import genesyscloud

client = genesyscloud.rest_api_client.RestApiClient(
client_id=GENESYS_CLIENT_ID,
client_secret=GENESYS_CLIENT_SECRET
)

analytics_api = genesyscloud.analytics.AnalyticsApi(client)

Fetching queue metrics for the last 24 hours

response = analytics_api.post_analytics_routing_queues_metrics(
body=body_params,
async_req=True
)

We’re building a nightly Python script to pull queue metrics from Genesys Cloud and dump them into S3 using boto3. The script works fine for small datasets, but when we try to pull data for a high-volume queue over a 24-hour window, the initial API call times out or returns incomplete data. I’ve read the docs and know that Genesys Cloud uses cursor-based pagination for analytics endpoints, but the Python SDK doesn’t seem to have a built-in helper like next_page() that some other SDKs offer.

Here’s what we’ve tried so far:

• Using async_req=True to fire off the request and poll for completion, but the response.data object doesn’t always contain the nextPageCursor field even when there’s more data.
• Manually parsing the JSON response to extract the cursor and making subsequent calls, but we keep hitting 429 Too Many Requests errors because we’re not backing off correctly between requests.
• Wrapping the S3 upload in a try/except block, but the script crashes when the analytics API returns a 502 Bad Gateway intermittently.

The environment is:

• Python 3.9
• genesyscloud-python SDK v7.0.1
• boto3 v1.26.0
• AWS Lambda (512 MB memory, 15 min timeout)

Can someone share a working snippet for handling pagination with the Genesys Cloud Python SDK? Specifically, how do you detect if there’s a next page and loop until all data is fetched without getting rate-limited? We’re also open to suggestions on how to chunk the data before uploading to S3 to avoid memory issues.