Our cloud data connector is consistently hitting SaaS API rate limits during synchronization, resulting in incomplete data loads. We’re getting HTTP 429 responses from the source API after processing about 60% of our dataset. The exponential backoff strategy doesn’t seem configured, batch size optimization hasn’t been implemented, and we have no rate limit monitoring in place. Adaptive throttling would help but we’re not sure how to configure it. The incomplete data sync is causing reporting gaps:
API Response: 429 Too Many Requests
Rate limit: 1000 requests/hour exceeded
Requests made: 1247 in 58 minutes
Data loaded: 64% complete before failure
Both actually. Increase your batch size to 500 records per call if the API supports it - this reduces total request count. Then implement intelligent throttling to stay under the rate limit. You should also add rate limit monitoring to track your consumption in real-time. Most SaaS APIs return rate limit headers that you can use to dynamically adjust your request rate. Have you checked if your API provides these headers?
You’ll need to implement custom logic in your connector script to parse those headers and adjust wait times between requests. It’s not built into the standard connector, but definitely achievable with some scripting.
You need to implement proper retry logic with exponential backoff. The default connector behavior doesn’t handle rate limits well. What’s your current batch size per API request?
We’re requesting 100 records per API call, which seemed reasonable. But with 1000+ API calls needed for our full dataset, we’re hitting the hourly limit before completion. Should we increase batch size or reduce request frequency?
Yes, the API returns X-RateLimit-Remaining and X-RateLimit-Reset headers. How can we use these in Qlik Sense connector configuration to implement adaptive throttling?