Logstash：Data转换，分析，提取，丰富及核心操作

Logstash：Data转换，分析，提取，丰富及核心操作

Logstash plugins

Logstash是一个非常容易进行扩张的框架。它可以对各种的数据进行分析处理。这依赖于目前提供的超过200多个plugin。
首先，我们来查看一下目前有哪些plugin：

Input plugins

首先进入到Logstash的安装目录下的bin子目录，并在命令行中打入如下的命令：

$ ./logstash-plugin list --group input

显示：

logstash-input-azure_event_hubs
logstash-input-beats
logstash-input-couchdb_changes
logstash-input-elasticsearch
logstash-input-exec
logstash-input-file
logstash-input-ganglia
logstash-input-gelf
logstash-input-generator
logstash-input-graphite
logstash-input-heartbeat
logstash-input-http
logstash-input-http_poller
logstash-input-imap
logstash-input-jdbc
logstash-input-jms
logstash-input-kafka
logstash-input-pipe
logstash-input-rabbitmq
logstash-input-redis
logstash-input-s3
logstash-input-snmp
logstash-input-snmptrap
logstash-input-sqs
logstash-input-stdin
logstash-input-syslog
logstash-input-tcp
logstash-input-twitter
logstash-input-udp
logstash-input-unix

Filter plugs

在命令行打入如下的命令：

$ ./logstash-plugin list --group filter
logstash-filter-aggregate
logstash-filter-anonymize
logstash-filter-cidr
logstash-filter-clone
logstash-filter-csv
logstash-filter-date
logstash-filter-de_dot
logstash-filter-dissect
logstash-filter-dns
logstash-filter-drop
logstash-filter-elasticsearch
logstash-filter-fingerprint
logstash-filter-geoip
logstash-filter-grok
logstash-filter-http
logstash-filter-jdbc_static
logstash-filter-jdbc_streaming
logstash-filter-json
logstash-filter-kv
logstash-filter-memcached
logstash-filter-metrics
logstash-filter-mutate
logstash-filter-prune
logstash-filter-ruby
logstash-filter-sleep
logstash-filter-split
logstash-filter-syslog_pri
logstash-filter-throttle
logstash-filter-translate
logstash-filter-truncate
logstash-filter-urldecode
logstash-filter-useragent
logstash-filter-uuid
logstash-filter-xml

Output plugins

在命令行打入如下的命令：

$ ./logstash-plugin list --group output
logstash-output-cloudwatch
logstash-output-csv
logstash-output-elastic_app_search
logstash-output-elasticsearch
logstash-output-email
logstash-output-file
logstash-output-graphite
logstash-output-http
logstash-output-lumberjack
logstash-output-nagios
logstash-output-null
logstash-output-pipe
logstash-output-rabbitmq
logstash-output-redis
logstash-output-s3
logstash-output-sns
logstash-output-sqs
logstash-output-stdout
logstash-output-tcp
logstash-output-udp
logstash-output-webhdfs

Codec plugins:

在命令行打入如下的命令：

$ ./logstash-plugin list codec
logstash-codec-avro
logstash-codec-cef
logstash-codec-collectd
logstash-codec-dots
logstash-codec-edn
logstash-codec-edn_lines
logstash-codec-es_bulk
logstash-codec-fluent
logstash-codec-graphite
logstash-codec-json
logstash-codec-json_lines
logstash-codec-line
logstash-codec-msgpack
logstash-codec-multiline
logstash-codec-netflow
logstash-codec-plain
logstash-codec-rubydebug

在这上面显示都是我们在安装Logstash后，已经给我们配置好的plugin。我们可以自己开发自己的plugin，并安装它。我们也可以安装一个别人已经开发好的plugin。

从上面我们可以看出来，因为file都在input及output之中，我们甚至可以做如下的配置：

input {
   file {
      path => "C:/Program Files/Apache Software Foundation/Tomcat 7.0/logs/*access*"
      type => "apache"
   }
} 
output {
   file {
      path => "C:/tpwork/logstash/bin/log/output.log"
   }
}

