Running a daily analytics export job in Python using the Genesys Cloud SDK and boto3. The goal is simple: pull conversation metrics for the last 24 hours, format them, and push to S3. Works fine for small datasets. Starts choking when the date range expands or the volume spikes.
The code looks like this:
from genesyscloud import analytics_api
from genesyscloud.analytics.models import ConversationSummaryQuery
import boto3
client = analytics_api.AnalyticsApi(configuration)
s3 = boto3.client('s3')
def fetch_data(start, end):
query = ConversationSummaryQuery(
interactions=['voice'],
date_from=start,
date_to=end,
size=2000
)
return client.post_analytics_conversations_queries_summary(body=query)
# ... loop through chunks, write to S3 ...
The post_analytics_conversations_queries_summary call hangs indefinitely after returning the first few pages. No exception thrown. Just silence. The SDK seems to drop the connection or the server stops sending data. Checked the network traffic with Wireshark, TCP FIN is sent by the server after about 15 seconds of inactivity between pages. The SDK doesn’t retry. It just sits there.
Tried adding a custom RetryConfiguration but the SDK’s internal paginator doesn’t seem to respect it for this specific endpoint. Also tried increasing the size parameter to reduce the number of requests, but the server caps it at 2000 regardless. If I set it higher, it silently reverts to 2000.
Is there a known issue with the Python SDK handling long-running analytics queries? Or am I missing a keep-alive setting in the configuration object? The docs don’t mention any special handling for large exports. Just says “use pagination.” Pagination breaks when the server drops the connection mid-stream.
Need a reliable way to handle this without switching to raw HTTP requests. The SDK is supposed to abstract this away. Feels like a bug in the generator or the underlying HTTP client library. Anyone hit this? What’s the workaround?