
config is None,
"Can't supply both the 'config' and 'partitions_def' arguments"
)
partitioned_config = PartitionedConfig(partitions_def,
lambda _: {})
if isinstance(config, ConfigMapping):
config_mapping = config
elif isinstance(config, PartitionedConfig):
partitioned_config = config
elif isinstance(config, dict):
presets = [PresetDefinition(name="default", run_config=config)]
# Using config mapping here is a trick to make it so that the preset will be used even
# when no config is supplied for the job.
config_mapping = _config_mapping_with_default_value(
self._get_config_schema(resource_defs_with_defaults,
executor_def, logger_defs),
config,
job_name,
self.name,
)
elif config is not None:
check.failed(
f"config param must be a ConfigMapping, a PartitionedConfig, or a dictionary, but "
f"is an object of type {type(config)}")
return JobDefinition(
name=job_name,
description=description or self.description,
graph_def=self,
mode_def=ModeDefinition(
resource_defs=resource_defs_with_defaults,
logger_defs=logger_defs,
executor_defs=[executor_def],
_config_mapping=config_mapping,
_partitioned_config=partitioned_config,
),
preset_defs=presets,
tags=tags,
hook_defs=hooks,
version_strategy=version_strategy,
op_retry_policy=op_retry_policy,
).get_job_def_for_op_selection(op_selection)