Running into a wall with the Genesys Cloud Analytics API. I need to pull detailed queue performance data for the last 90 days to feed into our adherence tracking dashboard. The standard GET /api/v2/analytics/queues/summary endpoint works fine for 24-hour windows, but scaling it up breaks things.
Here is the basic structure of the request body I am sending:
{
"select": ["queue/id", "queue/name", "abandonedCalls", "handledCalls"],
"groupBy": ["queue/id"],
"filter": {
"type": "equal",
"path": "queue/id",
"value": "5f8a9b2c-1d3e-4f5a-8b7c-9d0e1f2a3b4c"
},
"interval": "P1D",
"intervalCount": 90
}
When I set intervalCount to 90, the server responds with a 413 Entity Too Large. I have tried reducing the number of metrics in the select array, but the error persists as long as the date range is wide. I suspect the payload size calculation includes the projected response size, not just the request body.
The documentation mentions pagination, but it seems designed for time-series data within a smaller window, not for splitting a large date range into chunks. I cannot simply run 90 separate API calls for each day because that would take too long and hit rate limits on our tenant.
Has anyone figured out how to split a 90-day query into manageable chunks? I tried using multiple date ranges in a single request, but the API does not seem to support multiple start/end times. I am using the Python SDK version 2.4.0.
Is there a way to adjust the intervalCount or use a different endpoint to avoid this limit? I need this data daily for our SLA reports.