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https://github.com/datadog/datadog-api-client-python

Python client for the Datadog API
https://github.com/datadog/datadog-api-client-python

datadog datadog-api openapi python

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Python client for the Datadog API

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README 

        
# datadog-api-client-python



This repository contains a Python API client for the [Datadog API](https://docs.datadoghq.com/api/).



## Requirements



Building and using the API client library requires [Python 3.7+](https://www.python.org/downloads/).



## Installation



To install the API client library, simply execute:



```shell

pip install datadog-api-client

```



## Getting Started



Please follow the [installation](#installation) instruction and execute the following Python code:



```python

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:



```python

configuration.api_key["apiKeyAuth"] = ""

configuration.api_key["appKeyAuth"] = ""

```



### 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:



```python

configuration.unstable_operations[""] = True

```



where `` 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:



```python

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:



```python

configuration.compress = False

```



### Enable requests logging



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



```python

configuration.debug = True

```



### Enable retry



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



```python

    configuration.enable_retry = True

```



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



```python

    configuration.max_retries = 5

```



### Configure proxy



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



```python

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:



```python

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]`.



```python

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:



```python

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 are available on [readthedocs](https://datadog-api-client.readthedocs.io/).



## Documentation for Authorization



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



```python

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

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

```



## Author



[email protected]