docker-compose搭建EFK,继上篇使用filebeat+es对日志文件的过滤

前言--------上一期说到，通过这两天对EFKL方面的研究,发现Logstash在处理过滤日志，提取确实有一定的优势，配合es和Kibana简直可以完美的把我们需要的日志一目了然的展示出来，但是时间久了会发现Logstash简直太迟CPU了，我丢，这下去不行啊，赶紧研究别的方案。 先给大家展示一下我要收集的日志格式：

[2021-10-29 03:39:12] saveData.INFO: saveData {"params":{"index":"fulfillments_1","id":5941107,"body":{"id":5941107,"shippingMethodId":null,"shippingMethodName":null,"pluginId":null,"shipToName":"tan2","shipToPhone":null,"shipToSuburb":"FRASER RISE","shipToState":"VIC","shipToPostcode":"3336","shipToCountry":"AU","shipToAddress1":"Second St","shipToAddress2":null,"shipToCompanyName":"eiz","shipToEmail":null,"fromAddress1":"tet-1","fromAddress2":null,"fromSuburb":"Moorabbin","fromState":"VIC","fromCountry":"AU","fromPostcode":"3189","fromCompany_name":"eiz","fromName":"jin2","fromPhone":"47658975","fromEmail":null,"carrierName":null,"labelNumber":[],"fulfillmentStatus":1,"consignments":[],"products":[{"id":4,"account_id":1,"product_id":4,"sku":"124","title":"dsadasds","weight":1,"length":11,"width":11,"height":11,"quantity":0,"location":null,"insured_amount":null,"status":0,"custom_label":null,"custom_label2":null,"custom_label3":null,"img_url":null,"barcode":null,"wms_stock":0,"pivot":{"fulfillment_id":5941107,"product_id":4,"qty":1,"note":null,"sku":"124"}}],"consignmentStatus":0,"picklistStatus":0,"createdAt":"2021-10-26 13:33:03","updatedAt":"2021-10-29 14:39:11","package_info":[{"packObj":[],"qty":"2","weight":"13","length":"6","width":"7","height":"8","package_id":null}],"price":null,"note":null,"tags":[{"id":95,"account_id":1,"parent_id":null,"name":"test","description":"{"name":"test","color":"#eb2f96"}"}],"errors":null,"tracking_status":0,"packageNum":2,"productNum":1,"autoQuoteResult":[],"orders":[],"log":[],"shipToRef":"TJ0000212"}}} []

解决方案：通过filebeat+es的pipeline就可以实现我们预期的效果，且不耗CPU，我们来动手试试，先看看实现效果：可以看到我们使用这种方法，依然实现了上一篇的效果

第一步：

先来看看我的项目目录：关于dockerfiles文件夹下的文件内容我都写在上一篇了，还有这块我用的是opensearch+opensearch-dashboards，其实和Elasticsearch+Kibana是一样的，上篇文章有说到

编辑docker-compose.yaml文件

version: "2.2"
services:
  opensearch:
    build:
      context: dockerfiles
      dockerfile: opensearch-no-security.dockerfile
    restart: always
    container_name: opensearch
    image: wangyi/opensearch:latest
    environment:
      - discovery.type=single-node
    ports:
      - 9200:9200
      - 9600:9600 # required for Performance Analyzer
    volumes:
      - opensearch-data1:/usr/share/opensearch/data

  opensearch-dashboards:
    build:
      context: dockerfiles
      dockerfile: opensearch-dashboards-no-security.dockerfile
    image: wangyi/opensearch-dashboard:latest
    container_name: opensearch-dashboards
    ports:
      - 5601:5601
    environment:
      OPENSEARCH_HOSTS: '["http://opensearch:9200"]' # must be a string with no spaces when specified as an environment variable

  filebeat:
    build: ./filebeat
    restart: "always"
    container_name: filebeat
    volumes:
      - ./storage/logs/:/tools/logs/
    user: root

volumes:
  opensearch-data1:

第二步：

配置filebeat文件夹下的Dockerfile文件：

FROM docker.elastic.co/beats/filebeat-oss:7.11.0

# Copy our custom configuration file
COPY ./filebeat.yml /usr/share/filebeat/filebeat.yml

USER root
# Create a directory to map volume with all docker log files
RUN mkdir /usr/share/filebeat/dockerlogs
RUN chown -R root /usr/share/filebeat/
RUN chmod -R go-w /usr/share/filebeat/

编辑filebeat.yml文件

filebeat.inputs:
- type: log
  enabled: true
  paths:
    - /tools/logs/saveData/*/*/*.log
  pipeline: savedata_log  ##记住这个pipeline
  fields:
    type: savedata_log
  tail_files: true  ##设置监察文件写入时执行，不从头读取


- type: log
  enabled: true
  paths:
    - /tools/logs/condition/*/*/*.log
  pipeline: my_pipeline_id  ##记住这个pipeline
  fields:
    type: condition_log
  tail_files: true


setup.ilm.enabled: false

setup.template.settings:
  index.number_of_shards: 1
  index.number_of_replicas: 0
  index.codec: best_compression

output.elasticsearch:
  hosts: ["opensearch:9200"]
  indices:
    - index: "savedata_logs_%{+yyyy.MM.dd}"
      when.equals:
        fields.type: "savedata_log"
    - index: "condition_logs_%{+yyyy.MM.dd}"
      when.equals:
        fields.type: "condition_log"

第三步：

启动三个服务，注意启动顺序
1.先启动opensearch（ES）docker-compose up -d opensearch
2.再启动filebeat docker-compose up -d filebeat
3.再启动opensearch-dashboards（Kibana） docker-compose up -d opensearch-dashboards

待所有服务启动成功后，我们打开Kibana后台，配置pipeline

PUT _ingest/pipeline/my_pipeline_id  ##我们在这里创建两个对应filebeat文件的pipeline就行
{
  "description": "test pipeline",
  "processors": [
    {
      "grok": {
        "field": "message",
        "patterns": ["""[%{TIMESTAMP_ISO8601:logtime}] %{WORD:env}.(?[A-Z]{4,5}): %{WORD:params} %{GREEDYdata:message} """]  ##此配置仅限于提取Laravel文件配置
      },
      "json": {
        "field": "message"
      }
    }
  ]
}

大家可以通过GROK在线测试自己的正则

EFK一行就此结束，前前后后换了三次方案，最终得出这套完美的解决方案，CPU也看过了，非常完美！！！