Hey everyone,
Trying to set up a nightly job that pulls interaction data from Genesys Cloud and dumps it straight into an S3 bucket. I’m using the Python SDK to handle the OAuth and the actual API calls, then boto3 to push the JSON blobs to AWS.
The main headache is handling the pagination correctly without hammering the API or blowing up memory. The docs mention paging.pageSize and paging.pageNumber, but I’m not 100% sure on the most efficient way to loop until I hit the end.
Here’s the rough structure I’m working with:
from genesyscloud import analytics_api
import boto3
import json
client = analytics_api.AnalyticsApi()
s3_client = boto3.client('s3')
page_size = 200
current_page = 1
all_data = []
while True:
try:
# GET /api/v2/analytics/interactions/summary
response = client.post_analytics_interactions_summary(
body=body,
paging_page_size=page_size,
paging_page_number=current_page
)
if not response.data:
break
all_data.extend(response.data)
current_page += 1
except Exception as e:
print(f"Error on page {current_page}: {e}")
break
# Upload to S3
s3_client.put_object(
Bucket='my-analytics-bucket',
Key='daily_export.json',
Body=json.dumps(all_data)
)
This feels a bit clunky. I’m worried about the while True loop if the API returns an empty page for some reason, or if there’s a rate limit I’m hitting silently. Also, storing all_data in a list seems risky for larger exports. Should I be writing chunks directly to S3 or using a file stream instead?
Any tips on handling the pagination edge cases or optimizing the memory usage here?