Apache Hbase 系列文章
1、hbase-2.1.0介绍及分布式集群部署、HA集群部署、验证、硬件配置推荐 2、hbase-2.1.0 shell基本操作详解 3、HBase的java API基本操作（创建、删除表以及对数据的添加、删除、查询以及多条件查询） 4、HBase使用(namespace、数据分区、rowkey设计、原生api访问hbase) 5、Apache Phoenix(5.0.0-5.1.2) 介绍及部署、使用（基本使用、综合使用、二级索引示例）、数据分区示例 6、Base批量装载——Bulk load（示例一：基本使用示例） 7、Base批量装载-Bulk load（示例二：写千万级数据-mysql数据以ORCFile写入hdfs，然后导入hbase） 8、HBase批量装载-Bulk load（示例三：写千万级数据-mysql数据直接写成Hbase需要的数据，然后导入hbase）
(文章目录)
本文主要介绍Bulk load的用法，即MapReduce和bulk load的配合使用。 本文是介绍Bulk load用法的系列的第三篇。 本文前提依赖hbase可用、phoenix可用、mysql可用、hadoop是HA环境。 本文分为2个部分，即实现步骤和实现。
一、实现步骤
示例三：1260万数据，介绍bulk load的基本用法，包含mapper的写法、bulk load的导入、phoenix的客户端视图用法 1、从mysql数据库中读取1260万（12606948）数据并以HBase要求的数据格式写入HDFS中 2、加载数据到Hbase中 3、通过phoenix客户端查询数据 4、hadoop环境是HA
二、实现
1、创建Hbase表
create_namespace "USER"
create "USER:DBUSER", { NAME => "C1", COMPRESSION => "GZ"}, { NUMREGIONS => 6, SPLITALGO => "HexStringSplit"}
2、编码并运行
1）、pom.xml
<?xml version="1.0"?>
<project
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"
xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>com.okcard</groupId>
<artifactId>bigdata-component</artifactId>
<version>0.0.1-SNAPSHOT</version>
</parent>
<groupId>com.okcard</groupId>
<artifactId>hbase</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>hbase</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>2.1.0</version>
</dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>2.6</version>
</dependency>
<!-- hadoop的通用包 -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.5</version>
</dependency>
<!-- Xml操作相关 -->
<dependency>
<groupId>com.github.cloudecho</groupId>
<artifactId>xmlbean</artifactId>
<version>1.5.5</version>
</dependency>
<!-- 操作Office库 -->
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi</artifactId>
<version>4.0.1</version>
</dependency>
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi-ooxml</artifactId>
<version>4.0.1</version>
</dependency>
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi-ooxml-schemas</artifactId>
<version>4.0.1</version>
</dependency>
<!-- 操作JSON -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.62</version>
</dependency>
<!-- phoenix core -->
<dependency>
<groupId>org.apache.phoenix</groupId>
<artifactId>phoenix-core</artifactId>
<version>5.0.0-HBase-2.0</version>
<!-- 解决打包 Failure to find org.glassfish:javax.el:pom:3.0.1-b08-SNAPSHOT in xxx -->
<exclusions>
<exclusion>
<groupId>org.glassfish</groupId>
<artifactId>javax.el</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- phoenix 客户端 -->
<dependency>
<groupId>org.apache.phoenix</groupId>
<artifactId>phoenix-queryserver-client</artifactId>
<version>5.0.0-HBase-2.0</version>
<!-- 解决打包 Failure to find org.glassfish:javax.el:pom:3.0.1-b08-SNAPSHOT in xxx -->
<exclusions>
<exclusion>
<groupId>org.glassfish</groupId>
<artifactId>javax.el</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- HBase对mapreduce的支持 -->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-mapreduce</artifactId>
<version>2.1.0</version>
</dependency>
<!-- hadoop mr任务客户端 -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.7.5</version>
</dependency>
<!-- 客户端 -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.5</version>
</dependency>
<!-- hadoop权限认证相关 -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-auth</artifactId>
<version>2.7.5</version>
</dependency>
<!-- 方便操作文件apache的工具类包 -->
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>2.6</version>
</dependency>
<dependency>
<groupId>org.apache.htrace</groupId>
<artifactId>htrace-core</artifactId>
<version>3.2.0-incubating</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-core</artifactId>
<version>2.5.6</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.46</version>
</dependency>
<!-- ORC文件依赖 -->
<dependency>
<groupId>org.apache.orc</groupId>
<artifactId>orc-shims</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.orc</groupId>
<artifactId>orc-core</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>org.apache.orc</groupId>
<artifactId>orc-mapreduce</artifactId>
<version>1.6.3</version>
</dependency>
