Based on extensive experience with high-volume quote processing in Zendesk Sell, here’s a comprehensive strategy addressing all three critical areas:
API Rate Limit Handling:
Implement a multi-layered approach to rate limit management. First, add request throttling on your side to proactively stay under limits rather than reactively handling 429 errors. For 200 requests/minute limit, configure your client to send maximum 180 requests/minute, leaving 10% buffer for other system operations. Use a token bucket algorithm to smooth out burst traffic:
class RateLimiter:
def __init__(self, rate=180, per=60):
self.rate = rate
self.tokens = rate
self.updated_at = time.time()
def acquire(self):
while self.tokens < 1:
wait = self._refill_time()
time.sleep(wait)
self.tokens -= 1
When you do encounter rate limits (429 responses), parse the Retry-After header and wait that duration plus 10% buffer before retrying. Implement exponential backoff for repeated failures: 1s, 2s, 4s, 8s, max 32s. Log all rate limit events to identify patterns and optimize request timing.
Batch Processing Optimization:
Batch processing provides 10-15x throughput improvement for quote generation at your volume. Optimal batch size is 25-30 operations per request based on testing with similar workloads. Larger batches (40-50) risk timeout issues and make error recovery more expensive. Structure your batches to group related operations: create quote header, then all line items for that quote, then pricing calculations. This maintains data consistency if partial batch failures occur.
Implement intelligent batch error handling. The API response includes a results array with success/failure status for each operation. Parse this to identify exactly which operations failed:
for i, result in enumerate(batch_response['results']):
if result['status'] != 200:
failed_ops.append(operations[i])
log_failure(result['error'])
else:
success_count += 1
Retry only failed operations in a new batch rather than resubmitting the entire batch. This prevents duplicate quotes and optimizes retry processing time.
Quote Generation Throughput:
For your volume of 50-100 quotes/hour with 10-20 line items each, batch processing can achieve 100 quotes in 8-12 minutes versus 40-50 minutes with individual API calls. The key is parallel processing with multiple workers. Implement a producer-consumer pattern: main thread prepares quote data and populates a queue, 3-5 worker threads consume from queue and submit batches to API concurrently.
Each worker maintains its own rate limit token bucket to ensure combined throughput doesn’t exceed API limits. With 5 workers each processing batches of 25 operations, you can sustain approximately 150 quotes/hour (2.5 quotes/minute) well within rate limits.
Monitor these key performance metrics: average batch processing time, rate limit hit frequency, retry rate, and end-to-end quote generation latency. Set alerts for retry rate above 5% or rate limit hits above 10/hour, indicating need for throughput adjustment.
For complex pricing rules that may behave differently in batch mode, implement a validation step: after batch creation, retrieve created quotes via API and verify pricing calculations match expected values. If discrepancies exceed 1%, fall back to individual API processing for affected quotes and investigate batch processing compatibility issues with your pricing engine.
Implement comprehensive logging of all batch operations including: request payload, response status, processing time, retry attempts, and final outcome. This data is invaluable for troubleshooting and ongoing optimization of your integration.