Webhook Retries
Webhook retries are the mechanism of reattempting to deliver webhook events over a specified period, ensuring they eventually reach their destination. Given the inherent unreliability of network connections, retries play a critical role in guaranteeing that events are successfully delivered, even in cases of temporary disruptions.
Convoy is a flexible webhook gateway that can be configured to use either linear or exponential backoff retry strategy.
Comparing Retry Algorithms for Webhooks
Retry algorithms aren’t specific to webhooks, rather they’re a general technique used to solve various problems in distributed systems. But what would be appropriate retry configuration for a webhook provider? Most providers offer retries with exponential backoff. Let’s assess three retry strategies for webhooks
Linear RetryExponential BackoffExponential Backoff with capped duration
An ideal webhook retry strategy should meet the following requirements: [1]
- Requirement 1: The first 5 retries should happen in less than 30 minutes.
- Requirement 2: The maximum retry should not exceed 24 hours.
Why? Webhooks power real-time notifications across apps. The keyword here is real-time. As with many things in software engineering, that word is overloaded and can mean different things depending on the context. In high-frequency trading, even a few microseconds of delay can result in significant financial losses. But this isn’t the case for a vast majority of webhooks. For example, a webhook from Linear to Slack with a 1-minute delay wouldn’t result in any significant financial loss or serious disruption to your business, especially when the delay is a result of failure in initial attempts.
Here’s the configuration for each retry strategy compared:
| Strategy | Formula |
|---|---|
| Linear Retry | + 1 hour |
| Exponential Backoff | (delaySeconds * 2^attempt) + jitter |
| Exponential Backoff with Capped Duration | min(delaySeconds * 2^attempt, maxRetrySeconds) + jitter |
With the following parameters
- Base: 20 seconds
- Jitter: +/-10%
- Delay Growth:
delaySeconds * 2^attempt - Retry Limit: 20
To provide additional context, the formula is defined as follows:
- Delay Growth: The delay doubles with each retry attempt, allowing for a rapid increase in wait time for successive failures (
delaySeconds * 2^attempt). - Capping: The new strategy incorporates a default (but configurable) cap of 7200 seconds (
maxRetrySeconds) to prevent indefinitely long wait times, thus ensuring the system remains responsive and efficient. - Jitter: A
jittervalue is added to each calculated wait time, providing an additional layer of randomness that further helps distribute the retry attempts over time.
We wrote a script to run these comparisons here. Feel free to tweak the input and analyse the output.
The Results
A graph comparing the cumulative retry duration for two retry strategies
Given our requirements above, let’s evaluate the two retry strategies shown in the graph based on these criteria:
| Linear Retry | Exponential Backoff with Capped Duration | |
|---|---|---|
| Requirement 1 The first 5 retries should happen in less than 30 minutes | The graph shows that the linear retry strategy takes about 18,000 seconds by the 5th retry. 18,000 seconds is approximately 5 hours, which exceeds the 30-minute limit for the first 5 retries. | The graph shows that the capped duration strategy takes about 601 seconds by the 5th retry. 601 seconds is approximately 10 minutes and 1 second, which doesn’t exceed the 30-minute limit for the first 5 retries. |
| Requirement 2 The maximum retry should happen not greater than 24 hours | By the 20th retry attempt, the linear strategy takes about 72,000 seconds, which translates to 20 hours, which doesn’t exceed the 24 hours rule. | By the 20th retry attempt the capped duration strategy takes about 89559 seconds, which translates to 24 hours 52 minutes which slightly exceeds the 24 hours rule. |
We didn’t include the pure exponential backoff strategy above because the chart looks ridiculous compared to the rest.
An additional graph includes exponential backoff.
Appendix
- These numbers aren’t a hard and fast rule; rather, they’re a framework for reasoning about the latency requirements for your webhooks and tuning them accordingly. This would optimize your entire delivery process and save $$$ in compute. Feel free to use our script - compare-retries.
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