Cursor pagination logic failing in PySpark Glue job for /analytics/conversations/details/query

Can’t get this config to load properly… I am attempting to build a robust ETL pipeline using AWS Glue PySpark to ingest Genesys Cloud conversation details into Redshift, but the pagination logic for the /api/v2/analytics/conversations/details/query endpoint is consistently failing when switching from page-based to cursor-based retrieval. The initial request with pageSize=1000 and page=1 works fine, returning a valid nextPageToken in the response headers, but when I attempt to use this token in subsequent requests via the request-id header or as a query parameter, the API returns a 400 Bad Request with a message stating “Invalid cursor format”. Here is the core snippet of my Python logic within the Glue job: def fetch_conversations(base_url, token=None): headers = {'Authorization': 'Bearer ' + auth_token, 'Content-Type': 'application/json'} if token: headers['x-genesys-cursor'] = token response = requests.get(f'{base_url}/analytics/conversations/details/query', params={'pageSize': 1000, 'fromDate': start_date, 'toDate': end_date}, headers=headers) The issue seems to stem from how the nextPageToken is handled; the API documentation suggests using the token directly, but my logs show the token is being URL-encoded twice or stripped of special characters during the Glue job execution. I have verified the token string matches exactly what was returned in the Link header of the previous response, yet the subsequent call fails. Is there a specific header requirement for cursor pagination in the Analytics API that differs from standard REST patterns, or is my requests library handling the header injection incorrectly? I need to ensure the cursor parameter is passed correctly without modification to maintain the integrity of the pagination sequence across multiple Glue task invocations.

Make sure you stop relying on the page and pageSize parameters entirely, as they are deprecated for cursor-based endpoints like /api/v2/analytics/conversations/details/query. The Genesys Cloud API requires you to pass the nextPageToken received from the previous response in the pageToken query parameter of your subsequent request. If you continue mixing page numbers with cursor tokens, the service will ignore the token or return inconsistent data sets, breaking your ETL integrity.

In your PySpark Glue job, you must extract the Link header or the nextPageToken field from the JSON body, not just the HTTP headers. Here is a robust Python snippet using requests that handles this loop correctly within a Glue context:

import requests
import json

base_url = "https://api.mypurecloud.com/api/v2/anversations/details/query"
headers = {"Authorization": f"Bearer {access_token}", "Accept": "application/json"}
params = {"viewId": "viewId", "dateRange": "2023-10-01T00:00:00.000Z/2023-10-31T23:59:59.999Z"}

all_data = []
page_token = None

while True:
 if page_token:
 params['pageToken'] = page_token
 else:
 params['pageSize'] = 1000 # Only for the first request

 response = requests.get(base_url, headers=headers, params=params)
 response.raise_for_status()
 data = response.json()
 
 if not data.get('entities'):
 break
 
 all_data.extend(data['entities'])
 page_token = data.get('nextPageToken')
 
 if not page_token:
 break

Be aware that the nextPageToken is opaque and time-sensitive. Do not attempt to parse it or cache it across long-running sessions. In a Glue job, ensure your connection timeout is sufficient, as large analytics queries can take several seconds to return. If you encounter a 400 Bad Request, verify that your dateRange does not exceed the 90-day limit for this specific endpoint, and ensure your OAuth scope includes analytics:conversation:view. The cursor mechanism is strictly sequential; skipping tokens will result in missing records.

This is a standard migration issue. the previous answer is correct, but let’s break down why your spark job is choking. the /analytics/conversations/details/query endpoint strictly uses cursor pagination now. if you send page=2 alongside a valid pageToken, the api ignores the token or throws a 400. you need a loop that only reads the nextPageToken from the response headers.

here is a python snippet using requests to handle the iteration correctly:

import requests

headers = {"Authorization": "Bearer <token>"}
params = {"pageSize": 1000, "interval": "2023-01-01T00:00:00.000Z/2023-01-02T00:00:00.000Z"}
url = "https://api.us-east-1.mygenesys.com/api/v2/analytics/conversations/details/query"

while True:
 r = requests.get(url, headers=headers, params=params)
 data = r.json()
 # process data here
 if "nextPageToken" not in r.headers:
 break
 params["pageToken"] = r.headers["nextPageToken"]

stop using page numbers. this ensures you capture all records without hitting the 5000 row limit per request.

If I recall correctly, the Python requests approach lacks the type safety and automatic model mapping that the official SDKs provide, which often leads to subtle bugs when handling complex Analytics API responses. While the cursor logic described above is correct, you should leverage the TypeScript SDK’s built-in pagination helpers to handle the pageToken rotation automatically, even if you are bridging to PySpark via a wrapper. The SDK handles the header parsing and token persistence internally, reducing boilerplate. Here is how the TypeScript SDK handles this, which you can port to Python or use in a Node.js pre-processing step:

import { platformClient } from '@genesyscloud/genesyscloud';

async function fetchConversations() {
 const analyticsClient = platformClient.AnalyticsApi;
 const body = { dateFrom: '2023-01-01T00:00:00.000Z', dateTo: '2023-01-02T00:00:00.000Z', pageSize: 1000 };
 
 let response = await analyticsClient.postAnalyticsConversationsDetailsQuery(body);
 let allData = response.body;
 
 while (response.body.nextPageToken) {
 response = await analyticsClient.postAnalyticsConversationsDetailsQuery(body, { pageToken: response.body.nextPageToken });
 allData = { ...allData, interactions: [...allData.interactions, ...response.body.interactions] };
 }
 return allData;
}

Have you tried decoupling the pagination logic from your Spark transformation entirely? The suggestion above regarding the pageToken is technically correct, but it misses the critical detail about how Genesys Cloud Analytics endpoints behave under high-throughput ETL loads.

When you run this in Glue, you are likely hitting rate limits or connection timeouts that cause the nextPageToken to expire before your next request fires. The API does not guarantee token longevity for large datasets. You must implement an exponential backoff strategy with a strict maximum retry count.

1. Stop using requests directly for this volume. It lacks the robust retry logic needed for enterprise analytics queries.
2. Use the Python SDK with a custom session handler to manage the pageToken rotation and error handling.
3. Check the X-RateLimit-Reset header in the response. If you are close to the limit, pause the executor.

Here is the corrected pattern using the PureCloudPlatformClientV2 SDK within a PySpark context:

from platformclientv2 import AnalyticsApi, Configuration
from platformclientv2.rest import ApiException

def fetch_conversations(api: AnalyticsApi, query_body: dict):
 page_token = None
 while True:
 try:
 # Pass pageToken in the query parameters, not body
 response = api.post_analytics_conversations_details_query(
 body=query_body,
 page_token=page_token,
 page_size=1000
 )
 
 if not response or not response.entities:
 break
 
 yield response.entities
 page_token = response.next_page_token
 
 if not page_token:
 break
 
 except ApiException as e:
 if e.status == 429:
 # Implement backoff here
 time.sleep(int(e.headers.get('X-RateLimit-Reset', 10)))
 else:
 raise

The previous advice ignores the token expiration risk. If your job takes longer than 5 minutes to process a batch, the token becomes invalid. You must restart the query or handle the 400 Bad Request gracefully by falling back to time-window based queries.

Do not mix page numbers with pageToken. The API will silently ignore the token if both are present, leading to duplicate records in Redshift.