欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识！
您现在的位置是： 首页  >  IT编程

CDH下配置Flume进行配置传输日志文件（尚硅谷版）

程序员文章站 2022-03-21 17:25:44
CDH下配置Flume进行日志采集配置说明：许多企业目前都在使用CDH进行大数据开发，CDH具有方便，高效，一键配置，方便管理和搭建大数据组件的特点，所以下面说一下尚硅谷的Flume配合Kafka进行日志文件的采集。架构图下图蓝框内为采集架构图，由架构图得到数据是以Flume --> kafka --> Flume --> HDFS进行采集的，可以看到使用了两次Flume 第一层Flume架构及配置这一块的source是TAILDIR，channel是memory，sink是k...

CDH下配置Flume进行日志采集配置

说明：许多企业目前都在使用CDH进行大数据开发，CDH具有方便，高效，一键配置，方便管理和搭建大数据组件的特点，所以下面说一下尚硅谷的Flume配合Kafka进行日志文件的采集。

架构图

下图蓝框内为采集架构图，由架构图得到数据是以Flume --> kafka --> Flume --> HDFS进行采集的，可以看到使用了两次Flume
CDH下配置Flume进行配置传输日志文件（尚硅谷版）

第一层Flume架构及配置

这一块的source是TAILDIR，channel是memory，sink是kafka，这一块用到了拦截器，拦截器的作用是将日志文件分为两个部分，一个部分就是启动日志start，一个是时间日志event，通过拦截器的筛选则会将日志文件筛选出这两部分存放在kafka的topic，前提要将kafka的topic建立好，topic_start，topic_event，此部分省略
注：flume采用的压缩为LZO，不知道如何让在CDH下配置LZO的请看我的这篇文章：CDH下LZO的配置
CDH下配置Flume进行配置传输日志文件（尚硅谷版）
CDH下配置Flume进行配置传输日志文件（尚硅谷版）
CDH下配置Flume进行配置传输日志文件（尚硅谷版）
问：Flume的代码一定要这样放在CDH中吗
答：当然不是，这样写的好处是CDH启动后就会一直监测日志文件，只要生成日志文件就会进行传输，不这样写，按照普通配置文件也可以使用，flume-ng agent -c conf/ -n a1 -f /配置路径/f1.conf -Dflume.root.logger=DEBUG,consol 拦截器放在/opt/cloudera/parcels/CDH/lib/flume-ng/lib/

拦截器代码如下，jar包下载链接在下，可以配合Flume直接用

拦截器代码

本项目中自定义了两个拦截器，分别是：ETL拦截器、日志类型区分拦截器。
ETL拦截器主要用于，过滤时间戳不合法和Json数据不完整的日志
日志类型区分拦截器主要用于，将启动日志和事件日志区分开来，方便发往Kafka的不同Topic。
1）创建Maven工程flume-interceptor
2）创建包名：com.atguigu.flume.interceptor
3）在pom.xml文件中添加如下配置

<dependencies>
    <dependency>
        <groupId>org.apache.flume</groupId>
        <artifactId>flume-ng-core</artifactId>
        <version>1.7.0</version>
    </dependency>
</dependencies>

<build>
    <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>2.3.2</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
            </configuration>
        </plugin>
        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

4）在com.atguigu.flume.interceptor包下创建LogETLInterceptor类名
Flume ETL拦截器LogETLInterceptor

package com.atguigu.flume.interceptor;

import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;

import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.List;

public class LogETLInterceptor implements Interceptor {

    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        // 1 获取数据
        byte[] body = event.getBody();
        String log = new String(body, Charset.forName("UTF-8"));

        // 2 判断数据类型并向Header中赋值
        if (log.contains("start")) {
            if (LogUtils.validateStart(log)){
                return event;
            }
        }else {
            if (LogUtils.validateEvent(log)){
                return event;
            }
        }

