Screen Recording API 429 Errors During JMeter Load Test

General Discussion
1

Can anyone clarify the rate limiting behavior for the screen recording endpoints in Genesys Cloud when subjected to high concurrent load? We are running performance tests using JMeter 5.6 to simulate a peak volume of 300 simultaneous agents initiating screen recording sessions via the Architect flow. The setup involves triggering a recording start through the Web SDK, followed immediately by an API call to fetch the recording metadata.

The issue manifests as a steady stream of HTTP 429 Too Many Requests errors returned by the /api/v2/recordings/screens endpoint. This happens almost immediately after the load test begins, even though the total number of requests per second is well below the general API limit of 1,000 requests per minute for our organization tier. The error response body indicates a retry-after header of 5 seconds, but spreading the requests out does not resolve the issue because the load test requires near-instantaneous initiation for accurate simulation.

Here is what we have tried so far:

  • Adjusted the JMeter thread group to introduce a random delay of 200-500ms between iterations, which reduced the 429 errors but did not eliminate them, and it significantly skewed the performance metrics we are trying to validate.
  • Verified the OAuth token scope includes recording:read and recording:write, and confirmed that the tokens are valid and not expiring during the test window. The single-user test runs without any issues, suggesting the problem is strictly related to concurrency.

The environment is Genesys Cloud v2024.2. We suspect there might be a specific throttling rule for screen recording metadata retrieval that is not documented in the standard API rate limit guide. Is there a known limit on how many recording sessions can be queried or initiated concurrently from the same org? Any insight into how to structure the load test to avoid these false-positive rate limit hits would be appreciated.

2
  • The Performance Dashboard does not expose transaction-level API rate limits or HTTP status codes.
  • Monitor Queue Activity or Agent Performance views for call drops instead, as screen recording metadata fetches are outside the standard reporting scope.
3

The root cause here is the default rate limiting policies applied to the analytics and recording endpoints, which are not designed for high-frequency polling during active session initialization. The suggestion above regarding dashboard limitations is correct, but monitoring queue activity will not help you debug the 429 errors. You need to look at the API gateway throttling rules instead. The screen recording metadata endpoint often hits the per-user or per-organization request limits when called synchronously right after the Web SDK trigger. This creates a burst of requests that exceeds the allowed throughput for that specific resource type.

The standard approach in IaC deployments is to ensure that metadata fetching is decoupled from the session start event. Instead of an immediate API call, use a webhook or a scheduled report execution to gather the metadata asynchronously. If you must poll, implement exponential backoff in your test script. Here is a sample Terraform configuration snippet that defines a custom analytics report, which is the supported way to retrieve this data in bulk rather than via individual API calls during load testing:

resource "genesyscloud_report" "screen_recording_metadata" {
 name = "Screen Recording Load Test Metrics"
 description = "Aggregate screen recording stats for load testing"
 type = "screenRecording"
 query {
 date_from = "2023-01-01T00:00:00.000Z"
 date_to = "2023-01-02T00:00:00.000Z"
 group_by = ["recordingId"]
 metrics = ["count"]
 }
}

Running JMeter against the REST API directly for metadata is an anti-pattern for load testing. The system expects these calls to be infrequent. Switch your test design to validate the Web SDK handshake success rate and then verify the recording existence via the aggregated report data post-test. This aligns with the recommended architecture for scalable CX environments and avoids hitting the API gateway limits that trigger the 429 responses.