Python utilities for AWS Lambda functions including but not limited to tracing, logging and custom metric
Project description
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 - Compatible with Python >=3.6.
During beta, this library may change its API/methods, or environment variables as it receives feedback from customers.
- Status: Beta
- How long until GA?: Current progress
Features
- Capture cold start as annotation, and response and exceptions as metadata
- Run functions locally with SAM CLI without code change to disable tracing
- Explicitly disable tracing via env var
POWERTOOLS_TRACE_DISABLED="true"
- Support tracing async methods
- Capture key fields from Lambda context, cold start and structures logging output as JSON
- Log Lambda event when instructed (disabled by default)
- Enable via
POWERTOOLS_LOGGER_LOG_EVENT="true"
or explicitly via decorator param
- Enable via
- 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%
- Enable via
- Append additional keys to structured log at any point in time
- 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)
- 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
See example of all features, testing, and a SAM template with all Powertools env vars. All features also provide full docs, and code completion for VSCode and PyCharm.
Installation
With pip installed, run: pip install aws-lambda-powertools
Tracing
Tracing Lambda handler and a function
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):
ret = requests.post(PAYMENT_ENDPOINT) # logic
tracer.put_annotation("PAYMENT_STATUS", "SUCCESS") # custom annotation
return ret
@tracer.capture_lambda_handler
def handler(event, context)
charge_id = event.get('charge_id')
payment = collect_payment(charge_id)
...
Tracing asynchronous functions
import asyncio
from aws_lambda_powertools.tracing import Tracer
tracer = Tracer()
# tracer = Tracer(service="payment") # can also be explicitly defined
@tracer.capture_method
async def collect_payment(charge_id):
...
@tracer.capture_lambda_handler
def handler(event, context)
charge_id = event.get('charge_id')
payment = asyncio.run(collect_payment(charge_id)) # python 3.7+
...
Tracing concurrent asynchronous with gather
:warning: This will no longer be necessary after this X-Ray recorder issue is resolved as it's an edge case. :warning:
To safely workaround this issue, use @tracer.capture_method
on functions not being run with async.gather
, and instead use in_subsegment_async
context manager escape hatch to have the same tracing effect.
import asyncio
from aws_lambda_powertools.tracing import Tracer
tracer = Tracer()
# tracer = Tracer(service="payment") # can also be explicitly defined
async def another_async_task():
async with tracer.provider.in_subsegment_async("## another_async_task"):
...
async def another_async_task_2():
async with tracer.provider.in_subsegment_async("## another_async_task_2"):
...
@tracer.capture_method
async def collect_payment(charge_id):
asyncio.gather(another_async_task(), another_async_task_2())
...
@tracer.capture_lambda_handler
def handler(event, context)
charge_id = event.get('charge_id')
payment = asyncio.run(collect_payment(charge_id)) # python 3.7+
...
Using escape hatch mechanisms
You can use tracer.provider
attribute to access all methods provided by xray_recorder
. This is useful when you need a feature available in X-Ray that is not available in the Tracer middleware, for example thread-safe, or context managers.
Example using aiohttp with an async context manager
import asyncio
import aiohttp
from aws_lambda_powertools.tracing import Tracer, aiohttp_trace_config
tracer = Tracer()
# aiohttp_trace_config is x-ray extension for aiohttp trace config known as aws_xray_trace_config
async def aiohttp_task():
# Async context manager as opposed to `@tracer.capture_method`
async with tracer.provider.in_subsegment_async("## aiohttp escape hatch"):
async with aiohttp.ClientSession(trace_configs=[aiohttp_trace_config()]) as session:
async with session.get("https://httpbin.org/json") as resp:
resp = await resp.json()
return resp
@tracer.capture_method
async def async_tasks():
ret = await aiohttp_task()
...
return {
"task": "done",
**ret
}
@tracer.capture_lambda_handler
def handler(event, context)
ret = asyncio.run(async_tasks()) # python 3.7+
...
Using 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
Structuring logs with Lambda context info
from aws_lambda_powertools.logging import Logger
logger = Logger()
# Logger(service="payment", level="INFO") # also accepts explicit service name, 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",
"sampling_rate": 0.1,
"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",
"sampling_rate": 0.1,
"message":{
"operation":"collect_payment",
"charge_id": "ch_AZFlk2345C0"
}
}
Appending additional keys to current logger
from aws_lambda_powertools.logging import Logger
logger = Logger()
@logger.inject_lambda_context
def handler(event, context)
if "order_id" in event:
logger.structure_logs(append=True, order_id=event["order_id"])
logger.info("Collecting payment")
...
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",
"sampling_rate": 0.1,
"order_id": "order_id_value",
"message": "Collecting payment"
}
Metrics
This feature makes use of CloudWatch Embedded Metric Format (EMF), and metrics are created asynchronously by CloudWatch service.
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
If using multiple middlewares, use log_metrics
as the last decorator, or else it 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")
NOTE: When using Metrics() in multiple places in your code, make sure to use
POWERTOOLS_METRICS_NAMESPACE
env var, or setting namespace param.
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
Tracing middleware execution
This makes use of an existing Tracer instance that you may have initialized anywhere in your code. If no Tracer instance is found, it'll initialize one using default options.
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
Debug mode
By default, all log statements from AWS Lambda Powertools package are suppressed. If you'd like to enable them, use set_package_logger
utility:
import aws_lambda_powertools
aws_lambda_powertools.logging.logger.set_package_logger()
...
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for aws_lambda_powertools-0.9.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | dc7a8f829c636d9d3391d0fe7741fcddbb5ce8eebca381b291d42ad4f03d7844 |
|
MD5 | 041ce034e8f806481655a670eba7b2b4 |
|
BLAKE2b-256 | 32748c3c42a6d7b4b5322183b8d45dde646bc9351b9be5cac47b6ce3da8553ad |
Hashes for aws_lambda_powertools-0.9.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3bdef2911132d0f2c9206496e93151cc5334e37f2df0a347c95a4ce551805ba1 |
|
MD5 | c48a8b443349694f9eb4d231a9d75f75 |
|
BLAKE2b-256 | d3d8394bf3917dc6e981445486280d8fec4436ff89af92844d9c6ccaaa5e2891 |