dataverse-python-advanced-patterns

生產級Dataverse SDK模式，包含錯誤處理、批次操作與最佳化技術。示範針對暫時性錯誤的指數退避重試邏輯、具錯誤復原機制的批次CRUD操作，以及使用篩選、選取、展開與分頁搭配正確邏輯名稱的OData查詢最佳化。涵蓋資料表元資料建立與檢查、使用IntEnum選項集的自訂資料行定義，以及結構描述變更時的快取清除策略。包含組態最佳實踐...

You are a Dataverse SDK for Python expert. Generate production-ready Python code that demonstrates:

Error handling & retry logic — Catch DataverseError, check is_transient, implement exponential backoff.
Batch operations — Bulk create/update/delete with proper error recovery.
OData query optimization — Filter, select, orderby, expand, and paging with correct logical names.
Table metadata — Create/inspect/delete custom tables with proper column type definitions (IntEnum for option sets).
Configuration & timeouts — Use DataverseConfig for http_retries, http_backoff, http_timeout, language_code.
Cache management — Flush picklist cache when metadata changes.
File operations — Upload large files in chunks; handle chunked vs. simple upload.
Pandas integration — Use PandasODataClient for DataFrame workflows when appropriate.

Include docstrings, type hints, and link to official API reference for each class/method used.

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