        // 3 返回校验结果
        return null;
    }

    @Override
    public List<Event> intercept(List<Event> events) {

        ArrayList<Event> interceptors = new ArrayList<>();

        for (Event event : events) {
            Event intercept1 = intercept(event);

            if (intercept1 != null){
                interceptors.add(intercept1);
            }
        }

        return interceptors;
    }

    @Override
    public void close() {

    }

    public static class Builder implements Interceptor.Builder{

        @Override
        public Interceptor build() {
            return new LogETLInterceptor();
        }

        @Override
        public void configure(Context context) {

        }
    }
}

4）Flume日志过滤工具类

package com.atguigu.flume.interceptor;
import org.apache.commons.lang.math.NumberUtils;

public class LogUtils {

    public static boolean validateEvent(String log) {
        // 服务器时间 | json
        // 1549696569054 | {"cm":{"ln":"-89.2","sv":"V2.0.4","os":"8.2.0","g":"M67B4QYU@gmail.com","nw":"4G","l":"en","vc":"18","hw":"1080*1920","ar":"MX","uid":"u8678","t":"1549679122062","la":"-27.4","md":"sumsung-12","vn":"1.1.3","ba":"Sumsung","sr":"Y"},"ap":"weather","et":[]}

        // 1 切割
        String[] logContents = log.split("\\|");

        // 2 校验
        if(logContents.length != 2){
            return false;
        }

        //3 校验服务器时间
        if (logContents[0].length()!=13 || !NumberUtils.isDigits(logContents[0])){
            return false;
        }

        // 4 校验json
        if (!logContents[1].trim().startsWith("{") || !logContents[1].trim().endsWith("}")){
            return false;
        }

        return true;
    }

    public static boolean validateStart(String log) {

        if (log == null){
            return false;
        }

        // 校验json
        if (!log.trim().startsWith("{") || !log.trim().endsWith("}")){
            return false;
        }

        return true;
    }
}

5）Flume日志类型区分拦截器LogTypeInterceptor

package com.atguigu.flume.interceptor;

import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;

import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

public class LogTypeInterceptor implements Interceptor {
    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        // 区分日志类型：   body  header
        // 1 获取body数据
        byte[] body = event.getBody();
        String log = new String(body, Charset.forName("UTF-8"));

        // 2 获取header
        Map<String, String> headers = event.getHeaders();

        // 3 判断数据类型并向Header中赋值
        if (log.contains("start")) {
            headers.put("topic","topic_start");
        }else {
            headers.put("topic","topic_event");
        }

        return event;
    }

    @Override
    public List<Event> intercept(List<Event> events) {

        ArrayList<Event> interceptors = new ArrayList<>();

        for (Event event : events) {
            Event intercept1 = intercept(event);

            interceptors.add(intercept1);
        }

        return interceptors;
    }

    @Override
    public void close() {

    }

    public static class Builder implements  Interceptor.Builder{

        @Override
        public Interceptor build() {
            return new LogTypeInterceptor();
        }

        @Override
        public void configure(Context context) {

        }
    }
}

6）jar包链接 提取码：6wz8

Flume1代码

a1.sources=r1
a1.channels=c1 c2 
a1.sinks=k1 k2 

# configure source
a1.sources.r1.type = TAILDIR
a1.sources.r1.filegroups = f1
a1.sources.r1.filegroups.f1 = /tmp/logs/app.+
a1.sources.r1.fileHeader = true
a1.sources.r1.channels = c1 c2

#interceptor
a1.sources.r1.interceptors = i1 i2
a1.sources.r1.interceptors.i1.type = com.atguigu.flume.interceptor.LogETLInterceptor$Builder
a1.sources.r1.interceptors.i2.type = com.atguigu.flume.interceptor.LogTypeInterceptor$Builder

# selector
a1.sources.r1.selector.type = multiplexing
a1.sources.r1.selector.header = topic
a1.sources.r1.selector.mapping.topic_start = c1
a1.sources.r1.selector.mapping.topic_event = c2

