5).Spark-core读写HBase的数据
- 配置Spark
把HBase的lib目录下的一些jar文件拷贝到Spark中,这些都是编程时需要引入的jar包,需要拷贝的jar文件包括:所有hbase开头的jar文件、guava-12.0.1.jar、htrace-core-3.1.0-incubating.jar和protobuf-java-2.5.0.jar
<!--此时需要的Maven依赖 -->
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>wyq</groupId>
<artifactId>ScalaReadHBase</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<spark.version>2.0.0</spark.version>
<scala.version>2.11</scala.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-mllib_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-common -->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-common</artifactId>
<version>1.2.11</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-client -->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.2.11</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hbase/hbase-server -->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.2.11</version>
</dependency>
</dependencies>
<build>
<plugins>
<!--当前插件是用来让maven能够编译、测试、运行scala项目的 -->
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<version>2.15.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
# 进入spark的安装目录下,有一个专门放jar包的路径(在spark-2.10之后)
cd /usr/local/spark/jars
# 将HBase的jar包拷贝到当前目录下
cp /usr/local/hbase/lib/hbase*.jar ./ # 将hbase开头的所有jar包全部导入到jars目录下
cp /usr/local/hbase/lib/guava-12.0.1.jar ./
cp /usr/local/hbase/lib/htrace-core-3.1.0-incubating.jar ./
cp /usr/local/hbase/lib/protobuf-java-2.5.0.jar ./
cp /usr/local/hbase/lib/metrics-core-2.2.0.jar ./
- 编写Spark程序并读取HBase数据
import org.apache.hadoop.hbase._
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
object SparkOperateHBase {
def main(args: Array[String]) {
// 创建HBase的配置文件对象
val conf = HBaseConfiguration.create()
// 初始化一个SparkContext对象,传入对应的SparkConf()对象
val sc = new SparkContext(new SparkConf())
// 设置查询的表名
conf.set(TableInputFormat.INPUT_TABLE, "student")
// 传入配置文件对象,其余的是固定的
val stuRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
classOf[ImmutableBytesWritable],
classOf[Result])
// 查询当前数据集中有多少行数据
val count = stuRDD.count()
// 测试打印输出
println("Students RDD Count:" + count)
// 将数据持久化到内存,一般是直接调用cache()方法,它会默认调用persist(MEMORY_ONLY);防止在这之前调用了行动操作,然后会从头执行,在之后的行动操作中可以直接调用内存中保存的变量,而不用再从头执行
stuRDD.cache()
// 遍历输出
/*
1. case (_,result):中的"_"是占位符,表示其他的参数,是没有实际作用,都是给系统的变量,我们只用后边的result变量,其中保存了HBase中的数据
2.result.getRow:取出所有的行键
3.result.getValue(列族,某一列);
参数必须是字节数组对象,可以使用 getBytes方法转换为
*/
stuRDD.foreach({ case (_,result) =>
val key = Bytes.toString(result.getRow)
val name = Bytes.toString(result.getValue("info".getBytes,"name".getBytes))
val gender = Bytes.toString(result.getValue("info".getBytes,"gender".getBytes))
val age = Bytes.toString(result.getValue("info".getBytes,"age".getBytes))
println("Row key:"+key+" Name:"+name+" Gender:"+gender+" Age:"+age)
})
}
}
-
打包运行
可以使用任意已经测试通过的打包方式进行打包,然后在运行的时候必须使用“--driver-class-path”参数指定依赖JAR包的路径,而且必须把“/usr/local/hbase/conf”也加到路径中
spark-submit \
--class SparkOperateHBase \
/usr/local/spark/mycode/simple-project_2.11-1.0.jar
# 该命令执行的时候会自动的读取jars目录下的所有包,如果是在jars文件夹下又单独建立的文件存储hbase的jar包,则需要使用以下参数进行指定
--driver-class-path /usr/local/spark/jars/hbase/*:/usr/local/hbase/conf
-
编写Spark程序并将数据写入HBase
HBase写入数据格式.png
import org.apache.hadoop.hbase.client.{Put, Result}
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.{SparkConf, SparkContext}
object SparkWriteHBase {
def main(args: Array[String]): Unit = {
val conf = new SparkConf()
val sc = new SparkContext(conf)
val tableName = "student"
sc.hadoopConfiguration.set(TableOutputFormat.OUTPUT_TABLE, tableName)
val job = new Job(sc.hadoopConfiguration)
job.setOutputKeyClass(classOf[ImmutableBytesWritable])
job.setOutputValueClass(classOf[Result])
job.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])
val indataRDD = sc.makeRDD(Array("3,dufu,M,26","4,xingzhesun,M,27"))
val rdd = indataRDD.map(_.split(',')).map{arr=>{
// 行健的值
val put = new Put(Bytes.toBytes(arr(0)))
// info:name列的值
put.add(Bytes.toBytes("info"),Bytes.toBytes("name"),Bytes.toBytes(arr(1)))
// info:gender列的值
put.add(Bytes.toBytes("info"),Bytes.toBytes("gender"),Bytes.toBytes(arr(2)))
// info:age列的值
put.add(Bytes.toBytes("info"),Bytes.toBytes("age"),Bytes.toBytes(arr(3)))
// 表示返回数据类型,以及数据(put对象)
(new ImmutableBytesWritable, put)
}}
rdd.saveAsNewAPIHadoopDataset(job.getConfiguration())
}
}
-
查看HBase中数据表的内容
scan 'student'
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