Instrumenting Python Serverless Applications
Installation
Datadog offers many different ways to enable instrumentation for your serverless applications. Choose a method below that best suits your needs. Datadog generally recommends using the Datadog CLI. You must follow the instructions for “Container Image” if your application is deployed as a container image.
The Datadog CLI modifies existing Lambda functions’ configurations to enable instrumentation without requiring a new deployment. It is the quickest way to get started with Datadog’s serverless monitoring.
Install the Datadog CLI client
npm install -g @datadog/datadog-ci
If you are new to Datadog serverless monitoring, launch the Datadog CLI in the interactive mode to guide your first installation for a quick start, and you can ignore the remaining steps. To permanently install Datadog for your production applications, skip this step and follow the remaining ones to run the Datadog CLI command in your CI/CD pipelines after your normal deployment.
datadog-ci lambda instrument -i
Configure the AWS credentials
Datadog CLI requires access to the AWS Lambda service, and depends on the AWS JavaScript SDK to resolve the credentials. Ensure your AWS credentials are configured using the same method you would use when invoking the AWS CLI.
Configure the Datadog site
export DATADOG_SITE="<DATADOG_SITE>"
Replace <DATADOG_SITE>
with
(ensure the correct SITE is selected on the right).
Configure the Datadog API key
Datadog recommends saving the Datadog API key in AWS Secrets Manager for security and easy rotation. The key needs to be stored as a plaintext string (not a JSON blob). Ensure your Lambda functions have the required secretsmanager:GetSecretValue
IAM permission.
export DATADOG_API_KEY_SECRET_ARN="<DATADOG_API_KEY_SECRET_ARN>"
For quick testing purposes, you can also set the Datadog API key in plaintext:
export DATADOG_API_KEY="<DATADOG_API_KEY>"
Instrument your Lambda functions
Note: Instrument your Lambda functions in a dev or staging environment first. If the instrumentation result is unsatisfactory, run uninstrument
with the same arguments to revert the changes.
To instrument your Lambda functions, run the following command.
datadog-ci lambda instrument -f <functionname> -f <another_functionname> -r <aws_region> -v 99 -e 65
To fill in the placeholders:
- Replace
<functionname>
and <another_functionname>
with your Lambda function names. Alternatively, you can use --functions-regex
to automatically instrument multiple functions whose names match the given regular expression. - Replace
<aws_region>
with the AWS region name.
Additional parameters can be found in the CLI documentation.
The Datadog Serverless Plugin automatically configures your functions to send metrics, traces, and logs to Datadog through the Datadog Lambda Extension.
To install and configure the Datadog Serverless Plugin, follow these steps:
Install the Datadog Serverless Plugin:
serverless plugin install --name serverless-plugin-datadog
Update your serverless.yml
:
custom:
datadog:
site: <DATADOG_SITE>
apiKeySecretArn: <DATADOG_API_KEY_SECRET_ARN>
To fill in the placeholders:
- Replace
<DATADOG_SITE>
with
(ensure the correct SITE is selected on the right). - Replace
<DATADOG_API_KEY_SECRET_ARN>
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string (not a JSON blob). The secretsmanager:GetSecretValue
permission is required. For quick testing, you can instead use apiKey
and set the Datadog API key in plaintext.
For more information and additional settings, see the plugin documentation.
The Datadog CloudFormation macro automatically transforms your SAM application template to install Datadog on your functions using Lambda layers, and configures your functions to send metrics, traces, and logs to Datadog through the Datadog Lambda Extension.
Install the Datadog CloudFormation macro
Run the following command with your AWS credentials to deploy a CloudFormation stack that installs the macro AWS resource. You only need to install the macro once for a given region in your account. Replace create-stack
with update-stack
to update the macro to the latest version.
aws cloudformation create-stack \
--stack-name datadog-serverless-macro \
--template-url https://datadog-cloudformation-template.s3.amazonaws.com/aws/serverless-macro/latest.yml \
--capabilities CAPABILITY_AUTO_EXPAND CAPABILITY_IAM
The macro is now deployed and ready to use.
