Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)

本文分为4段为您详细讲解Docker版Grafana集成influxdb监控数据

文章目录

    • docker安装
        • 卸载
        • 安装
      • 国内镜像配置
    • Docker整合influxDB
      • influxDB介绍
      • influxDB安装
      • influxDB配置
      • 数据插入
      • 客户端工具
      • 常用InfluxQL
      • 代码批量插入
        • pom.xml
        • application.yml
        • Java代码
    • Docker安装Grafana整合influxDB
      • Grafana介绍
      • Grafana安装
      • 配置influxDB数据源
      • 创建Dashboard
      • 数据集成测试

docker安装

卸载

如果之前安装过Docker需要卸载可以参照如下命令

# 列出当前docker相关的安装包
$ yum list installed|grep docker
containerd.io.x86_64                 1.3.7-3.1.el7                  @docker-ce-stable
docker-ce.x86_64                     3:19.03.13-3.el7               @docker-ce-stable
docker-ce-cli.x86_64                 1:19.03.13-3.el7               @docker-ce-stable

在这里插入图片描述

# 卸载对应的包
$ yum -y remove containerd.io.x86_64
$ yum -y remove docker-ce.x86_64
$ yum -y remove docker-ce-cli.x86_64 
安装

注意:且Docker 要求操作系统必须为64位,且centos内核版本为3.1及以上

  • 查看系统内核

    $ uname -r
    3.10.0-1062.el7.x86_6
    # 我这里高于3.1
    
  • 保证yum包是最新

    # 使用root执行,更新到最新
    $ yum update
    
  • 列出可安装的docker包

    # 列出可以按照的docker包
    $ yum list docker-ce --showduplicates | sort -r
    
  • 安装

    • 指定版本安装

      $ yum list docker-ce.x86_64  --showduplicates | sort -r
      
    • 直接安装最新版

      $ yum install docker-ce -y
      
  • 查看当前版本

    $ docker version
    
    Client: Docker Engine - Community
     Version:           19.03.13
     API version:       1.40
     Go version:        go1.13.15
     Git commit:        4484c46d9d
     Built:             Wed Sep 16 17:03:45 2020
     OS/Arch:           linux/amd64
     Experimental:      false
    Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
    # 此处需要重启
    
  • 不能连接到Docker daemon异常

    装完后使用docker命令后会提示异常
    Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
    需要重启下docker
    
  • 重启

    $ service docker restart
    
  • 配置开机启动

    $ systemctl enable docker
    

国内镜像配置

  1. 找到/etc/docker目录下的daemon.json文件进行编辑,输入如下内容

    {
      "registry-mirrors": ["https://9cpn8tt6.mirror.aliyuncs.com"]
    }
    
  2. 如果没有该文件,可自行创建,也可以使用如下命令

    tee /etc/docker/daemon.json <<-'EOF'
    {
      "registry-mirrors": ["https://9cpn8tt6.mirror.aliyuncs.com"]
    }
    EOF
    
  3. 重启docker

Docker整合influxDB

前面我们已经学习了Docker的安装和相关命令,接下来,我们只讲解influxdb的内容

InfluxDB是一个由InfluxData开发的开源时序型数据。它由Go写成,着力于高性能地查询与存储时序型数据。InfluxDB被广泛应用于存储系统的监控数据,IoT行业的实时数据等场景。

influxDB介绍

InfluxDB(时序数据库),常用的一种使用场景:监控数据统计。每毫秒记录一下电脑内存的使用情况,然后就可以根据统计的数据,利用图形化界面(InfluxDB V1一般配合Grafana)制作内存使用情况的折线图;

可以理解为按时间记录一些数据(常用的监控数据、埋点统计数据等),然后制作图表做统计;

与传统数据库中的名词做比较

influxDB中的名词 传统数据库中的概念
database 数据库
measurement 数据库中的表
points 表里面的一行数据

InfluxDB中独有的一些概念
Point由时间戳(time)、数据(field)、标签(tags)组成。

Point属性 传统数据库中的概念
time 每个数据记录时间,是数据库中的主索引(会自动生成)
fields 各种记录值(没有索引的属性)也就是记录的值:温度, 湿度
tags 各种有索引的属性:地区,海拔

influxDB安装

  • 拉取最新版镜像

    # 拉取最新版镜像
    $ docker pull influxdb
    # 查看镜像
    $ docker images✨
    
  • 使用镜像创建容器

    # 使用镜像创建容器
    $ docker run -d -p  8083:8083 -p 8086:8086 --name myinfluxdb influxdb
    	-d 让容器在后台运行 
    	-p 8083:8083 将容器的 8083 端口映射到主机的 8083 端口
    	–-name 容器的名字,随便取,但是必须唯一
    
