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Co-authored-by: API Team <api-team@thousandeyes.com>
134 lines
8.2 KiB
Python
134 lines
8.2 KiB
Python
# coding: utf-8
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"""
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ThousandEyes for OpenTelemetry API
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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 [product documentation](https://docs.thousandeyes.com/product-documentation/integration-guides/opentelemetry).
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Generated by OpenAPI Generator (https://openapi-generator.tech)
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Do not edit the class manually.
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""" # noqa: E501
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from __future__ import annotations
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import pprint
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import re # noqa: F401
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import json
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from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictStr
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from typing import Any, ClassVar, Dict, List, Optional
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from thousandeyes_sdk.streaming.models.endpoint_agent_label import EndpointAgentLabel
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from thousandeyes_sdk.streaming.models.exporter_config import ExporterConfig
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from thousandeyes_sdk.streaming.models.filters import Filters
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from thousandeyes_sdk.streaming.models.tag_match import TagMatch
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from thousandeyes_sdk.streaming.models.test_match import TestMatch
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from typing import Optional, Set
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from typing_extensions import Self
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class PutStream(BaseModel):
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"""
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PutStream
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""" # noqa: E501
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custom_headers: Optional[Dict[str, StrictStr]] = Field(default=None, description="Custom headers.", alias="customHeaders")
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stream_endpoint_url: Optional[StrictStr] = Field(default=None, description="The URL ThousandEyes sends data stream to. For a URL to be valid, it needs to: - Be syntactically correct. - Be reachable. - Use the HTTPS protocol. - When using the `grpc` endpointType, streamEndpointUrl cannot contain paths: - Valid . `grpc` - `https://example.com` - Invalid . `grpc` - `https://example.com/collector`. - Valid . `http` - `https://example.com/collector`. - When using the `http` endpointType, the operation must match the exact final full URL (including the path if there is one) to which the data will be sent. Examples below: - `https://api.honeycomb.io:443/v1/metrics` - `https://ingest.eu0.signalfx.com/v2/datapoint/otlp`", alias="streamEndpointUrl")
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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. Tags are invalid if the tag key includes characters that are not allowed by the [OpenTelemetry naming recommendations for attributes](https://opentelemetry.io/docs/specs/semconv/general/naming/#recommendations-for-application-developers).", alias="tagMatch")
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test_match: Optional[List[TestMatch]] = Field(default=None, description="A collection of tests to be included in the data stream.", alias="testMatch")
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enabled: Optional[StrictBool] = Field(default=None, description="Flag to enable or disable the stream integration.")
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filters: Optional[Filters] = None
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exporter_config: Optional[ExporterConfig] = Field(default=None, alias="exporterConfig")
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endpoint_agent_label: Optional[List[EndpointAgentLabel]] = Field(default=None, description="A collection of Endpoint Agent label IDs that determines what local network data is included in the data stream.", alias="endpointAgentLabel")
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__properties: ClassVar[List[str]] = ["customHeaders", "streamEndpointUrl", "tagMatch", "testMatch", "enabled", "filters", "exporterConfig", "endpointAgentLabel"]
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model_config = ConfigDict(
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populate_by_name=True,
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validate_assignment=True,
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protected_namespaces=(),
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extra="allow",
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)
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def to_str(self) -> str:
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"""Returns the string representation of the model using alias"""
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return pprint.pformat(self.model_dump(by_alias=True))
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def to_json(self) -> str:
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"""Returns the JSON representation of the model using alias"""
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# TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
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return self.model_dump_json(by_alias=True, exclude_unset=True, exclude_none=True)
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@classmethod
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def from_json(cls, json_str: str) -> Optional[Self]:
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"""Create an instance of PutStream from a JSON string"""
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return cls.from_dict(json.loads(json_str))
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def to_dict(self) -> Dict[str, Any]:
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"""Return the dictionary representation of the model using alias.
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This has the following differences from calling pydantic's
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`self.model_dump(by_alias=True)`:
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* `None` is only added to the output dict for nullable fields that
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were set at model initialization. Other fields with value `None`
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are ignored.
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"""
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excluded_fields: Set[str] = set([
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])
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_dict = self.model_dump(
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by_alias=True,
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exclude=excluded_fields,
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exclude_none=True,
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)
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# override the default output from pydantic by calling `to_dict()` of each item in tag_match (list)
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_items = []
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if self.tag_match:
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for _item in self.tag_match:
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if _item:
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_items.append(_item.to_dict())
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_dict['tagMatch'] = _items
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# override the default output from pydantic by calling `to_dict()` of each item in test_match (list)
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_items = []
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if self.test_match:
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for _item in self.test_match:
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if _item:
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_items.append(_item.to_dict())
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_dict['testMatch'] = _items
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# override the default output from pydantic by calling `to_dict()` of filters
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if self.filters:
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_dict['filters'] = self.filters.to_dict()
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# override the default output from pydantic by calling `to_dict()` of exporter_config
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if self.exporter_config:
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_dict['exporterConfig'] = self.exporter_config.to_dict()
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# override the default output from pydantic by calling `to_dict()` of each item in endpoint_agent_label (list)
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_items = []
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if self.endpoint_agent_label:
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for _item in self.endpoint_agent_label:
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if _item:
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_items.append(_item.to_dict())
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_dict['endpointAgentLabel'] = _items
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return _dict
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@classmethod
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def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
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"""Create an instance of PutStream from a dict"""
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if obj is None:
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return None
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if not isinstance(obj, dict):
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return cls.model_validate(obj)
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_obj = cls.model_validate({
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"customHeaders": obj.get("customHeaders"),
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"streamEndpointUrl": obj.get("streamEndpointUrl"),
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"tagMatch": [TagMatch.from_dict(_item) for _item in obj["tagMatch"]] if obj.get("tagMatch") is not None else None,
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"testMatch": [TestMatch.from_dict(_item) for _item in obj["testMatch"]] if obj.get("testMatch") is not None else None,
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"enabled": obj.get("enabled"),
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"filters": Filters.from_dict(obj["filters"]) if obj.get("filters") is not None else None,
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"exporterConfig": ExporterConfig.from_dict(obj["exporterConfig"]) if obj.get("exporterConfig") is not None else None,
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"endpointAgentLabel": [EndpointAgentLabel.from_dict(_item) for _item in obj["endpointAgentLabel"]] if obj.get("endpointAgentLabel") is not None else None
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})
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return _obj
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