GET /api/v2/analytics/flows/flowlogs returns 413 Payload Too Large. Querying the full timestamp range for the new IVR triggers the limit because the Python SDK get_flow_flowlogs call lacks stream aggregation. The max_log_size limit hits instantly. We’re porting a heavy debug flow from TwiML to GC Architect expressions. How do I construct the retrieval payload with flow_run_id references? The redaction_pipeline just breaks schema_validation anyway.
from genesyscloud import platform_client
from datetime import datetime, timedelta
client = platform_client.Authenticate().get_default_client()
flows = client.flows_api
start = datetime(2024, 10, 1)
end = datetime(2024, 10, 2)
window = timedelta(hours=2)
cursor = start
while cursor < end:
stop = min(cursor + window, end)
try:
res = flows.get_flow_flowlogs(
date_from=cursor.isoformat(),
date_to=stop.isoformat(),
limit=250,
aggregate=False
)
for log in res.entities:
process_trace(log)
cursor = stop
except Exception as e:
print("chunk failed", e)
break
The 413 error isn’t about your request payload. It’s the response hitting the server memory cap. Genesys Cloud drops the connection when raw flow traces exceed the internal JSON limit. You’ll need to slice the date range into two hour blocks like above. If you already have the flow run IDs, skip the date sweep entirely. Pass them directly in the flow_log_id parameter. The redaction pipeline breaks schema validation because raw execution traces contain unmasked PII nodes that the SDK tries to deserialize into strict models. Switching to aggregate=True or filtering by specific IDs bypasses that parsing error.
Querying full ranges on heavy debug flows always blows up the payload. Chunk it down. The API doesn’t stream those logs natively. You’ll have to handle pagination on the client side anyway.