retries.md

Optimizing Job Retries

Implementing strategic job retry mechanisms enhances the fault tolerance of your job processing system, ensuring efficiency even in the face of failures.

Customizing Retry Delays

Adopting an exponential backoff strategy for retry delays helps balance between immediate retries and waiting too long, effectively managing both transient and persistent failures.

Exponential Backoff Strategy

import "math"

// Exponential backoff increases delay between retries, optimizing for temporary outage recovery.
func ExponentialBackoffDelay(attempts int, _ error) time.Duration {
    return time.Duration(math.Pow(float64(attempts), 2)) * time.Second
}

Applying to a Job Handler

handler := queue.NewHandler(
    "process_job",
    ProcessJobHandler,
    queue.WithRetryDelayFunc(ExponentialBackoffDelay), // Implement custom retry delay
)

This setup applies escalating delay times for retries, efficiently spacing out retry attempts.

Distinguishing Failure Types

Identifying the nature of failures allows for more intelligent retry decisions, conserving resources and reducing unnecessary retries.

Handling Temporary Failures

Temporary issues should trigger retries without impacting the retry count, aiding in self-recovery of transient problems.

func ProcessJobHandler(ctx context.Context, job *queue.Job) error {
    if temporaryIssue() {
        return queue.ErrTransientIssue // Signals a retry without penalty
    }
    // Job processing logic
    return nil
}

Addressing Permanent Failures

For errors unlikely to be resolved with retries, prevent further attempts to save resources.

func ProcessJobHandler(ctx context.Context, job *queue.Job) error {
    if irreversibleError() {
        return queue.NewSkipRetryError("Unrecoverable error identified") // Cease retries
    }
    // Job processing logic
    return nil
}