An empty effect to provide the retry requirements, e.g. MonadIO and MonadMask

Peel the Retry effect.

A RetryPolicyM is a function that takes an RetryStatus and possibly returns a delay in microseconds. Iteration numbers start at zero and increase by one on each retry. A *Nothing* return value from the function implies we have reached the retry limit.

Please note that RetryPolicyM is a Monoid. You can collapse multiple strategies into one using mappend or <>. The semantics of this combination are as follows:

1. If either policy returns Nothing, the combined policy returns Nothing. This can be used to inhibit after a number of retries, for example.
2. If both policies return a delay, the larger delay will be used. This is quite natural when combining multiple policies to achieve a certain effect.

Example:

One can easily define an exponential backoff policy with a limited number of retries:

> limitedBackoff = exponentialBackoff 50000 <> limitRetries 5

Naturally, mempty will retry immediately (delay 0) for an unlimited number of retries, forming the identity for the Monoid.

The default retry policy retryPolicyDefault implements a constant 50ms delay, up to 5 times:

> retryPolicyDefault = constantDelay 50000 <> limitRetries 5

For anything more complex, just define your own RetryPolicyM:

> myPolicy = retryPolicy $ \ rs -> if rsIterNumber rs > 10 then Just 1000 else Just 10000

Since 0.7.

Helper for making simplified policies that don't use the monadic context.

Default retry policy

How to handle a failed action.

Convert a boolean answer to the question "Should we retry?" into a RetryAction.

Datatype with stats about retries made thus far.

Initial, default retry status. Use fields or lenses to update.

For use with logRetries.

Implement a constant delay with unlimited retries.

Grow delay exponentially each iteration. Each delay will increase by a factor of two.

FullJitter exponential backoff as explained in AWS Architecture Blog article.

http://www.awsarchitectureblog.com/2015/03/backoff.html

temp = min(cap, base * 2 ** attempt)

sleep = temp / 2 + random_between(0, temp / 2)

Implement Fibonacci backoff.

Retry immediately, but only up to n times.

Add an upperbound to a policy such that once the given time-delay amount *per try* has been reached or exceeded, the policy will stop retrying and fail. If you need to stop retrying once *cumulative* delay reaches a time-delay amount, use limitRetriesByCumulativeDelay

Add an upperbound to a policy such that once the cumulative delay over all retries has reached or exceeded the given limit, the policy will stop retrying and fail.

Set a time-upperbound for any delays that may be directed by the given policy. This function does not terminate the retrying. The policy `capDelay maxDelay (exponentialBackoff n)` will never stop retrying. It will reach a state where it retries forever with a delay of maxDelay between each one. To get termination you need to use one of the limitRetries function variants.

Run given policy up to N iterations and gather results. In the pair, the Int is the iteration number and the Maybe Int is the delay in microseconds.

Run given policy up to N iterations and pretty print results on the console.