aws-lambda-powertools 0.3.0

Python utilities for AWS Lambda functions including but not limited to tracing, logging and custom metric

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

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

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.

HISTORY

November 15th, 2019

• Public beta release