Looking for advice on handling WebSocket instability when simulating high concurrency in Genesys Cloud messaging. We are running load tests using JMeter 5.6.2 to validate our platform’s capacity for digital channels. The goal is to sustain 200 concurrent WebSocket connections simulating active messaging sessions. Each thread group maintains a persistent connection to the /api/v2/analytics/events/realtime endpoint to monitor interaction states in real-time during the test run.
The issue arises when we ramp up to 150 concurrent threads. Within 30 seconds, approximately 40% of the connections drop unexpectedly. The JMeter response shows a generic Connection reset by peer error, followed by a 401 Unauthorized when the client attempts to re-authenticate with the stored JWT token. The tokens themselves are valid and not expired, as verified by decoding them in a separate utility script. This suggests the server is actively rejecting the reconnection attempts rather than the client failing to maintain the heartbeat.
We are using the standard Genesys Cloud OAuth2 client credentials flow to generate the tokens. The test environment is a dedicated tenant with no other active traffic. I have checked the JMeter HTTP Request defaults and ensured the Connection header is set to keep-alive. The WebSocket sampler is configured with a 30-second heartbeat interval, which aligns with the default server timeout settings mentioned in the developer documentation.
Is there a specific rate limit or connection cap for WebSocket endpoints that applies during load testing scenarios? We noticed that the disconnections correlate with spikes in API calls to the /api/v2/interactions endpoint for creating new messaging sessions. Could the high throughput on the REST API be impacting the WebSocket stability, or is this a known limitation with the number of simultaneous real-time event subscriptions? Any insights on tuning the JMeter configuration or adjusting the Genesys Cloud settings to prevent these premature drops would be appreciated.
Yep, this is a known issue. Do not use the analytics events endpoint for load testing; it is strictly for monitoring and will drop connections under pressure. Use the dedicated load testing utilities instead to validate channel capacity.
This is typically caused by the fundamental difference in how Genesys Cloud handles real-time streaming versus the batch-oriented event logs we relied on in Zendesk. The suggestion above is spot on regarding the /api/v2/analytics/events/realtime endpoint. In my experience migrating high-volume digital channels from Zendesk, treating this WebSocket stream like a standard API endpoint for load validation is a common pitfall. The GC infrastructure treats these connections as ephemeral monitoring streams, not persistent session carriers.
To properly validate capacity for 200 concurrent messaging sessions, you need to simulate the actual v2/conversations/messaging lifecycle rather than just monitoring the analytics output. Use the Genesys Cloud Load Testing Framework (LTF) or a custom script that initiates actual conversation objects. Here is a simplified cURL example of how you should structure the initial connection request for load testing, ensuring you rotate conversationId and participantId to avoid cache collisions:
curl -X POST "https://{{org}}.mygenesys.cloud/api/v2/conversations/messaging" \
-H "Authorization: Bearer {{access_token}}" \
-H "Content-Type: application/json" \
-d '{
"to": [{"id": "{{agent_id}}", "type": "person"}],
"from": {"id": "{{simulated_user_{{thread_id}}}}", "type": "person"},
"type": "message"
}'
In Zendesk, we often tested by hammering the API with ticket updates. Here, you must respect the WebSocket handshake limits for the actual messaging clients. If you insist on using JMeter, configure it to use the HTTP Request sampler for the REST API calls to create conversations, and only use WebSocket samplers for brief, authenticated bursts to verify connectivity, not sustained load. This mirrors the “ticket field validation” errors we saw in Zendesk when overloading the wrong endpoint. Focus on the conversation creation rate, not the analytics stream stability.
The way I solve this is by redirecting validation to the dedicated load testing utilities rather than the analytics endpoint. The suggestion above correctly identifies that the realtime events stream is designed for monitoring, not concurrency stress testing.