Our distribution-mgmt integration jobs are failing due to API rate limiting errors when syncing data from S/4HANA ERP to SAP IBP. We’re hitting the API gateway limits during peak processing times, causing a significant sync backlog.
The error response we’re getting:
{
"error": "rate_limit_exceeded",
"message": "API rate limit of 1000 requests per minute exceeded",
"retry_after": 60
}
Our integration jobs need to sync approximately 50,000 distribution records daily, which includes orders, deliveries, and shipment status updates. The jobs run every 15 minutes throughout the day. We’re using REST API calls to IBP’s distribution-mgmt endpoints with batch sizes of 100 records per request.
The rate limiting started affecting us after we onboarded three new distribution centers last month, which increased our transaction volume by about 40%. The sync backlog is now over 6 hours behind real-time, impacting our ability to optimize distribution plans.
Are there best practices for handling API rate limits in si-2302, or ways to optimize our batch processing to stay within limits?
Larger batch sizes generally improve performance up to a point. For distribution-mgmt in si-2302, the sweet spot is 500-750 records per batch. Beyond that, you risk timeout issues if the processing takes longer than the API gateway timeout (typically 60 seconds).
Also consider implementing exponential backoff retry logic. When you hit rate limits, the API returns a retry_after header telling you how long to wait. Your integration should honor this and retry with exponential delays rather than immediately retrying and making the problem worse.
You’re hitting the default API gateway limits which are designed to prevent system overload. With 50,000 records and batch size of 100, you’re making 500 API calls per job run. Running every 15 minutes means potentially 2,000 calls per hour, which exceeds most standard rate limits.
First step is to increase your batch size. IBP distribution-mgmt APIs can typically handle 500-1000 records per request. This would reduce your API calls by 5-10x and likely solve the rate limiting issue immediately.
Another approach is to optimize your job scheduling. Instead of running every 15 minutes with the same batch size, implement intelligent scheduling based on data volume. Run smaller, more frequent jobs during low-activity periods and larger, less frequent jobs during peak times.
You could also implement a queue-based system where records are added to a queue and processed in optimal batch sizes based on current API availability rather than fixed schedules. This smooths out the load and prevents rate limit spikes.
Don’t forget about API gateway load balancing. If you’re making all requests from a single integration server, you’re limited by that server’s IP address rate limit. Consider distributing the load across multiple integration servers or using a load balancer to spread requests across different source IPs.
Some customers also negotiate higher rate limits with SAP for production integrations that have legitimate high-volume needs. Contact SAP support to request a rate limit increase for your tenant if batch optimization isn’t sufficient.
That’s a good point about batch size. I wasn’t aware the API could handle 500+ records per request. Our documentation said 100 was the recommended size, but that might be outdated. I’ll test with larger batches. Are there any performance implications on the IBP side when processing larger batches?