hbase bulkload弊端 hbase bulkload命令

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mob64ca14116c53 2023-08-09 20:16:43

文章标签 hbase bulkload弊端 apache hadoop Text 文章分类 Hbase 数据库

三、课堂目标

1. 掌握hbase的客户端API操作

2. 掌握hbase集成MapReduce

3. 掌握hbase集成hive

4. 掌握hbase表的rowkey设计

5. 掌握hbase表的热点

6. 掌握hbase表的数据备份

7. 掌握hbase二级索引

四、知识要点

1. hbase客户端API操作

创建Maven工程，添加依赖

<dependencies>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
    </dependencies>

hbase表的增删改查操作

具体操作详细见==《hbase表的增删改查操作.md》==文档

1、初始化一个init方法

2、创建一个表

3、修改表属性

4、put添加数据

5、get查询单条数据

6、scan批量查询数据

7、delete删除表中的列数据

8、删除表

9、过滤器的使用

过滤器的类型很多，但是考科一分为两大类--比较过滤器，专用过滤器
过滤器的作用是在服务端判断数据是否满足条件，然后只将满足条件的数据返回给客户端

9.1、hbase过滤器的比较运算符

LESS <

LESS_OR_EQUAL <=

EQUAL =

NOT_EQUAL <>

GREATER_OR_EQUAL >=

GREATER >

9.2、hbase过滤器的比较器（指定比较机制）

BinaryComparator  按字节索引顺序比较指定字节数组
BinaryPrefixComparator 跟前面相同，只是比较左端的数据是否相同
NullComparator 判断给定的是否为空
BitComparator 按位比较
RegexStringComparator 提供一个正则的比较器，仅支持 EQUAL 和非EQUAL
SubstringComparator 判断提供的子串是否出现在value中。

9.3、过滤器使用实战

9.3.1、针对行键的前缀过滤器

PrefixFilter

public void testFilter1() throws Exception {

// 针对行键的前缀过滤器
  Filter pf = new PrefixFilter(Bytes.toBytes("liu"));//"liu".getBytes()
  testScan(pf);
}

     //定义一个方法，接受一个过滤器，返回结果数据
public void testScan(Filter filter) throws Exception {
        Table table = conn.getTable(TableName.valueOf("t_user_info"));

        Scan scan = new Scan();
        //设置过滤器
        scan.setFilter(filter);

        ResultScanner scanner = table.getScanner(scan);
        Iterator<Result> iter = scanner.iterator();
        //遍历所有的Result对象，获取结果
        while (iter.hasNext()) {
            Result result = iter.next();
            List<Cell> cells = result.listCells();
            for (Cell c : cells) {
                //获取行键
                byte[] rowBytes = CellUtil.cloneRow(c);
                //获取列族
                byte[] familyBytes = CellUtil.cloneFamily(c);
                //获取列族下的列名称
                byte[] qualifierBytes = CellUtil.cloneQualifier(c);
                //列字段的值
                byte[] valueBytes = CellUtil.cloneValue(c);

                System.out.print(new String(rowBytes)+" ");
                System.out.print(new String(familyBytes)+":");
                System.out.print(new String(qualifierBytes)+" ");
                System.out.println(new String(valueBytes));
            }
            System.out.println("-----------------------");
        }
        }

9.3.2 行过滤器

RowFilter

public void testFilter2() throws Exception {

// 行过滤器  需要一个比较运算符和比较器
RowFilter rf1 = new RowFilter(CompareFilter.CompareOp.LESS, new        BinaryComparator(Bytes.toBytes("user002")));
         testScan(rf1);

         RowFilter rf2 = new RowFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("01"));//rowkey包含"01"子串的
         testScan(rf2);
}

9.3.3 列族过滤器

FamilyFilter

public void testFilter3() throws Exception {

//针对列族名的过滤器   返回结果中只会包含满足条件的列族中的数据
        FamilyFilter ff1 = new FamilyFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("base_info")));
        FamilyFilter ff2 = new FamilyFilter(CompareFilter.CompareOp.EQUAL, new BinaryPrefixComparator(Bytes.toBytes("base")));
        testScan(ff2);

}

9.3.4 列名过滤器

QualifierFilter

public void testFilter4() throws Exception {

//针对列名的过滤器 返回结果中只会包含满足条件的列的数据
    QualifierFilter qf1 = new QualifierFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("password")));
    QualifierFilter qf2 = new QualifierFilter(CompareFilter.CompareOp.EQUAL, new BinaryPrefixComparator(Bytes.toBytes("user")));
    testScan(qf2);
}