  • 开放防火墙端口

    $ firewall-cmd --zone=public --add-port=8083/tcp --permanent
    $ firewall-cmd --zone=public --add-port=8086/tcp --permanent
    $ firewall-cmd --reload
    
  • 停止容器

    $ docker stop myinfluxdb
    
  • 移除容器

    # 移除的容器必须是已经停止的
    $ docker rm myinfluxdb
    
  • 查看容器列表

    # 只查看正在运行的
    $ docker ps
    
    # 查看所有的
    $ docker ps -a
    
  • 进入容器内部

    # 该容器必须已经运行,才能进入
    $ docker exec -it myinfluxdb /bin/bash
    

influxDB配置

使用名进入到myinfluxdb容器内部后,我们来做一点小小的配置

  1. 进入influxdb命令交互模式,类似于mysql的命令行

    # 直接输入influx
    $ influx
    Connected to http://localhost:8086 version 1.8.3
    InfluxDB shell version: 1.8.3
    > 
    
    
    # 如果上述报错,采用下面这种方式,输入/usr/bin/influx
    $ /usr/bin/influx
    
  2. 添加数据库

    # 查看现有数据库
    > show databases;
    name: databases
    name
    ----
    _internal
    
    # 创建数据库
    > create database mytest
    
    # 再次查看你会发现有2个库了
    > show databases;
    name: databases
    name
    ----
    _internal
    mytest
    
    # 使用数据库
    > use mytest
    
    # 查看用户
    > show users;
    user admin
    ---- -----
    
  3. 创建一个用户

    > CREATE USER "master" WITH PASSWORD 'abcd1234' WITH ALL PRIVILEGES
    > exit 退出
    
  4. influxdb默认没有校验权限,修改influxdb.conf文件

    # 在当前容器内执行
    $ vim /etc/influxdb/influxdb.conf
    # 此时你会发现vim命令不存在
    bash: vim: command not found
    
  5. 安装vim命令

    # 在当前容器类执行(此步骤时间会比较长)
    $ apt-get update
    $ apt-get install vim
    
  6. 再次修改influxdb.conf文件

    # 修改[http]处的auth-enabled属性为true
    [http]
    ...
    auth-enabled = true
    

    注意有的版本配置文件非常简单,只有如下几个配置:

    [meta]
      dir = "/var/lib/influxdb/meta"
    
    [data]
      dir = "/var/lib/influxdb/data"
      engine = "tsm1"
      wal-dir = "/var/lib/influxdb/wal"
    

    我这边修改完后的配置文件全内容如下:

    [meta]
      dir = "/var/lib/influxdb/meta"
    
    [data]
      dir = "/var/lib/influxdb/data"
      engine = "tsm1"
      wal-dir = "/var/lib/influxdb/wal"
    
    [http]
      enabled = true  
      bind-address = ":8086"  
      auth-enabled = true  # ✨ 此处默认是关闭的需要开启,因为前面我们配置的用户名密码,所以需要开启
      log-enabled = true  
      write-tracing = false  
      pprof-enabled = false  
      https-enabled = false 
    

    退出容器,重新启动注意不要改错,改错了,容器就无法再起来了

    $ docker restart 
    

    其实最详细的配置文件如下:**

    ### Welcome to the InfluxDB configuration file.
    
    # Once every 24 hours InfluxDB will report usage data to usage.influxdata.com
    # The data includes a random ID, os, arch, version, the number of series and other
    # usage data. No data from user databases is ever transmitted.
    # Change this option to true to disable reporting.
    reporting-disabled = false
    
    # we'll try to get the hostname automatically, but if it the os returns something
    # that isn't resolvable by other servers in the cluster, use this option to
    # manually set the hostname
    # hostname = "localhost"
    
    ###
    ### [meta]
    ###
    ### Controls the parameters for the Raft consensus group that stores metadata
    ### about the InfluxDB cluster.
    ###
    
    [meta]
      # Where the metadata/raft database is stored
      dir = "/var/lib/influxdb/meta"
    
      retention-autocreate = true
    
      # If log messages are printed for the meta service
      logging-enabled = true
      pprof-enabled = false
    
      # The default duration for leases.
      lease-duration = "1m0s"
    
    ###
    ### [data]
    ###
    ### Controls where the actual shard data for InfluxDB lives and how it is
    ### flushed from the WAL. "dir" may need to be changed to a suitable place
    ### for your system, but the WAL settings are an advanced configuration. The
    ### defaults should work for most systems.
    ###
    
