Spark连接HBase进行读写相关操作【CDH5.7.X】

文章内容：

1. 通过Spark读取HBase的表并通过转换RDD

2.Spark连接HBase进行表写入操作

版本：

CDH集群版本：CDH5.7.1

Spark版本：spark-1.6.0+cdh5.7.1+193                                                                                                               
HBase版本：hbase-1.2.0+cdh5.7.1+142                                       

准备工作：

1.集群环境准备

已经安装CDH5.7.X集群

集群安装Spark和HBase相关组件

2.开发环境准备

Maven工程需要添加Spark Core和HBase依赖

a.HBase的Maven依赖

注意其中必须去除掉servlet-api的依赖否则在SparkContext初始化的时候会出现servlet-api版本冲突异常

<dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-annotations</artifactId>    <version>${hbase.version}</version></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-common</artifactId>    <version>${hbase.version}</version>    <exclusions>        <exclusion>            <groupId>javax.servlet</groupId>            <artifactId>servlet-api</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api</artifactId>        </exclusion>        <exclusion>            <groupId>javax.servlet.jsp</groupId>            <artifactId>jsp-api</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api-2.5</artifactId>        </exclusion>    </exclusions></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-client</artifactId>    <version>${hbase.version}</version></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-server</artifactId>    <version>${hbase.version}</version>    <exclusions>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>jsp-api-2.1</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api-2.5</artifactId>        </exclusion>    </exclusions></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-hadoop-compat</artifactId>    <version>${hbase.version}</version></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-prefix-tree</artifactId>    <version>${hbase.version}</version></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-protocol</artifactId>    <version>${hbase.version}</version></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-rest</artifactId>    <version>${hbase.version}</version>    <exclusions>        <exclusion>            <groupId>javax.servlet</groupId>            <artifactId>*</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api-2.5</artifactId>        </exclusion>    </exclusions></dependency><dependency>    <groupId>org.apache.hbase</groupId>    <artifactId>hbase-thrift</artifactId>    <version>${hbase.version}</version>    <exclusions>        <exclusion>            <groupId>javax.servlet</groupId>            <artifactId>*</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api</artifactId>        </exclusion>        <exclusion>            <groupId>org.mortbay.jetty</groupId>            <artifactId>servlet-api-2.5</artifactId>        </exclusion>    </exclusions></dependency>

b.Spark Core的Maven依赖

<!-- spark core -->        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-core_2.10</artifactId>            <version>${spark.version}</version>            <exclusions>                <exclusion>                    <groupId>javax.servlet</groupId>                    <artifactId>*</artifactId>                </exclusion>                <exclusion>                    <groupId>org.slf4j</groupId>                    <artifactId>slf4j-api</artifactId>                </exclusion>            </exclusions>        </dependency>

3. 数据准备

HBase表通过hbase shell的方式创建test表,put几条测试数据

create 'test','cf'

put 'test','row1','cf:name','zs'

put 'test','row1','cf:age','23'

put 'test','row2','cf:name','we'

put 'test','row2','cf:age','15'

put 'test','row3','cf:name','ls'

put 'test','row3','cf:age','42'

put 'test','row4','cf:name','zs'

put 'test','row4','cf:age','75'

4. Spark读取HBase表

第一步：创建HBaseConf

//导入相关包

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase.TableNotFoundException
import org.apache.hadoop.hbase.client.{Put, Result}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat, TableOutputFormat}
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext

import org.apache.spark.SparkConf

//创建HBaseConf

val conf = HBaseConfiguration.create();

第二步：HBaseConf设置输入表"test"

conf.set(TableInputFormat.INPUT_TABLE, "test")

第三步：创建SparkContext

//注意本地设置为Spark本地运行，如果集群模式运行把setMaster("local")去掉

val sparkConf = new SparkConf().setAppName("my app").setMaster("local")

//创建SparkContext

val sc = new SparkContext(sparkConf)

第四步：读取HBaseConf转换RDD

//通过newAPIHadoopRDD方法读取hbase表转换为RDD

val hbaseRDD =  sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
      classOf[ImmutableBytesWritable],
      classOf[Result])

第五步：对hbaseRDD进行操作

//HBase 对test表进行统计

val testCount = hbaseRDD.count()

println("count :" + testCount) //4

 //循环每一行HBase的数据
    hBaseRDD.foreach{case (_,result) =>
      //获取行键
      val key = Bytes.toString(result.getRow)
      //通过列族和列名获取列
      val name = Bytes.toString(result.getValue("cf".getBytes,"name".getBytes))
      val age = Bytes.toInt(result.getValue("cf".getBytes,"age".getBytes))
      println("Row key:" + key + " ,name:" + name + ",age :" + age)
    }

//Row key: row1,name:zs,age:23

//....

5. Spark对HBase表进行写入

第一步：创建HBaseConf

//导入相关包

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase.TableNotFoundException
import org.apache.hadoop.hbase.client.{Put, Result}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.{TableInputFormat, TableOutputFormat}
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext

import org.apache.spark.SparkConf

//创建HBaseConf

val conf = HBaseConfiguration.create();

第二步：HBaseConf设置输入表"test"

conf.set(TableInputFormat.INPUT_TABLE, "test")

第三步：创建SparkContext

//注意本地设置为Spark本地运行，如果集群模式运行把setMaster("local")去掉

val sparkConf = new SparkConf().setAppName("my app").setMaster("local")

//创建SparkContext

val sc = new SparkContext(conf)

第四步：创建Job，并获取Configuration

//创建Job

 val job = new Job(sc.hadoopConfiguration)

//设置输出的KeyClass

 job.setOutputKeyClass(classOf[ImmutableBytesWritable])

//设置输出ValueClass
 job.setOutputValueClass(classOf[Result])

//设置OutputFormat
 job.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])

val hbaseWriterConf = job.getConfiguration()

第五步：创建写入HBase表的数据的RDD

//通过Array数组创建3条数据的RDD

 val inDataRDD = sc.makeRDD(Array("row5,Anm,15","row6,Lily,16","row7,Mak,29"))

//RDD转换成可以进行HBase表数据写入的格式的RDD
    val hbaseWriterRdd = inDataRDD.map(_.split(',')).map{arr=>{
      val put = new Put(Bytes.toBytes(arr(0)))
      put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("name"),Bytes.toBytes(arr(1)))
      put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("age"),Bytes.toBytes(arr(2).toInt))
      (new ImmutableBytesWritable, put)
    }}

第六步：数据写入HBase表

//调用saveAsNewAPIHadoopDataset写入HBase数据表

hbaseWriterRdd.saveAsNewAPIHadoopDataset(hbaseWriterConf)

第七步：关闭SparkContext

//自从写入HBase表操作完成，关闭SparkContest

sc.stop()

7. HBase写入数据检查

通过hbase shell的scan命令进行查看,如果出现下图情况，证明数据已经写入正常

scan 'test'