async-durable-execution 2.0.0rc1

Community-maintained durable execution SDK for AWS Lambda in Python

Async Durable Execution for Python

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Build reliable, long-running AWS Lambda workflows with checkpointed steps, waits, callbacks, and parallel execution.

This repository is a community-maintained fork of the original Apache-2.0 licensed AWS project and continues to ship under Apache License 2.0 with the upstream notices preserved.

This fork is specifically focused on making async Python work naturally with durable functions. The public API remains synchronous at the durable operation boundary, but user-provided durable callables must now use async def for handlers, steps, child contexts, callback submitters, and condition checks.

✨ Key Features

• Community-maintained fork of the official AWS SDK - This repository builds on aws/aws-durable-execution-sdk-python and preserves the upstream Apache-2.0 notices while evolving the Python experience independently
• Async-first user programming model - Unlike the official library, this fork requires user-provided durable code to use async def for handlers, steps, child contexts, callback submitters, and wait-for-condition checks
• Ergonomic async call-site helpers - Use @durable_callable together with top-level awaitable operations like step(...), wait(...), and run_in_child_context(...) to keep durable workflow code explicit and natural in async Python
• Same durable primitives, adapted for async Python - Checkpointed steps, waits, callbacks, parallel branches, maps, retries, and child contexts are all preserved, but tuned for an async execution style
• Replay-safe logging with stdlib logging - Use standard logging loggers enriched by the durable context filter instead of relying on ad hoc logging patterns
• SDK and runner shipped together - The execution SDK now includes the local/cloud runner through the async_durable_execution public API
• Stronger local and cloud validation workflow - The repo includes runner integration examples and GitHub Actions automation for build, test, and generated API docs

📦 Packages

Package	Description	Version
async-durable-execution	Execution SDK, local/cloud test runner, and pytest helpers for Lambda durable functions
async-durable-execution-examples	Example durable functions and integration tests for local and cloud workflows	Shared repo version

Install async-durable-execution and import runner helpers from async_durable_execution.

🚀 Quick Start

This fork now requires async callables for all user-provided durable code. Requires Python 3.10 or newer.

Install the execution SDK:

pip install async-durable-execution

For an async Lambda service client, install the optional aioboto extra:

pip install "async-durable-execution[aioboto]"

The aioboto extra installs the published aiobotocore package. When it is installed, the SDK creates an async Lambda client by default for durable checkpoint and state APIs. Without the extra, it continues to use the bundled botocore dependency through a threaded async adapter. Explicitly provided Lambda API clients are detected as sync or async and wrapped accordingly. Code that must force the sync botocore client can use async_durable_execution.client.create_default_sync_client().

To share the SDK through a Lambda layer instead of vendoring it in each function zip, publish the repository-built layer from GitHub Actions or build a local layer archive from this checkout:

hatch run python scripts/build_layer.py \
  --sdk-source async-durable-execution \
  --output dist/async-durable-execution-layer.zip

Publish the zip as an AWS::Serverless::LayerVersion or AWS::Lambda::LayerVersion, then add the layer ARN to Python durable functions.

Create a durable Lambda handler:

import asyncio
import logging
from datetime import timedelta

from async_durable_execution import (
    durable_callable,
    durable_execution,
    step,
    wait,
)

logger = logging.getLogger(__name__)


@durable_callable
async def validate_order(order_id: str) -> dict:
    await asyncio.sleep(0)
    logger.info("Validating order", extra={"order_id": order_id})
    return {"order_id": order_id, "valid": True}


@durable_callable
async def create_receipt(order_id: str) -> dict:
    await asyncio.sleep(0)
    logger.info("Creating receipt", extra={"order_id": order_id})
    return {"receipt_id": f"receipt-{order_id}", "order_id": order_id}


@durable_execution
async def handler(event: dict) -> dict:
    order_id = event["order_id"]
    logger.info("Starting workflow", extra={"order_id": order_id})

    validation = await step(validate_order(order_id), name="validate_order")
    if not validation["valid"]:
        return {"status": "rejected", "order_id": order_id}

    # simulate approval (real world: use wait_for_callback)
    await wait(duration=timedelta(seconds=5), name="await_confirmation")

    receipt = await step(create_receipt(order_id), name="create_receipt")

    return {"status": "approved", "order_id": order_id, "receipt": receipt}

Async callables are required anywhere the SDK accepts user code, including map() item functions, bound parallel() branch callables, child contexts, callback submitters, and wait-for-condition checks. Durable context operations are awaitable and run on the same event loop as your handler.

When decorating class or static methods, @durable_callable can be used in either order with @classmethod or @staticmethod; both of these are valid:

class Steps:
    @durable_callable
    @classmethod
    async def from_class(cls) -> str:
        return cls.__name__

    @staticmethod
    @durable_callable
    async def from_static() -> str:
        return "ok"

Handler input is deserialized from the durable execution payload before your code runs. Empty or whitespace payloads are normalized to {}, and malformed JSON fails the invocation before user code executes.

🧪 Testing Durable Functions

The SDK includes runner helpers for testing durable functions locally or against deployed Lambda functions:

pip install async-durable-execution

The local runner executes the durable handler in process, intercepts checkpoint operations with an in-memory service client, and returns a DurableFunctionTestResult that can be inspected by operation name.