这样我们把input文件读入到Logstash，经过它的处理后，就会变成下面的这种输出：

0:0:0:0:0:0:0:1 - - [
   25/Dec/2016:18:37:00 +0800] "GET / HTTP/1.1" 200 11418

{
   "path":"C:/Program Files/Apache Software Foundation/Tomcat 7.0/logs/
   localhost_access_log.2016-12-25.txt",
   "@timestamp":"2016-12-25T10:37:00.363Z","@version":"1","host":"Dell-PC",
   "message":"0:0:0:0:0:0:0:1 - - [25/Dec/2016:18:37:00 +0800] "GET /
   HTTP/1.1" 200 11418
","type":"apache","tags":[]
}

安装plugin

在标准的logstash中，有很多的plugin已经被安装了，但是在有些场合，我们需要手动来安装一些我们所需要的plugin，比如Exec output plugin。我们可以在bin目录先打人如下的命令：

./bin/logstash-plugin install logstash-output-exec

这样我们用如下的命令来检查上面的plugin是否已经被成功安装了：

./bin/logstash-plugin list --group output | grep exec

$ ./bin/logstash-plugin list --group output | grep exec
Java HotSpot(TM) 64-Bit Server VM warning: Option UseConcMarkSweepGC was deprecated in version 9.0 and will likely be removed in a future release.
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.bouncycastle.jcajce.provider.drbg.DRBG (file:/Users/liuxg/elastic/logstash-7.4.0/vendor/jruby/lib/ruby/stdlib/org/bouncycastle/bcprov-jdk15on/1.61/bcprov-jdk15on-1.61.jar) to constructor sun.security.provider.Sun()
WARNING: Please consider reporting this to the maintainers of org.bouncycastle.jcajce.provider.drbg.DRBG
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
logstash-output-exec

读取log文件

Logstash很容易设置来读取一个log文件。比如，我们可以通过如下的方式来读取一个Apache的log文件：

input {
  file { 
  	type => "apache"
  	path => "/Users/liuxg/data/apache_logs"
 	  start_position => "beginning"
	  sincedb_path => "null"
  }
}
 
output {
	stdout { 
		codec => rubydebug 
	}
}

我们甚至可以读取多个文件：

# Pull in application-log data. They emit data in JSON form.
input {
  file {
    path => [
      "/var/log/app/worker_info.log",
      "/var/log/app/broker_info.log",
      "/var/log/app/supervisor.log"
    ]
    exclude => "*.gz"
    type    => "applog"
    codec   => "json"
  }
}

数据的系列化

我们可以使用已经提供的Codec来把我们的数据进行系列化，比如：

input {
  // Deserialize newline separated JSON
  file  { path => “/some/sample.log”, codec => json }
}
 
output {
  // Serialize to the msgpack format
  redis { codec => msgpack }
  stdout {
    codec => rubydebug
  }
}

在我们的longstash运行起来后，我们可以通过如下的命令在一个terminal中向文件sample.json添加内容：
$ echo '{"name2", "liuxg2"}' >> ./sample.log

我们可以看到如下的输出：

{
      "@version" => "1",
       "message" => "{"name2", "liuxg2"}",
    "@timestamp" => 2019-09-12T07:37:56.639Z,
          "host" => "localhost",
          "tags" => [
        [0] "_jsonparsefailure"
    ],
          "path" => "/Users/liuxg/data/sample.log"
}

最常用的codec

  1. line 使用“message”中的数据将每行转换为logstash事件。 也可以将输出格式化为自定义行 。

  2. multiline: 允许您为“message”构成任意边界。 经常用于stacktraces等。也可以在filebeat中完成。

  3. json_lines: 解析换行符分隔的JSON数据

  4. json: 解析所有JSON。 仅适用于面向消息的输入/输出，如Redis / Kafka / HTTP等

还有很多其它的Codec。

解析及提取

Grok Filter

filter {
	grok {
		match => [
			"message", "%{TIMESTAMP_ISO8601:timestamp_string}%{SPACE}%{GREEDYDATA:line}"
		]
	}
}

上面的例子可以帮我们很方便地把如下的log信息变成一个机构化的数据：

2019-09-09T13:00:00Z Whose woods these are I think I know.

