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\r","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
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 。
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。
json_lines: 解析换行符分隔的JSON数据
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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