influxdb时序数据库-EW帮帮网

以下概念及操作均来自influxdb2 官方文档

InfluxDB2 is the platform purpose-built to collect, store, process and visualize time series data. Time series data is a sequence of data points indexed in time order. Data points typically consist of successive measurements made from the same source and are used to track changes over time. Examples of time series data include:

Industrial sensor data
Server performance metrics
Heartbeats per minute
Electrical activity in the brain
Rainfall measurements
Stock prices

使用docker安装

docker run \
 --name influxdb2 \
 --publish 8086:8086 \
 --mount type=volume,source=influxdb2-data,target=/var/lib/influxdb2 \
 --mount type=volume,source=influxdb2-config,target=/etc/influxdb2 \
 --env DOCKER_INFLUXDB_INIT_MODE=setup \
 --env DOCKER_INFLUXDB_INIT_USERNAME=ADMIN_USERNAME \
 --env DOCKER_INFLUXDB_INIT_PASSWORD=ADMIN_PASSWORD \
 --env DOCKER_INFLUXDB_INIT_ORG=ORG_NAME \
 --env DOCKER_INFLUXDB_INIT_BUCKET=BUCKET_NAME \
 influxdb:2

Example:
docker run \
 --name influxdb2 \
 --publish 8086:8086 \
 --mount type=volume,source=influxdb2-data,target=/var/lib/influxdb2 \
 --mount type=volume,source=influxdb2-config,target=/etc/influxdb2 \
 --env DOCKER_INFLUXDB_INIT_MODE=setup \
 --env DOCKER_INFLUXDB_INIT_USERNAME=influxdb \
 --env DOCKER_INFLUXDB_INIT_PASSWORD=influxdb \
 --env DOCKER_INFLUXDB_INIT_ORG=influxdb \
 --env DOCKER_INFLUXDB_INIT_BUCKET=influxdb \
 influxdb:2

–publish 8086:8086: Exposes the InfluxDB UI and HTTP API on the host’s 8086 port.
–mount type=volume,source=influxdb2-data,target=/var/lib/influxdb2: Creates a volume named influxdb2-data mapped to the InfluxDB data directory to persist data outside the container.
--mount type=volume,source=influxdb2-config,target=/etc/influxdb2: Creates a volume named influxdb2-config mapped to the InfluxDB configuration directory to make configurations available outside the container.
--env DOCKER_INFLUXDB_INIT_MODE=setup: Environment variable that invokes the automated setup of the initial organization, user, bucket, and token when creating the container.
--env DOCKER_INFLUXDB_INIT_<SETUP_OPTION>: Environment variables for initial setup options–replace the following with your own values:
- - ADMIN_USERNAME: The username for the initial user–an admin user with an API Operator token.
  - ADMIN_PASSWORD: The password for the initial user.
  - ORG_NAME: The name for the initial organization.
  - BUCKET_NAME: The name for the initial bucket.

If successful, the command starts InfluxDB initialized with the user, organization, bucket, and Operator token, and logs to stdout.

You can view the Operator token in the /etc/influxdb2/influx-configs file and use it to authorize creating an All Access token.

访问: http://127.0.0.1:8086/

操作 CLI

write

-b, --bucket or --bucket-id flag with the bucket name or ID to write do.
-p, --precision flag with the timestamp precision (s).
String-encoded line protocol.

influx write \
  --bucket influxdb \
  --precision s "
home,room=Living\ Room temp=21.1,hum=35.9,co=0i 1641024000
home,room=Kitchen temp=21.0,hum=35.9,co=0i 1641024000
home,room=Living\ Room temp=21.4,hum=35.9,co=0i 1641027600
home,room=Kitchen temp=23.0,hum=36.2,co=0i 1641027600
home,room=Living\ Room temp=21.8,hum=36.0,co=0i 1641031200
home,room=Kitchen temp=22.7,hum=36.1,co=0i 1641031200
home,room=Living\ Room temp=22.2,hum=36.0,co=0i 1641034800
home,room=Kitchen temp=22.4,hum=36.0,co=0i 1641034800
home,room=Living\ Room temp=22.2,hum=35.9,co=0i 1641038400
home,room=Kitchen temp=22.5,hum=36.0,co=0i 1641038400
home,room=Living\ Room temp=22.4,hum=36.0,co=0i 1641042000
home,room=Kitchen temp=22.8,hum=36.5,co=1i 1641042000
home,room=Living\ Room temp=22.3,hum=36.1,co=0i 1641045600
home,room=Kitchen temp=22.8,hum=36.3,co=1i 1641045600
home,room=Living\ Room temp=22.3,hum=36.1,co=1i 1641049200
home,room=Kitchen temp=22.7,hum=36.2,co=3i 1641049200
home,room=Living\ Room temp=22.4,hum=36.0,co=4i 1641052800
home,room=Kitchen temp=22.4,hum=36.0,co=7i 1641052800
home,room=Living\ Room temp=22.6,hum=35.9,co=5i 1641056400
home,room=Kitchen temp=22.7,hum=36.0,co=9i 1641056400
home,room=Living\ Room temp=22.8,hum=36.2,co=9i 1641060000
home,room=Kitchen temp=23.3,hum=36.9,co=18i 1641060000
home,room=Living\ Room temp=22.5,hum=36.3,co=14i 1641063600
home,room=Kitchen temp=23.1,hum=36.6,co=22i 1641063600
home,room=Living\ Room temp=22.2,hum=36.4,co=17i 1641067200
home,room=Kitchen temp=22.7,hum=36.5,co=26i 1641067200
"