<dependency>
<groupId>jdk.tools</groupId>
<artifactId>jdk.tools</artifactId>
<version>1.8</version>
<scope>system</scope>
<systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
</dependency>
</dependencies>
</project>
2）、javabean
package org.hbase.mr.largedata;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;
import lombok.Data;
/**
* 实现Hadoop序列化接口Writable 从数据库读取/写入数据库的对象应实现DBWritable
*
* @author chenw
*/
@Data
public class User implements Writable, DBWritable {
private String id;
private String userName;
private String password;
private String phone;
private String email;
private String createDay;
@Override
public void write(PreparedStatement ps) throws SQLException {
ps.setString(1, id);
ps.setString(2, userName);
ps.setString(3, password);
ps.setString(4, phone);
ps.setString(5, email);
ps.setString(6, createDay);
}
@Override
public void readFields(ResultSet rs) throws SQLException {
this.id = rs.getString(1);
this.userName = rs.getString(2);
this.password = rs.getString(3);
this.phone = rs.getString(4);
this.email = rs.getString(5);
this.createDay = rs.getString(6);
}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(id);
out.writeUTF(userName);
out.writeUTF(password);
out.writeUTF(phone);
out.writeUTF(email);
out.writeUTF(createDay);
}
@Override
public void readFields(DataInput in) throws IOException {
id = in.readUTF();
userName = in.readUTF();
password = in.readUTF();
phone = in.readUTF();
email = in.readUTF();
createDay = in.readUTF();
}
public String toString() {
return id + "\t" + userName + "\t" + password + "\t" + phone + "\t" + email + "\t" + createDay;
}
}
3）、Mapper和Driver
package org.hbase.mr.largedata.db;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
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.RegionLocator;
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.hbase.util.MD5Hash;
import org.apache.hadoop.hbase.util.MapReduceExtendedCell;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.hbase.mr.largedata.User;
import org.springframework.util.StopWatch;
/**
* 将mysql數據解析成Hbase导入所需文件，即须与Hbase导入文件的key-value保持一致
*
* @author chenw
*
*/
public class UserBulkLoadDBToHbase extends Configured implements Tool {
// static String in = "hdfs://HadoopHAcluster/hbasetest/user/textuser/out";
static String out = "hdfs://HadoopHAcluster/hbasetest/user/dbuser/data";
TableName tableName = TableName.valueOf("USER:DBUSER");
public static void main(String[] args) throws Exception {
StopWatch clock = new StopWatch();
clock.start(UserBulkLoadDBToHbase.class.getSimpleName());
// 1. 使用HBaseConfiguration.create()加载配置文件
System.setProperty("HADOOP_USER_NAME", "alanchan");
Configuration configuration = HBaseConfiguration.create();
configuration.set("fs.defaultFS", "hdfs://HadoopHAcluster");
configuration.set("dfs.nameservices", "HadoopHAcluster");
configuration.set("dfs.ha.namenodes.HadoopHAcluster", "nn1,nn2");
configuration.set("dfs.namenode.rpc-address.HadoopHAcluster.nn1", "server1:8020");
configuration.set("dfs.namenode.rpc-address.HadoopHAcluster.nn2", "server2:8020");
configuration.set("dfs.client.failover.proxy.provider.HadoopHAcluster","org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");
int status = ToolRunner.run(configuration, new UserBulkLoadDBToHbase(), args);
System.exit(status);
clock.stop();
System.out.println(clock.prettyPrint());
}
@Override
public int run(String[] args) throws Exception {
// 1. 创建HBase连接
Connection connection = ConnectionFactory.createConnection(getConf());
// 2. 获取HTable
Table table = connection.getTable(tableName);
DBConfiguration.configureDB(getConf(), "com.mysql.jdbc.Driver", "jdbc:mysql://192.168.10.44:3306/test", "root","root");
Job job = Job.getInstance(getConf(), this.getClass().getName());
job.setJarByClass(this.getClass());
job.setMapperClass(UserBulkLoadDBToHbaseMapper.class);
job.setMapOutputKeyClass(ImmutableBytesWritable.class);
job.setMapOutputValueClass(MapReduceExtendedCell.class);
job.setInputFormatClass(DBInputFormat.class);
