Influxdb xDB通量连接系列

我在XDB中有以下数据

server,operation=ADD queryMs=7.9810 1620608972904452000
server,operation=GET queryMs=12.2430 1620608972909339200
server,operation=UPDATE queryMs=11.5780 1620608972909655400
server,operation=ADD queryMs=11.2460 1620608972910445700
server,operation=GET queryMs=15.0620 1620608972911305000
etc...

在我的图表中，我看到了三个系列

我想实现一系列的所有

操作

s

我试图

|>分组（列：[“_字段]）

，这正是我需要的，但查询速度非常慢

from(bucket: "initial")
  |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
  |> filter(fn: (r) => r["_measurement"] == "server")
  |> filter(fn: (r) => r["_field"] == "queryMs")
  |> group(columns: ["_field"])
  |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
  |> yield(name: "mean")

我的问题有什么快速的解决方案吗？

这会更快

union(tables: [
  from(bucket: "initial")
    |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
    |> filter(fn: (r) => r["_measurement"] == "server")
    |> filter(fn: (r) => r["_field"] == "queryMs")
    |> filter(fn: (r) => r["operation"] == "GET")
    |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false),
  from(bucket: "initial")
    |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
    |> filter(fn: (r) => r["_measurement"] == "server")
    |> filter(fn: (r) => r["_field"] == "queryMs")
    |> filter(fn: (r) => r["operation"] == "ADD")
    |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false),
  from(bucket: "initial")
    |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
    |> filter(fn: (r) => r["_measurement"] == "server")
    |> filter(fn: (r) => r["_field"] == "queryMs")
    |> filter(fn: (r) => r["operation"] == "UPDATE")
    |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false),
  ])
  |> drop(columns:["operation"])
  |> sort(columns: ["_time"], desc: false)
  |> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
  |> yield(name: "mean")