线上问诊：业务数据采集

系列文章目录

线上问诊：业务数据采集

线上问诊：数仓数据同步

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文章目录

• 系列文章目录
• 前言
• 一、环境安装
• • 1.DataX
• 二、全量同步
• • 1.DataX配置文件生成
  • 2.启动hadoop测试一下。
  • 3.全量同步
• 三、增量同步
• • 1.配置Flume
  • 2.编写Flume拦截器
  • 3.通道测试
  • 4.修改Maxwell参数
  • 5.编写Flume启停脚本
  • 6.创建增量同步脚本
• 总结

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前言

上次博客记录的是数据的采集，这次我们记录一下数据从MYSQL到HDFS的数据同步。

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一、环境安装

1.DataX

上传并解压

尚硅谷提供的资料里边解压会报一个错误，所以自己寻找了一下。
DataX下载

用datax提供的自检命令测试一下。

python /opt/module/datax/bin/datax.py /opt/module/datax/job/job.json

二、全量同步

1.DataX配置文件生成

mkdir /opt/module/gen_datax_config
cd /opt/module/gen_datax_config

修改配置文件

mysql.username=root
mysql.password=000000
mysql.host=hadoop102
mysql.port=3306
mysql.database.import=medical
# mysql.database.export=
mysql.tables.import=dict,doctor,hospital,medicine,patient,user
# mysql.tables.export=
is.seperated.tables=0
hdfs.uri=hdfs://hadoop102:8020
import_out_dir=/opt/module/datax/job/medical/import
# export_out_dir=

执行文件

java -jar datax-config-generator-1.0-SNAPSHOT-jar-with-dependencies.jar

2.启动hadoop测试一下。

hadoop fs -mkdir -p /origin_data/medical/user_full/2023-05-09

数据同步测试

python /opt/module/datax/bin/datax.py -p"-Dtargetdir=/origin_data/medical/user_full/2023-05-09" /opt/module/datax/job/medical/import/medical.user.json

测试成功后编写全量同步脚本。

vim ~/bin/medical_mysql_to_hdfs_full.sh

#!/bin/bash

DATAX_HOME=/opt/module/datax
DATAX_DATA=/opt/module/datax/job/medical

#清理脏数据
handle_targetdir() {
  hadoop fs -rm -r $1 >/dev/null 2>&1
  hadoop fs -mkdir -p $1
}

#数据同步
import_data() {
  local datax_config=$1
  local target_dir=$2

  handle_targetdir "$target_dir"
  echo "正在处理$1"
  python $DATAX_HOME/bin/datax.py -p"-Dtargetdir=$target_dir" $datax_config >/tmp/datax_run.log 2>&1
  if [ $? -ne 0 ]
  then
    echo "处理失败, 日志如下:"
    cat /tmp/datax_run.log 
  fi
}

#接收表名变量
tab=$1
# 如果传入日期则do_date等于传入的日期，否则等于前一天日期
if [ -n "$2" ] ;then
    do_date=$2
else
    do_date=$(date -d "-1 day" +%F)
fi


case ${tab} in
dict|doctor|hospital|medicine|patient|user)
  import_data $DATAX_DATA/import/medical.${tab}.json /origin_data/medical/${tab}_full/$do_date
  ;;
"all")
  for tmp in dict doctor hospital medicine patient user
  do
    import_data $DATAX_DATA/import/medical.${tmp}.json /origin_data/medical/${tmp}_full/$do_date
  done
  ;;
esac

添加权限。

chmod +x ~/bin/medical_mysql_to_hdfs_full.sh

3.全量同步

medical_mysql_to_hdfs_full.sh all 2023-05-09

三、增量同步

1.配置Flume

在hadoop104进行配置

vim medical_kafka_to_hdfs.conf

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

2.编写Flume拦截器

创建项目

medical-flume-interceptor

编写pom.xml

<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>

创建包

com.atguigu.medical.flume.interceptors

创建类

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) {

        }
    }
}

idea打包

将文件上传

放入到hadoop104的/opt/module/flume/lib

3.通道测试

启动Zookeeper、Kafka和Maxwell

在Hadoop104启动Flume

bin/flume-ng agent -n a1 -c conf/ -f job/medical_kafka_to_hdfs.conf -Dflume.root.logger=INFO,console

然后再次进行数据模拟。

medical_mock.sh 1

增量同步完成

4.修改Maxwell参数

vim /opt/module/maxwell/config.properties

5.编写Flume启停脚本

vim medical-f1.sh

#!/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

chmod +x medical-f1.sh

6.创建增量同步脚本

vim medical_mysql_to_kafka_inc_init.sh

#!/bin/bash

# 该脚本的作用是初始化所有的增量表，只需执行一次

MAXWELL_HOME=/opt/module/maxwell

import_data() {
  for tab in $@
  do 
    $MAXWELL_HOME/bin/maxwell-bootstrap --database medical --table $tab --config $MAXWELL_HOME/config.properties
  done
}

case $1 in
consultation | payment | prescription | prescription_detail | user | patient | doctor)
  import_data $1
  ;;
"all")
  import_data consultation payment prescription prescription_detail user patient doctor
  ;;
esac

chmod +x medical_mysql_to_kafka_inc_init.sh

删除之前的增量数据，再次测试。

hadoop fs -ls /origin_data/medical  | grep _inc | awk '{print $8}' | xargs hadoop fs -rm -r -f

medical_mysql_to_kafka_inc_init.sh all

再次测试后出现增量数据，代表通过建立完成。

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总结

数据的同步到这里就结束了。