DBInputFormat.setInput(job, User.class, "select id, user_Name,pass_word,phone,email,create_day from dx_user",
// 12606948 条数据
"select count(*) from dx_user ");
// DBInputFormat.setInput(job, User.class,
// "select id, user_Name,pass_word,phone,email,create_day from dx_user where user_name = 'alan2452'",
// "select count(*) from dx_user where user_name = 'alan2452'");
Path outputDir = new Path(out);
outputDir.getFileSystem(this.getConf()).delete(outputDir, true);
FileOutputFormat.setOutputPath(job, outputDir);
RegionLocator regionLocator = connection.getRegionLocator(tableName);
HFileOutputFormat2.configureIncrementalLoad(job, table, regionLocator);
return job.waitForCompletion(true) ? 0 : 1;
}
static class UserBulkLoadDBToHbaseMapper
extends Mapper<LongWritable, User, ImmutableBytesWritable, MapReduceExtendedCell> {
LongWritable outKey = new LongWritable();
Text outValue = new Text();
// LongWritable 是数据库记录的符合条件读取的每行行号，不是数据库中的原始行号
// User 是每行记录的字段值，经过user的DBWrite实现
protected void map(LongWritable key, User value, Context context)
throws IOException, InterruptedException {
Counter counter = context.getCounter("mysql_records_counters", "User Records");
counter.increment(1);
//数据格式：1145643347 alan2452 837132 13977776789 alan.chan.chn@163.com 2021-12-28 00:00:00.0
// 1、 从实体类中获取ID，并转换为rowkey，hbase中的字段设置为大写，为phoenix建索引奠定基础
byte[] rowkeyByteArray = getRowkey(value);
byte[] columnFamily = Bytes.toBytes("C1");
byte[] colId = Bytes.toBytes("ID");
byte[] colUserName = Bytes.toBytes("USERNAME");
byte[] colPassword = Bytes.toBytes("PASSWORD");
byte[] colPhone = Bytes.toBytes("PHONE");
byte[] colEmail = Bytes.toBytes("EMAIL");
byte[] colCreateDay = Bytes.toBytes("CREATEDAY");
// 2、 构建输出key：new ImmutableBytesWrite(rowkey)
ImmutableBytesWritable immutableBytesWritable = new ImmutableBytesWritable(rowkeyByteArray);
// 3、 使用KeyValue类构建单元格，每个需要写入到表中的字段都需要构建出来单元格
KeyValue kvId = new KeyValue(rowkeyByteArray, columnFamily, colId, Bytes.toBytes(value.getId()));
KeyValue kvUserName = new KeyValue(rowkeyByteArray, columnFamily, colUserName,Bytes.toBytes(value.getUserName()));
KeyValue kvPassword = new KeyValue(rowkeyByteArray, columnFamily, colPassword,Bytes.toBytes(value.getPassword()));
KeyValue kvPhone = new KeyValue(rowkeyByteArray, columnFamily, colPhone, Bytes.toBytes(value.getPhone()));
KeyValue kvEmail = new KeyValue(rowkeyByteArray, columnFamily, colEmail, Bytes.toBytes(value.getEmail()));
KeyValue kvCreateDay = new KeyValue(rowkeyByteArray, columnFamily, colCreateDay,Bytes.toBytes(value.getCreateDay()));
// 4、使用context.write将输出输出
// 构建输出的value：new MapReduceExtendedCell(keyvalue对象)
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvId));
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvUserName));
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvPassword));
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvPhone));
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvEmail));
context.write(immutableBytesWritable, new MapReduceExtendedCell(kvCreateDay));
}
protected static byte[] getRowkey(User user) {
// rowkey = MD5Hash_id_userName_phone_createDay
StringBuilder rowkeyBuilder =new StringBuilder();
rowkeyBuilder.append(user.getId()).append("_").append(user.getUserName()).append("_").append(user.getPhone()).append("_").append(user.getCreateDay());
// 使用Bytes.toBytes将拼接出来的字符串转换为byte[]数组
// 使用MD5Hash.getMD5AsHex生成MD5值，并取其前8位
String md5AsHex = MD5Hash.getMD5AsHex(rowkeyBuilder.toString().getBytes());
String md5Hex8bit = md5AsHex.substring(0, 8);
return Bytes.toBytes(md5Hex8bit + "_" + rowkeyBuilder.toString());
}
}
}
4)、运行
3、加载HDFS数据到Hbase
hbase org.apache.hadoop.hbase.tool.LoadIncrementalHFiles /hbasetest/user/dbuser/data USER:DBUSER
hbase shell中执行
hbase(main):021:0> get "USER:DBUSER","26cfac1e_728232503_alan2452_13977776789_2021-12-28 00:00:00.0",{FORMATTER=>'toString'}
COLUMN CELL
C1:CREATEDAY timestamp=1665536417451, value=2021-12-28 00:00:00.0
C1:EMAIL timestamp=1665536417451, value=alan.chan.chn@163.com
C1:ID timestamp=1665536417451, value=728232503
C1:PASSWORD timestamp=1665536417451, value=337518
