Better avoid and handle AWS API throttling#459
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| while True: | ||
| try: | ||
| [self.task] = ( | ||
| await ecs.describe_tasks( | ||
| cluster=self.cluster_arn, tasks=[self.task_arn] | ||
| ) | ||
| )["tasks"] | ||
| except ClientError as e: | ||
| if e.response["Error"]["Code"] == "ThrottlingException": | ||
| wait_duration = min(wait_duration * 2, 20) | ||
| else: | ||
| raise | ||
| else: | ||
| break | ||
| await asyncio.sleep(wait_duration) |
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Am I right in understanding that we are removing our retry logic here and leveraging the built-in retries in aiobotocore?
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Correct. Sorry, I stuck this draft PR up as a placeholder and hadn't yet added a detailed description to describe this. Done now.
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The current ECS implementation has a couple issues regarding AWS API throttling that are especially problematic when starting a large cluster (over ~100 workers)
Workermakes its own call toecs.list_account_settingsto determine iftaskLongArnFormatis enabled rather than doing this just once at theECSClusterlevel. This PR optimized some of the easy cases like this. The most notable one that's NOT addressed isecs.describe_tasksthat each worker polls as it's waiting to come up.References: