我想知道在数据管理 API 中处理同一模型的新版本的最佳实践是什么 存储桶系统
目前，我每个用户有一个存储桶，并且在进行 svf/svf2 转换时，同名文件会覆盖现有模型。
为了以最佳方式处理模型版本控制，我应该：
- 为每个转换的文件创建一个存储桶
，还是
- 继续为每个用户创建一个存储桶。
如果 1)：可以创建的存储桶数量是否有限制？
否则2）：如何让翻译接受与文件名不同的bucketKey？ （现在，上传的文件需要是文件名才能进行翻译。）
提前为您的帮助欢呼。
I am wondering of what is the best practise for handling new version of the same model in the Data Management API Bucket system
Currently, I have one bucket per user and the files with same name overwrites the existing model when doing a svf/svf2 conversion.
In order to handle model versioning in be the best manner, should I :
- create one bucket per file converted
or
- continue with one bucket per user.
If 1): is there a limitation of number of buckets which is possible to create?
else 2): How do I get the translation to accept an bucketKey different than the file name? (As it is now, the uploaded file need to be the filename to get the translation going.)
In advance, cheers for the assistance.
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评论（1）
为了翻译文件，您不必必须保留原始文件名，但您需要保留文件扩展名（例如*.rvt)，以便模型衍生服务知道要使用哪个转换器。因此，您可以创建具有不同名称的文件：也许添加“_v1”等后缀，或者生成随机名称并跟踪哪个文件是版本的数据库中的>模型。由你决定。
存储桶的数量没有限制，但为每个文件分配一个单独的存储桶可能有点过分了。
In order to translate a file, you do not have to keep the original file name, but you do need to keep the file extension (e.g. *.rvt), so that the Model Derivative service knows which translator to use. So you could just create files with different names: perhaps add a suffix like "_v1" etc or generate random names and keep track of which file is what version of what model in a database. Up to you.
There is no limit on number of buckets, but it might be an overkill to have a separate one for each file.