Instrument your Lambda functions
Add the DatadogServerless
transform after the AWS::Serverless
transform under the Transform
section in your template.yml
file for SAM.
Transform:
- AWS::Serverless-2016-10-31
- Name: DatadogServerless
Parameters:
stackName: !Ref "AWS::StackName"
pythonLayerVersion: 99
extensionLayerVersion: 65
site: "<DATADOG_SITE>"
apiKeySecretArn: "<DATADOG_API_KEY_SECRET_ARN>"
To fill in the placeholders:
- Replace
<DATADOG_SITE>
with
(ensure the correct SITE is selected on the right). - Replace
<DATADOG_API_KEY_SECRET_ARN>
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string (not a JSON blob). The secretsmanager:GetSecretValue
permission is required. For quick testing, you can use apiKey
instead and set the Datadog API key in plaintext.
More information and additional parameters can be found in the macro documentation.
The Datadog CDK Construct automatically installs Datadog on your functions using Lambda Layers, and configures your functions to send metrics, traces, and logs to Datadog through the Datadog Lambda Extension.
Install the Datadog CDK constructs library
# For AWS CDK v1
pip install datadog-cdk-constructs
# For AWS CDK v2
pip install datadog-cdk-constructs-v2
Instrument your Lambda functions
# For AWS CDK v1
from datadog_cdk_constructs import Datadog
# For AWS CDK v2
from datadog_cdk_constructs_v2 import Datadog
datadog = Datadog(self, "Datadog",
python_layer_version=99,
extension_layer_version=65,
site="<DATADOG_SITE>",
api_key_secret_arn="<DATADOG_API_KEY_SECRET_ARN>",
)
datadog.add_lambda_functions([<LAMBDA_FUNCTIONS>])
To fill in the placeholders:
- Replace
<DATADOG_SITE>
with
(ensure the correct SITE is selected on the right). - Replace
<DATADOG_API_KEY_SECRET_ARN>
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string (not a JSON blob). The secretsmanager:GetSecretValue
permission is required. For quick testing, you can use apiKey
instead and set the Datadog API key in plaintext.
More information and additional parameters can be found on the Datadog CDK documentation.
Install the Datadog Lambda Library
If you are deploying your Lambda function as a container image, you cannot use the Datadog Lambda library as a Lambda Layer. Instead, you must install the Datadog Lambda library as a dependency of your function within the image.
pip install datadog-lambda
Note that the minor version of the datadog-lambda
package always matches the layer version. For example, datadog-lambda v0.5.0
matches the content of layer version 5.
Install the Datadog Lambda Extension
Add the Datadog Lambda Extension to your container image by adding the following to your Dockerfile:
COPY --from=public.ecr.aws/datadog/lambda-extension:<TAG> /opt/. /opt/
Replace <TAG>
with either a specific version number (for example, 65
) or with latest
. Alpine is also supported with specific version numbers (such as 65-alpine
) or with latest-alpine
. You can see a complete list of possible tags in the Amazon ECR repository.
Redirect the handler function
- Set your image’s
CMD
value to datadog_lambda.handler.handler
. You can set this in AWS or directly in your Dockerfile. Note that the value set in AWS overrides the value in the Dockerfile if you set both. - Set the environment variable
DD_LAMBDA_HANDLER
to your original handler, for example, myfunc.handler
.
Note: If you are using a third-party security or monitoring tool that is incompatible with the Datadog handler redirection, you can apply the Datadog wrapper in your function code instead.
Configure the Datadog site, API key, and tracing in your Dockerfile
- Set the environment variable
DD_SITE
to
(ensure the correct SITE is selected on the right). - Set the environment variable
DD_API_KEY_SECRET_ARN
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string (not a JSON blob). The secretsmanager:GetSecretValue
permission is required. For quick testing, you can use DD_API_KEY
instead and set the Datadog API key in plaintext. - Set the environment variable
DD_TRACE_ENABLED
to true
.
