10 API Retry Best Practices
Retrying failed API requests is a common practice, but there are a few things you should keep in mind to make sure you're doing it correctly. Here are 10 best practices to follow.
API retries are an important part of any API integration. They help ensure that your API requests are successful and that your users have a good experience. But, if not done correctly, API retries can cause more harm than good.
In this article, we’ll discuss 10 best practices for API retries. We’ll cover topics such as when to retry, how many times to retry, and how to handle errors. By following these best practices, you can ensure that your API retries are successful and that your users have a great experience.
1. Use exponential backoff
When an API call fails, it’s likely that the server is overloaded or experiencing some other issue. If you keep sending requests at a constant rate, you’ll just add to the problem and make it worse.
Exponential backoff helps solve this problem by gradually increasing the time between retries. This gives the server more time to recover before your next request comes in. It also reduces the load on the server, which can help prevent future failures.
When implementing exponential backoff, start with a short delay (e.g., 1 second) and then double the delay each time until you reach a maximum value (e.g., 60 seconds). This will ensure that your requests are spaced out enough to give the server time to recover without taking too long for the user.
2. Retry on server errors only
When an API call fails due to a server error, it’s likely that the problem is temporary and can be resolved by retrying the request. On the other hand, if the failure is caused by a client-side issue (e.g., invalid input data), then retrying won’t help and will only waste resources.
Therefore, when implementing API retry logic, make sure you are only retrying on server errors such as 5xx HTTP status codes or connection timeouts. This way, you’ll ensure that your retries are effective and don’t cause unnecessary load on your servers.
3. Don’t retry on throttling errors
When an API is throttled, it means that the server has reached its maximum capacity and can no longer handle requests. Retrying in this situation will only add to the load on the server, making the problem worse. Instead of retrying, you should wait for a period of time before trying again. This gives the server time to recover from the overload and allows your request to go through without any issues.
4. Implement a timeout for each request
When an API request is sent, it can take a long time for the response to come back. If the response doesn’t come back within a certain amount of time, then you should consider that request as failed and retry it.
This helps ensure that your application isn’t waiting too long for a response from the API, which could cause performance issues or even lead to errors. It also ensures that if there are any network issues, they don’t affect your application’s performance.
You should set the timeout value based on how quickly you expect the API to respond. For example, if you’re expecting a response in less than one second, then you should set the timeout to one second. This way, if the API takes longer than expected, you’ll know right away and can retry the request.
5. Consider the idempotency of your requests
Idempotency is the property of an operation that can be applied multiple times without changing the result beyond the initial application. In other words, if you make a request to an API and it fails, then retry the same request again, the result should be the same as if you had only made the request once.
This means that when implementing API retry best practices, you need to ensure that your requests are idempotent. This will help prevent any unexpected results from occurring due to duplicate requests being sent to the API.
6. Keep track of failed requests
When an API request fails, it’s important to know why. Was the failure due to a network issue? A server error? Or was the data simply not found?
By keeping track of failed requests, you can quickly identify and address any issues that may be causing them. This will help ensure your API is running smoothly and efficiently. Additionally, tracking failed requests can also provide valuable insights into how users are interacting with your API. You can use this information to improve user experience and optimize performance.
7. Avoid duplicate requests
When you make a request to an API, it can take some time for the response to come back. If your application makes multiple requests in quick succession, there’s a chance that the same request will be sent more than once.
This is especially true if the first request fails due to a network error or other issue. To avoid this problem, use retry logic when making API calls. This means that if a request fails, the application should wait a few seconds before trying again. This ensures that only one request is made and reduces the chances of duplicate data being returned.
8. Limit the number of retries
If you don’t limit the number of retries, your API could end up in an infinite loop of trying to make a request and failing. This can cause performance issues for your application as well as put unnecessary strain on the server hosting the API.
To avoid this issue, it’s important to set a maximum number of retries that will be attempted before giving up. This should be based on the type of request being made and how critical it is to the success of the application. For example, if the request is non-critical, then fewer retries may be acceptable than if the request is essential for the application to function properly.
9. Make sure you have enough capacity to handle retries
When an API call fails, the retry process can cause a surge in traffic to your system. If you don’t have enough capacity to handle this surge, it could lead to further failures and degraded performance.
To ensure that you have enough capacity for retries, you should monitor your system’s performance closely and adjust your capacity as needed. You may also want to consider implementing rate limiting or other strategies to help manage the load on your system during periods of high demand. Additionally, make sure that your retry logic is designed to back off when necessary so that it doesn’t overwhelm your system with too many requests at once.
10. Monitor your API response times and error rates
When an API request fails, it’s important to understand why. Was the failure due to a network issue? A server-side error? Or something else entirely? By monitoring your API response times and error rates, you can quickly identify any issues that may be causing requests to fail. This allows you to take corrective action before the problem becomes too severe.
Additionally, by tracking your API response times and error rates over time, you can spot trends in performance and make adjustments as needed. For example, if you notice that your API is consistently responding slowly during peak hours, you can adjust your retry strategy accordingly.
