Bulk upload of quality records via REST API fails with timeout

PLM ENOVIA
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1

We’re migrating quality records from our legacy system to ENOVIA Cloud R2020x using REST API batch uploads. The uploads consistently fail with timeout errors when payload size exceeds 5MB, and we’re occasionally seeing HTTP 413 errors.

Our current approach sends JSON payloads containing 500-1000 quality inspection records per request:

{
  "items": [
    {"recordId": "QR-001", "inspectionData": {...}},
    // ... 500-1000 records
  ]
}

We’ve tried increasing client timeout to 300 seconds, but the API gateway seems to have its own limits. The batch processing strategy doesn’t seem optimal for cloud deployment. Has anyone dealt with payload size configuration issues in cloud REST API uploads? Our migration is blocked and we need to upload 50K+ records within the next two weeks.

system 2

I’ll address all three critical aspects systematically:

API Gateway Limits Configuration: Your cloud gateway has a hard 6MB limit that cannot be modified in standard deployments. However, you can request a temporary increase through 3DS support for migration projects. Document your business case with timeline and record counts. We got approval for 10MB limit during our migration window.

Optimized Batch Processing Strategy: Implement a three-tier approach:

  1. Chunk your 50K records into batches of 50 records each (target 1-2MB per payload)
  2. Use parallel processing with 4-6 worker threads
  3. Implement circuit breaker pattern to handle transient failures
const batchSize = 50;
const maxWorkers = 5;
const chunks = chunkArray(qualityRecords, batchSize);

await processInParallel(chunks, maxWorkers, async (batch) => {
  return await uploadQualityRecords(batch);
});

Payload Size Configuration Best Practices:

  • Enable gzip compression in your HTTP client (reduces size by 50-70%)
  • Strip unnecessary metadata from JSON payloads
  • Use PATCH instead of PUT for updates to minimize payload
  • Implement request/response logging to track actual payload sizes
  • Set client timeout to 180s (sufficient for 50-record batches)
  • Add retry logic with exponential backoff (initial: 2s, max: 30s)

For your 50K records, this approach should complete in 3-4 hours with proper parallelization. Monitor cloud instance metrics during migration - if CPU exceeds 80%, reduce parallel workers. Also verify your API authentication token doesn’t expire during long migration runs.

One critical point: validate data integrity after migration by comparing record counts and spot-checking complex records. We found 0.5% of records needed reprocessing due to timeout-related partial commits.

This draft is based on general ENOVIA knowledge. It has not been verified against your specific version and environment. Practitioners: verify the steps and share your experience below.

3

The 413 error is definitely an API gateway limit issue. Cloud deployments typically enforce strict payload size restrictions at the gateway level, usually around 5-6MB. You need to reduce your batch size significantly - try 50-100 records per request instead of 500-1000. Also check if your cloud instance has custom gateway configuration that might allow slightly higher limits.

chloe_functional 4

“Tested this on our ENOVIA 3DEXPERIENCE R2022x migration, chunking 50K quality records into 50-record REST API batches eliminated all timeout failures completely.”

5

I’ve seen this exact scenario. The problem isn’t just batch size - it’s how the API processes large payloads. Even if you get past the gateway, the backend processing can timeout on large batches. Consider implementing exponential backoff retry logic and adding request compression (gzip) to reduce actual payload size. We reduced our payload by 60% with compression alone.

6

For large-scale migrations like yours, parallel processing is key. Split your 50K records into smaller chunks and use multiple threads to upload concurrently. We successfully migrated 100K+ quality records by running 5 parallel upload threads, each handling batches of 75 records. This approach respects gateway limits while maintaining reasonable migration speed. Monitor your cloud instance CPU and memory during parallel uploads to avoid overloading the system.

7

Have you checked the actual timeout source? Use browser dev tools or API monitoring to see where the timeout occurs - client side, gateway, or backend service. In our R2020x cloud setup, we found the issue was backend transaction timeout, not gateway limits. We had to adjust both API call batch size and enable async processing mode for quality record imports.

8

The HTTP 413 is a clear indicator you’re exceeding configured limits. Beyond reducing batch size, consider the quality record complexity - if each record has extensive inspection data, attachments, or related objects, the JSON serialization bloats quickly. We optimized by sending only essential fields in the initial upload, then using separate API calls to attach supplementary data. This two-phase approach kept payloads under 2MB consistently.

9

Check if your ENOVIA Cloud instance supports the bulk import API endpoint specifically designed for large data migrations. Standard REST endpoints aren’t optimized for this use case.