目录

日志采集Flume启动停止脚本

项目经验之Flume组件选型

消费者Flume配置

Flume时间戳拦截器

消费者Flume启动停止脚本

 采集通道启动/停止脚本

---

日志采集Flume启动停止脚本

[doudou@hadoop102 bin]$ vim fl.sh

#! /bin/bash
 
case $1 in
"start"){
        for i in hadoop102 hadoop103
        do
                echo " --------启动 $i 采集flume-------"
                ssh $i "nohup /opt/module/flume/bin/flume-ng agent --conf-file /opt/module/flume/conf/file-flume-kafka.conf --name a1 -Dflume.root.logger=INFO,LOGFILE >/opt/module/flume/log1.txt 2>&1  &"
        done
};;	
"stop"){
        for i in hadoop102 hadoop103
        do
                echo " --------停止 $i 采集flume-------"
                ssh $i "ps -ef | grep file-flume-kafka | grep -v grep |awk  '{print \$2}' | xargs -n1 kill -9 "
        done
 
};;
esac

[doudou@hadoop102 bin]$ chmod 777 fl.sh 

项目经验之Flume组件选型

FileChannel和MemoryChannel区别

MemoryChannel传输数据速度更快，但因为数据保存在JVM的堆内存中，Agent进程挂掉会导致数据丢失，适用于对数据质量要求不高的需求。

FileChannel传输速度相对于Memory慢，但数据安全保障高，Agent进程挂掉也可以从失败中恢复数据。

选型：

金融类公司、对钱要求非常准确的公司通常会选择FileChannel

传输的是普通日志信息（京东内部一天丢100万-200万条，这是非常正常的），通常选择MemoryChannel。

消费者Flume配置

Flume时间戳拦截器

在com.atguigu.flume.interceptor包下创建TimeStampInterceptor类

import com.alibaba.fastjson.JSONObject;
import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;
 
import java.nio.charset.StandardCharsets;
import java.util.List;
import java.util.Map;
 
public class TimeStampInterceptor implements Interceptor {
    @Override
    public void initialize() {
 
    }
 
    @Override
    public Event intercept(Event event) {
        //获取header
        Map headers=event.getHeaders();
        //获取body中的ts
        byte[] body=event.getBody();//获取body
        String log=new String(body, StandardCharsets.UTF_8);//转换编译语言
        JSONObject jsonObject=JSONObject.parseObject(log);//处理该日志
        String ts=jsonObject.getString("ts");//截取ts
 
        //将ts赋值给timestamp
        headers.put("timestamp",ts);
        return event;
    }
 
    @Override
    public List intercept(List list) {
        for (Event event:list){
            intercept(event);
        }
        return list;
    }
 
    @Override
    public void close() {
 
    }
 
    public static class Builder implements Interceptor.Builder{
 
        @Override
        public Interceptor build() {
            return new TimeStampInterceptor();
        }
 
        @Override
        public void configure(Context context) {
 
        }
    }
}

打包上传jar包

[doudou@hadoop104 lib]$ rz -E
 
[doudou@hadoop104 lib]$ ls | grep interceptor
flume-interceptor-1.0-SNAPSHOT-jar-with-dependencies.jar

在hadoop104的/opt/module/flume/conf目录下创建kafka-flume-hdfs.conf文件

[doudou@hadoop104 conf]$ vim kafka-flume-hdfs.conf

## 组件
a1.sources=r1
a1.channels=c1
a1.sinks=k1
 
## source1
a1.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r1.batchSize = 5000
a1.sources.r1.batchDurationMillis = 2000
a1.sources.r1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sources.r1.kafka.topics=topic_log
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.doudouflume.interceptor.TimeStampInterceptor$Builder
 
## channel1
a1.channels.c1.type = file
a1.channels.c1.checkpointDir = /opt/module/flume/checkpoint/behavior1
a1.channels.c1.dataDirs = /opt/module/flume/data/behavior1/
 
 
## sink1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = /origin_data/gmall/log/topic_log/%Y-%m-%d
a1.sinks.k1.hdfs.filePrefix = log-
a1.sinks.k1.hdfs.round = false
 
#控制生成的小文件
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.rollSize = 134217728
a1.sinks.k1.hdfs.rollCount = 0
 
## 控制输出文件是原生文件。
a1.sinks.k1.hdfs.fileType = CompressedStream
a1.sinks.k1.hdfs.codeC = lzop
 
## 拼装
a1.sources.r1.channels = c1
a1.sinks.k1.channel= c1

消费者Flume启动停止脚本

[doudou@hadoop102 bin]$ vim f2.sh

#! /bin/bash
 
case $1 in
"start"){
        for i in hadoop104
        do
                echo " --------启动 $i 消费flume-------"
                ssh $i "nohup /opt/module/flume/bin/flume-ng agent --conf-file /opt/module/flume/conf/kafka-flume-hdfs.conf --name a1 -Dflume.root.logger=INFO,LOGFILE >/opt/module/flume/log2.txt   2>&1 &"
        done
};;
"stop"){
        for i in hadoop104
        do
                echo " --------停止 $i 消费flume-------"
                ssh $i "ps -ef | grep kafka-flume-hdfs | grep -v grep |awk '{print \$2}' | xargs -n1 kill"
        done
 
};;
esac

[doudou@hadoop102 bin]$ chmod 777 f2.sh 

 采集通道启动/停止脚本

[doudou@hadoop102 bin]$ vim cluster.sh 

#!/bin/bash
 
case $1 in
"start"){
        echo ================== 启动 集群 ==================
 
        #启动 Zookeeper集群
        zk.sh start
 
        #启动 Hadoop集群
        hdp.sh start
 
        #启动 Kafka采集集群
        kf.sh start
 
        #启动 Flume采集集群
        f1.sh start
 
        #启动 Flume消费集群
        f2.sh start
 
        };;
"stop"){
        echo ================== 停止 集群 ==================
 
        #停止 Flume消费集群
        f2.sh stop
 
        #停止 Flume采集集群
        f1.sh stop
 
        #停止 Kafka采集集群
        kf.sh stop
 
        #停止 Hadoop集群
        hdp.sh stop
 
        #停止 Zookeeper集群
        zk.sh stop
 
};;
esac

[doudou@hadoop102 bin]$ chmod 777 cluster.sh