A recent discovery in Blender's Universal Scene Description (USD) file import functionality has sparked an interesting discussion about data structure choices and their impact on performance. The community has identified and analyzed a case where a seemingly simple implementation choice resulted in unexpected quadratic time complexity.
The Performance Problem
The issue stems from Blender's use of a sorted doubly linked list to store object IDs during USD file imports. What should have been an O(N) operation degraded into O(N²) performance when handling files with multiple references to the same entity. The community discussion revealed this is a classic example of accidentally quadratic behavior, where a seemingly reasonable implementation leads to unexpected performance degradation at scale.
For the life of me I can't figure out why people never consider replacing linked lists. Even without worst-case O(n) insertion, they usually behave worse than vectors, deques, or hives.
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| A flame graph depicting performance analysis related to Blender's USD file import functionality |
Alternative Solutions
The community proposed several alternative approaches to solve the performance issue. Suggestions included replacing the linked list with more efficient data structures like red-black trees, hash tables, or other tree-based implementations. Some developers noted that while C-based codebases often default to linked lists, modern programming practices favor more appropriate container types from standard libraries.
Technical note: O(N) refers to linear time complexity where processing time grows proportionally with input size, while O(N²) indicates quadratic growth where processing time grows with the square of input size.
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| The visualization of function calls in a flame graph highlights the performance impact of data structures in Blender |
Beyond Simple Fixes
While some suggested quick fixes like padding number formats (e.g., changing %s.%u to %s.%010u), the community emphasized the importance of addressing the root cause rather than implementing temporary workarounds. This highlights a broader discussion about technical debt and the value of choosing appropriate data structures from the start.
The Open Source Advantage
This case demonstrates both the strengths and limitations of open-source software development. While the transparency allowed for detailed analysis and potential solutions, some community members noted that the investigation resulted in a detailed write-up rather than an actual code contribution through a pull request.
The discussion serves as a reminder that performance optimization often requires careful consideration of data structure choices, and that what works well for small-scale operations may become problematic as data size increases.
Reference: A curious case of O(N^2) behavior which should be O(N)