官网:https://flume.apache.org/
[mall@mall software]$ tar -zxf /opt/software/apache-flume-1.9.0-bin.tar.gz -C /opt/module/
Flume是用java写的,所以需要确保JDK环境可用
需求描述:监控目录下多个文件写入Kafka
TAILDIR SOURCE:本质是tail -F [file]命令,只能监控文件的新增和修改,不能处理历史文件。
[mall@mall ~]$ cd /opt/module/apache-flume-1.9.0-bin/
[mall@mall apache-flume-1.9.0-bin]$ mkdir job
[mall@mall apache-flume-1.9.0-bin]$ cd job/
[mall@mall job]$ vim file_to_kafka.conf
内容:
# 0、配置agent:给source channel sink组件命名
a1.sources = r1
a1.channels = c1
# 1、配置source组件
a1.sources.r1.type = TAILDIR
a1.sources.r1.filegroups = f1
a1.sources.r1.filegroups.f1 = /opt/module/applog/app.*
# 断点续传标记信息存储位置
a1.sources.r1.positionFile = /opt/module/apache-flume-1.9.0-bin/taildir_position.json
# 2、配置channel组件:event临时缓冲区
a1.channels.c1.type = org.apache.flume.channel.kafka.KafkaChannel
a1.channels.c1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.channels.c1.kafka.topic = topic_mall_applog
# 按照字符串类型传到kafka去
a1.channels.c1.parseAsFlumeEvent = false
# 3、配置source、channel、sink之间的连接关系
a1.sources.r1.channels = c1
作用:拦截events,经拦截器处理,输出处理后的events。
开发:创建maven项目,打成jar包形式上传到flume所在机器
package com.songshuang.flume.interceptor;
import com.alibaba.fastjson.JSONException;
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.Iterator;
import java.util.List;
/**
* @date 2023/11/21 20:40
* 功能:剔除掉非json格式数据
*
* 1、实现接口
* 2、实现抽象方法
* 3、建造者模式:静态内部类
*/
public class ETLInterceptor implements Interceptor {
public void initialize() {
}
// 将log中event为非json格式数据置为null
public Event intercept(Event event) {
byte[] body = event.getBody();
// byte数组转为字符串
String log = new String(body, StandardCharsets.UTF_8);
boolean flag = false;
// 判断log是否是json格式
try {
JSONObject jsonObject = JSONObject.parseObject(log);
flag = true;
} catch (JSONException e) {
}
return flag ? event : null;
}
// 将log中event为null的删掉
public List<Event> intercept(List<Event> events) {
// 遍历events
Iterator<Event> iterator = events.iterator();
while (iterator.hasNext()) {
Event event = iterator.next();
if (intercept(event) == null) {
iterator.remove();
}
}
return events;
}
public void close() {
}
// 建造者模式
public static class Builder implements Interceptor.Builder {
@Override
public Interceptor build() {
return new ETLInterceptor();
}
@Override
public void configure(Context context) {
}
}
}
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0modelVersion>
<groupId>com.songshuanggroupId>
<artifactId>flume_interceptorartifactId>
<version>1.0-SNAPSHOTversion>
<dependencies>
<dependency>
<groupId>org.apache.flumegroupId>
<artifactId>flume-ng-coreartifactId>
<version>1.9.0version>
<scope>providedscope>
dependency>
<dependency>
<groupId>com.alibabagroupId>
<artifactId>fastjsonartifactId>
<version>1.2.62version>
dependency>
dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-compiler-pluginartifactId>
<version>2.3.2version>
<configuration>
<source>1.8source>
<target>1.8target>
configuration>
plugin>
<plugin>
<artifactId>maven-assembly-pluginartifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependenciesdescriptorRef>
descriptorRefs>
configuration>
<executions>
<execution>
<id>make-assemblyid>
<phase>packagephase>
<goals>
<goal>singlegoal>
goals>
execution>
executions>
plugin>
plugins>
build>
project>
上传到 /opt/module/apache-flume-1.9.0-bin/lib 目录下
[mall@mall job]$ vim file_to_kafka.conf
新增内容:
# 自定义拦截器
a1.sources.r1.interceptors = i1
# 指定自定义拦截器的建造者类名(入口)
a1.sources.r1.interceptors.i1.type = com.songshuang.flume.interceptor.ETLInterceptor$Builder
为什么要手动创建topic:flume自动创建的topic默认1个分区,每个分区1个副本。手动创建可以指定分区和副本数,可以有效利用Kafka集群资源。
–bootstrap-server参数作用:连接Kafka集群
[hadoop@hadoop102 kafka_2.11-2.4.1]$ bin/kafka-topics.sh --bootstrap-server hadoop102:9092,hadoop103:9092,hadoop104:9092 --create --replication-factor 2 --partitions 3 --topic topic_mall_applog
注意:放开Kafka集群所在机器9092端口,对Flume所在机器放开。
原因:Flume需要向Kafka集群写入数据,所以需要具有访问Kafka集群端口的权限。
– conf参数:配置文件存储所在目录
– name参数:agent名称,每个Flume配置文件就是一个agent。
– conf-file参数:flume本次启动读取的配置文件
nohup配合&:后台运行
&>/dev/null:将标准输出重定向到 /dev/null ,即丢弃所有输出
2>/dev/null:将标准错误输出重定向到 /dev/null ,即丢弃所有错误输出
[mall@mall ~]$ cd /opt/module/apache-flume-1.9.0-bin/
[mall@mall apache-flume-1.9.0-bin]$ nohup bin/flume-ng agent --conf conf/ --name a1 --conf-file job/file_to_kafka.conf &>/dev/null 2>/dev/null &
[mall@mall apache-flume-1.9.0-bin]$ ps -ef | grep file_to_kafka.conf
[mall@mall apache-flume-1.9.0-bin]$ kill 11001
需求描述:监控Kafka,将数据写入HDFS
如果想要从头消费需要设置kafka.consumer.auto.offset.reset = earliest,默认从最新offset开始
注意:需要在HDFS所在机器部署FLume,需要调用HADOOP相关jar包。
否则Flume向HDFS写数据时会失败!
