Getting Started

Installation

pip install capo-auto-scaling-plans

Usage

from capo_auto_scaling_plans import AsyncAutoScalingPlansClient


async def main():
    async with AsyncAutoScalingPlansClient() as auto_scaling_plans:
        # Example: call the create_scaling_plan operation
        response = await auto_scaling_plans.create_scaling_plan()
        print(response["scaling_plan_version"])

Error Handling

The SDK raises exceptions for errors returned by the API. Catch them to handle failures gracefully.

from capo_auto_scaling_plans import AsyncAutoScalingPlansClient
from capo_auto_scaling_plans.error import ConcurrentUpdateException


async def main():
    async with AsyncAutoScalingPlansClient() as auto_scaling_plans:
        try:
            await auto_scaling_plans.create_scaling_plan()
        except ConcurrentUpdateException as e:
            print(f"Error: {e}")
            print(e.data)  # additional error data

Retrying

The SDK retries failed operations automatically. Retry behaviour follows the Smithy specification: errors are retried based on their is_retryable and is_throttling_error attributes. Throttling errors use a longer base delay. Network-level failures (connection errors and timeouts) are also retried. Non-retryable errors, such as client errors without the @retryable trait, are raised immediately without further attempts.

The number of attempts defaults to 3 and can be changed at the client level via retry_max_attempts, or per call via config_overrides.

from capo_auto_scaling_plans import AsyncAutoScalingPlansClient


async def main():
    async with AsyncAutoScalingPlansClient() as auto_scaling_plans:
        # Default: 3 attempts for every operation
        response = await auto_scaling_plans.create_scaling_plan()

        # Override per operation
        response = await auto_scaling_plans.create_scaling_plan(config_overrides={"retry_max_attempts": 5})

        # Disable retries for this call
        response = await auto_scaling_plans.create_scaling_plan(config_overrides={"retry_max_attempts": 1})