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‎README.md

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# datadog-lambda-layer-python
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![CircleCI](https://img.shields.io/circleci/build/github/DataDog/datadog-lambda-layer-python)
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[![PyPI](https://img.shields.io/pypi/v/datadog-lambda)](https://pypi.org/project/datadog-lambda/)
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[![Slack](https://img.shields.io/badge/slack-%23serverless-blueviolet?logo=slack)](https://datadoghq.slack.com/channels/serverless/)
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[![License](https://img.shields.io/badge/license-Apache--2.0-blue)](https://github.com/DataDog/datadog-lambda-layer-python/blob/master/LICENSE)
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Datadog Lambda Layer for Python (2.7, 3.6 and 3.7) enables custom metric submission from AWS Lambda functions, and distributed tracing between serverful and serverless environments.
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## Installation
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The Datadog API must be defined as an environment variable via [AWS CLI](https://docs.aws.amazon.com/lambda/latest/dg/env_variables.html) or [Serverless Framework](https://serverless-stack.com/chapters/serverless-environment-variables.html):
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* DD_API_KEY or DD_KMS_API_KEY (if encrypted by KMS)
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- DD_API_KEY or DD_KMS_API_KEY (if encrypted by KMS)
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Set the following Datadog environment variable to `datadoghq.eu` to send your data to the Datadog EU site.
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* DD_SITE
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- DD_SITE
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If your Lambda function powers a performance-critical task (e.g., a consumer-facing API). You can avoid the added latencies of metric-submitting API calls, by setting the following Datadog environment variable to `True`. Custom metrics will be submitted asynchronously through CloudWatch Logs and [the Datadog log forwarder](https://github.com/DataDog/datadog-serverless-functions/tree/master/aws/logs_monitoring).
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* DD_FLUSH_TO_LOG
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- DD_FLUSH_TO_LOG
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### The Serverless Framework
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DD_API_KEY: <DD_API_KEY>
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```
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## Basic Usage
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```python
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requests.get("https://www.datadoghq.com")
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```
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## Custom Metrics
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Custom metrics can be submitted using `lambda_metric` and the Lambda handler function needs to be decorated with `@datadog_lambda_wrapper`. The metrics are submitted as [distribution metrics](https://docs.datadoghq.com/graphing/metrics/distributions/).
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Custom metrics can be submitted using `lambda_metric` and the Lambda handler function needs to be decorated with `@datadog_lambda_wrapper`. The metrics are submitted as [distribution metrics](https://docs.datadoghq.com/graphing/metrics/distributions/).
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**IMPORTANT NOTE:** If you have already been submitting the same custom metric as non-distribution metric (e.g., gauge, count, or histogram) without using the Datadog Lambda Layer, you MUST pick a new metric name to use for `lambda_metric`. Otherwise that existing metric will be converted to a distribution metric and the historical data prior to the conversion will be no longer queryable.
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```
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### VPC
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If your Lambda function is associated with a VPC, you need to ensure it has [access to the public internet](https://aws.amazon.com/premiumsupport/knowledge-center/internet-access-lambda-function/).
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If your Lambda function is associated with a VPC, you need to ensure it has [access to the public internet](https://aws.amazon.com/premiumsupport/knowledge-center/internet-access-lambda-function/).
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## Distributed Tracing
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[Distributed tracing](https://docs.datadoghq.com/tracing/guide/distributed_tracing/?tab=python) allows you to propagate a trace context from a service running on a host to a service running on AWS Lambda, and vice versa, so you can see performance end-to-end. Linking is implemented by injecting Datadog trace context into the HTTP request headers.
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[Distributed tracing](https://docs.datadoghq.com/tracing/guide/distributed_tracing/?tab=python) allows you to propagate a trace context from a service running on a host to a service running on AWS Lambda, and vice versa, so you can see performance end-to-end. Linking is implemented by injecting Datadog trace context into the HTTP request headers.
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Distributed tracing headers are language agnostic, e.g., a trace can be propagated between a Java service running on a host to a Lambda function written in Python.
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Distributed tracing headers are language agnostic, e.g., a trace can be propagated between a Java service running on a host to a Lambda function written in Python.
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Because the trace context is propagated through HTTP request headers, the Lambda function needs to be triggered by AWS API Gateway or AWS Application Load Balancer.
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requests.get("https://www.datadoghq.com")
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```
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Note, the Datadog Lambda Layer is only needed to enable *distributed* tracing between Lambda and non-Lambda services. For standalone Lambda functions, traces can be found in Datadog APM after configuring [the X-Ray integration](https://docs.datadoghq.com/integrations/amazon_xray/).
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Note, the Datadog Lambda Layer is only needed to enable _distributed_ tracing between Lambda and non-Lambda services. For standalone Lambda functions, traces can be found in Datadog APM after configuring [the X-Ray integration](https://docs.datadoghq.com/integrations/amazon_xray/).
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### Patching
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```
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The file content for `datadog-sampling-priority-1.json`:
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```json
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{
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"SamplingRule": {
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"RuleName": "Datadog-Sampling-Priority-1",
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"ResourceARN": "*",
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"Priority": 9998,
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"FixedRate": 1,
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"ReservoirSize": 100,
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"ServiceName": "*",
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"ServiceType": "AWS::APIGateway::Stage",
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"Host": "*",
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"HTTPMethod": "*",
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"URLPath": "*",
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"Version": 1,
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"Attributes": {
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"x-datadog-sampling-priority": "1"
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}
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"SamplingRule": {
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"RuleName": "Datadog-Sampling-Priority-1",
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"ResourceARN": "*",
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"Priority": 9998,
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"FixedRate": 1,
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"ReservoirSize": 100,
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"ServiceName": "*",
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"ServiceType": "AWS::APIGateway::Stage",
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"Host": "*",
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"HTTPMethod": "*",
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"URLPath": "*",
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"Version": 1,
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"Attributes": {
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"x-datadog-sampling-priority": "1"
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}
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}
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}
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```
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The file content for `datadog-sampling-priority-2.json`:
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```json
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{
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"SamplingRule": {
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"RuleName": "Datadog-Sampling-Priority-2",
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"ResourceARN": "*",
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"Priority": 9999,
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"FixedRate": 1,
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"ReservoirSize": 100,
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"ServiceName": "*",
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"ServiceType": "AWS::APIGateway::Stage",
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"Host": "*",
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"HTTPMethod": "*",
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"URLPath": "*",
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"Version": 1,
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"Attributes": {
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"x-datadog-sampling-priority": "2"
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}
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"SamplingRule": {
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"RuleName": "Datadog-Sampling-Priority-2",
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"ResourceARN": "*",
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"Priority": 9999,
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"FixedRate": 1,
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"ReservoirSize": 100,
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"ServiceName": "*",
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"ServiceType": "AWS::APIGateway::Stage",
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"Host": "*",
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"HTTPMethod": "*",
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"URLPath": "*",
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"Version": 1,
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"Attributes": {
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"x-datadog-sampling-priority": "2"
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}
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}
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}
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```
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If your Lambda function is deployed by the Serverless Framework, such a mapping template gets created by default.
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## Opening Issues
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If you encounter a bug with this package, we want to hear about it. Before opening a new issue, search the existing issues to avoid duplicates.
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You can also open an issue for a feature request.
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## Contributing
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If you find an issue with this package and have a fix, please feel free to open a pull request following the [procedures](CONTRIBUTING.md).
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## License
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Unless explicitly stated otherwise all files in this repository are licensed under the Apache License Version 2.0.

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