好程序员大数据学习路线之Logstach与flume对比

好程序员大数据学习路线之Logstach与flume对比，没有集群的概念,logstach与flume都称为组

logstash是用JRuby语言开发的

组件的对比:

logstach : input filter output

flume : source channel sink

优劣对比:

logstach :

安装简单,安装体积小

有filter组件,使得该工具具有数据过滤,数据切分的功能

可以与ES无缝结合

具有数据容错功能,在数据采集的时候,如果发生宕机或断开的情况,会断点续传(会记录读取的偏移量)

综上,该工具主要用途为采集日志数据

flume:

高可用方面要比logstach强大

flume一直在强调数据的安全性,flume在数据传输过程中是由事务控制的

flume可以应用在多类型数据传输领域

数据对接

将logstach.gz文件上传解压即可

可以在logstach目录下创建conf文件,用来存储配置文件

一 命令启动

1.bin/logstash -e 'input { stdin {} } output { stdout{} }'

stdin/stdout(标准输入输出流)

hello xixi

2018-09-12T21:58:58.649Z hadoop01 hello xixi

hello haha

2018-09-12T21:59:19.487Z hadoop01 hello haha

2.bin/logstash -e 'input { stdin {} } output { stdout }'

hello xixi

{

"message" => "hello xixi",

"@version" => "1",

"@timestamp" => "2018-09-12T22:00:49.612Z",

"host" => "hadoop01"

}

3.es集群中 ,需要启动es集群

bin/logstash -e 'input { stdin {} } output { elasticsearch stdout{} }'

输入命令后,es自动生成index,自动mapping.

hello haha

2018-09-12T22:13:05.361Z hadoop01 hehello haha

bin/logstash -e 'input { stdin {} } output { elasticsearch stdout{} }'

4.kafka集群中,启动kafka集群

bin/logstash -e 'input { stdin {} } output { elasticsearch stdout{} }'

二 配置文件启动

需要启动zookeeper集群,kafka集群,es集群

1.与kafka数据对接

vi logstash-kafka.conf

启动

bin/logstash -f logstash-kafka.conf (-f:指定文件)

在另一节点上启动kafka消费命令

input {

file {

path => "/root/data/test.log"

discover_interval => 5

start_position => "beginning"

}

}

output {

kafka {

topic_id => "test1"

codec => plain {

format => "%"

charset => "UTF-8"

}

bootstrap_servers => "node01:9092,node02:9092,node03:9092"

}

}

2.与kafka-es数据对接

vi logstash-es.conf

#启动logstash

bin/logstash -f logstash-es.conf

在另一节点上启动kafka消费命令

input {

file {

type => "gamelog"

path => "/log/*/*.log"

discover_interval => 10

start_position => "beginning"

}

}

output {

elasticsearch {

index => "gamelog-%{+YYYY.MM.dd}"

hosts => ["node01:9200", "node02:9200", "node03:9200"]

}

}

数据对接过程

logstach节点存放: 哪个节点空闲资源多放入哪个节点 (灵活存放)

1.启动logstach监控logserver目录,把数据采集到kafka

2.启动另外一个logstach,监控kafka某个topic数据,把他采集到elasticsearch

数据对接案例

需要启动两个logstach,调用各个配置文件,进行对接

1.采集数据到kafka

cd conf

创建配置文件: vi gs-kafka.conf

input {

file {

codec => plain {

charset => "GB2312"

}

path => "/root/basedir/*/*.txt"

discover_interval => 5

start_position => "beginning"

}

}

output {

kafka {

topic_id => "gamelogs"

codec => plain {

format => "%"

charset => "GB2312"

}

bootstrap_servers => "node01:9092,node02:9092,node03:9092"

}

}

创建kafka对应的topic

bin/kafka-topics.sh --create --zookeeper hadoop01:2181 --replication-factor 1 --partitions 1 --topic gamelogs

2.在hadoop01上启动logstach

bin/logstash -f conf/gs-kafka.conf

3.在hadoop02上启动另外一个logstach

cd logstach/conf

vi kafka-es.conf

input {

kafka {

type => "accesslogs"

codec => "plain"

auto_offset_reset => "smallest"

group_id => "elas1"

topic_id => "accesslogs"

zk_connect => "node01:2181,node02:2181,node03:2181"

}

kafka {

type => "gamelogs"

auto_offset_reset => "smallest"

codec => "plain"

group_id => "elas2"

topic_id => "gamelogs"

zk_connect => "node01:2181,node02:2181,node03:2181"

}

}

filter {

if [type] == "accesslogs" {

json {

source => "message"

remove_field => [ "message" ]

target => "access"

}

}

if [type] == "gamelogs" {

mutate {

split => { "message" => "" }

add_field => {

"event_type" => "%"

"current_map" => "%"

"current_X" => "%"

"current_y" => "%"

"user" => "%"

"item" => "%"

"item_id" => "%"

"current_time" => "%"

}

remove_field => [ "message" ]

}

}

}

output {

if [type] == "accesslogs" {

elasticsearch {

index => "accesslogs"

codec => "json"

hosts => ["node01:9200", "node02:9200", "node03:9200"]

}

}

if [type] == "gamelogs" {

elasticsearch {

index => "gamelogs1"

codec => plain {

charset => "UTF-16BE"

}

hosts => ["node01:9200", "node02:9200", "node03:9200"]

}

}

}

bin/logstash -f conf/kafka-es.conf

4.修改basedir文件中任意数据即可产生es的index文件

5.网页数据存储在设置的/data/esdata中

6.在网页中查找指定字段

默认分词器为term,只能查找单个汉字,query_string可以查找全汉字