spark-toHive、tomysql、tohbase

一、Spark to Hive
1、打开虚拟机，将Hive的配置文件（conf下的hive-site.xml)拷贝到Spark的配置文件目录下(conf)，软拷贝硬拷贝皆可以

ln -s /opt/software/hadoop/hive110/conf/hive-site.xml /opt/software/hadoop/spark/conf/hive-site.xml

2、拷贝jar包（mysql-connector-java-5.1.32.jar 拷贝到Spark的jars目录下

cp /opt/software/hadoop/hive110/lib/mysql-connector-java-5.1.32.jar /opt/software/hadoop/spark/jars/

3、启动Spark-shell

spark-shell --jars /opt/software/hadoop/spark/jars/mysql-connector-java-5.1.32.jar

4、在Hive中随便建一张表

5、在Spark SQL中插入数据，此处直接查询数据库做演示

scala> spark.sql("show databases").show()

6、在Hive中查询数据即可看到在Spark中的操作，说明已接通

7、IDEA中集成

Maven搜索Spark-Hive，选第一个Spark Project Hive » [2.4.4]，找到对应的scala版本号，导入对应的依赖

<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-hive -->
<dependency>
  <groupId>org.apache.spark</groupId>
  <artifactId>spark-hive_2.11</artifactId>
  <version>2.4.4</version>
</dependency>
<!-- mysql-connector-java -->
<dependency>
  <groupId>mysql</groupId>
  <artifactId>mysql-connector-java</artifactId>
  <version>5.1.31</version>
</dependency>

8、把hive110/conf/hive-site.xml文件拷贝到自建的resources资源包中，将日志输出文件导入（可不导）

把第一个property中的hive仓库路径添加hdfs端口hdfs://192.168.29.130:9000

<property>
<name>hive.metastore.warehouse.dir</name>
<value>hdfs://192.168.29.130:9000/opt/software/hadoop/hive110/warehouse</value>
</property>

9、mysql中创建Hive账号并赋予权限

mysql中输入以下命令：

grant all on *.* to 'root'@'%' identified by 'kb10';
grant all on *.* to 'root'@'localhost' identified by 'kb10';
flush privileges;

10、IDEA代码如下，即可连接成功

object SparkToHive{
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.master(“local[4]”)
.appName(this.getClass.getSimpleName)
.enableHiveSupport()
.getOrCreate()
spark.sql(“show databases”).show()
}
}

做完以上步骤后，在回到虚拟机下使用beeline -u jdbc:hive2://192.168.29.130:10000命令时，启动的是spark内置的beeline，因此无法启动，此时需要进入hive/bin目录下用bash启动即可

二、Spark to MySQLl
1、导入依赖

 <dependency>
  <groupId>org.apache.spark</groupId>
  <artifactId>spark-sql_2.11</artifactId>
  <version>${spark.version}</version>
</dependency>
<dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>5.1.32</version>
</dependency>

2、创建类

object SparkToSql{
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[4]")
      .appName(this.getClass.getSimpleName)
      .getOrCreate()
    //最后面是数据库名
    val url = "jdbc:mysql://192.168.29.130:3306/exam"
    val tableName = "cron_test"//表名
    // 设置连接用户、密码、数据库驱动类
    val prop = new java.util.Properties
    prop.setProperty("user","root")
    prop.setProperty("password","kb10")
    prop.setProperty("driver","com.mysql.jdbc.Driver")
    // 取得该表数据
    val jdbcDF = spark.read.jdbc(url,tableName,prop)
    jdbcDF.show
    //DF存为新的表
    jdbcDF.write.mode("append").jdbc(url,"t2",prop)
  }
}

三、Spark to Hbase
1、导入依赖

	<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>
	</dependency>

2、创建类
读操作

object SparkToHbase{
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
    .builder()
    .appName(this.getClass.getName)
    .master("local[*]")
    .getOrCreate()
    
    val sc= spark.sparkContext
    //" NAMESPACE : TABLE_NAME "
    val tablename=" NAMESPACE : TABLE_NAME "
    val conf = HBaseConfiguration.create()

    //" IP_ADDRESS " 处，输入对应的虚机的IP地址
    conf.set("hbase.zookeeper.quorum"," IP_ADDRESS ")
    
    conf.set("hbase.zookeeper.property.clientPort","2181")
    conf.set(TableInputFormat.INPUT_TABLE,tablename)

    val rdd1= sc.newAPIHadoopRDD(conf,classOf[TableInputFormat],
      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
      classOf[org.apache.hadoop.hbase.client.Result]
    ).cache()

    println("count="+rdd1.count())
    import spark.implicits._
    //遍历输出
    rdd1.foreach({case (_,result) =>
      //通过result.getRow来获取行键
      val key = Bytes.toString(result.getRow)
      //通过result.getValue("列簇","列名")来获取值
      //需要使用getBytes将字符流转化为字节流
      val buynum = Bytes.toString(result.getValue("列簇".getBytes,"列名".getBytes))
      val cust_id = Bytes.toString(result.getValue("列簇".getBytes,"列名".getBytes))
      val dt = Bytes.toString(result.getValue("列簇".getBytes,"列名".getBytes))
      val good_id = Bytes.toString(result.getValue("列簇".getBytes,"列名".getBytes))

      //举个栗子~~
      println("Row key:"+key+" buynum:"+buynum+" cust_id:"+cust_id+" dt:"+dt+" good_id:"+good_id)
    })
}

写操作

object SparkToHBase {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
    .builder()
    .appName(this.getClass.getName)
    .master("local[*]")
    .getOrCreate()
    
    val sc= spark.sparkContext
    //" NAMESPACE : TABLE_NAME "
    val tablename=" NAMESPACE : TABLE_NAME "
    val conf = HBaseConfiguration.create()

    //" IP_ADDRESS " 处，输入对应的虚机的IP地址
    conf.set("hbase.zookeeper.quorum"," IP_ADDRESS ")
    
    conf.set("hbase.zookeeper.property.clientPort","2181")
    conf.set(TableInputFormat.OUTPUT_TABLE,tablename)

    val job = new JobConf(conf)
    job.setOutputFormat(classOf[TableOutputFormat])

    val indataRDD = sc.makeRDD(Array("11,1,6,20200807,7"))

    val rdd = indataRDD.map(_.split(",")).map{arr=>
      val put = new Put(Bytes.toBytes(arr(0)))
      //通过addColumn("列簇","列名")来将数据写入
      put.addColumn(Bytes.toBytes("one"),Bytes.toBytes("buynum"),Bytes.toBytes(arr(1)))
      put.addColumn(Bytes.toBytes("one"),Bytes.toBytes("cust_id"),Bytes.toBytes(arr(2)))
      put.addColumn(Bytes.toBytes("one"),Bytes.toBytes("dt"),Bytes.toBytes(arr(3)))
      put.addColumn(Bytes.toBytes("one"),Bytes.toBytes("good_id"),Bytes.toBytes(arr(4)))
      (new ImmutableBytesWritable,put)
    }
    rdd.saveAsHadoopDataset(job)
}

最后

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