医疗数仓配置Flume

Flume配置概述

Flume配置概述

Flume需要将Kafka中各topic的数据传输到HDFS，因此选用KafkaSource以及HDFSSink。对于安全性要求高的数据（不允许丢失）选用FileChannel，允许部分丢失的数据如日志可以选用MemoryChannel以追求更高的效率。此处采集的是业务数据，不允许丢失，选用FileChannel，生产环境根据实际情况选择合适的组件。

KafkaSource订阅Kafka medical_ods主题的数据，HDFSSink将不同topic的数据写入不同路径，路径中应包含表名及日期，前者用于区分来源于不同业务表的数据，后者按天对数据进行划分。关键配置如下：

2）Flume配置实操

（1）创建Flume配置文件

在hadoop104节点的Flume家目录下创建job目录，在job下创建medical_kafka_to_hdfs.conf。

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[atguigu@hadoop104 flume]$ mkdir job 
[atguigu@hadoop104 flume]$ cd job/
[atguigu@hadoop104 job]$ vim medical_kafka_to_hdfs.conf

（2）配置文件内容如下

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

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_db
a1.sources.r1.kafka.consumer.group.id = medical-flume
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.atguigu.medical.flume.interceptors.TimestampAndTableNameInterceptor$Builder


a1.channels.c1.type = file
a1.channels.c1.checkpointDir = /opt/module/flume/checkpoint/medical
a1.channels.c1.dataDirs = /opt/module/flume/data/medical
a1.channels.c1.maxFileSize = 2146435071
a1.channels.c1.capacity = 1000000
a1.channels.c1.keep-alive = 6


## sink1
a1.sinks.k1.type = hdfs
a1.sinks.k1.hdfs.path = /origin_data/medical/%{tableName}_inc/%Y-%m-%d
a1.sinks.k1.hdfs.filePrefix = db
a1.sinks.k1.hdfs.round = false


a1.sinks.k1.hdfs.rollInterval = 10
a1.sinks.k1.hdfs.rollSize = 134217728
a1.sinks.k1.hdfs.rollCount = 0


a1.sinks.k1.hdfs.fileType = CompressedStream
a1.sinks.k1.hdfs.codeC = gzip

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

（3）编写Flume拦截器

① 创建名为medical-flume-interceptor的项目

② 在pom文件中添加如下内容。

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<dependencies>
    <dependency>
        <groupId>org.apache.flume</groupId>
        <artifactId>flume-ng-core</artifactId>
        <version>1.10.1</version>
        <scope>provided</scope>
    </dependency>

    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.68</version>
    </dependency>
</dependencies>

<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>3.0.0</version>
            <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>

③ 在com.atguigu.medical.flume.interceptors包下创建TimestampAndTableNameInterceptor类

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package com.atguigu.medical.flume.interceptors;

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 TimestampAndTableNameInterceptor implements Interceptor {
    @Override
    public void initialize() {

    }

    @Override
    public Event intercept(Event event) {

        Map<String, String> headers = event.getHeaders();
        String log = new String(event.getBody(), StandardCharsets.UTF_8);

        JSONObject jsonObject = JSONObject.parseObject(log);

        Long ts = jsonObject.getLong("ts");

        String tableName = jsonObject.getString("table");

        headers.put("timestamp", ts * 1000 + "");
        headers.put("tableName", tableName);
        return event;

    }

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

        for (Event event : events) {
            intercept(event);
        }

        return events;
    }

    @Override
    public void close() {

    }

    public static class Builder implements Interceptor.Builder {


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

        @Override
        public void configure(Context context) {

        }
    }
}

④ 打包

⑤ 在target目录下查看打好的包

⑥ 将打好的包放入到hadoop104的/opt/module/flume/lib文件夹下

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[atguigu@hadoop104 lib]$ ls | grep medical-flume-interceptor
medical-flume-interceptor-1.0-SNAPSHOT-jar-with-dependencies.jar

3）通道测试

（1）启动Zookeeper、Kafka集群

（2）启动hadoop104的Flume

[atguigu@hadoop104 flume]$ bin/flume-ng agent -n a1 -c conf/ -f job/medical_kafka_to_hdfs.conf -Dflume.root.logger=INFO,console

（3）执行模拟数据jar包，生成数据

[atguigu@hadoop102 bin]$ medical_mock.sh 1

（4）观察HDFS上的目标路径，如下。

增量表目录已生成。

4）编写Flume启停脚本

为方便使用，此处编写一个Flume的启停脚本

（1）在hadoop102节点的/home/atguigu/bin目录下创建脚本medical-f1.sh

[atguigu@hadoop102 bin]$ vim medical-f1.sh

在脚本中填写如下内容

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#!/bin/bash

case $1 in
"start")
        echo " --------启动 hadoop104 业务数据flume-------"
        ssh hadoop104 "nohup /opt/module/flume/bin/flume-ng agent -n a1 -c /opt/module/flume/conf -f /opt/module/flume/job/medical_kafka_to_hdfs.conf > /opt/module/flume/medical-f1.log 2>&1 &"
;;
"stop")

        echo " --------停止 hadoop104 业务数据flume-------"
        ssh hadoop104 "ps -ef | grep medical_kafka_to_hdfs | grep -v grep |awk '{print \$2}' | xargs -n1 kill"
;;
esac

（2）增加脚本执行权限

[atguigu@hadoop102 bin]$ chmod +x medical-f1.sh

（3）medical-f1启动

[atguigu@hadoop102 module]$ medical-f1.sh start

（4）medical-f1停止

[atguigu@hadoop102 module]$ medical-f1.sh stop