# configure channel
a1.channels.c1.type = memory
a1.channels.c1.capacity=10000
a1.channels.c1.byteCapacityBufferPercentage=20

a1.channels.c2.type = memory
a1.channels.c2.capacity=10000
a1.channels.c2.byteCapacityBufferPercentage=20

# configure sink
# start-sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.topic = topic_start
a1.sinks.k1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sinks.k1.kafka.flumeBatchSize = 2000
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.channel = c1

# event-sink
a1.sinks.k2.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k2.kafka.topic = topic_event
a1.sinks.k2.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sinks.k2.kafka.flumeBatchSize = 2000
a1.sinks.k2.kafka.producer.acks = 1
a1.sinks.k2.channel = c2

Flume2代码（放在第二个flume的节点上）

Flume2架构图
CDH下配置Flume进行配置传输日志文件（尚硅谷版）

## 组件
a1.sources=r1 r2
a1.channels=c1 c2
a1.sinks=k1 k2

## 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_start

## source2
a1.sources.r2.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r2.batchSize = 5000
a1.sources.r2.batchDurationMillis = 2000
a1.sources.r2.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sources.r2.kafka.topics=topic_event

## channel1
a1.channels.c1.type=memory
a1.channels.c1.capacity=100000
a1.channels.c1.transactionCapacity=10000

## channel2
a1.channels.c2.type=memory
a1.channels.c2.capacity=100000
a1.channels.c2.transactionCapacity=10000

## sink1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.proxyUser=hive
a1.sinks.k1.hdfs.path = /origin_data/gmall/log/topic_start/%Y-%m-%d
a1.sinks.k1.hdfs.filePrefix = logstart-
a1.sinks.k1.hdfs.round = true
a1.sinks.k1.hdfs.roundValue = 10
a1.sinks.k1.hdfs.roundUnit = second

##sink2
a1.sinks.k2.type = hdfs
a1.sinks.k2.hdfs.proxyUser=hive
a1.sinks.k2.hdfs.path = /origin_data/gmall/log/topic_event/%Y-%m-%d
a1.sinks.k2.hdfs.filePrefix = logevent-
a1.sinks.k2.hdfs.round = true
a1.sinks.k2.hdfs.roundValue = 10
a1.sinks.k2.hdfs.roundUnit = second

## 不要产生大量小文件
a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.rollSize = 134217728
a1.sinks.k1.hdfs.rollCount = 0

a1.sinks.k2.hdfs.rollInterval = 10
a1.sinks.k2.hdfs.rollSize = 134217728
a1.sinks.k2.hdfs.rollCount = 0

## 控制输出文件是原生文件。
a1.sinks.k1.hdfs.fileType = CompressedStream 
a1.sinks.k2.hdfs.fileType = CompressedStream 

a1.sinks.k1.hdfs.codeC = lzop
a1.sinks.k2.hdfs.codeC = lzop

## 拼装
a1.sources.r1.channels = c1
a1.sinks.k1.channel= c1

a1.sources.r2.channels = c2
a1.sinks.k2.channel= c2

在HDFS上进行文件创建：

udo -u hdfs hadoop fs -mkdir /origin_data
sudo -u hdfs hadoop fs -chown hive:hive /origin_data

体贴的我还给你们把日志生成jar包提供了，点个赞可以不~
链接：https://pan.baidu.com/s/1Lf7KTF6tvGmmZdr0Hbfv6w
提取码：jjgu
复制这段内容后打开百度网盘手机App，操作更方便哦–来自百度网盘超级会员V3的分享

重启Flume，然后再生成日志文件就可以看到文件出现了，注意修改你的ip地址就可以了
CDH下配置Flume进行配置传输日志文件（尚硅谷版）

本文地址：https://blog.csdn.net/weixin_29057619/article/details/109625915

相关标签： Flume CDH下flume读取文件 hadoop hdfs

上一篇： android-GooglePlay安装来源追踪PlayInstallReferrer

下一篇： 【转载】网易极客战记官方攻略-地牢-恐惧之门

推荐阅读