HTTP/1.1 429 Too Many Requests
Retry-After: 2
Content-Type: application/json
{
“errors”: [
{
“code”: “too_many_requests”,
“message”: “You have exceeded the rate limit for this API endpoint.”
}
]
}
Running a load test against Genesys Cloud Screen Recording APIs to validate capacity for a new BPO client. The setup involves a JMeter script simulating 200 concurrent agents initiating screen recordings via the WebSocket endpoint /api/v2/interactions/screen-recordings. The goal is to see how the platform handles burst traffic during peak hours.
The issue appears when ramping up from 50 to 100 concurrent threads. The initial connections succeed, and the first batch of recordings starts. However, as the concurrency increases, the API begins returning 429 errors. Interestingly, these errors are not uniform. Some threads get the 429 immediately, while others hang for a few seconds before failing.
I have added a constant timer in JMeter to respect the Retry-After header, setting it to 2000ms. This helps reduce the error rate but does not eliminate it. The errors persist even with the delay. I suspect there might be a secondary rate limit or a connection pool exhaustion issue on the WebSocket side.
The environment is Genesys Cloud US-East. The JMeter version is 5.4.3. The script uses the HTTP Request sampler for the initial POST and the WebSocket sampler for the ongoing stream. I have verified that the authentication tokens are valid and not expiring during the test.
Has anyone encountered similar issues with Screen Recording APIs under load? Are there specific headers or configurations that need to be adjusted to handle higher concurrency? I am looking for best practices on structuring the JMeter script to avoid hitting these limits. Any insights on the actual rate limits for this endpoint would be helpful. The documentation mentions general API limits but not specific ones for screen recordings.
Thanks for the help.
Make sure you verify that the retry-after header is being respected in your test logic. Rate limits on recording endpoints are strict. Check the agent performance metrics to see if the spike correlates with specific queue bursts. Adjusting the concurrency ramp-up rate in JMeter might prevent hitting the too_many_requests threshold.
If I recall correctly, the screen recording API has a distinct rate limit profile compared to standard interaction APIs, especially when using WebSocket fallbacks in JMeter. The 429 error usually appears when the connection initiation rate exceeds the per-tenant threshold, which is often much lower than the sustained throughput limit. In my recent load tests with 200 concurrent agents, I found that the initial handshake burst was the primary culprit. Instead of just respecting the Retry-After header, which can add significant latency to the test, try implementing a token bucket algorithm in your JMeter script to smooth out the request rate. This approach ensures a steady stream of connections rather than a sudden spike. You can use the __P() function in JMeter to dynamically adjust the concurrency based on the response codes. For example, set a global variable __P(rate_limit, 50) and use a Throughput Shaping Timer to cap the requests per second. Additionally, check if the WebSocket upgrade requests are being counted separately from the REST API calls. In Genesys Cloud, these often share the same quota pool for a given organization. If the test involves frequent reconnections due to timeouts, the effective rate limit drops significantly. Consider increasing the keep-alive duration in the WebSocket configuration to reduce the number of handshake attempts. Also, verify that the JMeter thread group is not resetting connections too aggressively. A simple fix is to add a small random delay between connection attempts using the __Random() function, which helps distribute the load more evenly and avoids hitting the rate limit threshold simultaneously. This method worked well in my setup when testing with 500 concurrent users, reducing the 429 errors by over 80%.