使用 Docker 快速部署 Elasticsearch 集群

服务器

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2019-3-24

本文将使用Docker容器(使用docker-compose编排)快速部署Elasticsearch 集群,可用于开发环境(单机多实例)或生产环境部署。

注意,6.x版本已经不能通过 -Epath.config 参数去指定配置文件的加载位置,文档说明:

For the archive distributions, the config directory location defaults to
$ES_HOME/config. The location of the >config directory
can be changed via the
ES_PATH_CONF environment variable as follows:


ES_PATH_CONF=/path/to/my/config ./bin/elasticsearch

Alternatively, you can export the ES_PATH_CONF environment variable via the command line or via your shell profile.

即交给环境变量 ES_PATH_CONF 来设定了(官方文档),单机部署多个实例且不使用容器的同学多多注意。

准备工作

安装 docker & docker-compose

这里推进使用 daocloud 做个加速安装:

#docker
curl -sSL https://get.daocloud.io/docker | sh

#docker-compose
curl -L \
https://get.daocloud.io/docker/compose/releases/download/1.23.2/docker-compose-`uname -s`-`uname -m` \
> /usr/local/bin/docker-compose

chmod +x /usr/local/bin/docker-compose

#查看安装结果
docker-compose -v

数据目录

#创建数据/日志目录 这里我们部署3个节点
mkdir /opt/elasticsearch/data/{node0,nod1,node2} -p
mkdir /opt/elasticsearch/logs/{node0,nod1,node2} -p
cd /opt/elasticsearch
#权限我也很懵逼啦 给了 privileged 也不行 索性0777好了
chmod 0777 data/* -R && chmod 0777 logs/* -R

#防止JVM报错
echo vm.max_map_count=262144 >> /etc/sysctl.conf
sysctl -p

docker-compse 编排服务

创建编排文件

vim docker-compose.yml

参数说明

- cluster.name=elasticsearch-cluster
集群名称

- node.name=node0
- node.master=true
- node.data=true
节点名称、是否可作为主节点、是否存储数据

- bootstrap.memory_lock=true
锁定进程的物理内存地址避免交换(swapped)来提高性能

- http.cors.enabled=true
- http.cors.allow-origin=*
开启cors以便使用Head插件

- "ES_JAVA_OPTS=-Xms512m -Xmx512m"
JVM内存大小配置

- "discovery.zen.ping.unicast.hosts=elasticsearch_n0,elasticsearch_n1,elasticsearch_n2"
- "discovery.zen.minimum_master_nodes=2"
由于5.2.1后的版本是不支持多播的,所以需要手动指定集群各节点的tcp数据交互地址,用于集群的节点发现failover,默认缺省9300端口,如设定了其它端口需另行指定,这里我们直接借助容器通信,也可以将各节点的9300映射至宿主机,通过网络端口通信。
设定failover选取的quorum = nodes/2 + 1

当然,也可以挂载自己的配置文件,ES镜像的配置文件是/usr/share/elasticsearch/config/elasticsearch.yml,挂载如下:

volumes:
  - path/to/local/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml:ro

docker-compose.yml

version: '3'
services:
  elasticsearch_n0:
    image: elasticsearch:6.6.2
    container_name: elasticsearch_n0
    privileged: true
    environment:
      - cluster.name=elasticsearch-cluster
      - node.name=node0
      - node.master=true
      - node.data=true
      - bootstrap.memory_lock=true
      - http.cors.enabled=true
      - http.cors.allow-origin=*
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - "discovery.zen.ping.unicast.hosts=elasticsearch_n0,elasticsearch_n1,elasticsearch_n2"
      - "discovery.zen.minimum_master_nodes=2"
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - ./data/node0:/usr/share/elasticsearch/data
      - ./logs/node0:/usr/share/elasticsearch/logs
    ports:
      - 9200:9200
  elasticsearch_n1:
    image: elasticsearch:6.6.2
    container_name: elasticsearch_n1
    privileged: true
    environment:
      - cluster.name=elasticsearch-cluster
      - node.name=node1
      - node.master=true
      - node.data=true
      - bootstrap.memory_lock=true
      - http.cors.enabled=true
      - http.cors.allow-origin=*
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - "discovery.zen.ping.unicast.hosts=elasticsearch_n0,elasticsearch_n1,elasticsearch_n2"
      - "discovery.zen.minimum_master_nodes=2"
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - ./data/node1:/usr/share/elasticsearch/data
      - ./logs/node1:/usr/share/elasticsearch/logs
    ports:
      - 9201:9200
  elasticsearch_n2:
    image: elasticsearch:6.6.2
    container_name: elasticsearch_n2
    privileged: true
    environment:
      - cluster.name=elasticsearch-cluster
      - node.name=node2
      - node.master=true
      - node.data=true
      - bootstrap.memory_lock=true
      - http.cors.enabled=true
      - http.cors.allow-origin=*
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - "discovery.zen.ping.unicast.hosts=elasticsearch_n0,elasticsearch_n1,elasticsearch_n2"
      - "discovery.zen.minimum_master_nodes=2"
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - ./data/node2:/usr/share/elasticsearch/data
      - ./logs/node2:/usr/share/elasticsearch/logs
    ports:
      - 9202:9200

