Is it possible to retrieve aggregated queue wait times for a specific 15-minute interval via the analytics API? The standard endpoint seems to enforce a minimum granularity that I cannot override.
Using curl against https://us-east-1.mypurecloud.com/api/v2/analytics/users/summary. Payload includes dateFrom: "2023-10-27T14:00:00.000Z" and dateTo: "2023-10-27T14:15:00.000Z". Response is 422 Unprocessable Entity. Error body says "message":"The requested time interval is too small".
Tried adjusting the groupBy parameter to interval and minute without success. The documentation suggests dynamic bucketing but does not specify a lower limit for custom ranges. This is blocking our Terraform module validation which requires precise interval data for drift detection.
Environment: Genesys Cloud US-East-1. SDK: Go 1.21. Provider version v1.70.2. Need to know if this is a hard limit or if a different endpoint supports sub-hourly aggregation for user-level metrics.
Pretty sure the 422 error stems from the groupBy parameter being incompatible with the specified interval or date range granularity. The /api/v2/analytics/users/summary endpoint does not support arbitrary 15-minute slices without explicitly defining the time bucketing strategy in the request body. When you send a narrow window like 15 minutes, the API expects the interval to match standard ISO 8601 durations such as PT15M. However, the critical missing piece is often the metrics array structure. You must ensure that the metrics requested, such as queueWaitTime, are compatible with the users summary type. A common fix is to switch to the /api/v2/analytics/queues/summary endpoint if you are looking for queue-specific wait times, as the users endpoint aggregates across all interactions and may reject the query if the data volume is too low for statistical significance. Try modifying your JSON payload to include "interval": "PT15M" and verify that the dateFrom and dateTo are strictly within the same day. Also, check if the groupBy field is set to none or omitted, as grouping by user in a 15-minute window with low volume can trigger validation errors. The documentation suggests that for precise interval data, using the realTime API is more efficient, but for historical data, ensuring the interval matches the groupBy logic is key. If the issue persists, inspect the response headers for x-request-id to correlate with backend logs, as this often reveals if the error is due to a transient schema validation issue or a permanent configuration mismatch. Ensure your authentication token has the analytics:users:view scope, as insufficient permissions can sometimes manifest as 422 errors rather than 403s in this specific API version.
TL;DR: Validate interval formats and check for hidden rate-limit triggers before assuming schema errors.
This looks like a classic case where the request payload is technically valid but trips up the analytics engine’s validation layer due to implicit defaults. The /api/v2/analytics/users/summary endpoint is notoriously strict about time-bucket alignment. If the interval field is omitted, the system attempts to infer it, which often fails for sub-hourly ranges, resulting in the 422 response.
Ensure the interval is explicitly set to PT15M in the request body. Additionally, verify that the groupBy array includes a valid metric like queueWaitTime and does not conflict with the time granularity.
A common gotcha for AppFoundry integrations hitting this endpoint at scale is hitting secondary rate limits on analytics queries. If the payload is correct, monitor the X-RateLimit-Remaining header. If it hits zero, the API may return confusing validation errors instead of standard 429s. Switching to a pre-aggregated report or using the analytics:events API for real-time data might be more reliable for high-frequency polling scenarios.
I normally fix this by aligning the interval with the exact duration in the payload, since GC’s analytics engine is stricter than Zendesk’s ticket filters. Here is the corrected structure:
{
"dateFrom": "2023-10-27T14:00:00.000Z",
"dateTo": "2023-10-27T14:15:00.000Z",
"groupBy": "interval",
"interval": "PT15M"
}
This looks like a throughput bottleneck on the analytics engine when processing narrow time windows. The 422 often triggers if the request hits rate limits before validation completes. Try adding a delay between requests in your load script.
- API rate limits
- Request payload validation
- JMeter concurrent thread count