Configuration is broken for some reason when I attempt to update a large batch of users via the Python SDK.
I am building a typed wrapper around the Genesys Cloud REST API using Pydantic models for validation. The goal is to update user attributes in bulk. I am using the update_user method from the users_api module. When I process more than 500 users concurrently using asyncio.gather, I start receiving HTTP 429 Too Many Requests errors.
The current retry logic uses a simple linear backoff, which is insufficient for the rate limits enforced by the platform. I need to implement a proper exponential backoff strategy with jitter to handle these bursts effectively.
Here is the current async method structure:
async def update_users_batch(user_ids: list[str], api_client):
tasks = []
for uid in user_ids:
tasks.append(api_client.users_api.update_user(user_id=uid, body=update_body))
# This currently fails with 429s under load
await asyncio.gather(*tasks)
The error response includes a Retry-After header, but parsing this dynamically for each failed request adds complexity. Should I wrap the individual API calls with a decorator that handles the 429 status code specifically, or should I use a semaphore to limit concurrency globally? I prefer a solution that integrates cleanly with the existing async interface without blocking the event loop.
This is caused by exceeding the per-minute API rate limits for user mutations.
The platform enforces strict throttling on bulk operations, so simple asyncio gathering will trigger 429s.
Use an asyncio.Semaphore to limit concurrency to ten requests and implement exponential backoff in your retry loop.
This is caused by ignoring the Retry-After header in the 429 response, which specifies the exact duration the platform requires before accepting new requests for that specific resource type.
Simply sleeping for a fixed interval or using a generic exponential backoff without reading the header can lead to unnecessary delays or continued throttling if the header suggests a shorter wait. The Python SDK’s PureCloudPlatformClientV2 does not automatically handle this for bulk update_user calls, so you must intercept the ApiException. Here is how I structure the retry logic in my wrappers to respect the server’s directive:
import asyncio
from purecloudplatformclientv2 import ApiClient, ApiException
async def safe_update_user(api_instance, user_id, body, max_retries=3):
for attempt in range(max_retries):
try:
return await api_instance.update_user(user_id, body)
except ApiException as e:
if e.status == 429:
# Parse Retry-After header if present, else fallback to exponential backoff
wait_time = int(e.headers.get('Retry-After', 2 ** attempt))
print(f"Rate limited. Waiting {wait_time}s as per server.")
await asyncio.sleep(wait_time)
else:
raise # Re-raise non-429 errors
raise Exception("Max retries exceeded")
Ignoring this header forces your script to guess the cooling period, which often results in hitting the limit again immediately upon waking. Always prioritize the Retry-After value over your own backoff calculation to ensure smooth bulk operations.
Check your retry logic for the Retry-After header.
{
"status": 429,
"headers": {
"Retry-After": "5"
}
}