9.3.5 列值的过滤器

SingleColumnValueFilter

public void testFilter4() throws Exception {
    
//针对指定一个列的value的比较器来过滤
        ByteArrayComparable comparator1 = new RegexStringComparator("^zhang"); //以zhang开头的
        ByteArrayComparable comparator2 = new SubstringComparator("si");       //包含"si"子串
        SingleColumnValueFilter scvf = new SingleColumnValueFilter("base_info".getBytes(), "username".getBytes(), CompareFilter.CompareOp.EQUAL, comparator2);
        testScan(scvf);

}

9.3.6 多个过滤器同时使用

public void testFilter4() throws Exception {
    
//多个过滤器同时使用   select * from t1 where id >10 and age <30
    
//构建一个列族的过滤器            
FamilyFilter cfff1 = new FamilyFilter(CompareFilter.CompareOp.EQUAL, new BinaryPrefixComparator(Bytes.toBytes("base")));

//构建一个列的前缀过滤器
            ColumnPrefixFilter cfff2 = new ColumnPrefixFilter("password".getBytes());

//指定多个过滤器是否同时都要满足条件
            FilterList filterList = new FilterList(FilterList.Operator.MUST_PASS_ONE);

            filterList.addFilter(cfff1);
            filterList.addFilter(cfff2);
            testScan(filterList);
}

2 hbase集成MapReduce

HBase表中的数据最终都是存储在HDFS上，HBase天生的支持MR的操作，我们可以通过MR直接处理HBase表中的数据，并且MR可以将处理后的结果直接存储到HBase表中。

参考地址：http://hbase.apache.org/book.html#mapreduce

2.1 实战一

需求

==读取hbase某张表中的数据，然后把结果写入到另外一张hbase表==

package com.kaikeba;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
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.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;

import java.io.IOException;

public class HBaseMR {

    public static class HBaseMapper extends TableMapper<Text,Put>{
        @Override
        protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
             //获取rowkey的字节数组
            byte[] bytes = key.get();
            String rowkey = Bytes.toString(bytes);
            //构建一个put对象
            Put put = new Put(bytes);
            //获取一行中所有的cell对象
            Cell[] cells = value.rawCells();
            for (Cell cell : cells) {
                  // f1列族
                if("f1".equals(Bytes.toString(CellUtil.cloneFamily(cell)))){
                    // name列名
                     if("name".equals(Bytes.toString(CellUtil.cloneQualifier(cell)))){
                          put.add(cell);
                     }
                     // age列名
                    if("age".equals(Bytes.toString(CellUtil.cloneQualifier(cell)))){
                        put.add(cell);
                    }
                }
            }
            if(!put.isEmpty()){
              context.write(new Text(rowkey),put);
            }

        }
    }

     public  static  class HbaseReducer extends TableReducer<Text,Put,ImmutableBytesWritable>{
         @Override
         protected void reduce(Text key, Iterable<Put> values, Context context) throws IOException, InterruptedException {
             for (Put put : values) {
                 context.write(null,put);
             }
         }
     }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();

        Scan scan = new Scan();

        Job job = Job.getInstance(conf);
        job.setJarByClass(HBaseMR.class);
        //使用TableMapReduceUtil 工具类来初始化我们的mapper
        TableMapReduceUtil.initTableMapperJob(TableName.valueOf(args[0]),scan,HBaseMapper.class,Text.class,Put.class,job);

        //使用TableMapReduceUtil 工具类来初始化我们的reducer
        TableMapReduceUtil.initTableReducerJob(args[1],HbaseReducer.class,job);

        //设置reduce task个数
         job.setNumReduceTasks(1);

        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }

}

打成jar包提交到集群中运行

hadoop jar hbase_java_api-1.0-SNAPSHOT.jar com.kaikeba.HBaseMR t1 t2

2.2 实战二

需求

==读取HDFS文件，把内容写入到HBase表中==

hdfs上数据文件 user.txt

0001 xiaoming 20
0002 xiaowang 30
0003 xiaowu 40

代码开发

package com.kaikeba;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
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.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import java.io.IOException;



public class Hdfs2Hbase {

    public static class HdfsMapper extends Mapper<LongWritable,Text,Text,NullWritable> {

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

    public static class HBASEReducer extends TableReducer<Text,NullWritable,ImmutableBytesWritable> {

        protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            String[] split = key.toString().split(" ");
            Put put = new Put(Bytes.toBytes(split[0]));
            put.addColumn("f1".getBytes(),"name".getBytes(),split[1].getBytes());
            put.addColumn("f1".getBytes(),"age".getBytes(), split[2].getBytes());
            context.write(new ImmutableBytesWritable(Bytes.toBytes(split[0])),put);
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(Hdfs2Hbase.class);

        job.setInputFormatClass(TextInputFormat.class);
        //输入文件路径
        TextInputFormat.addInputPath(job,new Path(args[0]));
        job.setMapperClass(HdfsMapper.class);
        //map端的输出的key value 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        //指定输出到hbase的表名
        TableMapReduceUtil.initTableReducerJob(args[1],HBASEReducer.class,job);

