Docker构建ELK Docker集群日志收集系统

当我们搭建好Docker集群后就要解决如何收集日志的问题 ELK就提供了一套完整的解决方案 本文主要介绍使用Docker搭建ELK 收集Docker集群的日志

ELK简介

ELK由ElasticSearch、Logstash和Kiabana三个开源工具组成

Elasticsearch是个开源分布式搜索引擎，它的特点有：分布式，零配置，自动发现，索引自动分片，索引副本机制，restful风格接口，多数据源，自动搜索负载等。

Logstash是一个完全开源的工具，他可以对你的日志进行收集、过滤，并将其存储供以后使用

Kibana 也是一个开源和免费的工具，它Kibana可以为 Logstash 和 ElasticSearch 提供的日志分析友好的 Web 界面，可以帮助您汇总、分析和搜索重要数据日志。

使用Docker搭建ELK平台

首先我们编辑一下 logstash的配置文件 logstash.conf

input { 
  udp {
  port => 5000
  type => json
 }
}
filter {
  json {
   source => "message"
  }
}
output {
  elasticsearch {
       hosts => "elasticsearch:9200" #将logstash的输出到 elasticsearch 这里改成你们自己的host 
  }
}

然后我们还需要需要一下Kibana 的启动方式

编写启动脚本 等待elasticserach 运行成功后启动

#!/usr/bin/env bash

# Wait for the Elasticsearch container to be ready before starting Kibana.
echo "Stalling for Elasticsearch" 
while true; do
  nc -q 1 elasticsearch 9200 2>/dev/null && break
done

echo "Starting Kibana"
exec kibana

修改Dockerfile 生成自定义的Kibana镜像

FROM kibana:latest

RUN apt-get update && apt-get install -y netcat

COPY entrypoint.sh /tmp/entrypoint.sh
RUN chmod +x /tmp/entrypoint.sh

RUN kibana plugin --install elastic/sense

CMD ["/tmp/entrypoint.sh"]

同时也可以修改一下Kibana 的配置文件 选择需要的插件

# Kibana is served by a back end server. This controls which port to use.
port: 5601

# The host to bind the server to.
host: "0.0.0.0"

# The Elasticsearch instance to use for all your queries.
elasticsearch_url: "http://elasticsearch:9200"

# preserve_elasticsearch_host true will send the hostname specified in `elasticsearch`. If you set it to false,
# then the host you use to connect to *this* Kibana instance will be sent.
elasticsearch_preserve_host: true

# Kibana uses an index in Elasticsearch to store saved searches, visualizations
# and dashboards. It will create a new index if it doesn't already exist.
kibana_index: ".kibana"

# If your Elasticsearch is protected with basic auth, this is the user credentials
# used by the Kibana server to perform maintence on the kibana_index at statup. Your Kibana
# users will still need to authenticate with Elasticsearch (which is proxied thorugh
# the Kibana server)
# kibana_elasticsearch_username: user
# kibana_elasticsearch_password: pass

# If your Elasticsearch requires client certificate and key
# kibana_elasticsearch_client_crt: /path/to/your/client.crt
# kibana_elasticsearch_client_key: /path/to/your/client.key

# If you need to provide a CA certificate for your Elasticsarech instance, put
# the path of the pem file here.
# ca: /path/to/your/CA.pem

# The default application to load.
default_app_id: "discover"

# Time in milliseconds to wait for elasticsearch to respond to pings, defaults to
# request_timeout setting
# ping_timeout: 1500

# Time in milliseconds to wait for responses from the back end or elasticsearch.
# This must be > 0
request_timeout: 300000

# Time in milliseconds for Elasticsearch to wait for responses from shards.
# Set to 0 to disable.
shard_timeout: 0

# Time in milliseconds to wait for Elasticsearch at Kibana startup before retrying
# startup_timeout: 5000

# Set to false to have a complete disregard for the validity of the SSL
# certificate.
verify_ssl: true

# SSL for outgoing requests from the Kibana Server (PEM formatted)
# ssl_key_file: /path/to/your/server.key
# ssl_cert_file: /path/to/your/server.crt

# Set the path to where you would like the process id file to be created.
# pid_file: /var/run/kibana.pid

# If you would like to send the log output to a file you can set the path below.
# This will also turn off the STDOUT log output.
log_file: ./kibana.log
# Plugins that are included in the build, and no longer found in the plugins/ folder
bundled_plugin_ids:
 - plugins/dashboard/index
 - plugins/discover/index
 - plugins/doc/index
 - plugins/kibana/index
 - plugins/markdown_vis/index
 - plugins/metric_vis/index
 - plugins/settings/index
 - plugins/table_vis/index
 - plugins/vis_types/index
 - plugins/visualize/index

好了下面我们编写一下 Docker-compose.yml 方便构建

端口之类的可以根据自己的需求修改 配置文件的路径根据你的目录修改一下 整体系统配置要求较高 请选择配置好点的机器

elasticsearch:
 image: elasticsearch:latest
 command: elasticsearch -Des.network.host=0.0.0.0
 ports:
  - "9200:9200"
  - "9300:9300"
logstash:
 image: logstash:latest
 command: logstash -f /etc/logstash/conf.d/logstash.conf
 volumes:
  - ./logstash/config:/etc/logstash/conf.d
 ports:
  - "5001:5000/udp"
 links:
  - elasticsearch
kibana:
 build: kibana/
 volumes:
  - ./kibana/config/:/opt/kibana/config/
 ports:
  - "5601:5601"
 links:
  - elasticsearch

#好了命令 就可以直接启动ELK了 
docker-compose up -d

访问之前的设置的kibanna的5601端口就可以看到是否启动成功了

使用logspout收集Docker日志

下一步我们要使用logspout对Docker日志进行收集 我们根据我们的需求修改一下logspout镜像

编写配置文件 modules.go

package main

import (
  _ "github.com/looplab/logspout-logstash"
  _ "github.com/gliderlabs/logspout/transports/udp"

)

编写Dockerfile

FROM gliderlabs/logspout:latest
COPY ./modules.go /src/modules.go

重新构建镜像后 在各个节点运行即可

 docker run -d --name="logspout" --volume=/var/run/docker.sock:/var/run/docker.sock \
         jayqqaa12/logspout logstash://你的logstash地址

现在打开Kibana 就可以看到收集到的 docker日志了

注意Docker容器应该选择以console输出 这样才能采集到

好了我们的Docker集群下的ELK 日志收集系统就部署完成了

如果是大型集群还需要添加logstash 和elasticsearch 集群 这个我们下回分解。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持呐喊教程。

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