C1:PHONE timestamp=1665536417451, value=13977776789
C1:USERNAME timestamp=1665536417451, value=alan2452
1 row(s)
Took 0.0233 second
执行完成后，表数据分布如下图（rowkey设计合理）
4、在phoenix创建表并验证
#1、在phoenix中创建namespace
CREATE SCHEMA IF NOT EXISTS USER;
#2、在phoenix中创建表（实际上就是与Hbase中的表进行映射）
CREATE TABLE IF NOT EXISTS USER.DBUSER(
rk varchar primary key,
C1.id varchar,
C1.username varchar,
C1.password varchar,
C1.phone varchar,
C1.email varchar,
C1.createday varchar
);
0: jdbc:phoenix:server2:2118> CREATE SCHEMA IF NOT EXISTS USER;
No rows affected (0.011 seconds)
0: jdbc:phoenix:server2:2118> CREATE TABLE IF NOT EXISTS USER.DBUSER(
. . . . . . . . . . . . . . > rk varchar primary key,
. . . . . . . . . . . . . . > C1.id varchar,
. . . . . . . . . . . . . . > C1.username varchar,
. . . . . . . . . . . . . . > C1.password varchar,
. . . . . . . . . . . . . . > C1.phone varchar,
. . . . . . . . . . . . . . > C1.email varchar,
. . . . . . . . . . . . . . > C1.createday varchar
. . . . . . . . . . . . . . > );
No rows affected (24.487 seconds)
0: jdbc:phoenix:server2:2118> !desc user.dbuser
+------------+--------------+-------------+--------------+------------+------------+--------------+----------------+-----------------+-----------------+-----------+----------+-------------+----------------+-------------------+--------------------+-------------------+--------------+------+
| TABLE_CAT | TABLE_SCHEM | TABLE_NAME | COLUMN_NAME | DATA_TYPE | TYPE_NAME | COLUMN_SIZE | BUFFER_LENGTH | DECIMAL_DIGITS | NUM_PREC_RADIX | NULLABLE | REMARKS | COLUMN_DEF | SQL_DATA_TYPE | SQL_DATETIME_SUB | CHAR_OCTET_LENGTH | ORDINAL_POSITION | IS_NULLABLE | SCOP |
+------------+--------------+-------------+--------------+------------+------------+--------------+----------------+-----------------+-----------------+-----------+----------+-------------+----------------+-------------------+--------------------+-------------------+--------------+------+
| | USER | DBUSER | RK | 12 | VARCHAR | null | null | null | null | 0 | | | null | null | null | 1 | false | |
| | USER | DBUSER | ID | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 2 | true | |
| | USER | DBUSER | USERNAME | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 3 | true | |
| | USER | DBUSER | PASSWORD | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 4 | true | |
| | USER | DBUSER | PHONE | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 5 | true | |
| | USER | DBUSER | EMAIL | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 6 | true | |
| | USER | DBUSER | CREATEDAY | 12 | VARCHAR | null | null | null | null | 1 | | | null | null | null | 7 | true | |
+------------+--------------+-------------+--------------+------------+------------+--------------+----------------+-----------------+-----------------+-----------+----------+-------------+----------------+-------------------+--------------------+-------------------+--------------+------+
0: jdbc:phoenix:server2:2118>
0: jdbc:phoenix:server2:2118> select count(*) from user.dbuser;
+-----------+
| COUNT(1) |
+-----------+
| 12606948 |
+-----------+
1 row selected (12.978 seconds)
发现如此创建的表，除了rowkey能查出来外，其他的查询均没有数据 此时创建的phoenix表看不到hbase中已有的数据，但若hbase表有新增数据或对phoenix表进行增删改查则可以看到hbase里的增量数据， 这个是因为phoenix4.10版本之后对列映射编码规则做了修改
0: jdbc:phoenix:server2:2118> drop table user.dbuser;
No rows affected (2.683 seconds)
0: jdbc:phoenix:server2:2118> !table
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
| TABLE_CAT | TABLE_SCHEM | TABLE_NAME | TABLE_TYPE | REMARKS | TYPE_NAME | SELF_REFERENCING_COL_NAME | REF_GENERATION | INDEX_STATE | IMMUTABLE_ROWS | SALT_BUCKETS | MULTI_TENANT | VIEW_STATEMENT | VIEW_TYPE | INDEX_TYPE | TRANSACTIONAL | IS_NAMESPACE_MAPPED | G |
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
| | SYSTEM | CATALOG | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | FUNCTION | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | LOG | SYSTEM TABLE | | | | | | true | 32 | false | | | | false | true | n |