        //设置reduce个数
        job.setNumReduceTasks(1);

        System.exit(job.waitForCompletion(true)?0:1);
    }
}

创建hbase表 t3

create 't3','f1'

打成jar包提交到集群中运行

hadoop jar hbase_java_api-1.0-SNAPSHOT.jar com.kaikeba.Hdfs2Hbase /data/user.txt t3

2.3 实战三

需求

==通过bulkload的方式批量加载数据到HBase表中==

把hdfs上面的这个路径/input/user.txt的数据文件，转换成HFile格式，然后load到user这张表里面中

知识点描述

加载数据到HBase当中去的方式多种多样，我们可以使用HBase的javaAPI或者使用sqoop将我们的数据写入或者导入到HBase当中去，但是这些方式不是慢就是在导入的过程的占用Region资料导致效率低下，我们也可以通过MR的程序，将我们的数据直接转换成HBase的最终存储格式HFile，然后直接load数据到HBase当中去即可

HBase数据正常写流程回顾

hbase bulkload弊端 hbase bulkload命令_Text

bulkload方式的处理示意图

hbase bulkload弊端 hbase bulkload命令_hbase bulkload弊端_02

好处

（1）.导入过程不占用Region资源

（2）.能快速导入海量的数据

（3）.节省内存

==1、开发生成HFile文件的代码==

package com.kaikeba;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat2;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class HBaseLoad {

    public static class LoadMapper  extends Mapper<LongWritable,Text,ImmutableBytesWritable,Put> {
        @Override
        protected void map(LongWritable key, Text value, Mapper.Context context) throws IOException, InterruptedException {
            String[] split = value.toString().split(" ");
            Put put = new Put(Bytes.toBytes(split[0]));
            put.addColumn("f1".getBytes(),"name".getBytes(),split[1].getBytes());
            put.addColumn("f1".getBytes(),"age".getBytes(), split[2].getBytes());
            context.write(new ImmutableBytesWritable(Bytes.toBytes(split[0])),put);
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
            final String INPUT_PATH=  "hdfs://node1:9000/input";
            final String OUTPUT_PATH= "hdfs://node1:9000/output_HFile";
            Configuration conf = HBaseConfiguration.create();

            Connection connection = ConnectionFactory.createConnection(conf);
            Table table = connection.getTable(TableName.valueOf("t4"));
            Job job= Job.getInstance(conf);

            job.setJarByClass(HBaseLoad.class);
            job.setMapperClass(LoadMapper.class);
            job.setMapOutputKeyClass(ImmutableBytesWritable.class);
            job.setMapOutputValueClass(Put.class);
            //指定输出的类型HFileOutputFormat2
            job.setOutputFormatClass(HFileOutputFormat2.class);

         HFileOutputFormat2.configureIncrementalLoad(job,table,connection.getRegionLocator(TableName.valueOf("t4")));
            FileInputFormat.addInputPath(job,new Path(INPUT_PATH));
            FileOutputFormat.setOutputPath(job,new Path(OUTPUT_PATH));
            System.exit(job.waitForCompletion(true)?0:1);


    }
}

==2、打成jar包提交到集群中运行==

hadoop jar hbase_java_api-1.0-SNAPSHOT.jar com.kaikeba.HBaseLoad

==3、观察HDFS上输出的结果==

hbase bulkload弊端 hbase bulkload命令_hbase bulkload弊端_03

hbase bulkload弊端 hbase bulkload命令_Text_04

==4、加载HFile文件到hbase表中==

代码加载

package com.kaikeba;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;

public class LoadData {
    public static void main(String[] args) throws Exception {
        Configuration configuration = HBaseConfiguration.create();
        configuration.set("hbase.zookeeper.quorum", "node1:2181,node2:2181,node3:2181");
    //获取数据库连接
    Connection connection =  ConnectionFactory.createConnection(configuration);
    //获取表的管理器对象
    Admin admin = connection.getAdmin();
    //获取table对象
    TableName tableName = TableName.valueOf("t4");
    Table table = connection.getTable(tableName);
    //构建LoadIncrementalHFiles加载HFile文件
    LoadIncrementalHFiles load = new LoadIncrementalHFiles(configuration);
    load.doBulkLoad(new Path("hdfs://node1:9000/output_HFile"), admin,table,connection.getRegionLocator(tableName));
 }
}

命令加载

命令格式

hadoop jar hbase-server-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable

先将hbase的jar包添加到hadoop的classpath路径下

export HBASE_HOME=/opt/bigdata/hbase
export HADOOP_HOME=/opt/bigdata/hadoop
export HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase mapredcp`

命令加载演示

hadoop jar /opt/bigdata/hbase/lib/hbase-server-1.2.1.jar completebulkload /output_HFile t5

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