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elk如何定义日志格式

发布网友 发布时间：2022-04-25 05:51

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共3个回答

懂视网 时间：2022-05-02 21:12

ELK版本：6.5.3

操作步骤（MariaDB5.5.60为例）

1、安装mariadb，准备数据库环境

1.1 安装服务

[root@test101 ~]# yum -y install mariadb* #这样安装默认是5.5.60版本

1.2 修改配置文件，开启慢查询日志

在/etc/my.conf文件加入下面三行内容，开启慢查询日志：

slow_query_log
long_query_time = 2 #阙值设置成2秒，只要超过2秒的操作，就会写入slow-log文件
slow_query_log_file = "/var/lib/mysql/test101-slow.log" #设置慢查询日志

重启MySQL

[root@test101 ~]# systemctl restart mysql

1.3 创建测试库，导入数据

1）先创建一个大数据量的文件：

[root@test101 ~]# seq 1 19999999 > /tmp/big

2）登录数据库创建测试数据库：

[root@test101 ~]# mysql
MariaDB [(none)]> create database test101; #创建测试库
MariaDB [(none)]> use test101;
MariaDB [test101]> create table table1(id int(10)not null)engine=innodb; #创建测试表

3）将刚刚创建好的大数据文件/tmp/big导入到MySQL的测试数据库中，生成测试数据表：

MariaDB [test101]> show tables;
+----------------------+
| Tables_in_test101 |
+----------------------+
| table1   |
+-----------------------+
1 row in set (0.00 sec)

MariaDB [test101]> load data local infile ‘/tmp/big‘ into table table1; #注意：local不能少，少了local可能会报错“ERROR 13 (HY000): Can‘t get stat of ‘/tmp/big‘ (Errcode: 2)”
Query OK, 19999999 rows affected (1 min 47.64 sec) 
Records: 19999999 Deleted: 0 Skipped: 0 Warnings: 0

MariaDB [test101]>

测试数据导入成功

1.4 生成慢查询日志

在数据库查询一条数据：

MariaDB [test101]> select * from table1 where id=258;
+-----+
| id |
+-----+
| 258 |
+-----+
1 row in set (11.76 sec)

MariaDB [test101]>

然后查看慢查询日志文件，已经生成了相应的日志。数据库准备完成：

[root@test101 mysql]# tailf /var/lib/mysql/test101-slow.log 

# Time: 181217 15:23:39
# User@Host: root[root] @ localhost []
# Thread_id: 2 Schema: test101 QC_hit: No
# Query_time: 11.758867 Lock_time: 0.000106 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545031419;
select * from table1 where id=258;

2、ELK工具部署

2.1 在test101安装好filebeat、test102安装elasticsearch/kibana

（安装和基础配置省略）

2.2 在test103服务器安装好logstash

（安装步骤和基础配置省略）

1）创建logstash采集日志文件：

[root@test103 conf.d]# cat /etc/logstash/conf.d/logstash-syslog.conf 
# Sample Logstash configuration for creating a simple
# Beats -> Logstash -> Elasticsearch pipeline.

input {
 beats {
 port => 5044
 }
}

output {
 elasticsearch {
 hosts => ["http://10.0.0.102:9200"]
 index => "%{[@metadata][beat]}-%{[@metadata][version]}-%{+YYYY.MM.dd}"
 #user => "elastic"
 #password => "changeme"
 }
 stdout {
 codec => rubydebug
 }

}

2.3 启动服务查看日志采集

启动101服务器的filebeat，102服务器的elasticsearch、前台启动103服务器的logstash，前台查看日志采集

1）在test101服务器的MySQL运行查询语句（注意，运行查询语句之前要保证logstash前台启动已经完成）：

MariaDB [test101]> select * from table1 where id=358;

+-----+
| id |
+-----+
| 358 |
+-----+
1 row in set (13.47 sec)

MariaDB [test101]>

2）查看test101服务器上慢查询日志已经生成：

[root@test101 mysql]# tailf test101-slow.log 

# Time: 181218 8:58:30
# User@Host: root[root] @ localhost []
# Thread_id: 3 Schema: test101 QC_hit: No
# Query_time: 13.405630 Lock_time: 0.000271 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545094710;
select * from table1 where id=358;

3）再查看logstash，已经采集到相关信息（但是上面的6句日志信息，是被分成了6段messages采集的）：

[root@test103 logstash]# /usr/share/logstash/bin/logstash -f /etc/logstash/conf.d/logstash-syslog.conf
.......#省略若干行启动信息
[INFO ] 2018-12-18 08:51:48.669 [Api Webserver] agent - Successfully started Logstash API endpoint {:port=>9600} 
#就是出现这个提示的时候才运行上面的mysql查询语句

