Retry
Clarissa Pfister
Enable an application to handle transient failures when it tries to connect to a service or network resource, by transparently retrying a failed operation. This can improve the stability of the application.
Retry. If the specific fault reported is unusual or rare, it might have been caused by unusual circumstances such as a network packet becoming corrupted while it was being transmitted. In this case, the application could retry the failing request again immediately because the same failure is unlikely to be repeated and the request will probably be successful.
Retry after delay. If the fault is caused by one of the more commonplace connectivity or busy failures, the network or service might need a short period while the connectivity issues are corrected or the backlog of work is cleared. The application should wait for a suitable time before retrying the request.
For the more common transient failures, the period between retries should be chosen to spread requests from multiple instances of the application as evenly as possible. This reduces the chance of a busy service continuing to be overloaded. If many instances of an application are continually overwhelming a service with retry requests, it'll take the service longer to recover.
If the request still fails, the application can wait and make another attempt. If necessary, this process can be repeated with increasing delays between retry attempts, until some maximum number of requests have been attempted. The delay can be increased incrementally or exponentially, depending on the type of failure and the probability that it'll be corrected during this time.
The application should wrap all attempts to access a remote service in code that implements a retry policy matching one of the strategies listed above. Requests sent to different services can be subject to different policies. Some vendors provide libraries that implement retry policies, where the application can specify the maximum number of retries, the time between retry attempts, and other parameters.
An application should log the details of faults and failing operations. This information is useful to operators. That being said, in order to avoid flooding operators with alerts on operations where subsequently retried attempts were successful, it is best to log early failures as informational entries and only the failure of the last of the retry attempts as an actual error. Here is an example of how this logging model would look like.
Microsoft Entity Framework provides facilities for retrying database operations. Also, most Azure services and client SDKs include a retry mechanism. For more information, see Retry guidance for specific services.
The retry policy should be tuned to match the business requirements of the application and the nature of the failure. For some noncritical operations, it's better to fail fast rather than retry several times and impact the throughput of the application. For example, in an interactive web application accessing a remote service, it's better to fail after a smaller number of retries with only a short delay between retry attempts, and display a suitable message to the user (for example, "please try again later"). For a batch application, it might be more appropriate to increase the number of retry attempts with an exponentially increasing delay between attempts.
An aggressive retry policy with minimal delay between attempts, and a large number of retries, could further degrade a busy service that's running close to or at capacity. This retry policy could also affect the responsiveness of the application if it's continually trying to perform a failing operation.
Consider whether the operation is idempotent. If so, it's inherently safe to retry. Otherwise, retries could cause the operation to be executed more than once, with unintended side effects. For example, a service might receive the request, process the request successfully, but fail to send a response. At that point, the retry logic might re-send the request, assuming that the first request wasn't received.
A request to a service can fail for a variety of reasons raising different exceptions depending on the nature of the failure. Some exceptions indicate a failure that can be resolved quickly, while others indicate that the failure is longer lasting. It's useful for the retry policy to adjust the time between retry attempts based on the type of the exception.
Consider how retrying an operation that's part of a transaction will affect the overall transaction consistency. Fine tune the retry policy for transactional operations to maximize the chance of success and reduce the need to undo all the transaction steps.
Ensure that all retry code is fully tested against a variety of failure conditions. Check that it doesn't severely impact the performance or reliability of the application, cause excessive load on services and resources, or generate race conditions or bottlenecks.
Implement retry logic only where the full context of a failing operation is understood. For example, if a task that contains a retry policy invokes another task that also contains a retry policy, this extra layer of retries can add long delays to the processing. It might be better to configure the lower-level task to fail fast and report the reason for the failure back to the task that invoked it. This higher-level task can then handle the failure based on its own policy.
The statement that invokes this method is contained in a try/catch block wrapped in a for loop. The for loop exits if the call to the TransientOperationAsync method succeeds without throwing an exception. If the TransientOperationAsync method fails, the catch block examines the reason for the failure. If it's believed to be a transient error the code waits for a short delay before retrying the operation.
If you don't want to use a fixed wait duration between retry attempts, you can configure an IntervalFunction which is used instead to calculate the wait duration for every attempt. Resilience4j provides several factory methods to simplify the creation of an IntervalFunction.
You fail the activity by throwing an exception (in Java SDK at least) to force a retry. You specify which failures are not retryable by adding them ActivityOptions.RetryOptions.DoNotRetry. Another option is to throw a non retryable application failure created through ApplicationFailure.newNonRetryableFailure.
You can, but it is not recommended. The reason is that each retry is going to add events to the workflow history and it can get too large pretty fast. The built in service side retries to not add events to the history on retries. There are cases when you want to retry sequences of activities, then in this case using workflow side retries is reasonable.
To me, the business process defined above belongs in the workflow, which would have a sleep-and-check-again loop, and the activity code would purely be about fetching the resource. In this example the retry would be infrequent (e.g. # of hours, with a maximum of N days). I am currently thinking of activity retries as more to deal with exceptional circumstances / failures, not as expected circumstances.
when you are saying that then you mean two different activity interface or two different methods of same activity interface. If you mean two different activity methods of same activity interface then can you please tell if we can have retry option as per activity methods??
in our test environment, unfortunately quite some requests are failing from time to time, due to timeouts, environment not being available, you-name-it, so I want to give that requests a retry, especially when running the collections in Jenkins via newman.
We have daily scheduled refresh at midnight and sometimes it would fail when the data is not ready. Any way we can set up auto retry when it fails instead of manually kick off the refresh in the morning?
Like Max shared there is no retry ability through the UI, but if you are doing refreshes from the API CreateIngestion, then you can create a lambda function to execute the API DescribeIngestion, where you can receive a status and if that status equals fail then it can trigger the API CreateIngestion.
Also, since you mentioned that the refresh is failing occasionally due to data not being available, I assume that you have got other dependencies on that data load process which is not always in your control. In that case, it is best to go with triggering the data ingestion with a CreateIngestion API call (made from the end of your data pipline; once you are sure that all data is loaded and available) rather than doing a time based schedule and retrying. This way, you are not baking unnecessary wait time into your processing and can kick off the ingestion as soon as your data is available. If this is not feasible at your end (Maybe due to data pipeline being owned by another department etc), you can fall back on the scheduled refresh with a check and retry option as suggested by Bhasi.
To get into this state, stop the array. If you are having this issue you will see "retry unmounting shares" in the lower left corner. Note: There are other reasons this message could happen (like if you left an SSH terminal open while cd'd into the array). This discussion assumes none of the usual suspects apply.
A transient sim has a convergence failure near the end of the analysis and ADEXL (I think) is restarting the sim automatically. The original results would have been good enough but I can't view them because the new sim is overwriting them. On the second attempt the sim fails at the same point and this time ADEXL does not retry and I can view the results.
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