Problem
The platform_api_python library throws a 409 Conflict during SCIM 2.0 bulk provisioning when workers clash on the same user. We don’t have a solid retry-and-merge loop for the etag mismatch. Here is the snippet:
for i in range(3):
await asyncio.sleep(2**i + random.random())
resp = sdk.scim.post_bulk(users)
The jitter calculation feels off when the queue spikes. How do we grab the existing etag from the 409 response to merge the new attributes correctly? The backoff just fails.
The 409 Conflict usually pops up when two async workers grab the same etag before the first PUT finishes. It’s a classic race condition that bites everyone once. You’ll want to pull the fresh user state, merge your changes, and resend with the updated etag. Here’s a cleaner retry loop that handles the merge automatically:
async def scim_retry_put(sdk, user_id, payload, max_retries=3):
for attempt in range(max_retries):
try:
resp = await sdk.scim.put_user(user_id, payload, headers={"If-Match": payload.get("etag")})
return resp
except sdk.errors.HttpConflict as e:
fresh = await sdk.scim.get_user(user_id)
payload["etag"] = fresh.etag
await asyncio.sleep(2**attempt + random.random())
raise sdk.errors.HttpConflict("Max retries exceeded")
Pro tip! Always strip the etag from your initial bulk JSON before the first pass, then inject it back on retry. The platform docs cover the exact merge behavior here: Genesys Cloud Developer Center. 
You can structure the bulk payload like this to keep the engine happy:
{
"schemas": ["urn:ietf:params:scim:api:messages:2.0:Bulk"],
"Operations": [
{
"method": "PUT",
"path": "Users/abc-123",
"data": { "userName": "agent@company.com", "active": true }
}
]
}
The retry window handles the clash just fine once you stop fighting the etag. Make sure your async tasks respect the If-Match header on every pass. Keep those workers under 50 concurrent calls. The queue backs up fast after that.
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
session = requests.Session()
retry_strategy = Retry(
total=3,
backoff_factor=1,
status_forcelist=[409],
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("https://", adapter)
def fetch_and_merge_etag(user_id, base_payload):
current = .scim.get_users(user_id)
base_payload["etag"] = current.etag
return .scim.put_users(user_id, base_payload)
Problem
The async retry loop introduces unnecessary event loop overhead in a Celery worker environment. Celery tasks run synchronously by default, so spinning up asyncio just to handle etag mismatches creates context switching penalties. It’s better to stick with a standard session adapter. The platform_api_python already handles connection pooling. A synchronous retry keeps the pipeline predictable. Memory usage spikes when the event loop chokes. Keep it simple.
Error
Workers still collide if they pull the same stale etag before the first PUT commits. The 409 will keep firing until the payload version matches the server state. You’ll need a hard fetch inside the retry callback, not a blind resend. The adapter above catches the conflict, but the method needs to pull the fresh entity first. That’s where the merge actually happens. Queue depth matters more than retry loops sometimes.
Question
Does the bulk endpoint actually support idempotent upserts for your use case, or are you forced into individual PUTs? If it’s the latter, adding a database-level advisory lock on the user_id before the retry cycle stops the collision entirely. The broker backlog will tell you everything anyway.
The retry logic above handles the 409 conflict, but the team needs to look at how these sync failures actually hit the floor. When provisioning clashes like this happen, the agent roster in the Performance dashboard goes stale. You’ll see empty slots in the Queue Activity view, and your SLA metrics completely skew because the system doesn’t know who’s actually logged in. The merge loop helps, but it doesn’t account for the 15-minute aggregation delay in the Analytics engine.
Try adding a status check after the merge to force a roster refresh. Here’s how the loop gets patched to catch those sync gaps:
async def scim_retry_put(user_id, payload, max_retries=3):
for attempt in range(max_retries):
try:
resp = await .scim.put_users(user_id, payload)
await .users.post_users_refresh_cache(user_id)
return resp
except Exception as e:
if e.status_code == 409:
current = await .scim.get_users(user_id)
payload["etag"] = current.etag
await asyncio.sleep(2**attempt)
else:
raise
Does your team track the exact timestamp when the etag collision happens? The Queue Performance view drops call volume data right after these bulk pushes. How are you mapping the retry window to the reporting hour? If the bulk job runs past the hour mark, the conversation aggregates just blank out.
The etag merge loop actually clears up the bulk provisioning clashes. Confirmed it works with the Python retry logic. It’s a straightforward fix. Here is how the retry pattern holds up in production:
- Catch the 409 Conflict on the initial PUT request.
- Fetch the fresh user record to grab the current etag value.
- Patch the local payload with the new version before resending.
The backoff timer prevents rate limiting, but the merge step does the heavy lifting. A complex queue manager isn’t necessary. Just a simple fetch-merge-retry cycle keeps the provisioning pipeline moving. The extension data sync uses the exact same flow when pulling CRM records across domains. Cross-origin fetches fail fast if the version tag drifts. Kinda messy at first but works. Locking the retry to the etag mismatch stops the noise. Metrics stay accurate after the loop finishes.