REST API calls timeout when syncing large test datasets between environments

PLM Windchill
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1

We’re using REST API v2 to sync test datasets between our staging and development environments. The sync works fine for small batches (under 100 parts), but consistently times out with 504 Gateway Timeout errors when transferring larger datasets (500+ parts with attachments).

Our current implementation uses standard POST requests with JSON payloads. We’ve noticed the timeout occurs exactly at the 30-second mark regardless of dataset size. We’ve tried adjusting connection timeout configuration in our client code, but the issue persists.


POST /Windchill/servlet/odata/v2/PTC/Parts
Content-Type: application/json
Timeout after 30s with 504 error

Has anyone successfully implemented chunked transfer encoding or multipart form data for large batch operations? We need a reliable way to handle datasets up to 2000 parts with associated CAD files.

2

I’ve seen this before. The 30-second timeout is likely a server-side gateway setting, not your client configuration. Have you checked the Apache or load balancer timeout settings? We had to increase ProxyTimeout to 300 seconds for similar bulk operations.

3

The 504 suggests the application server itself is timing out. Check your method server logs for any OOM errors or thread starvation during these operations. Large JSON payloads can cause memory pressure. You might need to implement pagination or streaming instead of sending everything in one request. What’s the total payload size you’re attempting?

4

Breaking this into smaller chunks with proper error handling is definitely the right approach. However, you also need to address the underlying timeout configuration issue.

Connection Timeout Configuration: First, verify your gateway/proxy timeout settings. For Apache, set ProxyTimeout to at least 300 seconds. For Windchill’s method server, check wt.method.server.connectionTimeout in wt.properties.

Chunked Transfer Encoding Implementation: Switch to chunked transfer encoding for streaming large payloads. This prevents the server from buffering the entire request in memory:


Transfer-Encoding: chunked
Content-Type: multipart/form-data
Connection: keep-alive

Multipart Form Data Approach: Separate metadata from binary attachments. Send part data as JSON in one form field and attachments as binary streams in separate fields. This reduces payload size by 30-35% compared to base64 encoding.

Retry Logic Implementation: Implement exponential backoff with jitter for failed chunks. Track successfully uploaded parts in a state file so you can resume from failure points:


// Pseudocode - Key implementation steps:
1. Split dataset into chunks of 50 parts each
2. For each chunk, attempt upload with 3 retries
3. On failure, wait (2^retry_count + random(0-1000ms))
4. Log successful chunk IDs to recovery file
5. On fatal error, rollback completed chunks
// See documentation: REST API Guide Section 8.4

Additional Recommendations:

  • Implement progress tracking with chunk-level granularity
  • Use HTTP 100-Continue to validate before sending large payloads
  • Enable compression (gzip) for JSON metadata portions
  • Monitor method server thread pool utilization during bulk operations
  • Consider implementing a queue-based async pattern for datasets over 1000 parts

State Machine Context: For lifecycle transitions in your synced parts, ensure you’re including proper state context in your API calls. Missing context can cause validation failures that look like timeouts.

Webhook Fallback: If you need real-time sync confirmation, set up webhook notifications for completion events rather than keeping connections open. This prevents timeout issues entirely for long-running operations.

With these changes, you should be able to reliably sync datasets of 2000+ parts. We’ve successfully transferred 5000-part datasets using this approach with 99.7% success rate.

5

For transactional consistency with large datasets, consider using the bulk import service instead of REST API. It’s specifically designed for this use case and handles chunking internally. Alternatively, implement a two-phase commit pattern: first create placeholder objects via REST, then update with attachments in separate calls. This gives you rollback capability if later steps fail.

6

We solved this exact problem last year. Don’t send attachments as base64 in JSON - that bloats the payload by 33%. Use multipart/form-data instead, which streams binary data efficiently. Also implement exponential backoff retry logic for transient failures. Our success rate went from 60% to 98% after these changes. The key is treating attachments separately from metadata.

7

Total payload is around 150MB including base64-encoded attachments. We’re hitting memory limits based on the logs. Pagination makes sense but we need transactional consistency - either all parts sync or none do. Any examples of implementing retry logic with partial failure handling?