How do I handle the payload size limit when querying conversation details for a 90-day window? I’m trying to pull analytics data for our entire East Coast region to build a custom dashboard, but the API is rejecting the request with a 413 Entity Too Large status. I’ve tried breaking it down by queue, but the date range itself seems to be the culprit since the generated JSON payload hits the limit.
Here’s the basic structure I’m sending to POST /api/v2/analytics/conversations/details/query:
{
"dateFrom": "2023-10-01T00:00:00.000Z",
"dateTo": "2023-12-30T23:59:59.999Z",
"viewId": "realtime",
"groupBy": ["queueId"],
"filter": {
"type": "queue",
"id": "my-queue-id"
}
}
The response body just says “Request entity too large”. I know I can’t just ask for more memory on the server side. Should I be splitting this into multiple 30-day requests and stitching the results together in my Python script, or is there a specific parameter I’m missing to paginate the date range? The docs aren’t clear on how to chunk date queries.
90 days is a massive chunk of data for a single query. The API isn’t just rejecting the payload size; it’s timing out the database fetch on the backend before it even hits the size limit. You’re hitting the wall because you’re asking for too much at once.
Break it down. Split that 90-day window into 7-day chunks. It’s boring, but it works. You’ll get clean JSON back every time. Here’s a quick Python snippet using the SDK to loop through the dates:
import datetime
start_date = datetime.date(2023, 1, 1)
end_date = datetime.date(2023, 3, 31)
current = start_date
while current < end_date:
chunk_end = min(current + datetime.timedelta(days=6), end_date)
# Build your body for /api/v2/analytics/conversations/details/query
body = {
"interval": f"{current.isoformat()}T00:00:00.000Z/{chunk_end.isoformat()}T23:59:59.999Z",
"view": "agent",
"select": ["id", "wrapUpCode", "acwDuration"],
# ... other filters
}
analytics_api.post_analytics_conversations_details_query(body)
current = chunk_end + datetime.timedelta(days=1)
Just aggregate the results locally. Trying to force one giant request is a waste of time.