Datadog Serverless Compatibility Layer for Python

Datadog library for Python to enable tracing and custom metric submission from Azure Functions and Google Cloud Run Functions (1st gen).

Installation

1. Install the Datadog Serverless Compatibility Layer.

pip install datadog-serverless-compat

2. Install the Datadog Tracing Library following the official documentation for Tracing Python Applications.

3. Add the Datadog Serverless Compatibility Layer and the Datadog Tracer in code.

from datadog_serverless_compat import start
from ddtrace import tracer, patch_all

start()
patch_all()

Configuration

1. Set Datadog environment variables

• DD_API_KEY = <YOUR API KEY>
• DD_SITE = datadoghq.com
• DD_ENV = <ENVIRONMENT
• DD_SERVICE = <SERVICE NAME>
• DD_VERSION = <VERSION>

The default Datadog site is datadoghq.com. To use a different site, set the DD_SITE environment variable to the desired destination site. See Getting Started with Datadog Sites for the available site values.

The DD_SERVICE, DD_ENV, and DD_VERSION settings are configured from environment variables in Azure and are used to tie telemetry together in Datadog as tags. Read more about Datadog Unified Service Tagging.

Trace Metrics are enabled by default but can be disabled with the DD_TRACE_STATS_COMPUTATION_ENABLED environment variable.

Enable debug logs for the Datadog Serverless Compatibility Layer with the DD_LOG_LEVEL environment variable:

DD_LOG_LEVEL=debug

Alternatively disable logs for the Datadog Serverless Compatibility Layer with the DD_LOG_LEVEL environment variable:

DD_LOG_LEVEL=off

1. For additional tracing configuration options, see the official documentation for Datadog trace client.

2. If installing to Azure Functions, install the Datadog Azure Integration and set tags on your Azure Functions to further extend unified service tagging. This allows for Azure Function metrics and other Azure metrics to be correlated with traces.