Running a load test with JMeter to sync 500 concurrent users via a custom Data Action. The integration hits a 429 Too Many Requests error after roughly 120 calls per second.
The payload size is small, around 2KB. The goal is to find the optimal retry strategy or batching method without dropping connections. Does increasing the WebSocket keep-alive help, or is a simple exponential backoff in the script sufficient?
The official documentation states that exponential backoff is the required mechanism for handling 429 errors, as WebSocket keep-alives do not bypass platform rate limits.
Implementing a simple retry logic in the client script is usually sufficient, but ensure the retry-after header is respected to avoid immediate rejections during high-volume syncs.
It depends, but generally… the retry-after header is the single source of truth for Genesys Cloud API rate limiting. The platform enforces strict concurrency limits per tenant and per integration type. Ignoring this header causes immediate 403 bans. The WebSocket keep-alive suggestion above is irrelevant here. Keep-alives manage connection state, not request throughput. The 429 response includes a retry-after value in seconds. This value is dynamic. It scales based on current load. Hardcoding a static delay like 5 seconds often fails during peak hours. The client must parse the header. If the header is missing, a default backoff of 1-2 seconds with jitter is safer than zero. This prevents thundering herd issues when the limit lifts.
Here is a robust Go snippet for handling the retry logic within a custom Data Action or external sync service. It respects the header and adds jitter to prevent synchronized retries.
func retryWithBackoff(client *http.Client, req *http.Request) (*http.Response, error) {
resp, err := client.Do(req)
if err != nil {
return nil, err
}
if resp.StatusCode == 429 {
retryAfter := 1
if val := resp.Header.Get("Retry-After"); val != "" {
if parsed, err := strconv.Atoi(val); err == nil {
retryAfter = parsed
}
}
// Add jitter to prevent thundering herd
jitter := time.Duration(rand.Intn(1000)) * time.Millisecond
time.Sleep(time.Duration(retryAfter)*time.Second + jitter)
return retryWithBackoff(client, req) // Recursive retry
}
return resp, nil
}
This approach works reliably in our Terraform-based CI/CD pipelines when seeding large datasets. It avoids manual polling loops. The jitter is critical. Without it, multiple parallel workers wake up at the exact same millisecond. This triggers another 429 wave. The retry-after value is the only reliable signal. Trust it. Do not guess.