Assuming the Quick Start handler above is saved in order_workflow.py, a local test can run the same durable function:

import json

from async_durable_execution import (
    DurableFunctionTestResult,
    InvocationStatus,
    create_local_runner,
)

from order_workflow import handler


async def test_my_durable_function() -> None:
    with create_local_runner(
        handler=handler,
        input={"order_id": "order-123"},
        timeout=10,
    ) as runner:
        result: DurableFunctionTestResult = await runner.run()

    receipt = {"receipt_id": "receipt-order-123", "order_id": "order-123"}

    assert result.status is InvocationStatus.SUCCEEDED
    assert result.result == json.dumps(
        {"status": "approved", "order_id": "order-123", "receipt": receipt}
    )

    validation_result = result.get_step("validate_order")
    assert validation_result.step_details is not None
    assert validation_result.step_details.result == json.dumps(
        {"order_id": "order-123", "valid": True}
    )

    receipt_result = result.get_step("create_receipt")
    assert receipt_result.step_details is not None
    assert receipt_result.step_details.result == json.dumps(receipt)

Use the runner-specific factory for the environment you want to test:

from async_durable_execution import create_cloud_runner, create_local_runner

from order_workflow import handler


async def test_with_factory() -> None:
    with create_local_runner(
        handler=handler,
        input={"order_id": "order-123"},
        timeout=12,
    ) as runner:
        local_result = await runner.run()

    with create_cloud_runner(
        function_name="order-workflow:$LATEST",
        region="us-east-1",
        input={"order_id": "order-123"},
        timeout=45,
    ) as runner:
        cloud_result = await runner.run()

🧩 Example Integration Tests

The examples package includes pytest coverage that can run against either the local in-memory runner or deployed AWS Lambda durable functions.

Local mode is the default and does not require AWS credentials:

# Run all example tests locally from the repo root.
hatch run test:examples

# Or run pytest directly with an explicit mode.
pytest --runner-mode=local async-durable-execution-examples/test_examples/

# Run a specific example test.
pytest --runner-mode=local -k test_hello_world async-durable-execution-examples/test_examples/

Cloud mode exercises deployed Lambda functions with DurableFunctionCloudTestRunner:

# Build the example bundle from the repo root.
hatch run examples:build

# Generate a one-example SAM template.
hatch run examples:generate-sam-template -- --example-name "Hello World"

# Deploy the function with SAM.
sam build --template-file async-durable-execution-examples/template.generated.json
sam deploy \
  --template-file .aws-sam/build/template.yaml \
  --stack-name hello-world-test \
  --resolve-s3 \
  --capabilities CAPABILITY_IAM \
  --no-confirm-changeset \
  --parameter-overrides \
    FunctionName=HelloWorld-Test \
    LambdaEndpoint=https://lambda.eu-south-1.amazonaws.com

# Configure cloud test discovery.
export AWS_REGION=eu-south-1
export LAMBDA_ENDPOINT=https://lambda.eu-south-1.amazonaws.com
export QUALIFIED_FUNCTION_NAME="HelloWorld-Test:$LATEST"

# Run one cloud-backed example test.
pytest --runner-mode=cloud -k test_hello_world async-durable-execution-examples/test_examples/

# Or run via hatch.
hatch run test:examples-integration -k test_hello_world

For full-suite cloud runs where functions share a deployment prefix:

export PYTEST_FUNCTION_NAME_PREFIX="py313-"
hatch run test:examples-integration

Example tests use the durable_runner pytest fixture as a factory context manager:

from async_durable_execution import InvocationStatus
from async_durable_execution_examples import hello_world


async def test_hello_world(durable_runner):
    with durable_runner(
        handler=hello_world.handler,
        input="test",
        timeout=30,
    ) as runner:
        result = await runner.run()

    assert result.status is InvocationStatus.SUCCEEDED
    assert result.get_deserialized_result() == {
        "statusCode": 200,
        "body": "Hello from Durable Lambda! (status: 200)",
    }

Cloud test configuration:

Setting	Description
AWS_REGION	AWS region for Lambda invocation. Defaults to eu-south-1.
LAMBDA_ENDPOINT	Optional Lambda endpoint URL for testing.
PYTEST_FUNCTION_NAME_PREFIX	Prefix used to derive deployed qualified function names for all examples.
QUALIFIED_FUNCTION_NAME	Optional fallback for single-function cloud runs.
--runner-mode	Pytest mode: local or cloud.

📚 Documentation

The complete documentation for the AWS Durable Execution SDK for Python lives on the AWS Documentation site:

• Generated API Reference - Auto-generated from Python docstrings and published with GitHub Pages
• AWS Durable Execution Documentation - Concepts, getting started, core operations, advanced topics, and API reference
• AWS Lambda Durable Functions Guide - How durable functions work on Lambda
• Migration Guide - Move from the official synchronous Python SDK to this async-first SDK
• Using Synchronous Code - Wrap existing synchronous business logic and blocking clients safely
• Runner Architecture - Local and cloud runner execution flow, components, and diagrams
• Contributing Guide - Development workflow, Hatch commands, testing, and pull request guidance

💬 Feedback & Support

• Bug report
• Feature request
• Documentation feedback
• Contributing guide

📄 License

See the LICENSE file for our project's licensing.