Retry Policies in Temporal
In this post, we will give you information about Retry Policies in Temporal. Here we will give you detail about Retry Policies in Temporal And how to use it also give you a demo for it if it is necessary
RetryPolicy instances in Temporal allow you to define how Temporal retries Activities.
You can specify options like the number of times to retry before failing and how long to wait between retries.
Below are the supported options:
backoffCoefficient: Temporal will multiply how long it waits between retries by this number after every failure
initialInterval: The amount of time Temporal should wait to retry after the first failure
maximumAttempts: The maximum number of times Temporal should retry before erroring out
maximumInterval: The maximum amount of time Temporal will wait between retries
nonRetryableErrorTypes: Array of strings containing the errors to skip retrying
Below is a tool that calculates whether an activity succeeds or fails for a given retry policy.
Temporal means of, relating to, or measured by time. It can also refer to a particular period of time or a specific point in time. For example, ‘temporal lobe’ is a part of the brain that is involved in processing information about time.
Here are some examples of how the word ‘temporal’ can be used in a sentence:
The temporal lobe is responsible for processing information about time.
The temporal resolution of a camera is the amount of time it takes for the camera to capture an image.
The temporal sequence of events is the order in which they occurred.
I hope this helps! Let me know if you have any other questions.
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