The lambda-datadog
Terraform module wraps the aws_lambda_function
resource and automatically configures your Lambda function for Datadog Serverless Monitoring by:
- Adding the Datadog Lambda layers
- Redirecting the Lambda handler
- Enabling the collection and sending of metrics, traces, and logs to Datadog
module "lambda-datadog" {
source = "DataDog/lambda-datadog/aws"
version = "1.3.0"
environment_variables = {
"DD_API_KEY_SECRET_ARN" : "<DATADOG_API_KEY_SECRET_ARN>"
"DD_ENV" : "<ENVIRONMENT>"
"DD_SERVICE" : "<SERVICE_NAME>"
"DD_SITE": "<DATADOG_SITE>"
"DD_VERSION" : "<VERSION>"
}
datadog_extension_layer_version = 58
datadog_python_layer_version = 96
# aws_lambda_function arguments
}
Replace the aws_lambda_function
resource with the lambda-datadog
Terraform module. Then, specify the source
and version
of the module.
Set the aws_lambda_function
arguments:
All of the arguments available in the aws_lambda_function
resource are available in this Terraform module. Arguments defined as blocks in the aws_lambda_function
resource are redefined as variables with their nested arguments.
For example, in aws_lambda_function
, environment
is defined as a block with a variables
argument. In the lambda-datadog
Terraform module, the value for the environment_variables
is passed to the environment.variables
argument in aws_lambda_function
. See inputs for a complete list of variables in this module.
Fill in the environment variable placeholders:
- Replace
<DATADOG_API_KEY_SECRET_ARN>
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string (not a JSON blob). The secretsmanager:GetSecretValue
permission is required. For quick testing, you can instead use the environment variable DD_API_KEY
and set your Datadog API key in plaintext. - Replace
<ENVIRONMENT>
with the Lambda function’s environment, such as prod
or staging
- Replace
<SERVICE_NAME>
with the name of the Lambda function’s service - Replace
<DATADOG_SITE>
with
. (Ensure the correct Datadog site is selected on this page). - Replace
<VERSION>
with the version number of the Lambda function
Select the versions of the Datadog Extension Lambda layer and Datadog Python Lambda layer to use. If left blank the latest layer versions will be used.
datadog_extension_layer_version = 58
datadog_python_layer_version = 96
If you are not using a serverless development tool that Datadog supports, such as the Serverless Framework or AWS CDK, Datadog strongly encourages you instrument your serverless applications with the
Datadog CLI.
Install the Datadog Lambda library
The Datadog Lambda Library can be imported either as a layer (recommended) OR as a Python package.
The minor version of the datadog-lambda
package always matches the layer version. For example, datadog-lambda v0.5.0 matches the content of layer version 5.
Option A: Configure the layers for your Lambda function using the ARN in the following format:
# Use this format for x86-based Lambda deployed in AWS commercial regions
arn:aws:lambda:<AWS_REGION>:464622532012:layer:Datadog-<RUNTIME>:99
# Use this format for arm64-based Lambda deployed in AWS commercial regions
arn:aws:lambda:<AWS_REGION>:464622532012:layer:Datadog-<RUNTIME>-ARM:99
# Use this format for x86-based Lambda deployed in AWS GovCloud regions
arn:aws-us-gov:lambda:<AWS_REGION>:002406178527:layer:Datadog-<RUNTIME>:99
# Use this format for arm64-based Lambda deployed in AWS GovCloud regions
arn:aws-us-gov:lambda:<AWS_REGION>:002406178527:layer:Datadog-<RUNTIME>-ARM:99
Replace <AWS_REGION>
with a valid AWS region, such as us-east-1
. The available <RUNTIME>
options are: Python38
, Python39
, Python310
, Python311
, Python312
.
Option B: If you cannot use the prebuilt Datadog Lambda layer, alternatively install the datadog-lambda
package and its dependencies locally to your function project folder using your favorite Python package manager, such as pip
.
pip install datadog-lambda -t ./
Note: datadog-lambda
depends on ddtrace
, which uses native extensions; therefore it must be installed and compiled in a Linux environment on the right architecture (x86_64
or arm64
). For example, you can use dockerizePip for the Serverless Framework and –use-container for AWS SAM. For more details, see how to add dependencies to your function deployment package.