{
 "message" => "# Time: 181218 8:58:30",
 "@timestamp" => 2018-12-18T00:58:33.396Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
   "name" => "test101",
 "containerized" => true,
  "architecture" => "x86_64",
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "offset" => 252,
 "source" => "/var/lib/mysql/test101-slow.log"
}
{
 "message" => "# User@Host: root[root] @ localhost []",
 "@timestamp" => 2018-12-18T00:58:33.398Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
 "containerized" => true,
   "name" => "test101",
  "architecture" => "x86_64",
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
  "version" => "6.5.3",
 "hostname" => "test101"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 276
}
{
 "message" => "# Thread_id: 3 Schema: test101 QC_hit: No",
 "@timestamp" => 2018-12-18T00:58:33.398Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
   "os" => {
  "family" => "redhat",
  "platform" => "centos",
  "version" => "7 (Core)",
  "codename" => "Core"
 },
   "name" => "test101",
 "containerized" => true,
  "architecture" => "x86_64",
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 315
}
{
 "message" => "# Query_time: 13.405630 Lock_time: 0.000271 Rows_sent: 1 Rows_examined: 19999999",
 "@timestamp" => 2018-12-18T00:58:33.398Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
   "name" => "test101",
  "architecture" => "x86_64",
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
 "containerized" => true,
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 359
}
{
 "message" => "SET timestamp=1545094710;",
 "@timestamp" => 2018-12-18T00:58:33.398Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
 "containerized" => true,
   "name" => "test101",
  "architecture" => "x86_64",
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 443
}
{
 "message" => "select * from table1 where id=358;",
 "@timestamp" => 2018-12-18T00:58:33.398Z,
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
 "@version" => "1",
  "host" => {
  "architecture" => "x86_64",
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
   "name" => "test101",
 "containerized" => true,
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "beat" => {
  "name" => "test101",
  "version" => "6.5.3",
 "hostname" => "test101"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 469
}

3、日志采集优化

因为慢日志里面，一条信息被分成了6段，不便于日志的分析，因此需要将上面采集到的6个messages优化合并成一个

3.1 修改filebeat配置，将上面的6段messages合并成一句日志信息

修改filebeat配置文件，在日志路径后面加入下面三行，然后重启filebeat：

 paths:
 - /var/lib/mysql/test101-slow.log
 #- c:programdataelasticsearchlogs*
 #加入下面三行
 multiline.pattern: "^# User@Host:"
 multiline.negate: true
 multiline.match: after

说明：

multiline.pattern：正则表达式，去匹配指定的一行，这里去匹配的以“# User@Host:”开头的那一行；
multiline.negate：true 或 false；默认是false，就是将multiline.pattern匹配到的那一行合并到上一行；如果配置是true，就是将除了multiline.pattern匹的那一行的其他所有行合并到其上一行；
multiline.match：after 或 before，就是指定将要合并到上一行的内容，合并到上一行的末尾或开头；

3.2 检查修改后的采集日志格式

1）再在MySQL执行一次查询语句：

MariaDB [test101]> select * from table1 where id=368;
+-----+
| id |
+-----+
| 368 |
+-----+
1 row in set (12.54 sec)

MariaDB [test101]>

2）查看看查询日志，已经生成日志：

[root@test101 mysql]# tailf test101-slow.log 

# Time: 181218 9:17:42
# User@Host: root[root] @ localhost []
# Thread_id: 3 Schema: test101 QC_hit: No
# Query_time: 12.541603 Lock_time: 0.000493 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545095862;
select * from table1 where id=368;

3）查看logstash的实时输出，采集到的日志就变成了一行：

[root@test103 logstash]# /usr/share/logstash/bin/logstash -f /etc/logstash/conf.d/logstash-syslog.conf
.......#省略若干行启动信息
{
 "message" => # User@Host: root[root] @ localhost []
# Thread_id: 3 Schema: test101 QC_hit: No
# Query_time: 12.541603 Lock_time: 0.000493 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545095862;
select * from table1 where id=368;", 
   #这一行就是在slow-log中的信息，已经被合并成了一行，慢日志的“"# Time: 181218 9:17:42” 单独成行，在前面没有贴出来
 "prospector" => {
 "type" => "log"
 },
  "input" => {
 "type" => "log"
 },
  "host" => {
  "architecture" => "x86_64",
   "os" => {
  "codename" => "Core",
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat"
 },
 "containerized" => true,
   "name" => "test101",
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc"
 },
  "log" => {
 "flags" => [
  [0] "multiline"
 ]
 },
 "@timestamp" => 2018-12-18T01:17:45.336Z,
 "@version" => "1",
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "source" => "/var/lib/mysql/test101-slow.log",
 "offset" => 504
}

