aws-lambda-powertools 0.7.0

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
• Log sampling enables DEBUG log level for a percentage of requests (disabled by default)
  • Enable via POWERTOOLS_LOGGER_SAMPLE_RATE=0.1, ranges from 0 to 1, where 0.1 is 10% and 1 is 100%

Metrics

• Aggregate up to 100 metrics using a single CloudWatch Embedded Metric Format object (large JSON blob)
• Context manager to create an one off metric with a different dimension than metrics already aggregated
• Validate against common metric definitions mistakes (metric unit, values, max dimensions, max metrics, etc)
• No stack, custom resource, data collection needed — Metrics are created async by CloudWatch EMF

Bring your own middleware

• Utility to easily create your own middleware
• Run logic before, after, and handle exceptions
• Receive lambda handler, event, context
• Optionally create sub-segment for each custom middleware

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_TRACE_MIDDLEWARES	Creates sub-segment for each middleware created by lambda_handler_decorator	"false"	middleware_factory
POWERTOOLS_LOGGER_LOG_EVENT	Logs incoming event	"false"	logging
POWERTOOLS_LOGGER_SAMPLE_RATE	Debug log sampling	0	logging
POWERTOOLS_METRICS_NAMESPACE	Metrics namespace	None	metrics
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)
  ...

Fetching a pre-configured tracer anywhere

# handler.py
from aws_lambda_powertools.tracing import Tracer
tracer = Tracer(service="payment")

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

# another_file.py
from aws_lambda_powertools.tracing import Tracer
tracer = Tracer(auto_patch=False) # new instance using existing configuration with auto patching overriden

Logging

Example SAM template using supported environment variables

Globals:
  Function:
    Environment:
        Variables:
            POWERTOOLS_SERVICE_NAME: "payment" 
            POWERTOOLS_LOGGER_SAMPLE_RATE: 0.1 # enable debug logging for 1% of requests, 0% by default
            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 log_metric will be removed once it's GA.

This feature makes use of CloudWatch Embedded Metric Format (EMF) and metrics are created asynchronously by CloudWatch service

Contrary to log_metric, you don't need any custom resource or additional CloudFormation stack anymore.

Metrics middleware validates against the minimum necessary for a metric to be published:

• At least of one Metric and Dimension
• Maximum of 9 dimensions
• Only one Namespace
• Any Metric unit supported by CloudWatch

Creating multiple metrics

log_metrics decorator calls the decorated function, so leave that for last decorator or will fail with SchemaValidationError if no metrics are recorded.

from aws_lambda_powertools.metrics import Metrics, MetricUnit

metrics = Metrics()
metrics.add_namespace(name="ServerlessAirline")
metrics.add_metric(name="ColdStart", unit="Count", value=1)
metrics.add_dimension(name="service", value="booking")

@metrics.log_metrics
@tracer.capture_lambda_handler
def lambda_handler(evt, ctx):
    metrics.add_metric(name="BookingConfirmation", unit="Count", value=1)
    some_code()
    return True

def some_code():
    metrics.add_metric(name="some_other_metric", unit=MetricUnit.Seconds, value=1)
    ...

CloudWatch EMF uses the same dimensions across all metrics. If you have metrics that should have different dimensions, use single_metric to create a single metric with any dimension you want. Generally, this would be an edge case since you pay for unique metric

unique metric = (metric_name + dimension_name + dimension_value)

from aws_lambda_powertools.metrics import MetricUnit, single_metric

with single_metric(name="ColdStart", unit=MetricUnit.Count, value=1) as metric:
    metric.add_dimension(name="function_context", value="$LATEST")

Utilities

Bring your own middleware

This feature allows you to create your own middleware as a decorator with ease by following a simple signature.

• Accept 3 mandatory args - handler, event, context
• Always return the handler with event/context or response if executed
  • Supports nested middleware/decorators use case

Middleware with no params

from aws_lambda_powertools.middleware_factory import lambda_handler_decorator

@lambda_handler_decorator
def middleware_name(handler, event, context):
    return handler(event, context)

@lambda_handler_decorator
def middleware_before_after(handler, event, context):
    logic_before_handler_execution()
    response = handler(event, context)
    logic_after_handler_execution()
    return response


# middleware_name will wrap Lambda handler 
# and simply return the handler as we're not pre/post-processing anything
# then middleware_before_after will wrap middleware_name
# run some code before/after calling the handler returned by middleware_name
# This way, lambda_handler is only actually called once (top-down)
@middleware_before_after # This will run last
@middleware_name # This will run first
def lambda_handler(event, context):
    return True

Middleware with params

@lambda_handler_decorator
def obfuscate_sensitive_data(handler, event, context, fields=None):
    # Obfuscate email before calling Lambda handler
    if fields:
        for field in fields:
            field = event.get(field, "")
            event[field] = obfuscate_pii(field)

    return handler(event, context)

@obfuscate_sensitive_data(fields=["email"])
def lambda_handler(event, context):
    return True

Optionally trace middleware execution

This makes use of an existing Tracer instance that you may have initialized anywhere in your code, otherwise it'll initialize one using default options and provider (X-Ray).

from aws_lambda_powertools.middleware_factory import lambda_handler_decorator

@lambda_handler_decorator(trace_execution=True)
def middleware_name(handler, event, context):
    return handler(event, context)

@middleware_name
def lambda_handler(event, context):
    return True

Optionally, you can enrich the final trace with additional annotations and metadata by retrieving a copy of the Tracer used.

from aws_lambda_powertools.middleware_factory import lambda_handler_decorator
from aws_lambda_powertools.tracing import Tracer

@lambda_handler_decorator(trace_execution=True)
def middleware_name(handler, event, context):
    tracer = Tracer() # Takes a copy of an existing tracer instance
    tracer.add_anotation...
    tracer.metadata...
    return handler(event, context)

@middleware_name
def lambda_handler(event, context):
    return True

Beta

Progress towards GA

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"))