Scala Example Code

Updated on 2025-02-21 GMT+08:00

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Development Description

The CloudTable HBase and MRS HBase can be connected to DLI as data sources.

Prerequisites
A datasource connection has been created on the DLI management console. For details, see Enhanced Datasource Connections.

NOTE:

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

Constructing dependency information and creating a Spark session

Import dependencies.

Maven dependency involved

         
            <dependency>
  <groupId>org.apache.spark</groupId>
  <artifactId>spark-sql_2.11</artifactId>
  <version>2.3.2</version>
</dependency>

Import dependency packages.

         
            import scala.collection.mutable
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types._

Create a session.

        
           val sparkSession = SparkSession.builder().getOrCreate()

Create a table to connect to an HBase data source.

The sample code is applicable, if Kerberos authentication is disabled for the interconnected HBase cluster:

          
             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')"
)

The sample code is applicable, if Kerberos authentication is enabled for the interconnected HBase cluster:

          
             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')")

**Table 1** Parameters for creating a table
Parameter	Description
ZKHost	ZooKeeper IP address of the HBase cluster. You need to create a datasource connection first. For details, see Enhanced Datasource Connections. To access the CloudTable cluster, specify the ZooKeeper connection address in the internal network. To access the MRS cluster, specify the IP addresses and port numbers of the ZooKeeper nodes. The format is as follows: ZK_IP1:ZK_PORT1,ZK_IP2:ZK_PORT2
RowKey	Row key field of the table connected to DLI. The single and composite row keys are supported. A single row key can be of the numeric or string type. The length does not need to be specified. The composite row key supports only fixed-length data of the string type. The format is **attribute name 1:Length, attribute name 2:Length**.
Cols	Mapping between the fields in the DLI table and the CloudTable table. In this mapping, the DLI table field is placed before the colon (:) and the CloudTable table field is placed after the colon (:). The period (.) is used to separate the column family and column name of the CloudTable table. For example: DLI table field 1:CloudTable table.CloudTable table field 1, DLI table field 2:CloudTable table.CloudTable table field 2, DLI table field 3:CLoudTable table.CloudTable table field 3
krb5conf	Path of the krb5.conf file. This parameter is required when Kerberos authentication is enabled. The format is './krb5.conf'. For details, see Completing Configurations for Enabling Kerberos Authentication.
keytab	Path of the keytab file. This parameter is required when Kerberos authentication is enabled. The format is './user.keytab.'. For details, see Completing Configurations for Enabling Kerberos Authentication.
principal	Username created for Kerberos authentication.

Accessing a Data Source Using a SQL API

Insert data.

      
         sparkSession.sql("insert into test_hbase values('12345','abc','guiyang',false,null,3,23,2.3,2.34)")

Query data.

      
         sparkSession.sql("select * from test_hbase").show ()

Response

Accessing a Data Source Using a DataFrame API

Construct a 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)

Construct data based on the schema type.

      
         val mutableRow: Seq[Any] = Seq("12345","abc","city1",false,null,3,23,2.3,2.34)
val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)

Import data to HBase.

      
         sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("test_hbase")

Read data from 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().show()

Returned result:

Submitting a Spark Job

Generate a JAR file based on the code file and upload the JAR file to the OBS bucket.
(Optional) Add the krb5.conf and user.keytab files to other dependency files of the job when creating a Spark job in an MRS cluster with Kerberos authentication enabled. Skip this step if Kerberos authentication is not enabled for the cluster.
Figure 1 Adding dependency files
In the Spark job editor, select the corresponding dependency module and execute the Spark job.
NOTE:
- If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, set Module to sys.datasource.hbase when you submit a job.
- If the Spark version is 3.1.1 or later, you do not need to select a module. Configure Spark parameters (--conf).
  spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*
  
  spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*
- For how to submit a job on the console, see Table 3 "Parameters for selecting dependency resources" in Creating a Spark Job.
- For details about how to submit a job through an API, see the description of the modules parameter in Table 2 "Request parameters" in Creating a Batch Processing Job.

Complete Example Code

Maven dependency

      
         <dependency>
  <groupId>org.apache.spark</groupId>
  <artifactId>spark-sql_2.11</artifactId>
  <version>2.3.2</version>
</dependency>

Connecting to data sources through SQL APIs

Sample code when Kerberos authentication is disabled

        
         
           
           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','city1',false,null,3,23,2.3,2.34)")
    sparkSession.sql("select * from test_hbase").collect()

    sparkSession.close()
  }
}

          

        
       

Sample code when Kerberos authentication is enabled

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("OBS address of krb5.conf")
    sc.addFile("OBS address of user.keytab")
    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()
  }
}

Connecting to data sources through DataFrame APIs

      
       
         
         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","city1",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()
  }
}

        

      
     