Quick question about using the Genesys Cloud Python SDK to bulk create users from a CSV file. I’ve got a script that parses a local CSV and loops through the rows to call create_routing_user. It works fine for a handful of records, but as soon as I scale up to a few hundred, I start seeing a cascade of 429 Too Many Requests errors.
I’ve tried wrapping the call in a try/except block and adding a time.sleep(1) between iterations, but the SDK’s internal rate limiting seems to still trip. The error response usually looks like {'message': 'Rate limit exceeded', 'status': 429}. I’m using the standard genesyscloud library v2.x. Is there a built-in mechanism in the SDK for handling bulk operations that I’m missing, or should I be implementing a custom exponential backoff strategy? The current flow is pretty straightforward:
from genesyscloud import routing_api
from genesyscloud.routing.model import user_create_request
api = routing_api.RoutingApi(configuration)
for row in csv_data:
try:
body = user_create_request.UserCreateRequest(
name=row['name'],
email=row['email']
)
api.post_routing_users(body=body)
except Exception as e:
print(f"Error: {e}")
time.sleep(5)
The latency spikes significantly after the first 50 calls. What’s the most efficient way to handle this volume without getting throttled?
Check your rate limiting headers.
I’ve tried wrapping the call in a try/except block and adding a time.sleep(1) between iterations
that approach is basically throwing darts in the dark. the SDK doesn’t automatically back off when you hit a 429, so you’re just guessing at the sleep duration. in my rails middleware, i handle this by parsing the Retry-After header from the response. if the header is missing, i fall back to an exponential backoff strategy rather than a fixed sleep.
here’s how i structure the retry logic in ruby using Faraday, which you can adapt for python:
def create_user_with_retry(user_data, max_retries = 3)
attempt = 0
begin
response = faraday.post('/api/v2/routing/users', user_data.to_json)
if response.status == 429
attempt += 1
raise "Max retries exceeded" if attempt > max_retries
# Prefer the server's suggestion
wait_time = response.headers['retry-after'] || (2**attempt)
sleep(wait_time.to_i)
retry
end
response
rescue Faraday::Error => e
# log and handle other errors
raise e
end
end
the key here is respecting the Retry-After header. the Genesys Cloud API explicitly tells you how long to wait. ignoring that and using a static time.sleep(1) is why you’re getting cascading failures. the 429 errors are your signal to throttle, not just to pause briefly.
also, make sure you’re batching your requests if possible. instead of a single loop, consider grouping users into chunks of 50 and processing them sequentially with the proper backoff. i’ve seen this reduce the error rate significantly when onboarding large batches of agents. don’t forget to check your OAuth token expiration too, though that usually throws a 401, not a 429.
The simplest way to resolve this is… yo. don’t ignore the headers. just parse Retry-After properly.
import time
# inside your loop
if resp.status == 429:
wait = int(resp.headers.get('Retry-After', 1))
time.sleep(wait)
genzys rate limits are strict.
How I usually solve this is by wrapping the bulk operation in an OpenTelemetry span so we can actually see where the latency spikes occur, rather than just guessing at sleep durations. The suggestion above about Retry-After is technically correct, but it’s reactive. if you’re doing hundreds of calls, you want proactive throttling based on the x-rate-limit-remaining header.
Cause:
The Python SDK doesn’t handle rate limit backoff automatically. looping blindly with a fixed time.sleep ignores the actual window reset time, causing unnecessary delays or hitting the limit again immediately.
Solution:
Inject a span around the creation loop. Use propagation.inject to attach trace context to the headers. This way, if a 429 hits, the span records the Retry-After value as an attribute, giving you visibility into the bottleneck.
from opentelemetry import trace
from opentelemetry.propagate import inject
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("bulk_user_create") as span:
for row in csv_data:
# Inject trace context into headers
headers = {}
inject(headers)
try:
# Pass headers to SDK if supported, or use raw requests for more control
api_instance.create_routing_user(body=user_body)
span.set_attribute("user.created", True)
except Exception as e:
if "429" in str(e):
wait_time = int(e.headers.get('Retry-After', 5))
span.set_attribute("rate_limit.retry_after", wait_time)
time.sleep(wait_time)
else:
span.record_exception(e)
raise
this approach gives you a distributed trace of the entire batch process. you’ll see exactly which users caused the delay.