AWS exponential back off with boto3

boto-backoff.py

import boto3
from botocore.exceptions import ClientError

class BotoBackoff(object):
    """
    Wrap a client for an AWS service such that every call is backed by exponential backoff with jitter.

    Examples:
        >>> ecs = BotoBackoff('ecs')
        >>> ecs.list_tasks(cluster='my-cluster')


    Args:
        service (str): Name of AWS Service to wrap.
        min_sleep_time (float): The minimum amount of time to sleep in case of failure.
        max_retries (int): The maximum amount of retries to perform.
    """

    def __init__(self, service, min_sleep_time=1e-2, max_retries=15):
        self._service = boto3.client(service)
        self.min_sleep_time = min_sleep_time
        self.max_retries = max_retries
        self.logger = logging.getLogger(LOGGER_NAME)

    def __getattr__(self, item):
        fn = getattr(self._service, item)
        if not callable(fn):
            return fn

        def call_with_backoff(**api_kwargs):

            num_retries = 0
            while True:
                try:
                    self.logger.debug('BotoBackoff Calling {}'.format(fn))
                    return fn(**api_kwargs)
                except ClientError as err:
                    if "Rate exceeded" in err.args[0]:
                        # if we hit the retry limit, we'll go to sleep for a bit then try again.
                        # the number of retries determines our sleep time. This thread will sleep for
                        # min_sleep_time * random.randint(1, 2 ** num_retries), up to at most
                        # min_sleep_time * max_retries.
                        # After max_retries, we can't give up, so we scale back the number of retries by a random int
                        # to avoid collision with other threads.
                        num_retries += 1
                        if num_retries > self.max_retries:
                            num_retries = random.randint(1, self.max_retries)
                        sleep_time = self.min_sleep_time * random.randint(1, 2 ** num_retries)
                        self.logger.debug("{} Hit retry limit, sleeping for {} seconds".format(item, sleep_time))
                        self.logger.debug("arguments: {}".format(json.dumps(api_kwargs, indent=4, separators=(',', ': '))))
                        self.logger.error(err)
                        time.sleep(sleep_time)
                    else:
                        # let the caller handle every other error.
                        raise

        return call_with_backoff
      

when the rate is exceeded, is there a way we can capture the revised payload UnprocessedItems or do we need to retry writing the whole payload again?