项目实战从0到1之大数据项目之电商数仓（用户行为数据采集四）

摘要：4.4 采集日志Flume4.4.1 日志采集Flume安装集群规划：4.4.2 项目经验之Flume组件1）Source （1）Taildir Source相比Exec Source、Spooling Directory Source的优...

4.4 采集日志Flume

4.4.1 日志采集Flume安装

集群规划：

4.4.2 项目经验之Flume组件

1）Source （1）Taildir Source相比Exec Source、Spooling Directory Source的优势 TailDir Source：断点续传、多目录。Flume1.6以前需要自己自定义Source记录每次读取文件位置，实现断点续传。 Exec Source可以实时搜集数据，但是在Flume不运行或者Shell命令出错的情况下，数据将会丢失。 Spooling Directory Source监控目录，不支持断点续传。 （2）batchSize大小如何设置？ 答：Event 1K左右时，500-1000合适（默认为100） 2）Channel 采用Kafka Channel，省去了Sink，提高了效率。

4.4.3 日志采集Flume配置

1）Flume配置分析

Flume直接读log日志的数据，log日志的格式是app-yyyy-mm-dd.log。 2）Flume的具体配置如下： （1）在/opt/module/flume/conf目录下创建file-flume-kafka.conf文件

[kgg@hadoop101 conf]$ vim file-flume-kafka.conf
在文件配置如下内容
在文件配置如下内容
a1.sources=r1
a1.channels=c1 c2

# configure source
a1.sources.r1.type = TAILDIR
a1.sources.r1.positionFile = /opt/module/flume/test/log_position.json
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.kgg.flume.interceptor.LogETLInterceptor$Builder
a1.sources.r1.interceptors.i2.type = com.kgg.flume.interceptor.LogTypeInterceptor$Builder
​
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 = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.c1.kafka.bootstrap.servers = hadoop101:9092,hadoop102:9092,hadoop103:9092
a1.channels.c1.kafka.topic = topic_start
a1.channels.c1.parseAsFlumeEvent = false
a1.channels.c1.kafka.consumer.group.id = flume-consumer
​
a1.channels.c2.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.c2.kafka.bootstrap.servers = hadoop101:9092,hadoop102:9092,hadoop103:9092
a1.channels.c2.kafka.topic = topic_event
a1.channels.c2.parseAsFlumeEvent = false
a1.channels.c2.kafka.consumer.group.id = flume-consumer

注意：com.kgg.flume.interceptor.LogETLInterceptor和com.kgg.flume.interceptor.LogTypeInterceptor是自定义的拦截器的全类名。需要根据用户自定义的拦截器做相应修改。

4.4.4 Flume的ETL和分类型拦截器

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.kgg.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.kgg.flume.interceptor包下创建LogETLInterceptor类名

Flume ETL拦截器LogETLInterceptor
package com.kgg.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.kgg.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) {
 // {"action":"1","ar":"MX","ba":"HTC","detail":"542","en":"start","entry":"2","extend1":"","g":"S3HQ7LKM@gmail.com","hw":"640*960","l":"en","la":"-43.4","ln":"-98.3","loading_time":"10","md":"HTC-5","mid":"993","nw":"WIFI","open_ad_type":"1","os":"8.2.1","sr":"D","sv":"V2.9.0","t":"1559551922019","uid":"993","vc":"0","vn":"1.1.5"}
​
        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.kgg.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）打包 拦截器打包之后，只需要单独包，不需要将依赖的包上传。打包之后要放入Flume的lib文件夹下面。

注意：为什么不需要依赖包？因为依赖包在flume的lib目录下面已经存在了。

7）需要先将打好的包放入到hadoop101的/opt/module/flume/lib文件夹下面。

ls | grep interceptor
flume-interceptor-1.0-SNAPSHOT.jar

4.4.5 日志采集Flume启动停止脚本

1）在/home/kgg/bin目录下创建脚本f1.sh

 vim f1.sh    
在脚本中填写如下内容
#! /bin/bash
​
case $1 in
"start"){
        for i in hadoop101 hadoop102
        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 > /dev/null 2>&1 &"
        done
};;    
"stop"){
        for i in hadoop101 hadoop102
        do
                echo " --------停止 $i 采集flume-------"
                ssh $i "ps -ef | grep file-flume-kafka | grep -v grep |awk "{print \$2}" | xargs kill"
        done
​
};;
esac

说明1：nohup，该命令可以在你退出帐户/关闭终端之后继续运行相应的进程。nohup就是不挂起的意思，不挂断地运行命令。 说明2：/dev/null代表linux的空设备文件，所有往这个文件里面写入的内容都会丢失，俗称“黑洞”。 标准输入0：从键盘获得输入 /proc/self/fd/0 标准输出1：输出到屏幕（即控制台） /proc/self/fd/1 错误输出2：输出到屏幕（即控制台） /proc/self/fd/2 2）增加脚本执行权限

 chmod 777 f1.sh

3）f1集群启动脚本

f1.sh start

4）f1集群停止脚本

f1.sh stop