mirror of
https://github.com/thousandeyes/thousandeyes-sdk-python.git
synced 2026-03-22 17:35:30 +00:00
100 lines
4.7 KiB
Python
100 lines
4.7 KiB
Python
# coding: utf-8
|
|
|
|
"""
|
|
ThousandEyes for OpenTelemetry API
|
|
|
|
ThousandEyes for OpenTelemetry provides machine-to-machine integration between ThousandEyes and its customers. It allows you to export ThousandEyes telemetry data in OTel format, which is widely used in the industry. With ThousandEyes for OTel, you can leverage frameworks widely used in the observability domain - such as Splunk, Grafana, and Honeycomb - to capture and analyze ThousandEyes data. Any client that supports OTel can use ThousandEyes for OpenTelemetry. ThousandEyes for OTel is made up of the following components: * Data streaming APIs that you can use to configure and enable your ThousandEyes tests with OTel-compatible streams, in particular to configure how ThousandEyes telemetry data is exported to client integrations. * A set of streaming pipelines called _collectors_ that actively fetch ThousandEyes network test data, enrich the data with some additional detail, filter, and push the data to the customer-configured endpoints, depending on what you configure via the public APIs. * Third-party OTel collectors that receive, transform, filter, and export different metrics to client applications such as AppD, or any other OTel-capable client configuration. For more information about ThousandEyes for OpenTelemetry, see the [documentation](https://docs.thousandeyes.com/product-documentation/api/opentelemetry).
|
|
|
|
The version of the OpenAPI document: 7.0.4
|
|
Generated by OpenAPI Generator (https://openapi-generator.tech)
|
|
|
|
Do not edit the class manually.
|
|
""" # noqa: E501
|
|
|
|
|
|
from __future__ import annotations
|
|
import pprint
|
|
import re # noqa: F401
|
|
import json
|
|
|
|
from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictStr
|
|
from typing import Any, ClassVar, Dict, List, Optional
|
|
from thousandeyes_sdk.streaming.models.tag_match import TagMatch
|
|
from typing import Optional, Set
|
|
from typing_extensions import Self
|
|
|
|
class PutStream(BaseModel):
|
|
"""
|
|
PutStream
|
|
""" # noqa: E501
|
|
custom_headers: Optional[Dict[str, StrictStr]] = Field(default=None, description="Custom headers", alias="customHeaders")
|
|
tag_match: Optional[List[TagMatch]] = Field(default=None, description="A collection of tags that determine what tests are included in the data stream. These tag values are also included as attributes in the data stream metrics.", alias="tagMatch")
|
|
enabled: Optional[StrictBool] = Field(default=None, description="Flag to enable or disable the stream integration.")
|
|
__properties: ClassVar[List[str]] = ["customHeaders", "tagMatch", "enabled"]
|
|
|
|
model_config = ConfigDict(
|
|
populate_by_name=True,
|
|
validate_assignment=True,
|
|
protected_namespaces=(),
|
|
)
|
|
|
|
|
|
def to_str(self) -> str:
|
|
"""Returns the string representation of the model using alias"""
|
|
return pprint.pformat(self.model_dump(by_alias=True))
|
|
|
|
def to_json(self) -> str:
|
|
"""Returns the JSON representation of the model using alias"""
|
|
# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
|
|
return json.dumps(self.to_dict())
|
|
|
|
@classmethod
|
|
def from_json(cls, json_str: str) -> Optional[Self]:
|
|
"""Create an instance of PutStream from a JSON string"""
|
|
return cls.from_dict(json.loads(json_str))
|
|
|
|
def to_dict(self) -> Dict[str, Any]:
|
|
"""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.
|
|
"""
|
|
excluded_fields: Set[str] = set([
|
|
])
|
|
|
|
_dict = self.model_dump(
|
|
by_alias=True,
|
|
exclude=excluded_fields,
|
|
exclude_none=True,
|
|
)
|
|
# override the default output from pydantic by calling `to_dict()` of each item in tag_match (list)
|
|
_items = []
|
|
if self.tag_match:
|
|
for _item in self.tag_match:
|
|
if _item:
|
|
_items.append(_item.to_dict())
|
|
_dict['tagMatch'] = _items
|
|
return _dict
|
|
|
|
@classmethod
|
|
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
|
|
"""Create an instance of PutStream from a dict"""
|
|
if obj is None:
|
|
return None
|
|
|
|
if not isinstance(obj, dict):
|
|
return cls.model_validate(obj)
|
|
|
|
_obj = cls.model_validate({
|
|
"customHeaders": obj.get("customHeaders"),
|
|
"tagMatch": [TagMatch.from_dict(_item) for _item in obj["tagMatch"]] if obj.get("tagMatch") is not None else None,
|
|
"enabled": obj.get("enabled")
|
|
})
|
|
return _obj
|
|
|
|
|