更多grok的pattern可以在地址grok pattern找到。

Date filter

filter {
  date {
    match => ["timestamp_string", "ISO8601"]
  }
}

Date filter可以帮我们把一个字符串，变成一个我们想要的格式的时间，并且把这个值赋予给@timestamp字段。

Dissect filter

是一个更快，轻量级的更小的grok：

filter {
  dissect {
    mapping => {“message” => “%{id} %{function->} %{server}”}
  }
}

字段和分隔符模式的格式类似于Grok。

例子：

    input {
      generator {
        message => "<1>Oct 16 20:21:22 www1 1,2016/10/16 20:21:20,3,THREAT,SCAN,6,2016/10/16 20:21:20,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54"
        count => 1
      }
    }
     
    filter {
      if [message] =~ "THREAT," {
        dissect {
          mapping => {
            message => "<%{priority}>%{syslog_timestamp} %{+syslog_timestamp} %{+syslog_timestamp} %{logsource} %{pan_fut_use_01},%{pan_rec_time},%{pan_serial_number},%{pan_type},%{pan_subtype},%{pan_fut_use_02},%{pan_gen_time},%{pan_src_ip},%{pan_dst_ip},%{pan_nat_src_ip},%{pan_nat_dst_ip},%{pan_rule_name},%{pan_src_user},%{pan_dst_user},%{pan_app},%{pan_vsys},%{pan_src_zone},%{pan_dst_zone},%{pan_ingress_intf},%{pan_egress_intf},%{pan_log_fwd_profile},%{pan_fut_use_03},%{pan_session_id},%{pan_repeat_cnt},%{pan_src_port},%{pan_dst_port},%{pan_nat_src_port},%{pan_nat_dst_port},%{pan_flags},%{pan_prot},%{pan_action},%{pan_misc},%{pan_threat_id},%{pan_cat},%{pan_severity},%{pan_direction},%{pan_seq_number},%{pan_action_flags},%{pan_src_location},%{pan_dst_location},%{pan_content_type},%{pan_pcap_id},%{pan_filedigest},%{pan_cloud},%{pan_user_agent},%{pan_file_type},%{pan_xff},%{pan_referer},%{pan_sender},%{pan_subject},%{pan_recipient},%{pan_report_id},%{pan_anymore}"
          }
        }
      }
    }
     
     
    output {
    	stdout { 
    		codec => rubydebug 
    	}
    }

运行后：

    {
                 "@timestamp" => 2019-09-12T09:20:46.514Z,
                 "pan_dst_ip" => "9",
             "pan_nat_src_ip" => "10",
                   "sequence" => 0,
                  "logsource" => "www1",
             "pan_session_id" => "23",
                   "pan_vsys" => "16",
                    "pan_cat" => "34",
              "pan_rule_name" => "12",
               "pan_gen_time" => "2016/10/16 20:21:20",
             "pan_seq_number" => "37",
                "pan_subject" => "50",
       
                    ....
       
