更新时间:2022-10-28 GMT+08:00
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scala样例代码

开发说明

支持对接CloudTable的HBase和MRS的HBase。

  • 前提条件

    在DLI管理控制台上已完成创建跨源连接。具体操作请参考《数据湖探索用户指南》。

  • 构造依赖信息,创建SparkSession
    1. 导入依赖
      涉及到的mvn依赖库
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      <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.11</artifactId>
        <version>2.3.2</version>
      </dependency>
      
      import相关依赖包
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      import scala.collection.mutable
      import org.apache.spark.sql.{Row, SparkSession}
      import org.apache.spark.rdd.RDD
      import org.apache.spark.sql.types._
      
    2. 创建会话。
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      val sparkSession = SparkSession.builder().getOrCreate()
      
    3. 创建DLI跨源访问 HBase的关联表。
      • 如果对接的HBase集群未开启Kerberos认证,则样例代码参考如下。
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        sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
                'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
        	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
                          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
                          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
        	'TableName'='table_DupRowkey1',
        	'RowKey'='id:5,location:6,city:7',
            'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')"
        )
        
      • 如果对接的HBase集群开启了Kerberos认证,则样例代码参考如下。
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        sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
                'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
        	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
                          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
                          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
        	'TableName'='table_DupRowkey1',
        	'RowKey'='id:5,location:6,city:7', 
            'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef',
            'krb5conf'='./krb5.conf',
            'keytab' = './user.keytab',
        	'principal' = 'krbtest')")
        
      表1 创建表参数

      参数

      说明

      ZKHost

      HBase集群的ZK连接地址。

      获取ZK连接地址需要先创建跨源连接。具体操作请参考《数据湖探索用户指南》。

      • 访问CloudTable集群,填写ZK连接地址(内网)。
      • 访问MRS集群,填写ZK所在节点IP与ZK对外端口,格式为:"ZK_IP1:ZK_PORT1,ZK_IP2:ZK_PORT2"。
      说明:

      访问MRS集群,只支持创建增强型跨源连接并且需要配置主机信息,管理控制台操作请参考《数据湖探索用户指南》中的“增强型跨源连接”,相关API信息请参考创建增强型跨源连接

      RowKey

      指定作为rowkey的dli关联表字段,支持单rowkey与组合rowkey。单rowkey支持数值与String类型,不需要指定长度。组合rowkey仅支持String类型定长数据,格式为:属性名1:长度,属性名2:长度。

      Cols

      定义dli表字段和ct表字段之间的对应关系;其中,“:”前放dli表字段,冒号后放ct表信息,用“.”分隔ct表的列族和列名。

      例如:“dli表字段1:ct表.ct表字段1, dli表字段2:ct表.ct表字段2, dli表字段3:ct表.ct表字段3”。

      krb5conf

      开启Kerberos认证后的krb5.conf文件路径,格式为'./krb5.conf'。具体详情参考开启Kerberos认证时的相关配置文件

      keytab

      开启Kerberos认证后的keytab文件路径,格式为'./user.keytab'。具体详情参考开启Kerberos认证时的相关配置文件

      principal

      开启Kerberos认证后创建的用户名。

  • 通过SQL API访问
    1. 插入数据
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      sparkSession.sql("insert into test_hbase values('12345','abc','guiyang',false,null,3,23,2.3,2.34)")
      
    2. 查询数据
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      sparkSession.sql("select * from test_hbase").show ()
      

      返回结果:

  • 通过DataFrame API访问
    1. 构造schema
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      val attrId = new StructField("id",StringType)
      val location = new StructField("location",StringType)
      val city = new StructField("city",StringType)
      val booleanf = new StructField("booleanf",BooleanType)
      val shortf = new StructField("shortf",ShortType)
      val intf = new StructField("intf",IntegerType)
      val longf = new StructField("longf",LongType)
      val floatf = new StructField("floatf",FloatType)
      val doublef = new StructField("doublef",DoubleType)
      val attrs = Array(attrId, location,city,booleanf,shortf,intf,longf,floatf,doublef)
      
    2. 根据schema的类型构造数据
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      val mutableRow: Seq[Any] = Seq("12345","abc","guiyang",false,null,3,23,2.3,2.34)
      val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
      
    3. 导入数据到HBase
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      sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("test_hbase")
      
    4. 读取HBase上的数据
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      val map = new mutable.HashMap[String, String]()
      map("TableName") = "table_DupRowkey1"
      map("RowKey") = "id:5,location:6,city:7"
      map("Cols") = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
      map("ZKHost")="cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
                     cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
                     cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
      sparkSession.read.schema(new StructType(attrs)).format("hbase").options(map.toMap).load().show()
      

      返回结果:

  • 提交Spark作业
    1. 将写好的代码生成jar包,上传至DLI中。控制台操作请参考《数据湖探索用户指南》。API操作请参考《数据湖探索API参考》>《上传资源包》。
    2. 如果MRS集群开启了Kerberos认证,创建Spark作业时需要将krb5.conf和user.keytab文件添加到作业的其他依赖文件中,未开启Kerberos认证该步骤忽略。如图1所示:
      图1 添加依赖文件
    3. 在Spark作业编辑器中选择对应的Module模块并执行Spark作业。控制台操作请参考《数据湖探索用户指南》。API操作请参考《数据湖探索API参考》>《创建批处理作业》。
      • 提交作业时,需要指定Module模块,名称为:sys.datasource.hbase。
      • 通过控制台提交作业请参考《数据湖探索用户指南》中的“选择依赖资源参数说明”表说明。
      • 通过API提交作业请参考《数据湖探索API参考》>《创建批处理作业》中“表2-请求参数说明”关于“modules”参数的说明。

