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Retry

April 14, 2023 4 min read Behavioral
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Intent

Transparently retry certain operations that involve communication with external resources, particularly over the network, isolating calling code from the retry implementation details.

Explanation

Retry pattern consists retrying operations on remote resources over the network a set number of times. It closely depends on both business and technical requirements: How much time will the business allow the end user to wait while the operation finishes? What are the performance characteristics of the remote resource during peak loads as well as our application as more threads are waiting for the remote resource’s availability? Among the errors returned by the remote service, which can be safely ignored in order to retry? Is the operation idempotent?

Another concern is the impact on the calling code by implementing the retry mechanism. The retry mechanics should ideally be completely transparent to the calling code (service interface remains unaltered). There are two general approaches to this problem: From an enterprise architecture standpoint (strategic), and a shared library standpoint (tactical).

From a strategic point of view, this would be solved by having requests redirected to a separate intermediary system, traditionally an ESB, but more recently a Service Mesh.

From a tactical point of view, this would be solved by reusing shared libraries like Hystrix (please note that Hystrix is a complete implementation of the Circuit Breaker pattern, of which the Retry pattern can be considered a subset of). This is the type of solution showcased in the simple example that accompanies this README.md.

Real world example

Our application uses a service providing customer information. Once in a while the service seems to be flaky and can return errors or sometimes it just times out. To circumvent these problems we apply the retry pattern.

In plain words

Retry pattern transparently retries failed operations over network.

Microsoft documentation says

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.

Programmatic Example

In our hypothetical application, we have a generic interface for all operations on remote interfaces.

1public interface BusinessOperation<T> {
2  T perform() throws BusinessException;
3}

And we have an implementation of this interface that finds our customers by looking up a database.

1public final class FindCustomer implements BusinessOperation<String> {
2  @Override
3  public String perform() throws BusinessException {
4    ...
5  }
6}

Our FindCustomer implementation can be configured to throw BusinessExceptions before returning the customer’s ID, thereby simulating a flaky service that intermittently fails. Some exceptions, like the CustomerNotFoundException, are deemed to be recoverable after some hypothetical analysis because the root cause of the error stems from “some database locking issue”. However, the DatabaseNotAvailableException is considered to be a definite showstopper - the application should not attempt to recover from this error.

We can model a recoverable scenario by instantiating FindCustomer like this:

1final var op = new FindCustomer(
2    "12345",
3    new CustomerNotFoundException("not found"),
4    new CustomerNotFoundException("still not found"),
5    new CustomerNotFoundException("don't give up yet!")
6);

In this configuration, FindCustomer will throw CustomerNotFoundException three times, after which it will consistently return the customer’s ID (12345).

In our hypothetical scenario, our analysts indicate that this operation typically fails 2-4 times for a given input during peak hours, and that each worker thread in the database subsystem typically needs 50ms to “recover from an error”. Applying these policies would yield something like this:

 1final var op = new Retry<>(
 2    new FindCustomer(
 3        "1235",
 4        new CustomerNotFoundException("not found"),
 5        new CustomerNotFoundException("still not found"),
 6        new CustomerNotFoundException("don't give up yet!")
 7    ),
 8    5,
 9    100,
10    e -> CustomerNotFoundException.class.isAssignableFrom(e.getClass())
11);

Executing op once would automatically trigger at most 5 retry attempts, with a 100 millisecond delay between attempts, ignoring any CustomerNotFoundException thrown while trying. In this particular scenario, due to the configuration for FindCustomer, there will be 1 initial attempt and 3 additional retries before finally returning the desired result 12345.

If our FindCustomer operation were instead to throw a fatal DatabaseNotFoundException, which we were instructed not to ignore, but more importantly we did not instruct our Retry to ignore, then the operation would have failed immediately upon receiving the error, not matter how many attempts were left.

Class diagram

alt text

Applicability

Whenever an application needs to communicate with an external resource, particularly in a cloud environment, and if the business requirements allow it.

Consequences

Pros:

  • Resiliency
  • Provides hard data on external failures

Cons:

  • Complexity
  • Operations maintenance

Credits