                    "message" => "<1>Oct 16 20:21:22 www1 1,2016/10/16 20:21:20,3,THREAT,SCAN,6,2016/10/16 20:21:20,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54",
             "pan_fut_use_02" => "6",
                  "pan_flags" => "29",
           "syslog_timestamp" => "Oct 16 20:21:22",
                "pan_anymore" => "53,54"
    }

KV filter

解析键/值对中数据的简便方法

    filter {
      kv {
        source => “message”
        target => “parsed”
        value_split => “:”
      }
    }

我们运行这样的conf文件：

    input {
      generator {
        message => "pin=12345~0&d=123&e=foo@bar.com&oq=bobo&ss=12345"
        count => 1
      }
    }
     
    filter {
    	kv {
    		source => "message"
    		target => "parsed"
    		field_split => "&?"
    	}
    }
     
    output {
    	stdout { 
    		codec => rubydebug 
    	}
    }

显示的结果是：

    {
        "@timestamp" => 2019-09-12T09:46:04.944Z,
              "host" => "localhost",
            "parsed" => {
             "ss" => "12345",
              "e" => "foo@bar.com",
            "pin" => "12345~0",
             "oq" => "bobo",
              "d" => "123"
        },
           "message" => "pin=12345~0&d=123&e=foo@bar.com&oq=bobo&ss=12345",
          "sequence" => 0,
          "@version" => "1"
    }

对于kv flter来说，我们也可以使用一个target来把信息组织到一个object里，比如：

    filter {
      kv {
        source => “message”
        target => “parsed”
        value_split => “:”
      }
    }

核心操作Mutate filter

这个filter提供很多功能：

  • 转换字段类型（从字符串到整数等）
  • 添加/重命名/替换/复制字段
  • 大-小写转换
  • 将数组连接在一起（对于Array => String操作很有用）
  • 合并哈希
  • 将字段拆分为数组
  • 剥去空白
    input {
      generator {
        message => "pin=12345~0&d=123&e=foo@bar.com&oq=bobo&ss=12345"
        count => 1
      }
    }
     
    filter {
    	kv {
    		source => "message"
    		field_split => "&?"
    	}
     
    	if [pin] == "12345~0" {
        	mutate { add_tag => [ 'metrics' ]
        }
     
        mutate {
        	split => ["message", "&"]
        	add_field => {"foo" => "bar-%{pin}"}
      	}
      }
    }
     
    output {
    	stdout { 
    		codec => rubydebug 
    	}
     
    	if "metrics" in [tags] {
          stdout {
             codec => line { format => "custom format: %{message}" }
          }
       }
    }

显示的结果是：

    {
         "foo" => "bar-12345~0",
          "e" => "foo@bar.com",
          "sequence" => 0,
           "message" => [
            [0] "pin=12345~0",
            [1] "d=123",
            [2] "e=foo@bar.com",
            [3] "oq=bobo",
            [4] "ss=12345"
        ],
               "pin" => "12345~0",
                 "d" => "123",
              "host" => "localhost",
                "ss" => "12345",
        "@timestamp" => 2019-09-14T15:03:15.141Z,
                "oq" => "bobo",
          "@version" => "1",
              "tags" => [
            [0] "metrics"
        ]
    }
    custom format: pin=12345~0,d=123,e=foo@bar.com,oq=bobo,ss=12345

最核心的转化filters

  • Mute - 修改/添加每个项
  • Split - 把一个事件转化为多个事件
  • Drop - 丢掉一个事件

条件逻辑

if/else

  • 可以用 =~来使用regexps（正则）
  • 可以在一个数组里检查一个会员
    filter {
      mutate { lowercase => “account” }
      if [type] == “batch” {
        split { 
            field => actions 
           target => action 
        }
      }
     
      if { “action” =~ /special/ } {
        drop {}
      }
    }

GeoIP

丰富IP地址信息：

filter { geoip { fields => “my_geoip_field” }}

运行如下的配置：

    input {
      generator {
        message => "83.149.9.216"
        count => 1
      }
    }
     
    filter {
    	grok {
        	match => {
          		"message" => '%{IPORHOST:clientip}'
        	}
        }
     