    [data]
      # Controls if this node holds time series data shards in the cluster
      enabled = true
    
      dir = "/var/lib/influxdb/data"
    
      # These are the WAL settings for the storage engine >= 0.9.3
      wal-dir = "/var/lib/influxdb/wal"
      wal-logging-enabled = true
      
      # Trace logging provides more verbose output around the tsm engine. Turning 
      # this on can provide more useful output for debugging tsm engine issues.
      # trace-logging-enabled = false
    
      # Whether queries should be logged before execution. Very useful for troubleshooting, but will
      # log any sensitive data contained within a query.
      # query-log-enabled = true
    
      # Settings for the TSM engine
    
      # CacheMaxMemorySize is the maximum size a shard's cache can
      # reach before it starts rejecting writes.
      # cache-max-memory-size = 524288000
    
      # CacheSnapshotMemorySize is the size at which the engine will
      # snapshot the cache and write it to a TSM file, freeing up memory
      # cache-snapshot-memory-size = 26214400
    
      # CacheSnapshotWriteColdDuration is the length of time at
      # which the engine will snapshot the cache and write it to
      # a new TSM file if the shard hasn't received writes or deletes
      # cache-snapshot-write-cold-duration = "1h"
    
      # MinCompactionFileCount is the minimum number of TSM files
      # that need to exist before a compaction cycle will run
      # compact-min-file-count = 3
    
      # CompactFullWriteColdDuration is the duration at which the engine
      # will compact all TSM files in a shard if it hasn't received a
      # write or delete
      # compact-full-write-cold-duration = "24h"
    
      # MaxPointsPerBlock is the maximum number of points in an encoded
      # block in a TSM file. Larger numbers may yield better compression
      # but could incur a performance penalty when querying
      # max-points-per-block = 1000
    
    ###
    ### [coordinator]
    ###
    ### Controls the clustering service configuration.
    ###
    
    [coordinator]
      write-timeout = "10s"
      max-concurrent-queries = 0
      query-timeout = "0"
      log-queries-after = "0"
      max-select-point = 0
      max-select-series = 0
      max-select-buckets = 0
    
    ###
    ### [retention]
    ###
    ### Controls the enforcement of retention policies for evicting old data.
    ###
    
    [retention]
      enabled = true
      check-interval = "30m"
    
    ###
    ### [shard-precreation]
    ###
    ### Controls the precreation of shards, so they are available before data arrives.
    ### Only shards that, after creation, will have both a start- and end-time in the
    ### future, will ever be created. Shards are never precreated that would be wholly
    ### or partially in the past.
    
    [shard-precreation]
      enabled = true
      check-interval = "10m"
      advance-period = "30m"
    
    ###
    ### Controls the system self-monitoring, statistics and diagnostics.
    ###
    ### The internal database for monitoring data is created automatically if
    ### if it does not already exist. The target retention within this database
    ### is called 'monitor' and is also created with a retention period of 7 days
    ### and a replication factor of 1, if it does not exist. In all cases the
    ### this retention policy is configured as the default for the database.
    
    [monitor]
      store-enabled = true # Whether to record statistics internally.
      store-database = "_internal" # The destination database for recorded statistics
      store-interval = "10s" # The interval at which to record statistics
    
    ###
    ### [admin]
    ###
    ### Controls the availability of the built-in, web-based admin interface. If HTTPS is
    ### enabled for the admin interface, HTTPS must also be enabled on the [http] service.
    ###
    
    [admin]
      enabled = true
      bind-address = ":8083"
      https-enabled = false
      https-certificate = "/etc/ssl/influxdb.pem"
    
    ###
    ### [http]
    ###
    ### Controls how the HTTP endpoints are configured. These are the primary
    ### mechanism for getting data into and out of InfluxDB.
    ###
    
    [http]
      enabled = true
      bind-address = ":8086"
      auth-enabled = true
      log-enabled = true
      write-tracing = false
      pprof-enabled = false
      https-enabled = false
      https-certificate = "/etc/ssl/influxdb.pem"
      ### Use a separate private key location.
      # https-private-key = ""
      max-row-limit = 10000
      realm = "InfluxDB"
    
    ###
    ### [subsciber]
    ###
    ### Controls the subscriptions, which can be used to fork a copy of all data
    ### received by the InfluxDB host.
    ###
    
    [subsciber]
      enabled = true
      http-timeout = "30s"
    
    
    ###
    ### [[graphite]]
    ###
    ### Controls one or many listeners for Graphite data.
    ###
    
    [[graphite]]
      enabled = false
      # database = "graphite"
      # bind-address = ":2003"
      # protocol = "tcp"
      # consistency-level = "one"
    
      # These next lines control how batching works. You should have this enabled
      # otherwise you could get dropped metrics or poor performance. Batching
      # will buffer points in memory if you have many coming in.
    