4、logstash配置grok插件，格式化分析日志

在实现了上一步骤，即将日志的messages合并之后，就需要采用grok插件将messages格式化成json格式了。

4.1 编辑logstash-syslog.conf文件配置grok插件

编辑logstash-syslog.conf文件，在input和output中间，加入整个filter模块，配置如下：

[root@test103 conf.d]# cat logstash-syslog.conf
# Sample Logstash configuration for creating a simple
# Beats -> Logstash -> Elasticsearch pipeline.

input {
 beats {
 port => 5044
 }
}

filter {

 #这一步格式化messages为json格式
 grok {
 match => [ "message", "(?m)^# User@Host: %{USER:query_user}[[^]]+] @ (?:(?<query_host>S*) )?[(?:%{IP:query_ip})?]s# Thread_id:s+%{NUMBER:thread_id:int}s+Schema: %{USER:schema}s+QC_hit: %{WORD:QC_hit}s*# Query_time: %{NUMBER:query_time:float}s+Lock_time: %{NUMBER:lock_time:float}s+Rows_sent: %{NUMBER:rows_sent:int}s+Rows_examined: %{NUMBER:rows_examined:int}s*(?:use %{DATA:database};s*)?SET timestamp=%{NUMBER:timestamp};s*(?<query>(?<action>w+)s+.*)" ]

 }
#这一步是将日志中的时间那一行（如：# Time: 181218 9:17:42）加上一个“drop”的tag
 grok {
 match => { "message" => "# Time: " }
 add_tag => [ "drop" ]
 tag_on_failure => []
 }
#删除标签中含有drop的行。也就是要删除慢日志里面的“# Time: 181218 9:17:42”这样的内容
 if "drop" in [tags] {
 drop {}
 }

# 时间转换
 date {
 match => ["mysql.slowlog.timestamp", "UNIX", "YYYY-MM-dd HH:mm:ss"]
 target => "@timestamp"
 timezone => "Asia/Shanghai"
 }

 ruby {
 code => "event.set(‘[@metadata][today]‘, Time.at(event.get(‘@timestamp‘).to_i).localtime.strftime(‘%Y.%m.%d‘))"
 }

#删除字段message
 mutate {
 remove_field => [ "message" ]
 }
}

output {
 elasticsearch {
 hosts => ["http://10.0.0.102:9200"]
 #index => "%{tags[0]}" 
 index => "%{[@metadata][beat]}-%{[@metadata][version]}-%{+YYYY.MM.dd}"
 #user => "elastic"
 #password => "changeme"
 }
 stdout {
 codec => rubydebug
 }

}

4.2 检查格式化后的日志采集

1）清除elasticsearch的旧索引，保证索引干净：

[root@test102 filebeat]# curl 10.0.0.102:9200/_cat/indices
green open .kibana_1 udOUvbprSnKWUJISwD0r_g 1 0 8 0 74.4kb 74.4kb
[root@test102 filebeat]#

2）重启filebeat，重新启动logstash前台输出，在mariadb执行查询语句：

MariaDB [test101]> select * from table1 where id=588;
+-----+
| id |
+-----+
| 588 |
+-----+
1 row in set (13.00 sec)

MariaDB [test101]>

3）查看慢查询日志已经生成：

[root@test101 mysql]# tailf test101-slow.log 

# Time: 181218 14:39:38
# User@Host: root[root] @ localhost []
# Thread_id: 4 Schema: test101 QC_hit: No
# Query_time: 12.999487 Lock_time: 0.000303 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545115178;
select * from table1 where id=588;

4）查看logstash的实时采集输出，已经是json格式了：

[INFO ] 2018-12-18 14:25:02.802 [Api Webserver] agent - Successfully started Logstash API endpoint {:port=>9600}

