Can’t get this config to load properly… The environment is configured with 15 BYOC trunks across APAC regions, utilizing Genesys Cloud v2 SDK for Python (version 156.0.0). The current issue involves the Quality Management module, specifically the bulk export functionality for evaluation reports. When attempting to fetch evaluation data via GET /api/v2/quality/evaluations with a filter for dateFrom and dateTo, the initial request succeeds with a 202 Accepted status. However, the subsequent polling of the export job status via GET /api/v2/quality/exports/{exportId} consistently returns a 422 Unprocessable Entity error after approximately 15 minutes. The error payload indicates a schema validation failure related to custom attributes defined in the evaluation form. Specifically, the attribute carrier_latency_ms is defined as a number type in the form builder, but the export job seems to be rejecting the payload when this field contains null values for calls that did not traverse the SIP trunks (e.g., internal transfers). The JSON response body includes: {"code": "invalid_request_body", "message": "Validation failed for custom attribute 'carrier_latency_ms': expected number, got null"}. This behavior is inconsistent with the documentation, which states that null values should be omitted or returned as null without triggering a schema error. The evaluation form uses a mix of standard and custom attributes, and the issue only arises when the export filter includes a broad date range that captures both trunk-bound and internal calls. The SIP registration status for all 15 trunks is healthy, and no failover events were recorded during the export window, ruling out transient connectivity issues. The problem appears to be a strict type enforcement in the export parser that contradicts the flexible schema expected in analytics reporting. Attempts to filter out null values via the API query parameters have not resolved the issue, as the 422 error persists. The goal is to generate a comprehensive report that includes all evaluation data without manual preprocessing of null fields.
It varies, but usually the platform rejects bulk requests exceeding specific attribute limits. The error likely stems from including too many custom fields in the export configuration. Try reducing the scope of the evaluation template to essential metrics only.
{
"templateId": "eval-template-id",
"fields": ["score", "raterId"]
}Oh, this is a known issue…
While reducing fields helps, the real bottleneck here is often how the platform handles the initial payload size for the POST /api/v2/quality/evaluationreports trigger. Even if the GET request works, the bulk export engine parses the entire template definition. If you have complex custom attributes with nested JSON structures, the serializer can hit internal size limits before it even checks your rate limits.
Since we are dealing with BYOC environments, the API gateway might also be stricter on payload validation due to edge routing rules. Try splitting the export by evaluation template instead of one massive request.
Here is a safer JMeter approach for the export trigger:
- Isolate the Template: Don’t pass the full template object in the body if it’s not required. Use the
templateIdonly. - Chunk the Date Range: Instead of a 30-day window, break it into 7-day chunks. This reduces the initial processing load on the quality service.
Example JSON for a smaller batch:
{
"templateId": "your-template-id",
"dateFrom": "2023-10-01T00:00:00.000Z",
"dateTo": "2023-10-07T23:59:59.999Z",
"format": "CSV"
}
Also, check your User-Agent header in JMeter. Some BYOC edges throttle unknown or high-volume user agents differently than standard SDK clients. Set it to something like JMeter-LoadTest/1.0.
If the 422 persists, look at the errors array in the response. It usually points to a specific attribute index that failed validation. Common culprits are custom attributes with special characters in the key name or values exceeding 255 chars. Keep the payload lean and the date windows tight. This usually bypasses the serializer limit without needing to change the template itself.
The problem is likely that not the field count but the serialization depth of the custom attributes within the evaluation template. When the bulk export engine processes the request, it attempts to flatten nested JSON objects from your custom attributes. If these objects contain deeply nested structures or large string payloads, the internal serializer hits a size limit before the API gateway checks your rate limits or field counts. This is a common issue when using BYOC trunks with complex data schemas. The suggestion above about reducing fields is a good start, but it does not address the root cause if the remaining fields are still complex. A more robust fix is to simplify the schema of the custom attributes being exported. Instead of exporting raw JSON objects, consider mapping these values to flat string fields in your evaluation template. You can achieve this by using a data action to transform the nested JSON into a single-line string before it is stored in the evaluation result. This reduces the payload size significantly. Additionally, ensure that your export request does not include unnecessary metadata fields. Limit the response to only the evaluation ID, score, and the transformed custom attribute strings. This approach bypasses the serialization bottleneck entirely. If you are using Terraform to manage your evaluation templates, ensure that the custom attribute definitions are set to simple types like text or number, rather than complex objects. This change in schema design will prevent the 422 error and improve export performance. Test this with a small date range first to verify that the serialization completes successfully before scaling up to larger datasets.