Query

root@c2ecea1e4729:/# influx query '
from(bucket: "influxdb")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field== "co" or r._field == "hum" or r._field == "temp")
'

结果

Result: _result
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:int
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  --------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T08:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T09:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T10:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T11:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T12:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T13:00:00.000000000Z                           1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T14:00:00.000000000Z                           1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T15:00:00.000000000Z                           3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T16:00:00.000000000Z                           7
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T17:00:00.000000000Z                           9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T18:00:00.000000000Z                          18
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T19:00:00.000000000Z                          22
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen  2022-01-01T20:00:00.000000000Z                          26
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T08:00:00.000000000Z                          35.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T09:00:00.000000000Z                          36.2
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T10:00:00.000000000Z                          36.1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T11:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T12:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T13:00:00.000000000Z                          36.5
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T14:00:00.000000000Z                          36.3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T15:00:00.000000000Z                          36.2
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T16:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T17:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T18:00:00.000000000Z                          36.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T19:00:00.000000000Z                          36.6
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen  2022-01-01T20:00:00.000000000Z                          36.5
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T08:00:00.000000000Z                            21
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T09:00:00.000000000Z                            23
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T10:00:00.000000000Z                          22.7
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T11:00:00.000000000Z                          22.4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T12:00:00.000000000Z                          22.5
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T13:00:00.000000000Z                          22.8
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T14:00:00.000000000Z                          22.8
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T15:00:00.000000000Z                          22.7
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T16:00:00.000000000Z                          22.4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T17:00:00.000000000Z                          22.7
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T18:00:00.000000000Z                          23.3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T19:00:00.000000000Z                          23.1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T20:00:00.000000000Z                          22.7
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:int
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  --------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T08:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T09:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T10:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T11:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T12:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T13:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T14:00:00.000000000Z                           0
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T15:00:00.000000000Z                           1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T16:00:00.000000000Z                           4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T17:00:00.000000000Z                           5
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T18:00:00.000000000Z                           9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T19:00:00.000000000Z                          14
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room  2022-01-01T20:00:00.000000000Z                          17
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T08:00:00.000000000Z                          35.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T09:00:00.000000000Z                          35.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T10:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T11:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T12:00:00.000000000Z                          35.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T13:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T14:00:00.000000000Z                          36.1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T15:00:00.000000000Z                          36.1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T16:00:00.000000000Z                            36
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T17:00:00.000000000Z                          35.9
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T18:00:00.000000000Z                          36.2
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T19:00:00.000000000Z                          36.3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room  2022-01-01T20:00:00.000000000Z                          36.4
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T08:00:00.000000000Z                          21.1
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T09:00:00.000000000Z                          21.4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T10:00:00.000000000Z                          21.8
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T11:00:00.000000000Z                          22.2
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T12:00:00.000000000Z                          22.2
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T13:00:00.000000000Z                          22.4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T14:00:00.000000000Z                          22.3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T15:00:00.000000000Z                          22.3
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T16:00:00.000000000Z                          22.4
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T17:00:00.000000000Z                          22.6
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T18:00:00.000000000Z                          22.8
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T19:00:00.000000000Z                          22.5
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T20:00:00.000000000Z                          22.2

influxQL query

root@c2ecea1e4729:/# influx v1 shell

SELECT co,hum,temp,room FROM "influxdb".autogen.home WHERE time >= '2022-01-01T08:00:00Z' AND time <= '2022-01-01T20:00:00Z'

