Hey folks,
Running into a bit of confusion on the best approach for pulling active conversation details. We’ve got a custom dashboard that needs to show real-time queue stats and active conversation metadata for supervisors.
I’ve been using the standard /api/v2/conversations endpoint to fetch live data. It works fine for small batches, but as the concurrent call volume spikes, the response times get sluggish. I’m hitting rate limits pretty hard when polling every 5 seconds.
I noticed there’s also /api/v2/analytics/conversations/queries/post. The docs say it’s for historical data, but I’m seeing some examples where people use it for near-real-time metrics. Is it safe to use the analytics endpoint for current state, or am I risking data lag?
Here’s the Python snippet I’m currently using with the Genesys Cloud SDK:
from genesyscloud import conversations_api
api_instance = conversations_api.ConversationsApi(api_client)
try:
# Polling every 5 seconds
api_response = api_instance.get_conversations(conversations_type='voice', max_records=100)
for conv in api_response.records:
print(f"Conv ID: {conv.id}, State: {conv.state}")
except Exception as e:
print("Exception: %s\n" % e)
The issue is that get_conversations doesn’t give me the same level of aggregated metrics as the analytics endpoint. If I switch to the analytics query, I have to build a JSON payload with date ranges and groupings. It feels clunky for real-time monitoring.
Does anyone have a working example of using the analytics endpoint for current active conversations? Or is there a better way to handle high-frequency polling without getting throttled? I’m trying to avoid switching to WebSockets if I can help it, but open to ideas.
Any pointers on the trade-offs here would be great. I’m just trying to get the dashboard to load without timing out.