在sql server,等待狀態。等待狀態發生在多個處理器(個vcpu)處理和多個線程正在通過處理器。依賴性較長的運行的線程,可以導致的線程可能已經開始在一個不同的過程,但依賴於一個正在運行的線程完成的時間越長,會導致一個等待狀態和減緩相關查詢。sysadmin的觀點,這也可以視為硬故障在內存中。從SQL的角度上麵的問題是增加的最大約束添加端點(線程)和邏輯上創建額外的流-流,更快的流動。sysadmin的觀點,添加額外的集群增加端點的大小也會解決問題,因為允許更快的內存數量執行。在這兩種情況下,它應該解決這個問題。它將更有意義,讓Azure或AWS區分出來可靠Optimizied Serverless特性。然而,功能隻會執行擴展集群的壯舉。假設AWS / Azure處理,和他們的方法是集群規模,更好的答案……根據他們的處理情況,與集群規模向上。 I suspect the answer here is the Databricks people aren't always thinking like SQL Server database administrators. They have lots of cluster power. Frankly, if it were my issue, I would try the maximum bound increase first. If that didn't work then I'd go for the increased cluster size. But my thinking is a cost optimization. There is not a part of the question that asks how to do this in a cost effective way. So - final answer - C: boost the cluster.
