Need some help troubleshooting pagination for conversation details.
I am fetching high-volume data from my Chrome extension. The docs mention pageSize and pageNumber, but the response lacks nextPageId. Is this endpoint cursor-based or strictly page-based?
When I increment pageNumber, the response is slow. If I try to use a cursor field from the response, I get:
“Invalid query parameter: cursor”
Is cursor only for aggregates? How do I efficiently page through details?
This is caused by mixing pagination models. The details endpoint uses offset-based paging. Stop looking for cursors. Use pageNumber and pageSize. Increase pageSize to 2000. Cache results locally. Do not loop in the browser.
Note: Browser extensions hit rate limits quickly. Use a backend service.
This is actually a known issue… The /api/v2/analytics/conversations/details/query endpoint strictly enforces offset-based pagination; it does not support cursor-based retrieval. You must rely on pageNumber and pageSize as correctly identified in the previous response.
The performance degradation you observe when incrementing pageNumber is inherent to the offset model, as the server must scan and discard preceding records for each request. To mitigate latency and rate-limit exhaustion in your Chrome extension, implement a server-side proxy that aggregates these offset queries into a single batch process.
# Example: Server-side batch aggregation to reduce client-side latency
import requests
def fetch_conversation_details(start_page, max_pages, page_size=1000):
base_url = "https://api.mynice.com/api/v2/anversations/details/query"
headers = {"Authorization": "Bearer YOUR_TOKEN", "Content-Type": "application/json"}
all_results = []
for page in range(start_page, start_page + max_pages):
payload = {
"pageSize": page_size,
"pageNumber": page,
"dateRange": { "startDate": "2023-10-01T00:00:00.000Z", "endDate": "2023-10-02T00:00:00.000Z" }
}
response = requests.post(base_url, json=payload, headers=headers)
if response.status_code == 200:
all_results.extend(response.json().get("entities", []))
else:
# Implement exponential backoff here
break
return all_resultsIt’s worth reviewing at the offset-based nature of this endpoint. The suggestion above is correct, but for high-volume data, consider batching requests to avoid rate limits. Injecting a span for each page fetch helps trace performance bottlenecks in your extension. Use pageNumber and pageSize strictly.
It depends, but generally… offset pagination degrades performance significantly at scale. For sentiment analysis pipelines, I recommend switching to the analytics API’s streaming or query-splitting approach to avoid server-side scanning bottlenecks.
“When I increment pageNumber, the response is slow.”
Use this Python snippet to batch requests efficiently:
def fetch_conversations(page, size=2000):
return client.analytics_api.post_analytics_conversations_details_query(
body=QueryRequest(page_size=size, page_number=page)
)