        geoip {
        	source => "clientip"
      	}
    }
     
    output {
    	stdout {
    		codec => rubydebug
    	}
    }

显示的结果如下：

    {
              "host" => "localhost",
          "@version" => "1",
          "clientip" => "83.149.9.216",
           "message" => "83.149.9.216",
        "@timestamp" => 2019-09-15T06:54:46.695Z,
          "sequence" => 0,
             "geoip" => {
                  "timezone" => "Europe/Moscow",
               "region_code" => "MOW",
                  "latitude" => 55.7527,
             "country_code3" => "RU",
            "continent_code" => "EU",
                 "longitude" => 37.6172,
              "country_name" => "Russia",
                  "location" => {
                "lat" => 55.7527,
                "lon" => 37.6172
            },
                        "ip" => "83.149.9.216",
               "postal_code" => "102325",
             "country_code2" => "RU",
               "region_name" => "Moscow",
                 "city_name" => "Moscow"
        }
    }

我们可以看到在geoip之下，有很多具体的信息。

DNS filter

用DNS信息丰富主机名的更多信息

filter { dns { fields => “my_dns_field” }}

我们定义如下的一个Logstash配置文件：

    input {
      generator {
        message => "www.google.com"
        count => 1
      }
    }
     
    filter {
     	mutate {
        	add_field => { "hostname" => "172.217.160.110"}
    	}
     
     
    	dns {
    		reverse => ["hostname"]
    		action => "replace"	 
    	}   
     
    }
     
    output {
    	stdout {
    		codec => rubydebug
    	}
    }

上面是谷歌的地址，那么它的输出结果是：

    {
              "host" => "localhost",
          "sequence" => 0,
           "message" => "www.google.com",
        "@timestamp" => 2019-09-15T11:35:43.791Z,
          "hostname" => "tsa03s06-in-f14.1e100.net",
          "@version" => "1"
    }

在这里我们可以看到hostname的值。

Useragent filer

让浏览器的useragent信息更加丰富。我们使用如下的Logstash配置：

    input {
      generator {
        message => '83.149.9.216 - - [17/May/2015:10:05:50 +0000] "GET /presentations/logstash-monitorama-2013/images/kibana-dashboard.png HTTP/1.1" 200 321631 "http://semicomplete.com/presentations/logstash-monitorama-2013/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"'
        count => 1
      }
    }
     
    filter {
    	grok {
    	    match => {
    	      "message" => '%{IPORHOST:clientip} %{USER:ident} %{USER:auth} [%{HTTPDATE:timestamp}] "%{WORD:verb} %{DATA:request} HTTP/%{NUMBER:httpversion}" %{NUMBER:response:int} (?:-|%{NUMBER:bytes:int}) %{QS:referrer} %{QS:agent}'
    	    }
    	  }
     
    	useragent {
        	source => "agent"
        	target => "useragent"
      	}
    }
     
    output {
    	stdout {
    		codec => rubydebug
    	}
    }

运行出来的结果是：

    {
            "request" => "/presentations/logstash-monitorama-2013/images/kibana-dashboard.png",
          "useragent" => {
                "name" => "Chrome",
               "build" => "",
              "device" => "Other",
            "os_major" => "10",
                  "os" => "Mac OS X",
               "minor" => "0",
               "major" => "32",
             "os_name" => "Mac OS X",
               "patch" => "1700",
            "os_minor" => "9"
        },
           "sequence" => 0,
            "message" => "83.149.9.216 - - [17/May/2015:10:05:50 +0000] "GET /presentations/logstash-monitorama-2013/images/kibana-dashboard.png HTTP/1.1" 200 321631 "http://semicomplete.com/presentations/logstash-monitorama-2013/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"",
          "timestamp" => "17/May/2015:10:05:50 +0000",
           "referrer" => ""http://semicomplete.com/presentations/logstash-monitorama-2013/"",
           "clientip" => "83.149.9.216",
              "ident" => "-",
               "auth" => "-",
           "response" => 200,
           "@version" => "1",
               "verb" => "GET",
               "host" => "localhost",
         "@timestamp" => 2019-09-15T12:03:34.650Z,
        "httpversion" => "1.1",
              "bytes" => 321631,
              "agent" => ""Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36""
    }

