Analytics API 500 Internal Server Error during high-concurrency JMeter load test

General Discussion
1

Encountering a 500 Internal Server Error when hitting the /api/v2/analytics/interactions/query endpoint. This happens consistently when concurrent sessions exceed 200.

Using JMeter 5.6.2 to simulate a spike in reporting requests. The initial requests (up to ~150 concurrent) return 200 OK successfully. Once the load increases, the response time spikes to over 30 seconds before failing with a 500 error. The payload is a standard JSON body requesting interaction data for the last 24 hours.

{
 "date_from": "2023-10-25T00:00:00.000Z",
 "date_to": "2023-10-26T00:00:00.000Z",
 "size": 100
}

I checked the Genesys Cloud status page, and everything shows green. No rate limit 429 errors are appearing, which is strange. The error seems to originate from the backend processing the query rather than a gateway timeout.

Is there a known limit on concurrent query execution for this endpoint? Or is this a transient issue with the analytics service under load? I need to understand if this is a hard limit for our capacity planning or a temporary instability.

Thank you.

2

Have you tried reducing the date range in your query payload? In Zendesk, the search API could handle broader date spans under load, but Genesys Cloud’s analytics engine is much stricter about processing limits. Breaking the query into smaller, hourly chunks instead of a single large request often prevents the backend from timing out. It feels like a step back compared to the old ticketing system, but it stabilizes the connection significantly.

Also, check your JMeter thread group settings. Adding a constant throughput timer to space out the requests helps avoid hitting the API gateway’s concurrency ceiling. Unlike Zendesk, where we could blast concurrent calls, Genesys requires a more gentle approach for analytics. Try limiting the concurrent threads to 50 and see if the 500 errors disappear. This adjustment usually resolves the instability without needing complex infrastructure changes.