Just noticed that our AppFoundry integration is hitting rate limits when polling screen recording jobs. We are using the /api/v2/recordings/screencapture/jobs endpoint to sync status for a multi-org client. The response returns 429 Too Many Requests despite adhering to standard polling intervals. Is there a specific header or pagination strategy required for high-volume retrieval in the US-East region? Here is our current config:
polling_interval_ms: 5000
batch_size: 50
user_agent: "VendorApp/3.1"
How I usually solve this is by switching from synchronous polling to webhook-based notifications. Constant GET requests on the jobs endpoint will trigger rate limits, especially in US-East where traffic volume is higher. Instead, configure a webhook endpoint to receive recording.job.completed events. This shifts the load from your application to the platform’s event bus.
For legal discovery workflows, maintaining a strict chain of custody requires accurate timestamps. Webhooks provide an immediate eventTime which is more precise than polling intervals. Ensure your webhook handler returns a 200 OK status quickly to prevent retry storms. If you must poll, implement exponential backoff starting at 5 seconds, doubling on each 429 response.
Check the specific rate limit headers in the 429 response, specifically Retry-After. The documentation outlines the exact limits for recording APIs here: https://developer.genesys.cloud/apidocs/recording/screen-capture. Avoid linear polling; it is inefficient and risky for bulk export jobs.
Check your retry logic and pagination strategy.
The webhook approach mentioned above is valid for real-time triggers, but bulk historical retrieval still requires careful API management. The 429 error often stems from aggressive polling without respecting the X-RateLimit-Reset header. Instead of fixed intervals, implement an exponential backoff mechanism based on the reset timestamp returned in the response headers.
Also, ensure you are using cursor-based pagination correctly. The pageSize parameter has a maximum limit; requesting larger chunks does not help and may worsen throttling. Here is a Terraform snippet for a basic retry policy in a CI/CD pipeline that handles this gracefully:
resource "genesyscloud_api_client" "example" {
name = "ScreenRecordingSync"
scopes = ["recording:read"]
# Configure rate limit handling in your orchestration tool
# Do not rely on default SDK retries alone
}
Adjust your polling interval to start at 5 seconds, doubling on each 429 response until the X-RateLimit-Reset time is reached. This aligns better with the platform’s rate limiting algorithm than static delays.