datadog-api-client-python

This repository contains the autogenerated Python client for the Datadog API. The client code, feature files, and test data are generated from Datadog's public OpenAPI specifications by internal tooling.

We welcome contributions that expand or improve the client's capabilities. As part of the pull request review process, Datadog's internal tooling may regenerate the affected code, feature files, and test data based on the proposed changes.

Requirements

Building and using the API client library requires Python 3.10+.

Installation

To install the API client library, simply execute:

pip install datadog-api-client

Getting Started

Please follow the installation instruction and execute the following Python code:

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.monitors_api import MonitorsApi
from datadog_api_client.v1.model.monitor import Monitor
from datadog_api_client.v1.model.monitor_type import MonitorType

body = Monitor(
    name="example",
    type=MonitorType("log alert"),
    query='logs("service:foo AND type:error").index("main").rollup("count").by("source").last("5m") > 2',
    message="some message Notify: @hipchat-channel",
    tags=["test:example", "env:ci"],
    priority=3,
)

configuration = Configuration()
with ApiClient(configuration) as api_client:
    api_instance = MonitorsApi(api_client)
    response = api_instance.create_monitor(body=body)
    print(response)

Authentication

By default the library will use the DD_API_KEY and DD_APP_KEY environment variables to authenticate against the Datadog API. To provide your own set of credentials, you need to set some keys on the configuration:

configuration.api_key["apiKeyAuth"] = "<API KEY>"
configuration.api_key["appKeyAuth"] = "<APPLICATION KEY>"

Unstable Endpoints

This client includes access to Datadog API endpoints while they are in an unstable state and may undergo breaking changes. An extra configuration step is required to enable these endpoints:

configuration.unstable_operations["<OperationName>"] = True

where <OperationName> is the name of the method used to interact with that endpoint. For example: list_log_indexes, or get_logs_index

Changing Server

When talking to a different server, like the eu instance, change the server_variables on your configuration object:

configuration.server_variables["site"] = "datadoghq.eu"

Disable compressed payloads

If you want to disable GZIP compressed responses, set the compress flag on your configuration object:

configuration.compress = False

Enable requests logging

If you want to enable requests logging, set the debug flag on your configuration object:

configuration.debug = True

Enable retry

If you want to enable retry when getting status code 429 rate-limited, set enable_retry to True

    configuration.enable_retry = True

The default max retry is 3, you can change it with max_retries

    configuration.max_retries = 5

Custom retry policy

You can provide a custom retry policy by passing a urllib3.util.Retry instance to the configuration. When a custom retry policy is provided, it takes precedence over the enable_retry, retry_backoff_factor, and max_retries settings.

import urllib3
from datadog_api_client import Configuration

# Create a custom retry policy
custom_retry = urllib3.util.Retry(
    total=5,
    backoff_factor=2,
    status_forcelist=[429, 500, 502, 503, 504],
    allowed_methods=["GET", "POST", "PUT", "DELETE"]
)

configuration = Configuration(retry_policy=custom_retry)

See urllib3.util.Retry documentation for more details.

Configure proxy

You can configure the client to use proxy by setting the proxy key on configuration object:

configuration.proxy = "http://127.0.0.1:80"

Threads support

You can run API calls in a thread by using ThreadedApiClient in place of ApiClient. API calls will then return a AsyncResult instance on which you can call get to retrieve the result:

from datadog_api_client import Configuration, ThreadedApiClient
from datadog_api_client.v1.api.dashboards_api import DashboardsApi

configuration = Configuration()
with ThreadedApiClient(configuration) as api_client:
    api_instance = DashboardsApi(api_client)
    result = api_instance.list_dashboards()
    dashboards = result.get()
    print(dashboards)

Asyncio support

The library supports asynchronous operations when using AsyncApiClient for the transport. When that client is used, the API methods will then return coroutines that you can wait for.

To make async support available, you need to install the extra async qualifiers during installation: pip install datadog-api-client[async].

import asyncio

from datadog_api_client import Configuration, AsyncApiClient
from datadog_api_client.v1.api.dashboards_api import DashboardsApi

async def main():
    configuration = Configuration()
    async with AsyncApiClient(configuration) as api_client:
        api_instance = DashboardsApi(api_client)
        dashboards = await api_instance.list_dashboards()
        print(dashboards)

asyncio.run(main())

Pagination

Several listing operations have a pagination method to help consume all the items available. For example, to retrieve all your incidents:

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.incidents_api import IncidentsApi

configuration = Configuration()
configuration.unstable_operations["list_incidents"] = True
with ApiClient(configuration) as api_client:
    api_instance = IncidentsApi(api_client)
    for incident in api_instance.list_incidents_with_pagination():
        print(incident.id)

Documentation for API Endpoints and Models

Documentation for API endpoints and models is available in the API Client docs.

Documentation for Authorization

Authenticate with the API by providing your API and Application keys in the configuration:

configuration.api_key["apiKeyAuth"] = "YOUR_API_KEY"
configuration.api_key["appKeyAuth"] = "YOUR_APPLICATION_KEY"

Author

support@datadoghq.com