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wait_chaina� Chain two or more waiting strategies.
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@retry(wait=wait_chain(*[wait_fixed(1) for i in range(3)] +
[wait_fixed(2) for j in range(5)] +
[wait_fixed(5) for k in range(4)))
def wait_chained():
print("Wait 1s for 3 attempts, 2s for 5 attempts and 5s
thereafter.")
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d �Z dS ) �wait_incrementingz�Wait an incremental amount of time after each attempt.
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�wait_exponentiala Wait strategy that applies exponential backoff.
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upper and lower limits to some maximum and minimum value.
The intervals are fixed (i.e. there is no jitter), so this strategy is
suitable for balancing retries against latency when a required resource is
unavailable for an unknown duration, but *not* suitable for resolving
contention between multiple processes for a shared resource. Use
wait_random_exponential for the latter case.
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r r r r r6 � s 4r6 c s( e Zd ZdZded�� fdd� Z� ZS )�wait_random_exponentiala Random wait with exponentially widening window.
An exponential backoff strategy used to mediate contention between multiple
uncoordinated processes for a shared resource in distributed systems. This
is the sense in which "exponential backoff" is meant in e.g. Ethernet
networking, and corresponds to the "Full Jitter" algorithm described in
this blog post:
https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
Each retry occurs at a random time in a geometrically expanding interval.
It allows for a custom multiplier and an ability to restrict the upper
limit of the random interval to some maximum value.
Example::
wait_random_exponential(multiplier=0.5, # initial window 0.5s
max=60) # max 60s timeout
When waiting for an unavailable resource to become available again, as
opposed to trying to resolve contention for a shared resource, the
wait_exponential strategy (which uses a fixed interval) may be preferable.
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