我们在useragent里可以看到更加详细的信息啊。

Translate Filter

使用本地的数据来使得数据更加丰富。我们使用如下的Logstash配置文件：

    input {
      generator {
        message => '83.149.9.216 - - [17/May/2015:10:05:50 +0000] "GET /presentations/logstash-monitorama-2013/images/kibana-dashboard.png HTTP/1.1" 200 321631 "http://semicomplete.com/presentations/logstash-monitorama-2013/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"'
        count => 1
      }
    }
     
    filter {
    	grok {
    	    match => {
    	      "message" => '%{IPORHOST:clientip} %{USER:ident} %{USER:auth} [%{HTTPDATE:timestamp}] "%{WORD:verb} %{DATA:request} HTTP/%{NUMBER:httpversion}" %{NUMBER:response:int} (?:-|%{NUMBER:bytes:int}) %{QS:referrer} %{QS:agent}'
    	    }
    	 }
     
    	translate {
    		field => "[response]"
    		destination => "[http_status_description]"
    		dictionary => {
             	"100" => "Continue"
              	"101" => "Switching Protocols"
              	"200" => "OK"
              	"500" => "Server Error"
    		}
    		
    		fallback => "I'm a teapot"
    	}
    	
    }
     
     
    output {
    	stdout {
    		codec => rubydebug
    	}
    }

运行显示的结果是：

    {
                           "auth" => "-",
                           "host" => "localhost",
                      "timestamp" => "17/May/2015:10:05:50 +0000",
                        "message" => "83.149.9.216 - - [17/May/2015:10:05:50 +0000] "GET /presentations/logstash-monitorama-2013/images/kibana-dashboard.png HTTP/1.1" 200 321631 "http://semicomplete.com/presentations/logstash-monitorama-2013/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"",
                    "httpversion" => "1.1",
                       "@version" => "1",
                       "response" => 200,
                       "clientip" => "83.149.9.216",
                           "verb" => "GET",
                       "sequence" => 0,
                       "referrer" => ""http://semicomplete.com/presentations/logstash-monitorama-2013/"",
                          "agent" => ""Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36"",
        "http_status_description" => "OK",
                          "ident" => "-",
                     "@timestamp" => 2019-09-15T12:30:09.575Z,
                          "bytes" => 321631,
                        "request" => "/presentations/logstash-monitorama-2013/images/kibana-dashboard.png"
    }

我们可以看到一项http_status_description，它的值变为“OK”。

Elasticsearch Filter

从Elasticsearch中的index得到数据，并丰富事件。为了做这个测试，我们先建立一个叫做elasticsearch_filter的index:

    PUT ç/_doc/1
    {
      "name":"liuxg",
      "age": 20,
      "@timestamp": "2019-09-15"
    }

在这里，我必须指出来的是：我们必须有一个叫做@timestamp的项，否则会有错误。这个是用来做sort用的。

我们采用如下的Logstash配置：

    input {
      generator {
        message => "liuxg"
        count => 1
      }
    }
     
    filter {
    	elasticsearch {
    		hosts => ["http://localhost:9200"]
    		index => ["elasticsearch_filter"]
    		query => "name.keyword:%{[message]}"
    		result_size => 1
    		fields => {"age" => "user_age"}
    	}
    }
     
    output {
    	stdout {
    		codec => rubydebug
    	}
    }

运行上面的例子显示的结果是：

    {
          "user_age" => 20,
              "host" => "localhost",
           "message" => "liuxg",
          "@version" => "1",
        "@timestamp" => 2019-09-15T13:21:29.742Z,
          "sequence" => 0
    }

我们可以看到user_age是20。这个是通过搜索name:liuxg来得到的。

参考：https://opensource.com/article/17/10/logstash-fundamentals

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原文地址：https://www.cnblogs.com/sanduzxcvbnm/p/12076477.html