We’re planning to onboard 50,000+ parts with classification attributes from our legacy system into Windchill 12.0 CPS05. Debating between two approaches:
-
REST API approach: Iterate through parts, POST each with classification data via the Parts API. Gives us programmatic control and real-time error handling.
-
Bulk import: Use Windchill’s native import utilities (LoadFromFile or similar) with CSV files containing part and classification data.
The API approach seems more modern and fits our automation strategy, but I’m concerned about performance with 50K+ parts. Bulk import speed vs API control is the key tradeoff. On the other hand, error handling differences matter - with API we get immediate feedback per part, while bulk import might fail partway through a large file.
Anyone have real-world experience with large-scale part classification loading? What’s the practical performance difference, and how suitable for automation is each approach when you need to run this monthly as new parts arrive?
Having implemented both approaches across multiple client engagements, here’s a detailed comparison addressing the three critical factors:
Bulk Import Speed vs API Control: For your 50K part scenario, bulk import via LoadFromFile will complete in 4-8 hours depending on hardware and classification complexity. API approach with sequential calls would take 30-50 hours. However, with parallelization (15-20 threads), you can reduce API time to 8-12 hours while maintaining programmatic control. The speed gap narrows significantly with proper API optimization.
Error Handling Differences: This is where approaches diverge substantially. Bulk import provides:
- All-or-nothing batch processing (though Windchill supports partial commits)
- Error logs generated after completion
- Difficult to pinpoint exact failure causes in large batches
- Requires reprocessing entire failed batches
API approach offers:
- Per-record error handling with immediate feedback
- Granular retry logic for transient failures
- Detailed error messages per part
- Ability to skip problematic records and continue processing
For data quality issues, API wins decisively. You can implement validation, transformation, and error recovery inline.
Suitability for Automation: This is crucial for monthly ongoing loads. API integration fits modern CI/CD pipelines naturally:
- Easy to trigger from scheduling tools
- Integrates with monitoring and alerting systems
- Programmatic status checking and reporting
- Version control for integration logic
Bulk import requires more orchestration - generating CSV files, moving them to Windchill server, triggering loader utilities, parsing log files. It’s automatable but requires more glue code.
Recommendation: Use a phased approach:
-
Initial 50K Load: Use bulk import with thorough pre-validation. Write a validation script that checks data quality against Windchill rules before generating import files. This maximizes speed for one-time migration.
-
Ongoing Monthly Loads: Implement API-based integration for steady-state operations. The smaller volumes (likely hundreds or low thousands monthly) make API overhead acceptable, and you gain error handling and automation benefits.
-
Hybrid Safety Net: Keep bulk import capability for emergency scenarios where you need to reload large datasets quickly.
For the API implementation, use batch processing patterns - group parts into batches of 50-100, commit per batch, implement exponential backoff for retries. This balances throughput with error recovery granularity.
API approach gives you way better control. You can implement retry logic, validation before submission, and handle errors gracefully. We process about 5K parts per day via API and it works great. Performance-wise, if you parallelize the API calls (10-20 concurrent threads), you can achieve decent throughput. Not as fast as bulk import for one-time loads, but for ongoing automation it’s much more maintainable.
Always pre-validate before bulk import. Write a Python or Java script that checks required fields, data types, classification node existence, etc. Run your 50K records through validation first, fix issues, then do the actual import. This saves massive time versus discovering problems during a 6-hour import run. For API approach, you can validate inline but it’s slower overall for large initial loads.
The hybrid approach is compelling. For the initial load, how do you handle validation before bulk import? Do you build a pre-processing script to check data quality, or just let the bulk loader catch errors and iterate?
Consider a hybrid approach. Use bulk import for the initial 50K part load to get speed benefits, then switch to API for ongoing monthly additions. The initial load is a one-time event where raw speed matters most. Monthly additions are smaller volumes where API control and error handling provide more value. You get best of both worlds - fast initial migration and maintainable automation for steady-state operations.
We did 75K parts via bulk import last year and it took about 6 hours total. The native loader handles batching and transaction management efficiently. Error logs are comprehensive but you only see issues after the batch completes, which can be frustrating if you have data quality problems.