使用Flux查询数据

from(bucket: "db/rp")
  |> range(start: -1h)
  |> filter(fn: (r) =>
      r._measurement == "example-measurement" and
      r._field == "example-field" and
      r.tag == "example-tag"
  )

data
    |> group(columns: ["host"], mode: "by")

data
    |> sort(columns: ["host", "_value"])
    |> limit(n: 4)

data
    |> aggregateWindow(every: 20m, fn: mean)

data
    |> map(fn: (r) => ({ r with _value: r._value * r._value }))

data
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> map(
      fn: (r) => ({
          _time: r._time,
          _field: "used_percent",
          _value: float(v: r.used) / float(v: r.total) * 100.0,
      }),
  )

data
    |> increase()

data
    |> movingAverage(n: 3)

data
    |> timedMovingAverage(every: 2m, period: 4m)

data
    |> derivative(unit: 1m, nonNegative: true)

import "experimental/aggregate"

data
    |> aggregate.rate(every: 20m, unit: 1m)

data
    |> histogram(
        column: "_value",
        upperBoundColumn: "le",
        countColumn: "_value",
        bins: [100.0, 200.0, 300.0, 400.0],
    )

data
    |> fill(usePrevious: true)

data
    |> fill(value: 0.0)

data
    |> median()

data
    |> quantile(q: 0.99, method: "estimate_tdigest")

t1 = from(bucket: "example-bucket")
    |> range(start: 2020-01-01T00:00:00Z)
    |> filter(fn: (r) => r.m == "foo")

t2 = from(bucket: "example-bucket")
    |> range(start: 2020-01-01T00:00:00Z)
    |> filter(fn: (r) => r.m == "bar")
    
join(tables: {t1: t1, t2: t2}, on: ["_time"])

data
    |> cumulativeSum()

data
    |> first()

data
    |> last()

data
  |> filter(fn: (r) => exists r._value)

scalarValue = {
  _record =
    data
      |> tableFind(fn: key => true)
      |> getRecord(idx: 0)
  return _record._value
}

import "sql"

sql.from(
    driverName: "postgres",
    dataSourceName: "postgresql://user:password@localhost",
    query: "SELECT * FROM example_table",
)

if color == "green" then "008000" else "ffffff"

data
    |> filter(fn: (r) => r.tag =~ /^foo[1-3]/)

import "experimental/geo"

sampleGeoData
  |> geo.filterRows(region: {lat: 30.04, lon: 31.23, radius: 200.0})
  |> geo.groupByArea(newColumn: "geoArea", level: 5)