完整示例代码

  • Maven依赖
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    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.3.2</version>
    </dependency>
    
  • 通过SQL API访问
    • 未开启Kerberos认证样例代码
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      import org.apache.spark.sql.SparkSession
      
      object Test_SparkSql_HBase {
        def main(args: Array[String]): Unit = {
          // Create a SparkSession session.
          val sparkSession = SparkSession.builder().getOrCreate()
      
          /**
           * Create an association table for the DLI association Hbase table
           */
          sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
              'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
      	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
      	          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
      	          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
      	'TableName'='table_DupRowkey1',
      	'RowKey'='id:5,location:6,city:7',
      	'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,
      		longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')")
      
          //*****************************SQL model***********************************
          sparkSession.sql("insert into test_hbase values('12345','abc','guiyang',false,null,3,23,2.3,2.34)")
          sparkSession.sql("select * from test_hbase").collect()
      
          sparkSession.close()
        }
      }
      
    • 开启Kerberos认证样例代码
      import org.apache.spark.SparkFiles
      import org.apache.spark.sql.SparkSession
       
      import java.io.{File, FileInputStream, FileOutputStream}
       
      object Test_SparkSql_HBase_Kerberos {
       
        def copyFile2(Input:String)(OutPut:String): Unit ={
          val fis = new FileInputStream(Input)
          val fos = new FileOutputStream(OutPut)
          val buf = new Array[Byte](1024)
          var len = 0
          while ({len = fis.read(buf);len} != -1){
            fos.write(buf,0,len)
          }
          fos.close()
          fis.close()
        }
       
        def main(args: Array[String]): Unit = {
          // Create a SparkSession session.
          val sparkSession = SparkSession.builder().getOrCreate()
          val sc = sparkSession.sparkContext
          sc.addFile("krb5.conf的obs地址")
          sc.addFile("user.keytab的obs地址")
          Thread.sleep(10)
       
          val krb5_startfile = new File(SparkFiles.get("krb5.conf"))
          val keytab_startfile = new File(SparkFiles.get("user.keytab"))
          val path_user = System.getProperty("user.dir")
          val keytab_endfile = new File(path_user + "/" + keytab_startfile.getName)
          val krb5_endfile = new File(path_user + "/" + krb5_startfile.getName)
          println(keytab_endfile)
          println(krb5_endfile)
       
          var krbinput = SparkFiles.get("krb5.conf")
          var krboutput = path_user+"/krb5.conf"
          copyFile2(krbinput)(krboutput)
       
          var keytabinput = SparkFiles.get("user.keytab")
          var keytaboutput = path_user+"/user.keytab"
          copyFile2(keytabinput)(keytaboutput)
          Thread.sleep(10)
          /**
           * Create an association table for the DLI association Hbase table
           */
          sparkSession.sql("CREATE TABLE testhbase(id string,booleanf boolean,shortf short,intf int,longf long,floatf float,doublef double) " +
            "using hbase OPTIONS(" +
            "'ZKHost'='10.0.0.146:2181'," +
            "'TableName'='hbtest'," +
            "'RowKey'='id:100'," +
            "'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF2.longf,floatf:CF1.floatf,doublef:CF2.doublef'," +
            "'krb5conf'='" + path_user + "/krb5.conf'," +
            "'keytab'='" + path_user+ "/user.keytab'," +
            "'principal'='krbtest') ")
       
        //*****************************SQL model***********************************
        sparkSession.sql("insert into testhbase values('newtest',true,1,2,3,4,5)")
        val result = sparkSession.sql("select * from testhbase")
        result.show()
       
        sparkSession.close()
        }
      }
  • 通过DataFrame API访问
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    import scala.collection.mutable
    
    import org.apache.spark.sql.{Row, SparkSession}
    import org.apache.spark.rdd.RDD
    import org.apache.spark.sql.types._
    
    object Test_SparkSql_HBase {
      def main(args: Array[String]): Unit = {
        // Create a SparkSession session.
        val sparkSession = SparkSession.builder().getOrCreate()
    
        // Create an association table for the DLI association Hbase table
        sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
            'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
    	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
    	          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
    	          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
    	'TableName'='table_DupRowkey1',
    	'RowKey'='id:5,location:6,city:7',
    	'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')")
    
        //*****************************DataFrame model***********************************
        // Setting schema
        val attrId = new StructField("id",StringType)
        val location = new StructField("location",StringType)
        val city = new StructField("city",StringType)
        val booleanf = new StructField("booleanf",BooleanType)
        val shortf = new StructField("shortf",ShortType)
        val intf = new StructField("intf",IntegerType)
        val longf = new StructField("longf",LongType)
        val floatf = new StructField("floatf",FloatType)
        val doublef = new StructField("doublef",DoubleType)
        val attrs = Array(attrId, location,city,booleanf,shortf,intf,longf,floatf,doublef)
    
        // Populate data according to the type of schema
        val mutableRow: Seq[Any] = Seq("12345","abc","guiyang",false,null,3,23,2.3,2.34)
        val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
    
        // Import the constructed data into Hbase
        sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("test_hbase")
    
        // Read data on Hbase
        val map = new mutable.HashMap[String, String]()
        map("TableName") = "table_DupRowkey1"
        map("RowKey") = "id:5,location:6,city:7"
        map("Cols") = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
        map("ZKHost")="cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
                       cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
                       cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
        sparkSession.read.schema(new StructType(attrs)).format("hbase").options(map.toMap).load().collect()
    
        sparkSession.close()
      }
    }
    
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