HBase基础知识（五）：HBase 对接 Hadoop 的 MapReduce

通过 HBase 的相关 JavaAPI，我们可以实现伴随 HBase 操作的 MapReduce 过程，比如使用 MapReduce 将数据从本地文件系统导入到 HBase 的表中，比如我们从 HBase 中读取一些原 始数据后使用 MapReduce 做数据分析。

1 官方 HBase-MapReduce

1．查看 HBase 的 MapReduce 任务的执行

./bin/hbase mapredcp
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/module/hbase-1.3.1/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/module/hadoop-3.1.3/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
/opt/module/hbase-1.3.1/lib/zookeeper-3.4.6.jar:/opt/module/hbase-1.3.1/lib/guava-12.0.1.jar:/opt/module/hbase-1.3.1/lib/metrics-core-2.2.0.jar:/opt/module/hbase-1.3.1/lib/protobuf-java-2.5.0.jar:/opt/module/hbase-1.3.1/lib/hbase-common-1.3.1.jar:/opt/module/hbase-1.3.1/lib/hbase-protocol-1.3.1.jar:/opt/module/hbase-1.3.1/lib/htrace-core-3.1.0-incubating.jar:/opt/module/hbase-1.3.1/lib/hbase-client-1.3.1.jar:/opt/module/hbase-1.3.1/lib/hbase-hadoop-compat-1.3.1.jar:/opt/module/hbase-1.3.1/lib/netty-all-4.0.23.Final.jar:/opt/module/hbase-1.3.1/lib/hbase-server-1.3.1.jar:/opt/module/hbase-1.3.1/lib/hbase-prefix-tree-1.3.1.jar

2．环境变量的导入

（1）执行环境变量的导入（临时生效，在命令行执行下述操作）

$ export HBASE_HOME=/opt/module/hbase
$ export HADOOP_HOME=/opt/module/hadoop-2.7.2
$ export HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase mapredcp`

（2）永久生效：在/etc/profile 配置

export HBASE_HOME=/opt/module/hbase
export HADOOP_HOME=/opt/module/hadoop-2.7.2

并在 hadoop-env.sh 中配置：（注意：在 for 循环之后配）

export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:/opt/module/hbase-1.3.1/lib/*

3．运行官方的 MapReduce 任务 --

案例一：统计 Student 表中有多少行数据

 /opt/module/hadoop-3.1.3/bin/yarn jar lib/hbase-server-1.3.1.jar rowcounter stu

案例二：使用 MapReduce 将本地数据导入到 HBase

1）在本地创建一个 tsv 格式的文件：fruit.tsv（注意这里的分隔符是TAB键）

1001    Apple   Red
1002    Pear    Yellow
1003    Pineapple   Yellow

3）上传到hadoop

 hadoop fs -put fruit.tsv /

4）执行 MapReduce 到 HBase 的 fruit 表中

/opt/module/hadoop-3.1.3/bin/yarn jar lib/hbase-server-1.3.1.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,info:name,info:color fruit hdfs://hadoop101:9000/input_fruit

2）创建 Hbase 表

Hbase(main):001:0> create 'fruit','info'

5）使用 scan 命令查看导入后的结果

hbase(main):011:0> scan 'fruit'
ROW                                   COLUMN+CELL                                                                                               
 1001                                 column=info:color, timestamp=1642253156646, value=Red                                                     
 1001                                 column=info:name, timestamp=1642253156646, value=Apple                                                    
 1002                                 column=info:color, timestamp=1642253156646, value=Yellow                                                  
 1002                                 column=info:name, timestamp=1642253156646, value=Pear                                                     
 1003                                 column=info:color, timestamp=1642253156646, value=Yellow                                                  
 1003                                 column=info:name, timestamp=1642253156646, value=Pineapple                                                
3 row(s) in 0.2760 seconds

2 自定义 HBase-MapReduce1

目标：将 fruit 表中的一部分数据，通过 MR 迁入到 fruit_mr 表中。

分步实现：

1．构建 ReadFruitMapper 类，用于读取 fruit 表中的数据

package com.atguigu.mr;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class FruitMapper extends Mapper<LongWritable, Text,LongWritable,Text> {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        context.write(key,value);
    }
}

2． 构建 WriteFruitMRReducer 类，用于将读取到的 fruit 表中的数据写入到 fruit_mr 表中

package com.atguigu.mr;
​
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
​
import java.io.IOException;
​
public class FruitReducer extends TableReducer<LongWritable, Text, NullWritable> {
​
    //可以进行动态传参
    String cf1;
​
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
​
        Configuration configuration = context.getConfiguration();
​
        cf1 = configuration.get("cf1");
    }
​
    @Override
    protected void reduce(LongWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
​
        //1.遍历values
        for (Text value : values) {
            //获取每一行数据
            String[] fields = value.toString().split("\t");
​
            //3.构建put对象
            Put put = new Put(Bytes.toBytes(fields[0]));
​
            //4.给put对象赋值
            put.addColumn(Bytes.toBytes("info"),Bytes.toBytes("name"),Bytes.toBytes(fields[1]));
            put.addColumn(Bytes.toBytes("info"),Bytes.toBytes("color"),Bytes.toBytes(fields[2]));
​
            //5. 写出
            context.write(NullWritable.get(),put);
        }
​
    }
}
​

