Need some help troubleshooting an Analytics API aggregation query that fails to return data when grouping by both queue and mediaType.
I am constructing a GET request to /api/v2/analytics/interactions/queues/aggregates to feed data into a New Relic custom event pipeline. The goal is to capture interaction counts per queue, segmented by media type (voice, chat, etc.). When I use a single group, the query works. However, adding the second group results in an empty entities array in the response.
Here is the current request structure:
{
"dateFrom": "2023-10-01T00:00:00.000Z",
"dateTo": "2023-10-02T00:00:00.000Z",
"interval": "PT1H",
"groupBy": ["queue", "mediaType"],
"metrics": ["volume"],
"filters": {
"queueIds": ["abc-123-def"]
}
}
The response returns a 200 OK, but the entities list is empty. I have verified that the queueId is valid and that interactions occurred during this window.
- Environment: Genesys Cloud US1
- SDK: Genesys Cloud Python SDK v2
- Use Case: Ingesting metrics into New Relic via webhook
Is there a specific constraint on multi-group aggregations in the Analytics API, or is the JSON payload structure incorrect for this combination?
The aggregates endpoint requires explicit metric definitions in the request body, which you cannot attach to a GET request. Switch to POST and include the metrics array:
POST /api/v2/analytics/interactions/queues/aggregates
{
"dateRange": { "from": "2023-01-01T00:00:00.000Z", "to": "2023-01-02T00:00:00.000Z" },
"groupBy": ["queue", "mediaType"],
"metrics": ["offered", "answered"]
}
It varies, but usually the POST body structure is correct, though you must ensure the metrics array matches the specific queue interaction schema to avoid silent filtering. Refer to this guide for valid metric definitions.
Make sure you verify the granularity parameter when using multi-group aggregations. The suggestion above regarding the POST body is technically correct, but you will hit a silent failure if the granularity does not align with your date range. I usually enforce granularity: "P1D" for daily rollups to prevent the API from attempting sub-hourly bucketing that exceeds the 24-hour window constraint.
const body: AnalyticsApi.PostAnalyticsInteractionsQueuesAggregatesBody = {
dateRange: { from: startIso, to: endIso },
groupBy: ["queue", "mediaType"],
metrics: ["offered", "answered"],
granularity: "P1D"
};
await platformClient.AnalyticsApi.postAnalyticsInteractionsQueuesAggregates(body);
Warning: If you omit granularity, the service defaults to PT1H. When grouping by queue and mediaType, this creates a massive combinatorial explosion of buckets. The API often returns an empty array or a 504 Gateway Timeout because it cannot process the high-cardinality matrix within the execution time limit. Always pin the granularity to match your aggregation window to ensure predictable payload sizes.
Ah, this is a recognized issue…
When building CLI tools for bulk analytics pulls, I often see developers trip up on the granularity field. The suggestion above is correct about switching to POST, but you also need to ensure your dateRange does not exceed the 24-hour limit when using P1D granularity. If you span multiple days, the API silently returns empty results.
Here is a robust Python snippet using PureCloudPlatformClientV2 that handles multi-day ranges by chunking the query. This prevents timeout errors and ensures consistent data retrieval for your pipelines.
from PureCloudPlatformClientV2 import Configuration, AnalyticsApi
from datetime import datetime, timedelta
def get_queue_aggregates(api: AnalyticsApi, start_date: datetime, end_date: datetime):
config = Configuration()
chunk_size = timedelta(days=1)
current = start_date
while current < end_date:
end_chunk = min(current + chunk_size, end_date)
body = {
"dateRange": {
"from": current.isoformat(),
"to": end_chunk.isoformat()
},
"groupBy": ["queue", "mediaType"],
"metrics": ["offered", "answered"],
"granularity": "P1D"
}
response = api.post_analytics_interactions_queues_aggregates(body=body)
# Process response data here
current = end_chunk
Make sure you handle pagination in your CLI output if you are aggregating large datasets.