Lambda Powertools

A suite of utilities for AWS Lambda Functions that makes tracing with AWS X-Ray, structured logging and creating custom metrics asynchronously easier - Currently available for Python only and compatible with Python >=3.6.

Status: Beta

Features

Tracing

It currently uses AWS X-Ray

• Decorators that capture cold start as annotation, and response and exceptions as metadata
• Run functions locally without code change to disable tracing
• Explicitly disable tracing via env var POWERTOOLS_TRACE_DISABLED="true"

Logging

• Decorators that capture key fields from Lambda context, cold start and structures logging output as JSON
• Optionally log Lambda request when instructed (disabled by default)
  • Enable via POWERTOOLS_LOGGER_LOG_EVENT="true" or explicitly via decorator param
• Logs canonical custom metric line to logs that can be consumed asynchronously

Environment variables used across suite of utilities

Environment variable	Description	Default	Utility
POWERTOOLS_SERVICE_NAME	Sets service name used for tracing namespace, metrics dimensions and structured logging	"service_undefined"	all
POWERTOOLS_TRACE_DISABLED	Disables tracing	"false"	tracing
POWERTOOLS_LOGGER_LOG_EVENT	Logs incoming event	"false"	logging
LOG_LEVEL	Sets logging level	"INFO"	logging

Usage

Installation

With pip installed, run: pip install aws-lambda-powertools

Tracing

Example SAM template using supported environment variables

Globals:
  Function:
    Tracing: Active # can also be enabled per function
    Environment:
        Variables:
            POWERTOOLS_SERVICE_NAME: "payment" 
            POWERTOOLS_TRACE_DISABLED: "false" 

Pseudo Python Lambda code

from aws_lambda_powertools.tracing import Tracer
tracer = Tracer()
# tracer = Tracer(service="payment") # can also be explicitly defined

@tracer.capture_method
def collect_payment(charge_id):
  # logic
  ret = requests.post(PAYMENT_ENDPOINT)
  # custom annotation
  tracer.put_annotation("PAYMENT_STATUS", "SUCCESS")
  return ret

@tracer.capture_lambda_handler
def handler(event, context)
  charge_id = event.get('charge_id')
  payment = collect_payment(charge_id)
  ...

Logging

Example SAM template using supported environment variables

Globals:
  Function:
    Environment:
        Variables:
            POWERTOOLS_SERVICE_NAME: "payment" 
            LOG_LEVEL: "INFO"

Pseudo Python Lambda code

from aws_lambda_powertools.logging import logger_setup, logger_inject_lambda_context

logger = logger_setup()  
# logger_setup(service="payment") # also accept explicit service name
# logger_setup(level="INFO") # also accept explicit log level

@logger_inject_lambda_context
def handler(event, context)
  logger.info("Collecting payment")
  ...
  # You can log entire objects too
  logger.info({
    "operation": "collect_payment",
    "charge_id": event['charge_id']
  })
  ...

Exerpt output in CloudWatch Logs

{  
   "timestamp":"2019-08-22 18:17:33,774",
   "level":"INFO",
   "location":"collect.handler:1",
   "service":"payment",
   "lambda_function_name":"test",
   "lambda_function_memory_size":"128",
   "lambda_function_arn":"arn:aws:lambda:eu-west-1:12345678910:function:test",
   "lambda_request_id":"52fdfc07-2182-154f-163f-5f0f9a621d72",
   "cold_start": "true",
   "message": "Collecting payment"
}

{  
   "timestamp":"2019-08-22 18:17:33,774",
   "level":"INFO",
   "location":"collect.handler:15",
   "service":"payment",
   "lambda_function_name":"test",
   "lambda_function_memory_size":"128",
   "lambda_function_arn":"arn:aws:lambda:eu-west-1:12345678910:function:test",
   "lambda_request_id":"52fdfc07-2182-154f-163f-5f0f9a621d72",
   "cold_start": "true",
   "message":{  
      "operation":"collect_payment",
      "charge_id": "ch_AZFlk2345C0"
   }
}

Custom Metrics async

NOTE: This will likely change after Beta in light of new Amazon CloudWatch embedded metric format, meaning we won't need an additional stack and interface could change.

This feature requires Custom Metrics SAR App in order to process canonical metric lines in CloudWatch Logs.

If you're starting from scratch, you may want to see a working example, tune to your needs and deploy within your account - Serverless Airline Log Processing Stack

from aws_lambda_powertools.logging import MetricUnit, log_metric

def handler(event, context)
  log_metric(name="SuccessfulPayment", unit=MetricUnit.Count, value=10, namespace="MyApplication")
  
  # Optional dimensions
  log_metric(name="SuccessfulPayment", unit=MetricUnit.Count, value=10, namespace="MyApplication", customer_id="123-abc", charge_id="abc-123")
  
  # Explicit service name
  log_metric(service="paymentTest", name="SuccessfulPayment", namespace="MyApplication".....)
  ...

Exerpt output in CloudWatch Logs

MONITORING|10|Count|SuccessfulPayment|MyApplication|service="payment
MONITORING|10|Count|SuccessfulPayment|MyApplication|customer_id="123-abc",charge_id="abc-123",service="payment
MONITORING|10|Count|SuccessfulPayment|MyApplication|service="paymentTest

Beta

This library may change its API/methods or environment variables as it receives feedback from customers. Currently looking for ideas in the following areas before making it stable:

• Should Tracer patch all possible imported libraries by default or only AWS SDKs?
  • Patching all libraries may have a small performance penalty (~50ms) at cold start
  • Alternatively, we could patch only AWS SDK if available and to provide a param to patch multiple Tracer(modules=("boto3", "requests"))
• Create a Tracer provider to support additional tracing
  • Either duck typing or ABC to allow additional tracing providers

TODO

• Enable CI
• Add an example code using powertools
• Automate release and version bumping in CI

Credits

• Structured logging initial implementation from aws-lambda-logging
• Powertools idea DAZN Powertools

License

This library is licensed under the MIT-0 License. See the LICENSE file.