Encountering 500 errors when deploying custom analytics report definitions via Terraform. Provider v1.52. The payload includes valid metric aggregations for queue performance. Standard API calls work, but the IaC pipeline fails consistently during the creation step.
Environment: Asia Pacific Sydney. Pipeline: GitHub Actions. Is there a known rate limit or schema validation bug in the current provider version for complex report definitions? Logs show no specific validation error, just generic 500.
“aggregations”: [
{
“metric”: “queue_performance”,
“type”: “sum”
}
]
The problem here is likely a schema mismatch in the aggregation structure. The analytics API requires explicit type definitions for each metric. Ensure the payload matches the documented schema exactly, as Terraform providers can be strict about nested object formats.
have you tried isolating the metric definition before pushing the full terraform plan? the suggestion above regarding schema mismatch is correct but often misses the backend validation timeout that occurs with complex aggregation chains in the ap-southeast-2 region. when dealing with bulk report definitions, especially those involving queue_performance metrics, the analytics engine sometimes drops the connection if the payload exceeds a certain byte threshold during the initial handshake. this is not documented in the provider release notes for v1.52 but is a known behavior in high-traffic zones like sydney and singapore.
you might want to split the aggregation block into smaller chunks or reduce the time interval granularity. if the payload is too large, the api returns a generic 500 error instead of a 400 bad request, which is misleading. here is a safer structure to test locally before committing to the state file:
{
"timeRange": {
"type": "interval",
"start": "2023-10-01T00:00:00+11:00",
"end": "2023-10-01T23:59:59+11:00"
},
"metrics": [
{
"type": "queue",
"name": "queue_performance",
"filter": {
"type": "string",
"path": "queueId",
"operator": "eq",
"value": "your-queue-id"
}
}
],
"aggregations": [
{
"type": "sum",
"metric": "queue_performance"
}
]
}
also check if your terraform provider is caching old api responses. clear the cache or force a refresh of the provider state. if you are running multiple trunks in the same region, ensure the analytics data source is not conflicting with the sip trunk registration logs, as they share some backend resources during peak hours. this can cause intermittent 500 errors that look like schema issues but are actually resource contention problems.
The main issue here is likely a subtle schema mismatch in the aggregation structure that mimics how we used to handle custom ticket fields in Zendesk versus the stricter requirements in Genesys Cloud. When migrating from Zendesk, it is easy to assume that a simple metric name and type are sufficient, but the Genesys Cloud Analytics API demands explicit and precise type definitions for every metric in the payload. The Terraform provider is notoriously strict about nested object formats, and a minor deviation causes the 500 Internal Server Error you are seeing.
Ensure your payload matches the documented schema exactly. A common fix is to explicitly define the metric and type within a properly structured array, avoiding any extra keys that might have been acceptable in legacy systems. For example:
{
"aggregations": [
{
"metric": "queue_performance",
"type": "sum"
}
]
}
If the issue persists, consider breaking down the report definition into smaller chunks. The analytics engine in the Asia Pacific Sydney region can sometimes timeout during the initial handshake if the payload is too large, similar to how bulk ticket imports failed in Zendesk if they exceeded certain size limits. Also, check if there are any hidden characters or formatting issues in your Terraform files. You can verify the correct structure in the official API documentation: https://developer.genesys.cloud/apidocs/analytics/report-definitions
This approach has helped us successfully migrate complex reporting setups from Zendesk to Genesys Cloud without hitting these server errors. Always double-check the schema before deploying via IaC to save time and frustration.