Standard pagination does not apply to the time dimension in this endpoint. How should I structure the request to avoid the 413 error? Split into monthly chunks manually or is there a batch endpoint?
The simplest way to resolve this is… to stop sending monolithic date ranges to the analytics endpoint. The 413 error is not a bug. It is the platform protecting itself from payload bloat when you request 90 days of granular queue data in a single POST. You are hitting the request size limit because the SDK serializes the entire query object, including all date intervals, into the body.
Just noticed that POST /api/v2/analytics/queues/details fails with HTTP 413 Entity Too Large when the date range spans 90 days.
Split the query. Break the 90-day window into smaller chunks, such as 7-day or 14-day intervals. Execute these sequentially or in parallel using a thread pool, then aggregate the results on your side. This is standard practice for large analytics datasets in Genesys Cloud.
Here is a minimal Python example using genesyscloud SDK:
from genesyscloud import analytics
from datetime import datetime, timedelta
def fetch_queue_data(api_client, queue_id, start_date, end_date, chunk_days=14):
results = []
current_start = start_date
while current_start < end_date:
current_end = min(current_start + timedelta(days=chunk_days), end_date)
# Define the query for this specific chunk
query_body = analytics.AnalyticsQuery(
query_type="queue",
group_by=["interval"],
date_from=current_start.isoformat(),
date_to=current_end.isoformat(),
view="realtime", # or 'summary' depending on need
entities=[{"id": queue_id, "type": "queue"}]
)
try:
# Execute the smaller query
resp = api_client.create_analytics_query(body=query_body)
results.extend(resp.data)
except Exception as e:
print(f"Chunk failed: {current_start} to {current_end}: {e}")
current_start = current_end
return results
This approach keeps each request payload under the limit. It also allows you to handle transient errors on a per-chunk basis without retrying the entire 90-day set. Adjust chunk_days based on your specific data volume and API rate limits. Do not ignore the 413. It means your architecture is inefficient.
You might want to check at splitting the query into smaller intervals. The 413 error hits hard with 90-day spans. I use Celery to chunk requests by week. This avoids payload bloat.
# Split date range into 7-day chunks
for start in range(0, 90, 7):
payload['interval'] = f'P{start}D/P{start+7}D'
platformClient.AnalyticsApi.create_analytics_query(payload)