[hadoop@hadoop104 ~]$ rm /opt/module/apache-flume-1.9.0-bin/lib/guava-11.0.2.jar
作用:按照kafka消息中的时间字段,决定消息存储到hdfs的哪个文件中。
代码:
package com.songshuang.flume.interceptor;
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;
/**
* @date 2023/11/22 16:52
* 作用:获取kafka中时间戳字段,放入event头中,flume写入hdfs时,从头部获取时间,作为该event放入hdfs的文件夹名称
*/
public class TimestampInterceptor implements Interceptor {
@Override
public void initialize() {
}
// 获取kafka时间戳字段,放入event的header
@Override
public Event intercept(Event event) {
byte[] body = event.getBody();
String log = new String(body, StandardCharsets.UTF_8);
JSONObject jsonObject = JSONObject.parseObject(log);
String ts = jsonObject.getString("ts");
Map<String, String> headers = event.getHeaders();
headers.put("timestamp",ts); // event是引用变量类型,存储的是地址,header变了,自然event所对应地址上的值就变了
return event;
}
@Override
public List<Event> intercept(List<Event> events) {
for (Event event : events) {
intercept(event);
}
return events;
}
@Override
public void close() {
}
// 建造者模式
public static class Builder implements Interceptor.Builder {
@Override
public Interceptor build() {
return new TimestampInterceptor();
}
@Override
public void configure(Context context) {
}
}
}
[hadoop@hadoop104 job]$ vim kafka_to_hdfs.conf
内容:
a1.sources.r1.kafka.consumer.group.id:消费者组名。
a1.channels.c1.type:file类型channel,缓冲数据放在磁盘中,而不是内存中。
a1.channels.c1.dataDirs:file channel缓冲内容落盘地址。
a1.channels.c1.checkpointDir:检查点存放位置,用于断点续传。
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# 配置source
a1.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
a1.sources.r1.kafka.bootstrap.servers = hadoop102:9092,hadoop103:9092,hadoop104:9092
a1.sources.r1.kafka.topics = topic_mall_applog
a1.sources.r1.kafka.consumer.group.id = consumer_group_flume
# 指定consumer从哪个offset开始消费,默认latest
# a1.sources.r1.kafka.consumer.auto.offset.reset = earliest
# 自定义拦截器
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.songshuang.flume.interceptor.TimestampInterceptor$Builder
# 配置sink
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = /warehouse/applog/%Y-%m-%d
a1.sinks.k1.hdfs.codeC = gzip
# 配置channel
a1.channels.c1.type = file
a1.channels.c1.dataDirs = /opt/module/apache-flume-1.9.0-bin/data/kafka_to_hdfs
a1.channels.c1.checkpointDir = /opt/module/apache-flume-1.9.0-bin/checkpoint/kafka_to_hdfs
# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
注意1:需要放开kafka端口,即9092端口,Flume要读Kafka。
[hadoop@hadoop104 job]$ cd /opt/module/apache-flume-1.9.0-bin/
[hadoop@hadoop104 apache-flume-1.9.0-bin]$ nohup bin/flume-ng agent --conf conf/ --name a1 --conf-file job/kafka_to_hdfs.conf &>/dev/null 2>/dev/null &
[mall@mall apache-flume-1.9.0-bin]$ ps -ef | grep kafka_to_hdfs.conf
[mall@mall apache-flume-1.9.0-bin]$ kill 11001