亚马逊跨境电商抖音拼多多京东亚马逊淘宝电商跨境电子商务专业平台官网

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