We’re trying to update 2k users via /api/v2/users/{id} but keep getting 429 Too Many Requests. I’ve got a simple retry loop with a hardcoded 2s sleep, but it’s not enough. How are you guys handling the exponential backoff in Python? The Retry-After header is usually 1-5 seconds, but my current script still fails after a few hundred records. Here’s the basic request loop I’m using:
Hardcoding the sleep is risky because Genesys Cloud rate limits are dynamic. You need to parse the Retry-After header explicitly. The platform tells you exactly how long to wait, so ignoring it is why you’re getting blocked after a few hundred records. It’s not just about waiting; it’s about waiting the correct amount of time. Here is a simple retry helper that handles the exponential backoff and respects the header. It uses time.sleep but adds a small jitter to prevent thundering herd issues if you run this in parallel.
import requests
import time
import random
def update_user_with_backoff(session, user_id, payload, max_retries=5):
url = f"https://api.mypurecloud.com/api/v2/users/{user_id}"
headers = {"Content-Type": "application/json"}
for attempt in range(max_retries):
response = session.put(url, json=payload, headers=headers)
if response.status_code == 200:
return True
if response.status_code == 429:
wait_time = float(response.headers.get('Retry-After', 2 ** attempt))
# Add jitter to avoid synchronized retries
jitter = random.uniform(0, 0.1 * wait_time)
time.sleep(wait_time + jitter)
print(f"Rate limited. Waiting {wait_time:.2f}s...")
else:
response.raise_for_status() # Fail on other errors
return False
Make sure you’re reusing the session object to keep the TCP connection alive. Creating a new session for every user adds latency and might trigger different rate limit buckets. Also, check your OAuth token expiry. If the token dies mid-batch, you’ll get 401s which look nothing like 429s but will still break your loop. I usually batch updates in chunks of 50 to keep the load steady. It feels slower, but the adherence data stays clean.