{
  "QC_hit" => "No",
 "rows_sent" => 1,
  "action" => "select",
  "input" => {
 "type" => "log"
 },
  "offset" => 1032,
  "@version" => "1",
 "query_host" => "localhost",
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "query" => "select * from table1 where id=428;",
 "@timestamp" => 2018-12-18T06:25:34.017Z,
 "prospector" => {
 "type" => "log"
 },
 "lock_time" => 0.000775,
 "query_user" => "root",
  "log" => {
 "flags" => [
  [0] "multiline"
 ]
 },
  "schema" => "test101",
 "timestamp" => "1545114330",
 "thread_id" => 4,
 "query_time" => 14.155133,
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
  "host" => {
   "name" => "test101",
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc",
 "containerized" => true,
   "os" => {
  "codename" => "Core",
  "family" => "redhat",
  "version" => "7 (Core)",
  "platform" => "centos"
 },
  "architecture" => "x86_64"
 },
 "rows_examined" => 19999999,
  "source" => "/var/lib/mysql/test101-slow.log"
{
  "QC_hit" => "No",
 "rows_sent" => 1,
  "action" => "select",
  "input" => {
 "type" => "log"
 },
  "offset" => 1284,
  "@version" => "1",
 "query_host" => "localhost",
  "beat" => {
  "name" => "test101",
 "hostname" => "test101",
  "version" => "6.5.3"
 },
  "query" => "select * from table1 where id=588;",
 "@timestamp" => 2018-12-18T06:39:44.082Z,
 "prospector" => {
 "type" => "log"
 },
 "lock_time" => 0.000303,
 "query_user" => "root",
  "log" => {
 "flags" => [
  [0] "multiline"
 ]
 },
  "schema" => "test101",
 "timestamp" => "1545115178",
 "thread_id" => 4,
 "query_time" => 12.999487,
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
  "host" => {
   "name" => "test101",
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc",
 "containerized" => true,
   "os" => {
  "codename" => "Core",
  "family" => "redhat",
  "version" => "7 (Core)",
  "platform" => "centos"
 },
  "architecture" => "x86_64"
 },
 "rows_examined" => 19999999,
  "source" => "/var/lib/mysql/test101-slow.log"
}

再打开kibana界面，建立索引，然后查看kibana的界面，日志OK：
技术分享图片

技术分享图片

其他版本MySQL慢日志及grok语句

1、MySQL5.7.20版本

慢查询日志语句：
mysql5.7.20版本慢日志：

# Time: 2018-12-18T08:43:24.828892Z
# User@Host: root[root] @ localhost [] Id: 7
# Query_time: 15.819314 Lock_time: 0.000174 Rows_sent: 1 Rows_examined: 19999999
SET timestamp=1545122604;
select * from table1 where id=258;

grok语法：

match => [ "message", "(?m)^# User@Host: %{USER:query_user}[[^]]+] @ (?:(?<query_host>S*) )?[(?:%{IP:query_ip})?]s+Id:s+%{NUMBER:id:int}s# Query_time: %{NUMBER:query_time:float}s+Lock_time: %{NUMBER:lock_time:float}s+Rows_sent: %{NUMBER:rows_sent:int}s+Rows_examined: %{NUMBER:rows_examined:int}s*(?:use %{DATA:database};s*)?SET timestamp=%{NUMBER:timestamp};s*(?<query>(?<action>w+)s+.*)" ]

logstash采集查询结果：

{
 "rows_examined" => 19999999,
  "input" => {
 "type" => "log"
 },
  "tags" => [
 [0] "beats_input_codec_plain_applied"
 ],
 "query_user" => "root",
 "query_time" => 15.461892,
 "query_host" => "localhost",
  "@version" => "1",
  "query" => "select * from table1 where id=258;",
  "host" => {
 "containerized" => true,
   "os" => {
  "platform" => "centos",
  "version" => "7 (Core)",
  "family" => "redhat",
  "codename" => "Core"
 },
   "id" => "9fcb0c7f28e449aabad922d1ba1f0afc",
   "name" => "test101",
  "architecture" => "x86_64"
 },
  "source" => "/home/data/test101-mysql5.7.20-slow.log",
  "action" => "select",
 "lock_time" => 0.000181,
 "prospector" => {
 "type" => "log"
 },
  "offset" => 1416,
  "log" => {
 "flags" => [
  [0] "multiline"
 ]
 },
 "rows_sent" => 1,
 "timestamp" => "1545122934",
  "beat" => {
 "hostname" => "test101",
  "version" => "6.5.3",
  "name" => "test101"
 },
 "@timestamp" => 2018-12-18T08:48:58.531Z,
  "id" => 7
}

ELK如何收集并用grok格式化MySQL的slow日志？

标签：转换   4.4   打开   格式   设置   前言   sample   action   实现   

热心网友 时间：2022-05-02 18:20

在平常处理JAVA系统日志，或者其他日志的时候，我们一般是按照log4j 或者logback 进行日志的记录。  在收集到elasticsearch之后，方便查询或统计。

定义日志格式，首先在收集的时候就要处理好。如，利用logstash的 grok插件进行切割。将日志记录按需求分成数据库字段一样的。然后存储到elasticsearch中，这样在kibana中就可以方便的按照字段统计、查询等了。

具体的操作方式，你可以看下这个链接，看是不是你要的

网页链接

热心网友 时间：2022-05-02 19:38

如果要读取Elasticsearch中的内容，可使用Elasticsearch提供的客户端，或者使用 Spring Data Elasticsearch

如果只是要用ELK收集各个地方的日志，则推荐使用Filebeat传输日志文件到Logstash或者Elasticsearch
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