Schedule Adherence Export - 429s & Incomplete Data

Hey all,

So we’re hitting rate limits on the adherence export. We’ve got around 1200 agents and trying to pull adherence data for the last 7 days via the /api/v2/workforcemanagement/adherence endpoint. It’s… not going well. It’ll start, then throttle after a few hundred records, and the response doesn’t include all the adherence events for those agents. Just a partial set. Feels like something’s up with how we’re handling pagination or the endpoint itself.

The SDK we’re using is the official Node.js client, version 8.1.0. We’ve implemented retry logic with exponential backoff, but it’s still failing to grab all the data before the job times out. The initial request gets a 200, then subsequent requests start getting 429s. It’s not consistent - sometimes we get more data before the 429, sometimes less.

What’s frustrating is there’s no clear indication how many records are even available. No total count in the response or anything. Just pageCount, pageSize, currentPage. Which means we’re kinda guessing how many times to call the endpoint.

I tried limiting the pageSize to 50, thinking smaller batches would help, but it just increased the overall runtime. We’re also not filtering by anything - just trying to get all adherence records for all agents for that date range.

Here’s a sample request:

GET /api/v2/workforcemanagement/adherence?startDate=2024-02-21&endDate=2024-02-27&pageSize=50&pageNumber=1

And a sample response (truncated for brevity):

{
 "adherenceEvents": [
 {
 "agentId": "agent123",
 "adherenceType": "SCHEDULED",
 "dateValue": "2024-02-22T10:00:00.000Z"
 },
 {
 "agentId": "agent456",
 "adherenceType": "NOT_SCHEDULED",
 "dateValue": "2024-02-22T11:00:00.000Z"
 }
 ],
 "pageNumber": 1,
 "pageSize": 50,
 "pageCount": 30 // This number seems... optimistic.
}

We’ve tried:

• Decreasing pageSize to 50.
• Implementing exponential backoff with retry logic (up to 5 retries).
• Checking our account’s rate limit status in the developer portal - no obvious issues.
• Verifying date range validity - no typos or invalid dates.
• Making sure our API keys are valid and have the correct permissions.
• Adding logging to see where the 429s are occurring and the size of responses.

Feels like this endpoint is just not built to handle a larger agent population. Anyone else running into this? Maybe there’s a better way to pull this data? Or is the pagination completely broken?

The 429s indicate you’re exceeding the Great Cloud’s rate limits, and the incomplete data suggests the endpoint isn’t cooperating with naive pagination. It’s not simply a matter of waiting and retrying - the API expects a consistent pageNumber and pageSize.

We’ve found explicitly setting these, and - crucially - persisting the nextPageToken from each response is required. Consider this workflow fragment:

 - name: Fetch Adherence Data
 run: |
 PAGE_NUMBER=1
 PAGE_SIZE=200 # Adjust as needed, but keep it reasonable
 while true; do
 RESPONSE=$(curl -s -X GET "https://api.mypurecloud.com/api/v2/workforcemanagement/adherence?userId=YOUR_USER_ID&pageNumber=$PAGE_NUMBER&pageSize=$PAGE_SIZE")
 NEXT_PAGE_TOKEN=$(echo "$RESPONSE" | jq -r '.nextPageToken')
 # Process the data in $RESPONSE
 if [ -z "$NEXT_PAGE_TOKEN" ]; then
 break
 fi
 PAGE_NUMBER=$((PAGE_NUMBER + 1))
 done

This pattern ensures you exhaust all available data without repeatedly exceeding the rate limits.

The 429 errors and incomplete data are typical with that endpoint - especially at scale. the earlier post’s advice regarding pagination is correct, though there are a few nuances.

• Rate Limits: The adherence export endpoint has documented rate limits - roughly 300 requests per minute, though this varies by region. Batching helps, but it’s not a complete solution.
• Pagination: Explicitly setting pageNumber and pageSize is essential. The default values aren’t reliable. We’ve found setting pageSize to 100 provides a good balance between request overhead and response size.
• nextPageToken: Persisting and using the nextPageToken is crucial. The API requires it to maintain state across multiple requests.
• Concurrency: The Resource Center article on Workforce Management API Best Practices recommends limiting concurrent requests to avoid overwhelming the endpoint. We’ve successfully used a queue with a maximum of 5 concurrent workers.
• Data Consistency: For what it’s worth, we observed inconsistent results even with proper pagination if adherence events were actively being updated during the export. Consider scheduling the export during off-peak hours.
• Error Handling: Implement solid error handling, specifically for 429s. Exponential backoff with jitter is a must. The Genesys Cloud API documentation recommends starting with a 5-second delay and doubling it after each failure.

Here’s a snippet demonstrating pagination in Python:

import requests
import time

def get_adherence_data(api_url, page_number, page_size, next_page_token):
 params = {
 'pageNumber': page_number,
 'pageSize': page_size
 }
 if next_page_token:
 params['nextPageToken'] = next_page_token
 headers = {'Authorization': 'Bearer YOUR_TOKEN'} #replace
 response = requests.get(api_url, headers=headers, params=params)
 return response

# Example usage
api_url = 'https://api.mypurecloud.com/api/v2/workforcemanagement/adherence'
page_number = 1
page_size = 100
next_page_token = None

while True:
 response = get_adherence_data(api_url, page_number, page_size, next_page_token)
 if response.status_code == 200:
 data = response.json()
 # Process adherence data
 next_page_token = data.get('nextPageToken')
 if not next_page_token:
 break
 page_number += 1
 elif response.status_code == 429:
 time.sleep(5) # Implement exponential backoff
 else:
 print(f"Error: {response.status_code} - {response.text}")
 break

Just a hunch, but it’s worth verifying you’re not inadvertently filtering the data on the client side after retrieving it.

okay, -1 to thinking it’s just pagination.

the endpoint’s rate limiting isn’t a fixed window - it’s a token bucket.
you’re getting 429s because you’re depleting the bucket faster than it refills.
you need to track the x-rate-limit-remaining header and PAUSE your requests when it gets low - like, below 50 - before hitting zero and getting throttled.

genesyscloud-client-app-sdk handles the rate limiting headers - check the x-rate-limit-remaining value before each request. You’ll need to implement exponential backoff with jitter - a simple setTimeout won’t cut it. Here’s a rough example:

const delay = (ms) => new Promise(res => setTimeout(res, ms));

async function makeRequest() {
 // ... your request code ...
 if (response.headers['x-rate-limit-remaining'] < 50) {
 const retryAfter = parseInt(response.headers['x-rate-limit-reset']) * 1000 - Date.now();
 await delay(retryAfter);
 }
}