v1.24.0 #932

heitorlessa · 2021-12-31T17:51:08Z

heitorlessa
Dec 31, 2021

Summary

For the last release of the year (happy 2022!), we bring a major enhancements to Idempotency and Feature Flags.

We also addresses important papercuts like caching parsed JSON data in Event Sources, support for datetime format codes in Logger and the ability to ignore certain endpoints from being traced.

Did I say that 90% of this release was contributed by the community? thank you everyone!!!

HUGE shoutout to @DanyC97 on helping us make all of our documentation banners (warning, tip) consistent.

Big thanks to new contributors too (you rock!)

@trey-rosius for adding a medium-size GraphQL API example using a myriad of Lambda Powertools features
@nayaverdier for a future proof change on how we internally convert string to bool (distutils being deprecated in Python 3.12)

idempotent_function now supports dataclasses & Pydantic models

Docs with samples

When using idempotent_function to make any Python synchronous function idempotent, you might have data available as Dataclasses or Pydantic models, not just dictionaries.

This release gives you more flexibility on what data can be used as your idempotency token - it could be an entire Dataclass, Pydantic model, or fields within these models.

Going beyond boolean feature flags

Docs with sample

You can now use the new boolean_feature: false parameter in your schema to signal Feature Flags that you will return any JSON valid value.

Example scenario: you might have a list of features to unlock for premium customers, or a set of beta features for select customers

{
    "premium_features": {
        "boolean_type": false,
        "default": [],
        "rules": {
            "customer tier equals premium": {
                "when_match": ["no_ads", "no_limits", "chat"],
                "conditions": [
                    {
                        "action": "EQUALS",
                        "key": "tier",
                        "value": "premium"
                    }
                ]
            }
        }
    }
}

Translating to the following API:

from aws_lambda_powertools.utilities.feature_flags import FeatureFlags, AppConfigStore

app_config = AppConfigStore(
    environment="dev",
    application="product-catalogue",
    name="features"
)

feature_flags = FeatureFlags(store=app_config)

def lambda_handler(event, context):
    # Get customer's tier from incoming request
    ctx = { "tier": event.get("tier", "standard") }

    # Evaluate `has_premium_features` base don customer's tier
    premium_features: list[str] = feature_flags.evaluate(name="premium_features",
                                                        context=ctx, default=False)
    for feature in premium_features:
        # enable premium features
        ...

Ignoring HTTP endpoints from tracer

AWS X-Ray has a limit of 64K tracing data. This could be a problem if you're making hundreds of HTTP requests to the same endpoint.

Alternatively, there are sensitive endpoints you might want them to not be included in your tracing data.

You can now use ignore_endpoint for this purpose - globs (*) are allowed!

from aws_lambda_powertools import Tracer

tracer = Tracer()
# ignore all calls to `ec2.amazon.com`
tracer.ignore_endpoint(hostname="ec2.amazon.com")
# ignore calls to `*.sensitive.com/password` and  `*.sensitive.com/credit-card`
tracer.ignore_endpoint(hostname="*.sensitive.com", urls=["/password", "/credit-card"])


def ec2_api_calls():
    return "suppress_api_responses"

@tracer.capture_lambda_handler
def handler(event, context):
    for x in long_list:
        ec2_api_calls()