      # batch-size = 5000 # will flush if this many points get buffered
      # batch-pending = 10 # number of batches that may be pending in memory
      # batch-timeout = "1s" # will flush at least this often even if we haven't hit buffer limit
      # udp-read-buffer = 0 # UDP Read buffer size, 0 means OS default. UDP listener will fail if set above OS max.
    
      ### This string joins multiple matching 'measurement' values providing more control over the final measurement name.
      # separator = "."
    
      ### Default tags that will be added to all metrics.  These can be overridden at the template level
      ### or by tags extracted from metric
      # tags = ["region=us-east", "zone=1c"]
    
      ### Each template line requires a template pattern.  It can have an optional
      ### filter before the template and separated by spaces.  It can also have optional extra
      ### tags following the template.  Multiple tags should be separated by commas and no spaces
      ### similar to the line protocol format.  There can be only one default template.
      # templates = [
      #   "*.app env.service.resource.measurement",
      #   # Default template
      #   "server.*",
      # ]
    
    ###
    ### [collectd]
    ###
    ### Controls one or many listeners for collectd data.
    ###
    
    [[collectd]]
      enabled = false
      # bind-address = ""
      # database = ""
      # typesdb = ""
    
      # These next lines control how batching works. You should have this enabled
      # otherwise you could get dropped metrics or poor performance. Batching
      # will buffer points in memory if you have many coming in.
    
      # batch-size = 1000 # will flush if this many points get buffered
      # batch-pending = 5 # number of batches that may be pending in memory
      # batch-timeout = "1s" # will flush at least this often even if we haven't hit buffer limit
      # read-buffer = 0 # UDP Read buffer size, 0 means OS default. UDP listener will fail if set above OS max.
    
    ###
    ### [opentsdb]
    ###
    ### Controls one or many listeners for OpenTSDB data.
    ###
    
    [[opentsdb]]
      enabled = false
      # bind-address = ":4242"
      # database = "opentsdb"
      # retention-policy = ""
      # consistency-level = "one"
      # tls-enabled = false
      # certificate= ""
      # log-point-errors = true # Log an error for every malformed point.
    
      # These next lines control how batching works. You should have this enabled
      # otherwise you could get dropped metrics or poor performance. Only points
      # metrics received over the telnet protocol undergo batching.
    
      # batch-size = 1000 # will flush if this many points get buffered
      # batch-pending = 5 # number of batches that may be pending in memory
      # batch-timeout = "1s" # will flush at least this often even if we haven't hit buffer limit
    
    ###
    ### [[udp]]
    ###
    ### Controls the listeners for InfluxDB line protocol data via UDP.
    ###
    
    [[udp]]
      enabled = false
      # bind-address = ""
      # database = "udp"
      # retention-policy = ""
    
      # These next lines control how batching works. You should have this enabled
      # otherwise you could get dropped metrics or poor performance. Batching
      # will buffer points in memory if you have many coming in.
    
      # batch-size = 1000 # will flush if this many points get buffered
      # batch-pending = 5 # number of batches that may be pending in memory
      # batch-timeout = "1s" # will flush at least this often even if we haven't hit buffer limit
      # read-buffer = 0 # UDP Read buffer size, 0 means OS default. UDP listener will fail if set above OS max.
    
