Let me provide a comprehensive solution that addresses all the key areas you need to tackle:
1. API Timeout Configuration:
Set your client-side timeout to 90 seconds minimum, but understand this is just a safety net. The real fix is reducing the work per API call. Configure connection pooling with max 10 concurrent connections to Workday to prevent overwhelming their infrastructure.
2. Batch Processing Strategy:
Implement chunking with 25 records per batch as your starting point. Here’s the approach:
// Batch submission logic
BatchSize = 25 records
DelayBetweenBatches = 8 seconds
For each chunk: Submit -> Log batch ID -> Continue
This keeps each API call under 20 seconds and respects rate limits. Track each batch submission in a control table with status (SUBMITTED, PROCESSING, COMPLETED, FAILED).
3. Asynchronous Integration Pattern:
Shift to a submit-and-poll model. After submitting each batch, store the Workday batch ID and implement a separate polling process:
- Poll every 45 seconds for status updates
- Max 10 poll attempts before marking as stuck
- Use Workday’s Get_Integration_Events API to check batch completion
- This decouples submission from completion and prevents timeout errors
4. Payload Optimization:
This is critical - reduce your 2KB per record to under 800 bytes:
- Send expense category IDs instead of full category objects (saves ~400 bytes)
- Reference approval chain by worker ID only, not full hierarchy (saves ~600 bytes)
- Omit optional fields that can be derived or defaulted in Workday
- Use compressed JSON if your middleware supports it
Example optimized payload structure:
{
"expense_id": "EXP-2025-0001",
"worker_ref": "WID-123",
"amount": 245.50,
"category_id": "CAT-TRV-001",
"date": "2025-03-15"
}
Implementation Steps:
- Build the chunking logic in your integration layer first - this gives immediate relief
- Optimize payloads to reduce processing time per record - aim for 40% size reduction
- Implement async polling mechanism - this prevents future scaling issues
- Add retry logic with exponential backoff (retry after 2min, 5min, 10min)
- Set up monitoring alerts for batch failures and processing time trends
Expected Results:
With these changes, your 150-record sync should complete in 90-120 seconds total (6 batches × 15 seconds processing time + polling overhead) versus timing out at 60 seconds. You’ll also be able to scale to 500+ records without architectural changes.
The async pattern is the most important change - it transforms this from a blocking operation that fails under load to a resilient queue-based system that handles volume gracefully.
This draft is based on general Workday knowledge. It has not been verified against your specific version and environment. Practitioners: verify the steps and share your experience below.