这里我们分别为node0/node1/node2开放宿主机的9200/9201/9202作为http服务端口,各实例的tcp数据传输用默认的9300通过容器管理通信。

如果需要多机部署,则将EStransport.tcp.port: 9300端口映射至宿主机xxxx端口,discovery.zen.ping.unicast.hosts填写各主机代理的地址即可:

#比如其中一台宿主机为192.168.1.100
    ...
    - "discovery.zen.ping.unicast.hosts=192.168.1.100:9300,192.168.1.101:9300,192.168.1.102:9300"
    ...
ports:
  ...
  - 9300:9300

创建并启动服务

[root@localhost elasticsearch]# docker-compose up -d
[root@localhost elasticsearch]# docker-compose ps
      Name                    Command               State                Ports              
--------------------------------------------------------------------------------------------
elasticsearch_n0   /usr/local/bin/docker-entr ...   Up      0.0.0.0:9200->9200/tcp, 9300/tcp
elasticsearch_n1   /usr/local/bin/docker-entr ...   Up      0.0.0.0:9201->9200/tcp, 9300/tcp
elasticsearch_n2   /usr/local/bin/docker-entr ...   Up      0.0.0.0:9202->9200/tcp, 9300/tcp

#启动失败查看错误
[root@localhost elasticsearch]# docker-compose logs
#最多是一些访问权限/JVM vm.max_map_count 的设置问题

查看集群状态

192.168.20.6 是我的服务器地址

访问http://192.168.20.6:9200/_cat/nodes?v即可查看集群状态:

ip         heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
172.25.0.3           36          98  79    3.43    0.88     0.54 mdi       *      node0
172.25.0.2           48          98  79    3.43    0.88     0.54 mdi       -      node2
172.25.0.4           42          98  51    3.43    0.88     0.54 mdi       -      node1

验证 Failover

通过集群接口查看状态

模拟主节点下线,集群开始选举新的主节点,并对数据进行迁移,重新分片。

[root@localhost elasticsearch]# docker-compose stop elasticsearch_n0
Stopping elasticsearch_n0 ... done

集群状态(注意换个http端口 原主节点下线了),down掉的节点还在集群中,等待一段时间仍未恢复后就会被剔出

ip         heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
172.25.0.2           57          84   5    0.46    0.65     0.50 mdi       -      node2
172.25.0.4           49          84   5    0.46    0.65     0.50 mdi       *      node1
172.25.0.3                                                       mdi       -      node0

等待一段时间

ip         heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
172.25.0.2           44          84   1    0.10    0.33     0.40 mdi       -      node2
172.25.0.4           34          84   1    0.10    0.33     0.40 mdi       *      node1

恢复节点 node0

[root@localhost elasticsearch]# docker-compose start elasticsearch_n0
Starting elasticsearch_n0 ... done

等待一段时间

ip         heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
172.25.0.2           52          98  25    0.67    0.43     0.43 mdi       -      node2
172.25.0.4           43          98  25    0.67    0.43     0.43 mdi       *      node1
172.25.0.3           40          98  46    0.67    0.43     0.43 mdi       -      node0

配合 Head 插件观察

git clone git://github.com/mobz/elasticsearch-head.git
cd elasticsearch-head
npm install
npm run start

集群状态图示更容易看出数据自动迁移的过程

1、集群正常 数据安全分布在3个节点上

2、下线 node1 主节点 集群开始迁移数据

迁移中

迁移完成

3、恢复 node1 节点

问题小记

  1. elasticsearch watermark
    部署完后创建索引发现有些分片处于 Unsigned 状态,是由于 elasticsearch watermark:low,high,flood_stage的限定造成的,默认硬盘使用率高于85%就会告警,开发嘛,手动关掉好了,数据会分片到各节点,生产自行决断。

    curl -X PUT http://192.168.20.6:9201/_cluster/settings \
    -H 'Content-type':'application/json' \
    -d '{"transient":{"cluster.routing.allocation.disk.threshold_enabled": false}}'