| | SYSTEM | SEQUENCE | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | STATS | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | | WB | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
| | BANK_BU | TRANSFER_RECORD | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
| | USER | TEXTUSER | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
运行程序生成数据
加载hdfs数据到hbase
hbase org.apache.hadoop.hbase.tool.LoadIncrementalHFiles /hbasetest/user/dbuser/data USER:DBUSER
创建表，增加column_encoded_bytes=0 设置，具体如下
https://phoenix.apache.org/columnencoding.html
CREATE TABLE IF NOT EXISTS USER.DBUSER(
rk varchar primary key,
C1.id varchar,
C1.username varchar,
C1.password varchar,
C1.phone varchar,
C1.email varchar,
C1.createday varchar
)
column_encoded_bytes=0;
0: jdbc:phoenix:server2:2118> CREATE TABLE IF NOT EXISTS USER.DBUSER(
. . . . . . . . . . . . . . > rk varchar primary key,
. . . . . . . . . . . . . . > C1.id varchar,
. . . . . . . . . . . . . . > C1.username varchar,
. . . . . . . . . . . . . . > C1.password varchar,
. . . . . . . . . . . . . . > C1.phone varchar,
. . . . . . . . . . . . . . > C1.email varchar,
. . . . . . . . . . . . . . > C1.createday varchar
. . . . . . . . . . . . . . > )
. . . . . . . . . . . . . . > column_encoded_bytes=0;
0: jdbc:phoenix:server2:2118> !table
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
| TABLE_CAT | TABLE_SCHEM | TABLE_NAME | TABLE_TYPE | REMARKS | TYPE_NAME | SELF_REFERENCING_COL_NAME | REF_GENERATION | INDEX_STATE | IMMUTABLE_ROWS | SALT_BUCKETS | MULTI_TENANT | VIEW_STATEMENT | VIEW_TYPE | INDEX_TYPE | TRANSACTIONAL | IS_NAMESPACE_MAPPED | G |
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
| | SYSTEM | CATALOG | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | FUNCTION | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | LOG | SYSTEM TABLE | | | | | | true | 32 | false | | | | false | true | n |
| | SYSTEM | SEQUENCE | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | SYSTEM | STATS | SYSTEM TABLE | | | | | | false | null | false | | | | false | true | n |
| | USER | DBUSER | TABLE | | | | | | false | null | false | | | | false | true | n |
| | | WB | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
| | BANK_BU | TRANSFER_RECORD | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
| | USER | TEXTUSER | VIEW | | | | | | false | null | false | | MAPPED | | false | true | n |
+------------+--------------+------------------+---------------+----------+------------+----------------------------+-----------------+--------------+-----------------+---------------+---------------+-----------------+------------+-------------+----------------+----------------------+---+
0: jdbc:phoenix:server2:2118> select * from user.dbuser where rk = '26cfac1e_728232503_alan2452_13977776789_2021-12-28 00:00:00.0';
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
| RK | ID | USERNAME | PASSWORD | PHONE | EMAIL | CREATEDAY |
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
| 26cfac1e_728232503_alan2452_13977776789_2021-12-28 00:00:00.0 | 728232503 | alan2452 | 337518 | 13977776789 | alan.chan.chn@163.com | 2021-12-28 00:00:00.0 |
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
1 row selected (0.025 seconds)
0: jdbc:phoenix:server2:2118> select count(*) from user.dbuser;
+-----------+
| COUNT(1) |
+-----------+
| 12606948 |
+-----------+
1 row selected (13.648 seconds)
0: jdbc:phoenix:server2:2118> select * from user.dbuser where id = '728232503';
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
| RK | ID | USERNAME | PASSWORD | PHONE | EMAIL | CREATEDAY |
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
| 26cfac1e_728232503_alan2452_13977776789_2021-12-28 00:00:00.0 | 728232503 | alan2452 | 337518 | 13977776789 | alan.chan.chn@163.com | 2021-12-28 00:00:00.0 |
+----------------------------------------------------------------+------------+-----------+-----------+--------------+------------------------+------------------------+
1 row selected (19.569 seconds)
至此已经完成了从数据库到hbase的数据导入，并且可以使用phoenix客户端进行查询。如果需要使用join等相关操作，则需要建立二级索引进行操作
