How do I properly handle rate limiting when bulk-creating users using the Python Platform SDK? I’ve been working on a script to ingest a CSV file of new agents and push them into Genesys Cloud via the genesyscloud SDK. The logic seems sound, but the API keeps choking on the volume of requests.
The script reads the CSV, loops through each row, and calls the post_users method. I’ve added a simple time.sleep(1) between calls, assuming that would be enough to stay under the radar. It’s not. After about 15 successful creates, the script crashes with a 429 Too Many Requests error. The error payload is pretty generic, just telling me to back off.
Here is the core loop I am using:
from genesyscloud import PlatformClient
import csv
import time
api_instance = PlatformClient.get_users_api()
with open('agents.csv', 'r') as f:
reader = csv.DictReader(f)
for row in reader:
body = {
"email": row['email'],
"name": row['name'],
"division_id": "default",
"state": "active",
"role_ids": ["agent_role_id"]
}
try:
api_instance.post_users(body=body)
print(f"Created {row['name']}")
except Exception as e:
print(f"Failed {row['name']}: {e}")
break
time.sleep(1)
I have tried a few things to mitigate this:
- Increased the sleep interval to 3 seconds, which helps but makes the script incredibly slow for 500+ users.
- Added a try/except block to catch the 429 and retry with exponential backoff, but the retry logic is flaky and sometimes fails on the second attempt too.
- Checked the
RetryConfigoptions in the SDK initialization, but I’m not sure which parameters actually impact the per-second rate limits for write operations.
Is there a built-in mechanism in the Python SDK to handle bulk operations more gracefully? Or should I be batching the requests differently? The documentation mentions rate limits, but it doesn’t specify the exact thresholds for user creation endpoints. I’m trying to avoid writing a custom rate-limiter from scratch if the SDK already provides hooks for this. The current approach feels brittle and prone to failure in a CI/CD pipeline where I need predictable execution times.