3．构建 Fruit2FruitMRRunner extends Configured implements Tool 用于组装运行 Job任务

package com.atguigu.mr;
​
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
​
​
/**
 * @author:左泽林
 * @date:日期:2022-01-16-时间:8:55
 * @message:
 */
public class FruitDriver implements Tool {
​
    //定义一个COnfiguration
    private Configuration configuration = null;
​
​
    public int run(String[] args) throws Exception {
​
        //1.获取Job对象
        Job job = Job.getInstance(configuration);
​
        //2. 设置驱动类路径
        job.setJarByClass(FruitDriver.class);
​
        //3. 设置mapper&mapper输出的KV类型
        job.setMapperClass(FruitMapper.class);
        job.setMapOutputKeyClass(LongWritable.class);
        job.setMapOutputValueClass(Text.class);
​
        //4. 设置Reducer类
        TableMapReduceUtil.initTableReducerJob(args[1] , FruitReducer.class , job);
​
        //5. 设置输入输出的参数
        FileInputFormat.setInputPaths(job,new Path(args[0]));
​
        //6. 提交任务
        boolean result = job.waitForCompletion(true);
​
        return result ? 0 : 1;
    }
​
​
    public void setConf(Configuration configuration) {
        this.configuration = configuration;
    }
​
    public Configuration getConf() {
        return null;
    }
​
    public static void main(String[] args) throws Exception {
​
        Configuration configuration = new Configuration();
​
        int run = ToolRunner.run(configuration, new FruitDriver(), args);
​
        System.exit(run);
    }
}
​

4．主函数中调用运行该 Job 任务

5．打包运行任务

1. 1. 上传jar包到虚拟机，在hbase中创建fruit1表，在运行创建的jar包

创建fruit表:

hbase(main):003:0* create 'fruit1','info'
0 row(s) in 1.8160 seconds

=> Hbase::Table - fruit1

运行jar包

yarn jar hbase-1.0-SNAPSHOT.jar com.atguigu.mr.FruitDriver /input/fruit.tsv fruit1

查看fruit1表中的结果:

hbase(main):004:0> scan 'fruit1'
ROW                                   COLUMN+CELL                                                                                               
 1001                                 column=info:color, timestamp=1642298137576, value=Red                                                     
 1001                                 column=info:name, timestamp=1642298137576, value=Apple                                                    
 1002                                 column=info:color, timestamp=1642298137576, value=Yellow                                                  
 1002                                 column=info:name, timestamp=1642298137576, value=Pear                                                     
 1003                                 column=info:color, timestamp=1642298137576, value=Yellow                                                  
 1003                                 column=info:name, timestamp=1642298137576, value=Pineapple                                                
3 row(s) in 0.4790 seconds

3 自定义 Hbase-MapReduce2

目标：实现将 HDFS 中的数据写入到 Hbase 表中。

分步实现：

1．构建 ReadFruitFromHDFSMapper 于读取 HDFS 中的文件数据

package com.atguigu.mr2;
​
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.util.Bytes;
​
import java.io.IOException;

public class Fruit2Mapper extends TableMapper<ImmutableBytesWritable , Put> {
​
    @Override
    protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
​
        //构建Put对象
        Put put = new Put(key.get());
​
        //1.获取数据
        for (Cell cell : value.rawCells()) {
​
            //2.判断当前的cell是否为”name“列
            if ("name".equals(Bytes.toString(CellUtil.cloneQualifier(cell)))){
​
                //3.给Put对象赋值
                put.add(cell);
​
            }
        }
​
        //4.写出
        context.write(key,put);
    }
}

2．构建 WriteFruitMRFromTxtReducer 类

package com.atguigu.mr2;
​
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.io.NullWritable;
​
import java.io.IOException;
​

public class Fruit2Reducer extends TableReducer<ImmutableBytesWritable , Put , NullWritable> {
​
    @Override
    protected void reduce(ImmutableBytesWritable key, Iterable<Put> values, Context context) throws IOException, InterruptedException {
​
        //1。遍历写出
        for (Put value : values) {
​
            context.write(NullWritable.get(),value);
​
        }
​
    }
}
​

3．创建 Txt2FruitRunner 组装 Job

package com.atguigu.mr2;
​
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
​

public class Fruit2Driver implements Tool {
​
    //定义配置信息
    private Configuration configuration = null;
​
    public int run(String[] args) throws Exception {
​
        //1.获取Job对象
        Job job = Job.getInstance(configuration);
​
        //2. 设置主类路径
        job.setJarByClass(Fruit2Driver.class);
​
        //3.设置Mapper&输出KV类型
        TableMapReduceUtil.initTableMapperJob(
                "fruit",
                new Scan(),
                Fruit2Mapper.class,
                ImmutableBytesWritable.class,
                Put.class,
                job
        );
​
        //4.设置Reducer&输出的表
        TableMapReduceUtil.initTableReducerJob(
                "fruit12",
                Fruit2Reducer.class,
                job
        );
​
        //5.提交任务
        boolean result = job.waitForCompletion(true);
​
        return result ? 0 : 1;
    }
​
    public void setConf(Configuration configuration) {
        this.configuration = configuration;
    }
​
    public Configuration getConf() {
        return configuration;
    }
​
    public static void main(String[] args) throws Exception {
​
​
        Configuration configuration = HBaseConfiguration.create();
​
        ToolRunner.run(configuration, new Fruit2Driver() , args);
​
    }
}
​