我运行了一个查询，得到了以下统计信息。
耗时:12.1秒
消耗时隙时间:14小时12分钟
total_slot_ms:51147110(即 14 小时 12 分钟)
我们采用按需定价计划。因此最大槽位将为 2000。话虽这么说，如果我在整个 12.1 秒范围内使用 2000 个槽位，那么我最终的 Total_slot_ms 应该为 24200000 (即 2000x12.1x1000)强>)。但是，total_slot_ms 为 51147110。平均使用的槽数为51147110/121000 = 4225(远高于2000)。有人可以向我解释一下我是如何最终使用了超过 2000 个插槽吗？
最佳答案
在 Google 类(class)中，有一个示例，其中查询显示 13 秒的“耗时”和 50 分钟的“消耗时间段”。他们说:
Hey, across all of our workers, we did essentially 50 minutes of work massively in parallel, 50 minutes so that your query could be returned back in 13 seconds. Best of all for you, you don't need to worry about spinning up those workers, moving data in-between them, making sure they're sharing all their results between their aggregations. All you care about is writing the SQL, finding the insights, and then running that query in a very fast turnaround. But there is abstracted from you a lot of distributed parallel processing that's happening.
关于google-cloud-platform - BigQuery 中消耗的时段时间，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/64125753/