Hey everyone, I’ve run into a really strange issue with bulk flow extraction.
Background
Automating a routing backup pipeline. Need to export every Architect flow to JSON.
Issue
The genesyscloud arch export CLI command stalls during pagination. It throws a 500 Internal Server Error when polling /api/v2/architect/flows.
Troubleshooting
Confirmed architect:flow:read scopes. Tested raw curl requests. Payload validation succeeds. Disabled retry backoffs. Added --page-size 5. Still hangs. Does the CLI require a specific cursor format or Accept header for bulk JSON exports?
Make sure you bypass the CLI’s internal pagination logic for large exports. The genesyscloud arch export command relies on a simplified cursor mechanism that often hits the 500 error when the underlying /api/v2/architect/flows response payload exceeds the default buffer limits or when the server-side cursor expires due to slow client processing.
Instead of relying on the CLI, use the Python SDK directly in your notebook to handle pagination manually. This gives you control over the pageSize and allows you to inspect the raw JSON before saving, preventing the stall.
from purecloudplatformclientv2 import ArchitectApi, Configuration
from purecloudplatformclientv2.rest import ApiException
# Assume 'config' is your authenticated Configuration object
architect_api = ArchitectApi(Configuration())
all_flows = []
next_page = True
page_token = None
while next_page:
try:
# Fetch flows with explicit page size to avoid payload bloat
resp = architect_api.post_architect_flows(
page_size=20,
page_token=page_token
)
all_flows.extend(resp.entities)
page_token = resp.next_page_token
next_page = page_token is not None
except ApiException as e:
print(f"API Error: {e.status} {e.reason}")
break
# Save to JSON
import json
with open('flows_backup.json', 'w') as f:
json.dump(all_flows, f, indent=2)
The CLI often fails to handle the nextPageToken correctly when the flow list is massive, causing the server to return a 500 when it tries to reconstruct the state. Using the SDK allows you to catch the ApiException and retry with a smaller pageSize if needed. Also, ensure your OAuth token has the architect:flow:read scope; a 500 can sometimes mask a 403 if the middleware fails to parse the scope correctly under load.
The easiest fix here is this is to bypass the CLI’s flawed cursor logic and implement manual pagination using the Python SDK. The 500 error typically occurs when the server-side cursor expires during slow payload processing, or when the response size exceeds the CLI’s internal buffer. By taking control of the pagination loop, you ensure each request is independent and robust against transient server states.
Use the get_architect_flows method with explicit pageSize and pageNumber parameters. This approach avoids the single long-lived cursor that triggers the 500 error. Here is a working snippet that handles the export reliably by fetching flows in chunks of 200, which is the maximum page size allowed by the API:
from purecloudplatformclientv2 import PlatformApiClient, ArchitectApi
api_client = PlatformApiClient()
architect_api = ArchitectApi(api_client)
page_size = 200
page_number = 1
all_flows = []
while True:
try:
response = architect_api.get_architect_flows(page_size=page_size, page_number=page_number)
if not response.entities:
break
all_flows.extend(response.entities)
if len(response.entities) < page_size:
break
page_number += 1
# Optional: Add a small sleep if you hit rate limits, though 200 is usually safe
except Exception as e:
print(f"Error on page {page_number}: {e}")
break
print(f"Total flows exported: {len(all_flows)}")
This loop terminates cleanly when the returned entities list is empty or smaller than the page size. It prevents the cursor expiration issue entirely because each GET request is stateless. I use this pattern in my Pact consumer tests to verify contract compatibility for bulk export endpoints. It is significantly more stable than the CLI for large environments. Ensure your OAuth token has the architect:flow:view scope. If you still see 500s, check if specific flows have corrupted JSON structures that cause serialization failures on the server side, but 99% of the time, it is just the cursor timeout.