Hey folks,
Building a daily cron job to pull queue performance metrics from Genesys Cloud and dump them into S3. We’re using the official Python SDK (genesyscloud-platform-client-python) for the API calls and boto3 for the storage side.
The issue is rate limiting. The analytics endpoints are pretty strict. When the script tries to paginate through a large dataset (say, 30 days of hourly data for 100 queues), it hits the 429 Too Many Requests wall almost immediately. The SDK doesn’t seem to have a built-in retry mechanism that respects the Retry-After header specifically for analytics, so I’m rolling my own wrapper.
Here’s the rough flow:
def get_analytics_data(api_client, query):
results = []
while True:
try:
response = api_client.get_query_analytics(query)
results.extend(response.entities)
if not response.next_page_token:
break
query.page_token = response.next_page_token
except Exception as e:
if "429" in str(e):
time.sleep(5) # crude retry
continue
raise
return results
This works okay for small volumes, but when the dataset gets big, the 5-second sleep isn’t dynamic enough. I keep getting throttled. Is there a better way to handle the pagination tokens and backoff logic with the SDK? Or should I just drop the SDK for this part and use raw requests with a proper exponential backoff decorator?
Also, writing to S3 via boto3 is straightforward, but I’m batching the rows into CSV chunks. If the API call fails mid-stream, the script crashes and leaves a partial file in S3. Should I write to a temp file first and then upload_fileobj once the batch is complete? That feels safer but uses more disk I/O.
Any thoughts on the best practice here?