OptimizerRun

class openapi_client.models.optimizer_run.OptimizerRun(**data)

Bases: BaseModel

Information about an optimizer run

event_type: Optional[StrictStr]

start_timestamp: Optional[datetime]

end_timestamp: Optional[datetime]

compaction_metrics: Optional[Dict[str, Any]]

retention_metrics: Optional[Dict[str, Any]]

orphan_file_deletion_metrics: Optional[Dict[str, Any]]

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of OptimizerRun from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of OptimizerRun from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'compaction_metrics': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, alias='CompactionMetrics', alias_priority=2, description='Metrics for compaction optimizer runs'), 'end_timestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, alias='EndTimestamp', alias_priority=2, description='End timestamp of the run'), 'event_type': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='EventType', alias_priority=2, description='Type of event for the run'), 'orphan_file_deletion_metrics': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, alias='OrphanFileDeletionMetrics', alias_priority=2, description='Metrics for orphan file deletion optimizer runs'), 'retention_metrics': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, alias='RetentionMetrics', alias_priority=2, description='Metrics for retention optimizer runs'), 'start_timestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, alias='StartTimestamp', alias_priority=2, description='Start timestamp of the run')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_post_init(__context)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• __context (Any) – The context.

Return type:

None