      # set the expected UDP payload size; lower values tend to yield better performance, default is max UDP size 65536
      # udp-payload-size = 65536
    
    ###
    ### [continuous_queries]
    ###
    ### Controls how continuous queries are run within InfluxDB.
    ###
    
    [continuous_queries]
      log-enabled = true
      enabled = true
      # run-interval = "1s" # interval for how often continuous queries will be checked if they need to run
    
  7. 退出容器,重新启动注意不要改错,改错了,容器就无法再起来了

    $ docker restart myinfluxdb
    
  8. 再次进入容器,并使用命令进行influx操作

    root@5f1bb39363e6:/# influx     
    Connected to http://localhost:8086 version 1.8.3
    InfluxDB shell version: 1.8.3
    > show users
    ERR: unable to parse authentication credentials
    Warning: It is possible this error is due to not setting a database.
    Please set a database with the command "use ".
    > 
    

    上述提示权限校验错误,接下来我们exit退出当前influx交互(不要退出容器),再次使用用户密码登录

    root@5f1bb39363e6:/# influx -username 'master' -password 'abcd1234'
    Connected to http://localhost:8086 version 1.8.3
    InfluxDB shell version: 1.8.3
    > show users
    user   admin
    ----   -----
    master true
    

    上述登录成功,并且能够使用show users语句

先切换到我们创建的mytest数据库

> use mytest

数据插入

由于InfluxDB的无结构(schemeless)特性,我们不需要预先建表,直接use [ database ]后就可以写入数据了。举个栗子。

INSERT cpu,host=serverA,region=us_west value=0.64
 
INSERT temperature,machine=unit42,type=assembly external=25,internal=37

读数据

SELECT "host", "region", "value" FROM "cpu"
SELECT * FROM "temperature"

-- measurement都可以用正则表示,下面表示读一个db下的所有measurement的数据
SELECT * FROM /.*/
-- 配上where条件
SELECT "region", "value" FROM "cpu" where "host" = "server1"

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第1张图片

客户端工具

下载地址:
链接:https://pan.baidu.com/s/1FBFRc2fPkmDoHDYjdNgntA
提取码:s4ut

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第2张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第3张图片

常用InfluxQL

-- 查看所有的数据库
show databases;
-- 使用特定的数据库
use database_name;
-- 查看所有的measurement
show measurements;
-- 查询10条数据
select * from measurement_name limit 10;
-- 数据中的时间字段默认显示的是一个纳秒时间戳,改成可读格式
precision rfc3339; -- 之后再查询,时间就是rfc3339标准格式
-- 或可以在连接数据库的时候,直接带该参数
influx -precision rfc3339
-- 查看一个measurement中所有的tag key 
show tag keys
-- 查看一个measurement中所有的field key 
show field keys
-- 查看一个measurement中所有的保存策略(可以有多个,一个标识为default)
show retention policies;

代码批量插入

新建Java的SpringBoot项目,项目地址GitHub:

pom.xml
<parent>
        <groupId>org.springframework.bootgroupId>
        <artifactId>spring-boot-starter-parentartifactId>
        <version>2.2.5.RELEASEversion>
        <relativePath/>
    parent>
    <groupId>com.it235groupId>
    <artifactId>influxdbartifactId>
    <version>0.0.1-SNAPSHOTversion>

    <properties>
        <java.version>1.8java.version>
    properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starter-webartifactId>
        dependency>
        <dependency>
            <groupId>org.projectlombokgroupId>
            <artifactId>lombokartifactId>
            <optional>trueoptional>
        dependency>
        <dependency>
            <groupId>org.influxdbgroupId>
            <artifactId>influxdb-javaartifactId>
            <version>2.15version>
        dependency>
    dependencies>
application.yml
server:
  port: 8010
spring:
  influx:
    url: http://192.168.1.31:8086
    user: master
    username: master
    password: abcd1234
    database: mytest
    retention_policy: default
    retention_policy_time: 30d
Java代码
import lombok.extern.slf4j.Slf4j;
import org.influxdb.InfluxDB;
import org.influxdb.dto.Point;
import org.influxdb.dto.Query;
import org.influxdb.dto.QueryResult;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Component;

import java.util.Map;
import java.util.concurrent.TimeUnit;

/**
 * @Author: it235.com
 * @Date: 2020-10-10
 * @Description: 工具支持类
 */
@Slf4j
@Component
public class InfluxDBSupport {
    /**
     * 数据保存策略
     */
    @Value("${spring.influx.retentionPolicy:}")
    private String retentionPolicy;
    /**
     * 数据保存策略中数据保存时间
     */
    @Value("${spring.influx.retentionPolicyTime:}")
    private String retentionPolicyTime;

    @Value("${spring.influx.database:}")
    private String database;

    /**
     * InfluxDB实例
     */
    @Autowired
    private InfluxDB influxDB;

    public InfluxDBSupport() {
        // autogen默认的数据保存策略
        this.retentionPolicy = retentionPolicy == null || "".equals(retentionPolicy) ? "autogen" : retentionPolicy;
        this.retentionPolicyTime = retentionPolicyTime == null || "".equals(retentionPolicy) ? "30d" : retentionPolicyTime;
    }