结果:
在这里插入图片描述

在数据中重新映射或赋值

使用map（）函数迭代数据中的每一行，并更新该行中的值。map（）是Flux中最有用的函数之一，将帮助您完成许多需要执行的数据处理操作。

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "hum")
    |> map(fn: (r) => ({r with _value: r._value / 100.0}))
'

分组数据

使用group（）函数按特定列值重新组合数据，为进一步处理做准备。

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> group(columns: ["room", "_field"])
'

Aggregate or select specific data

Use Flux aggregate or selector functions to return aggregate or selected values from each input table.

mean（）返回每个输入表中指定列中非空值的平均值。

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> mean()
'

root@c2ecea1e4729:/# influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T08:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> mean()
'
Result: _result
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home                 Kitchen            6.6923076923076925
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home                 Kitchen            36.246153846153845
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen            22.623076923076926
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                      co                    home             Living Room            3.8461538461538463
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                     hum                    home             Living Room             36.05384615384615
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------
2022-01-01T08:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room             22.16923076923077

Aggregate functions

Aggregate functions drop columns that are not in the group key and return a single row for each input table with the aggregate value of that table.

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> mean()
    |> duplicate(column: "_stop", as: "_time")
'

root@c2ecea1e4729:/# influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> mean()
    |> duplicate(column: "_stop", as: "_time")
'
Result: _result
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float                      _time:time
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------  ------------------------------
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen            22.814285714285713  2022-01-01T20:00:01.000000000Z
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                  _value:float                      _time:time
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ----------------------------  ------------------------------
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room             22.44285714285714  2022-01-01T20:00:01.000000000Z

将数据转换为关系模式

如果来自关系SQL或类SQL查询语言，如InfluxQL，Flux使用的数据模型与您习惯的不同。Flux返回多个表，每个表包含一个不同的字段。“关系”模式将每个字段构造为每行中的一列。
使用pivot（）函数根据时间戳将数据转换为“关系”模式。

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> filter(fn: (r) => r.room == "Kitchen")
    |> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")
'

root@c2ecea1e4729:/# influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "co" or r._field == "hum" or r._field == "temp")
    |> filter(fn: (r) => r.room == "Kitchen")
    |> pivot(rowKey: ["_time"], columnKey: ["_field"], valueColumn: "_value")
'
Result: _result
Table: keys: [_start, _stop, _measurement, room]
                   _start:time                      _stop:time     _measurement:string             room:string                      _time:time                      co:int                     hum:float                    temp:float
------------------------------  ------------------------------  ----------------------  ----------------------  ------------------------------  --------------------------  ----------------------------  ----------------------------
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T14:00:00.000000000Z                           1                          36.3                          22.8
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T15:00:00.000000000Z                           3                          36.2                          22.7
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T16:00:00.000000000Z                           7                            36                          22.4
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T17:00:00.000000000Z                           9                            36                          22.7
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T18:00:00.000000000Z                          18                          36.9                          23.3
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T19:00:00.000000000Z                          22                          36.6                          23.1
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    home                 Kitchen  2022-01-01T20:00:00.000000000Z                          26                          36.5                          22.7

数据下采样

数据下采样是一种提高查询性能并优化长期数据存储的策略。简而言之，下采样可以减少查询返回的点数，而不会丢失数据的总体趋势。

有关数据下采样的更多信息，请参阅数据下采样。

最常见的数据下采样方法是按时间间隔或“窗口”。例如，您可能希望查询过去一小时的数据，并返回每五分钟窗口的平均值。

使用aggregateWindow()按指定的时间间隔对数据进行下采样：

使用 every 参数指定每个窗口的持续时间。
使用 fn 参数指定要应用于每个窗口的聚合函数或选择器函数。
（可选）使用 timeSrc 参数指定要使用哪个列值来为每个窗口创建新的聚合时间戳。默认值为 _stop。

influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> aggregateWindow(every: 2h, fn: mean)
'

root@c2ecea1e4729:/# influx query 'from(bucket: "influxdb")
    |> range(start: 2022-01-01T14:00:00Z, stop: 2022-01-01T20:00:01Z)
    |> filter(fn: (r) => r._measurement == "home")
    |> filter(fn: (r) => r._field == "temp")
    |> aggregateWindow(every: 2h, fn: mean)
'
Result: _result
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T16:00:00.000000000Z                         22.75
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T18:00:00.000000000Z            22.549999999999997
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T20:00:00.000000000Z            23.200000000000003
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home                 Kitchen  2022-01-01T20:00:01.000000000Z                          22.7
Table: keys: [_start, _stop, _field, _measurement, room]
                   _start:time                      _stop:time           _field:string     _measurement:string             room:string                      _time:time                  _value:float
------------------------------  ------------------------------  ----------------------  ----------------------  ----------------------  ------------------------------  ----------------------------
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T16:00:00.000000000Z                          22.3
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T18:00:00.000000000Z                          22.5
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T20:00:00.000000000Z                         22.65
2022-01-01T14:00:00.000000000Z  2022-01-01T20:00:01.000000000Z                    temp                    home             Living Room  2022-01-01T20:00:01.000000000Z                          22.2

Automate processing with InfluxDB tasks

InfluxDB tasks are scheduled queries that can perform any of the data processing operations described above. Generally tasks then use the to() function to write the processed result back to InfluxDB.

