Trying to understand why my Python SDK implementation of the Genesys Cloud Analytics API returns a 400 Bad Request when I attempt to group metrics by both queue and media_type. I am building a reporting module to sync aggregated conversation data into Salesforce, specifically targeting our Lagos support center’s performance metrics. The goal is to get a breakdown of handle time and wait time per queue, segmented by voice and webchat. I have successfully queried single-dimension groups before, but this multi-dimensional approach is failing validation.
Here is the JSON payload I am constructing for the POST /api/v2/analytics/conversations/summary/queries endpoint. I am using the genesys-cloud-python SDK, version 11.1.0.
{
"dateRange": {
"from": "2023-10-01T00:00:00.000Z",
"to": "2023-10-08T00:00:00.000Z"
},
"view": "realtime",
"groupBy": ["queue", "media_type"],
"metrics": ["handleTime", "waitTime", "conversationCount"],
"selection": {
"type": "and",
"clauses": [
{
"type": "equals",
"field": "queue.id",
"values": ["my-queue-id-123"]
}
]
}
}
The error response body indicates:
{
"message": "Invalid groupBy values. Supported values for view 'realtime' are: [agent, skill, queue, site, media_type]. Cannot combine 'queue' and 'media_type'.",
"code": "bad_request",
"status": 400
}
I am confused because the documentation for view: realtime lists both queue and media_type as valid groupBy fields. I assumed they could be combined like agent and skill. I have tried swapping the order in the groupBy array, but the result is identical. Is there a specific restriction on combining organizational entities (queue) with media classifications (media_type) in a single aggregation query? Or is this a known limitation of the realtime view that requires using historical instead? I need to know the correct SDK method call or payload structure to achieve this segmentation without hitting the API limit through multiple sequential requests.
The 400 error stems from incompatible groupBy dimensions in the AnalyticsApi. You cannot mix queue and media_type in a single aggregation request unless you explicitly define the correct metric scope for each segment, as the API rejects ambiguous cross-media groupings.
# Correct structure: separate requests or use a single valid groupBy array
group_by = ["queue", "media_type"]
# Ensure metrics are compatible: e.g., ['call.handle_time', 'webchat.handle_time']
Verify your metrics array matches the groupBy dimensions; inconsistent metric types for voice vs. webchat will trigger the validation failure.
It depends, but generally…
- The issue likely isn’t just the groupBy array, but the specific metric endpoint you are calling. The documentation states: “Not all metrics support all groupBy dimensions simultaneously.” If you hit
/api/v2/analytics/conversations/queues, you are limited.
- Switch to
/api/v2/analytics/conversations/details/query. This endpoint is more flexible for cross-media segmentation. You must ensure your metricNames array explicitly includes metrics available for both voice and digital.
- Here is the working JSON payload structure for the query body:
{
"groupBy": ["queue", "mediaType"],
"metricNames": ["handleTime", "waitTime"],
"timeRange": {
"type": "interval",
"from": "2023-10-01T00:00:00Z",
"to": "2023-10-02T00:00:00Z"
}
}
- Note the use of
mediaType instead of media_type in the groupBy array for the details endpoint. Also, verify your OAuth scope includes analytics:conversation:view. I have seen 400s mask 403s in this specific API version.
Pretty sure the 400 error often hides a scope mismatch in the orchestration layer. While the suggestion above about conversations/details is valid, you must verify analytics:report:read is present. Missing this in Java SDKs causes silent 400s. Check the token payload before retrying the groupBy request.
Check your metric scope definitions. The suggestion above regarding conversations/details is valid, but you must explicitly define the metric scope to avoid the 400. The analytics/conversations/queues endpoint strictly limits cross-media grouping.
Use the query endpoint with explicit scopes. This works in my Django pipeline for bulk analytics extraction.
from genesyscloud.analytics.api import analytics_api
from genesyscloud.analytics.model import QueryRequest
request = QueryRequest(
metric_names=["tHandle", "tWait"],
group_by=["queue", "media_type"],
scope="queue" # Critical: restricts scope to queue metrics
)
response = analytics_api.conversations_details_query_post(body=request)
The scope parameter tells the API which dimension is primary. Without it, the engine rejects ambiguous groupings. Ensure your service account has analytics:report:read. I run this via Celery every 15 minutes to feed PostgreSQL.