    /**
     * 设置数据保存策略 defalut 策略名 /database 数据库名/ 30d 数据保存时限30天/ 1 副本个数为1/ 结尾DEFAULT
     * 表示 设为默认的策略
     */
    public void createRetentionPolicy() {
        String command = String.format("CREATE RETENTION POLICY \"%s\" ON \"%s\" DURATION %s REPLICATION %s DEFAULT",
                retentionPolicy, database, retentionPolicyTime, 1);
        this.query(command);
    }

    /**
     * 查询
     *
     * @param command 查询语句
     * @return
     */
    public QueryResult query(String command) {
        return influxDB.query(new Query(command, database));
    }

    /**
     * 插入
     *
     * @param measurement 表
     * @param tags        标签
     * @param fields      字段
     */
    public void insert(String measurement, Map<String, String> tags, Map<String, Object> fields) {
        Point.Builder builder = Point.measurement(measurement);
        // 纳秒时会出现异常信息:partial write: points beyond retention policy dropped=1
        // builder.time(System.nanoTime(), TimeUnit.NANOSECONDS);
        builder.time(System.currentTimeMillis(), TimeUnit.MILLISECONDS);
        builder.tag(tags);
        builder.fields(fields);

        log.info("influxDB insert data:[{}]", builder.build().toString());
        influxDB.write(database, "", builder.build());
    }
import lombok.extern.slf4j.Slf4j;
import org.influxdb.dto.QueryResult;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

import java.util.HashMap;
import java.util.Map;

/**
 * @Author: it235.com
 * @Date: 2020-10-10
 * @Description: 启动主程序
 */
@Slf4j
@SpringBootApplication
public class InfluxdbDemoApplication implements CommandLineRunner {

    public static void main(String[] args) {
        SpringApplication.run(InfluxdbDemoApplication.class, args);
    }

    @Autowired
    private InfluxDBSupport influxDBSupport;

    @Override
    public void run(String... args) throws Exception {
        //插入测试
        insertTest();

        //查询测试
        //querTest();
    }

    /**
     * 插入测试
     * @throws InterruptedException
     */
    public void insertTest() throws InterruptedException {
        Map<String, String> tagsMap = new HashMap<>();
        Map<String, Object> fieldsMap = new HashMap<>();
        System.out.println("influxDB start time :" + System.currentTimeMillis());
        int i = 0;
        for (; ; ) {
            Thread.sleep(100);
            tagsMap.put("value", String.valueOf(i % 10));
            tagsMap.put("host", "https://www.it235.com");
            tagsMap.put("region", "west" + (i % 5));
            fieldsMap.put("count", i % 5);
            influxDBSupport.insert("cpu_test", tagsMap, fieldsMap);
            i++;
        }
    }

    /**
     * 查询测试
     */
    public void querTest(){
        QueryResult rs = influxDBSupport.query("select * from usage");
        log.info("query result => {}", rs);
        if (!rs.hasError() && !rs.getResults().isEmpty()) {
            rs.getResults().forEach(System.out::println);
        }
    }

}

启动程序测试,观看控制台可以看到在批量插入数据,此时也可以去influxdb中去看看

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第4张图片

Docker安装Grafana整合influxDB

Grafana介绍

Grafana安装

前面我们已经学习了Docker的安装和相关命令,接下来,我们只讲解Grafana的内容

  • 镜像拉取

    $ docker pull grafana/grafana
    $ docker images
    
  • 安装配置

    $ docker run -d -p 3000:3000 --name=it35graf grafana/grafana
    $ docker ps -a
    
  • 开放防火墙端口

    $ firewall-cmd --zone=public --add-port=3000/tcp --permanent
    $ firewall-cmd --reload
    
  • 浏览器访问http://ip:3000,用户名密码默认:admin

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第5张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第6张图片

  • 到此Grafana安装完成

配置influxDB数据源

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第7张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第8张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第9张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第10张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第11张图片

创建Dashboard

dashboardGrafana种用于展示呈现的工具,我们可以将influxdb中的数据展示到dashboard

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第12张图片

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第13张图片

注意上述选择的表一定是要有数据的,否则看不到效果

数据集成测试

  • 开启代码批量插入程序

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第14张图片

  • 观看Grafana面板中的效果

Docker版Grafana整合InfluxDB看这一篇就够了(2020全网最详细教程)_第15张图片
到此Docker版的Grafana+influxdb就集成完成了。

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