HBase之Bulkload（三）
作者：互联网
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
bulkload方式的处理示意图
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==
==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
标签：hadoop,new,apache,import,Bulkload,org,HBase,hbase 来源： https://www.cnblogs.com/hanchaoyue/p/13198566.html