Is it possible to efficiently bulk-create users from a CSV file using the Genesys Cloud Platform SDK for Python without hitting rate limits or encountering silent failures?
I am building a script to onboard a large batch of agents. The current implementation reads a CSV, constructs User objects, and calls post_users() in a tight loop. While this works for small batches, it becomes unreliable with larger datasets. I need a robust pattern for handling the asynchronous nature of the API and managing errors gracefully.
Here is the current YAML configuration for the script’s settings:
script_config:
csv_path: 'agents_batch_01.csv'
default_division_id: 'default'
retry_attempts: 3
rate_limit_pause_ms: 100
batch_size: 50
The core logic looks like this:
from geniecloud.platform.client import ApiClient, Configuration
from geniecloud.platform.models import User
import csv
def create_users_from_csv(client, csv_file):
with open(csv_file, 'r') as f:
reader = csv.DictReader(f)
for row in reader:
user = User(
name=row['name'],
email=row['email'],
username=row['username'],
division_id='default'
)
try:
api_response = client.users_api.post_users(body=user)
print(f"Created user: {api_response.id}")
except Exception as e:
print(f"Error creating user {row['username']}: {e}")
The issue is that post_users() returns a 202 Accepted, but the actual creation happens asynchronously. I am seeing a mix of 400 Bad Request errors (likely due to duplicate emails or invalid data) and 429 Too Many Requests.
How should I structure the retry logic and error handling to ensure no users are lost or duplicated? Should I be checking the location header from the 202 response for status polling, or is there a better batch endpoint I am missing?