See the latest release.
Install the Datadog Lambda Extension
Configure the layers for your Lambda function using the ARN in the following format:
# Use this format for x86-based Lambda deployed in AWS commercial regions
arn:aws:lambda:<AWS_REGION>:464622532012:layer:Datadog-Extension:65
# Use this format for arm64-based Lambda deployed in AWS commercial regions
arn:aws:lambda:<AWS_REGION>:464622532012:layer:Datadog-Extension-ARM:65
# Use this format for x86-based Lambda deployed in AWS GovCloud regions
arn:aws-us-gov:lambda:<AWS_REGION>:002406178527:layer:Datadog-Extension:65
# Use this format for arm64-based Lambda deployed in AWS GovCloud regions
arn:aws-us-gov:lambda:<AWS_REGION>:002406178527:layer:Datadog-Extension-ARM:65
Replace <AWS_REGION>
with a valid AWS region, such as us-east-1
.
Redirect the handler function
- Set your function’s handler to
datadog_lambda.handler.handler
. - Set the environment variable
DD_LAMBDA_HANDLER
to your original handler, for example, myfunc.handler
.
Note: If you are using a third-party security or monitoring tool that is incompatible with the Datadog handler redirection, you can apply the Datadog wrapper in your function code instead.
Configure the Datadog site, API key, and tracing
- Set the environment variable
DD_SITE
to
(ensure the correct SITE is selected on the right). - Set the environment variable
DD_API_KEY_SECRET_ARN
with the ARN of the AWS secret where your Datadog API key is securely stored. The key needs to be stored as a plaintext string, instead of being inside a json blob. The secretsmanager:GetSecretValue
permission is required. For quick testings, you can use DD_API_KEY
instead and set the Datadog API key in plaintext. - Set the environment variable
DD_TRACE_ENABLED
to true
.
(AWS Chalice only) Register the middleware
If you are using AWS Chalice, you must install datadog-lambda
using pip
, and register datadog_lambda_wrapper
as a middleware in your app.py
:
from chalice import Chalice, ConvertToMiddleware
from datadog_lambda.wrapper import datadog_lambda_wrapper
app = Chalice(app_name='hello-chalice')
app.register_middleware(ConvertToMiddleware(datadog_lambda_wrapper))
@app.route('/')
def index():
return {'hello': 'world'}
Minimize Cold Start Duration (Beta)
Starting with version 63 of the Datadog Extension, you can set the environment variable DD_EXTENSION_VERSION
to next
to use an optimized version of the Datadog Extension that reduces instrumentation overhead by up to 70%. To leave feedback or report a bug, please add an issue on Github and tag your issue with version/next
.
What’s next?
- You can now view metrics, logs, and traces on the Serverless Homepage.
- Turn on threat monitoring to get alerted on attackers targeting your service.
- See the sample code to monitor custom business logic
- See the troubleshooting guide if you have trouble collecting the telemetry
- See the advanced configurations to
- connect your telemetry using tags
- collect telemetry for Amazon API Gateway, SQS, etc.
- capture the Lambda request and response payloads
- link errors of your Lambda functions to your source code
- filter or scrub sensitive information from logs or traces
Monitor custom business logic
To monitor your custom business logic, submit a custom metric or span using the sample code below. For additional options, see custom metric submission for serverless applications and the APM guide for custom instrumentation.
import time
from ddtrace import tracer
from datadog_lambda.metric import lambda_metric
def lambda_handler(event, context):
# add custom tags to the lambda function span,
# does NOT work when X-Ray tracing is enabled
current_span = tracer.current_span()
if current_span:
current_span.set_tag('customer.id', '123456')
# submit a custom span
with tracer.trace("hello.world"):
print('Hello, World!')
# submit a custom metric
lambda_metric(
metric_name='coffee_house.order_value',
value=12.45,
tags=['product:latte', 'order:online']
)
return {
'statusCode': 200,
'body': get_message()
}
# trace a function
@tracer.wrap()
def get_message():
return 'Hello from serverless!'
Further Reading
Additional helpful documentation, links, and articles: