Is there a clean way to implement a daily analytics export job that writes to S3 using Python and boto3 without hitting rate limits? We are using analytics_api.get_analytics_user in a loop, which causes 429 Too Many Requests errors when processing 500+ agents. Should we switch to the batch export endpoints like /api/v2/analytics/users/summary/export or is there a pagination strategy within the SDK that handles this more efficiently?
This is caused by client-side looping triggering rate limits. Stop iterating users. Use the batch export API endpoint /api/v2/analytics/users/summary/export with an S3 bucket configuration. This shifts processing to the server.
body = {
"interval": "2023-10-01T00:00:00Z/2023-10-02T00:00:00Z",
"groupBy": ["user.id"],
"s3": {
"bucket": "my-analytics-bucket",
"region": "us-east-1"
}
}
api_instance.post_analytics_users_summary_export(body)
The SDK method post_analytics_users_summary_export handles the job creation. Monitor status via the returned job ID. This avoids 429 Too Many Requests entirely.
Make sure you verify the S3 bucket policy permissions before triggering the export. The batch endpoint offloads the heavy lifting to Genesys servers, but if your IAM role lacks s3:PutObject on the target bucket, the job will fail silently or return a 403 after a long wait. Also, do not use groupBy: ["user.id"] alone if you need metric aggregation; it creates a flat list. Use groupBy: ["user.id", "metric.name"] to get structured data.
Here is the corrected Python SDK implementation using PureCloudPlatformClientV2. This handles the export request and polls for completion without client-side loops.
from purecloudplatformclientv2 import ApiClient, Configuration, AnalyticsApi
import time
def run_s3_export(api_client, start, end, bucket, region):
analytics_api = AnalyticsApi(api_client)
# 1. Configure the export body
body = {
"interval": f"{start}/{end}",
"groupBy": ["user.id"],
"s3": {
"bucket": bucket,
"region": region
}
}
# 2. Trigger the export
try:
response = analytics_api.post_analytics_users_summary_export(body=body)
export_id = response.id
print(f"Export started: {export_id}")
# 3. Poll until complete
while response.status != "complete":
time.sleep(30) # Wait 30s to avoid 429s
response = analytics_api.get_analytics_users_summary_export(export_id)
if response.status == "complete":
print(f"Data written to S3: {response.s3.bucket}")
else:
print(f"Export failed: {response.status}")
except Exception as e:
print(f"Error: {e}")
This approach respects rate limits because the server processes the aggregation. You only poll the status endpoint, which is lightweight. Ensure your OAuth scope includes analytics:export:view and analytics:export:write.
If I remember correctly… Switching to the batch export endpoint resolved the 429 errors for us. We configured the S3 destination via Pulumi genesyscloud.OAuth2Client scopes and triggered the export using PureCloudPlatformClientV2. The server-side processing handled the 500+ agents without client-side looping, significantly reducing latency and error rates.
The documentation actually says Retry-After headers are mandatory. I confirmed the batch export works, but you must parse that header explicitly. My Python script now sleeps for the specified seconds before polling /api/v2/analytics/export/{id}. This prevents hard tenant blocks during high-volume S3 syncs.