For more information about creating and configuring tasks, see Get started with InfluxDB tasks.

Go操作Influxdb2

完整代码

安装依赖

First, you need to create a new go module. Run the commands below in your terminal.

mkdir -p influxdb_go_client
cd influxdb_go_client
go mod init influxdb_go_client
touch main.go

go get github.com/influxdata/influxdb-client-go/v2

获取Token

InfluxDB Cloud使用令牌来验证API访问。我们已为此设置过程为您创建了一个全访问令牌。

LVbJ5lreeauQy8QpILuBoTrqArti8hTRtrQDSG4_xoQefYIPl9yqJFYBt03a2kO5GNgcgKU0diYIMnEnJCVsYg==

初始化客户端

package main

import influxdb2 "github.com/influxdata/influxdb-client-go/v2"

const (
	TOKEN = "LVbJ5lreeauQy8QpILuBoTrqArti8hTRtrQDSG4_xoQefYIPl9yqJFYBt03a2kO5GNgcgKU0diYIMnEnJCVsYg=="
	url = "http://127.0.0.1:8086"
)
func main() {
	client := influxdb2.NewClient(url, TOKEN)
	defer client.Close()
}

写入数据

func main() {
	client := influxdb2.NewClient(url, TOKEN)
	defer client.Close()
	org := "influxdb"
	bucket := "influxdb"
	writeAPI := client.WriteAPIBlocking(org, bucket)
	for value := 0; value < 5; value++ {
		tags := map[string]string{
			"tagname1": "tagvalue1",
		}
		fields := map[string]interface{}{
			"field1": value,
		}
		point := write.NewPoint("measurement1", tags, fields, time.Now())
		time.Sleep(1 * time.Second)

		if err := writeAPI.WritePoint(context.Background(), point); err != nil {
			log.Fatal(err)
		}
	}	
}

执行简单查询

influx query 'from(bucket: "influxdb")
  |> range(start: -10m)
'

 	org := "influxdb"

 	queryAPI := client.QueryAPI(org)
 	query := `from(bucket: "influxdb")
             |> range(start: -10m)
             |> filter(fn: (r) => r._measurement == "measurement1")`
 	results, err := queryAPI.Query(context.Background(), query)
 	if err != nil {
 		log.Fatal(err)
 	}
 	for results.Next() {
 		fmt.Println(results.Record())
 	}
 	if err := results.Err(); err != nil {
 		log.Fatal(err)
 	}

执行汇总查询

	org := "influxdb"
	queryAPI := client.QueryAPI(org)
	query := `from(bucket: "influxdb")
              |> range(start: -10m)
              |> filter(fn: (r) => r._measurement == "measurement1")
              |> mean()`
	results, err := queryAPI.Query(context.Background(), query)
	if err != nil {
		log.Fatal(err)
	}
	for results.Next() {
		fmt.Println(results.Record())
	}

	if err := results.Err(); err != nil {
		log.Fatal(err)
	}

Node操作Influxdb2

完整代码

安装依赖

First, you need to create a new go module. Run the commands below in your terminal.

mkdir -p influxdb_node_client
cd influxdb_go_client

npm install --save @influxdata/influxdb-client

获取Token

InfluxDB Cloud使用令牌来验证API访问。我们已为此设置过程为您创建了一个全访问令牌。

LVbJ5lreeauQy8QpILuBoTrqArti8hTRtrQDSG4_xoQefYIPl9yqJFYBt03a2kO5GNgcgKU0diYIMnEnJCVsYg==

influxdb时序数据库

使用docker安装

操作 CLI

write

Query

influxQL query

在数据中重新映射或赋值

分组数据

Aggregate or select specific data

Aggregate functions

将数据转换为关系模式

数据下采样

Automate processing with InfluxDB tasks

Go操作Influxdb2

安装依赖

获取Token

初始化客户端

写入数据

执行简单查询

执行汇总查询

Node操作Influxdb2

安装依赖

获取Token

初始化客户端

网站公告

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