We’ve built a Python scheduler to pull outbound dialer metrics from CXone. The script hits GET /api/v2/outbound/campaigns/{campaignId}/stats every hour to grab answer rates and connect rates. Everything works fine locally, but the production instance keeps throwing HTTP 429 Too Many Requests when it tries to batch historical baselines for anomaly detection.
Steps attempted to resolve the throttle:
- Implemented exponential backoff with
time.sleep() - Switched to
requests.Session()with persistent connections - Added local Redis caching for baseline data
- Reduced batch window from 24 hours to 6 hours
- Patched ReportLab to render partial data frames
The rate limit kicks in regardless. We’re making around 45 requests in a 60-second window to backfill metrics. The CXone docs mention a 100 request per minute cap, but the error payload keeps returning {"code":"too_many_requests","message":"Rate limit exceeded"}.
The pandas aggregation step takes longer than expected because the API paginates the stats endpoint weirdly. Each page only returns 100 records, and we’ve got roughly 15 campaign variations running. The retry logic feels clunky. Drift between cached and live metrics causes false anomaly flags anyway. Stakeholders keep asking why the morning briefing PDF shows gaps.
Current environment specs:
- Python 3.11.4
- pandas 2.1.0
- ReportLab 4.0.4
- CXone REST API v2
- APScheduler 3.10.1
The PDF generation hangs if the data frame isn’t fully populated. We’ve patched the email template to drop the executive summary when the query fails, but that’s not ideal. The retry queue just keeps stacking up requests.