Python SDK bulk user creation rate limiting and concurrency

General Discussion
1

Just noticed that the genesyscloud Python SDK does not expose a native bulk endpoint for user creation, forcing me to iterate through a CSV of 500 agents and call api_client.users_api.post_user individually. My Django management command wraps this in a Celery task, but the sequential execution is painfully slow, and parallelizing with concurrent.futures triggers immediate 429 Too Many Requests errors from the platform. I need a robust pattern to handle the rate limiting without blocking the worker. Currently, I am using a simple time.sleep(0.5) between calls, which is inefficient. Is there a recommended way to implement exponential backoff or token bucket logic within the SDK’s auth client, or should I be batching requests manually? Here is the core loop: for row in csv_data: user = models.UserApiModel(...); try: api_client.users_api.post_user(body=user); except Exception as e: logging.error(e). The error payload is standard {"errors":[{"code":"too_many_requests","message":"Too Many Requests"}]}. I want to avoid writing a custom retry decorator if the SDK has a built-in mechanism I am missing.

2

The official documentation states the platform enforces strict rate limits per client ID, so you need to implement exponential backoff. Here is a Node.js async-retry pattern that handles the 429s gracefully while maintaining concurrency.

const retry = require('async-retry');
const axios = require('axios');

const createUser = async (userData) => {
 return retry(async (bail) => {
 try {
 return await axios.post('/api/v2/users', userData, {
 headers: { 'Authorization': `Bearer ${token}` }
 });
 } catch (err) {
 if (err.response?.status === 429) {
 console.log('Rate limited, retrying...');
 throw err; // Triggers retry
 }
 bail(err); // Stops retrying for other errors
 }
 }, { retries: 5, minTimeout: 1000, factor: 2 });
};
3

This happens because the platform enforcing strict rate limits per client ID, which triggers immediate 429 errors when using parallel execution without backoff. The suggestion above correctly identifies exponential backoff as the solution, but you must implement it in Python to match your stack.

import time
import requests

def create_user_with_backoff(api_client, user_data, max_retries=5):
 for attempt in range(max_retries):
 try:
 return api_client.users_api.post_user(body=user_data)
 except requests.exceptions.HTTPError as e:
 if e.response.status_code == 429:
 wait_time = 2 ** attempt
 time.sleep(wait_time)
 else:
 raise
4

Have you tried leveraging tenacity for robust retries? It handles jitter better than manual loops. See docs.

from tenacity import retry, wait_exponential
@retry(wait=wait_exponential(multiplier=1, min=4, max=10))
def post_user(client, data):
 return client.users_api.post_user(**data)
5

Ah, yeah, this is a known issue with synchronous loops. The tenacity wrapper is decent, but you need to control the concurrency pool to avoid hitting the rate limit wall before the backoff kicks in.

Use asyncio.Semaphore to throttle requests. This keeps your Django worker efficient without triggering 429s.

import asyncio

async def safe_create(sem, client, data):
 async with sem:
 return client.users_api.post_user(**data)