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Help Center/ Data Lake Insight/ Developer Guide/ Spark Jar Jobs/ Using Spark Jobs to Access Data Sources of Datasource Connections/ Connecting to HBase/ PySpark Example Code

PySpark 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.

Code implementation

Import dependency packages.

        
           from __future__ import print_function
from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
from pyspark.sql import SparkSession

Create a session.

        
           sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()

Connecting to data sources through SQL APIs

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 testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
    'ZKHost' = '192.168.0.189:2181',\
    'TableName' = 'hbtest',\
    'RowKey' = 'id:5',\
    'Cols' = 'location:info.location,city:detail.city')")

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

sparkSession.sql(
    "CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
    'ZKHost' = '192.168.0.189:2181',\
    'TableName' = 'hbtest',\
    'RowKey' = 'id:5',\
    'Cols' = 'location:info.location,city:detail.city',\
    'krb5conf' = './krb5.conf',\
    'keytab'='./user.keytab',\
    'principal' ='krbtest')")

If Kerberos authentication is enabled, you need to set three more parameters, as listed in Table 1.

**Table 1** Description
Parameter and Value	Description
'krb5conf' = './krb5.conf'	Path of the krb5.conf file.
'keytab'='./user.keytab'	Path of the keytab file.
'principal' ='krbtest'	Authentication username.

For details about how to obtain the krb5.conf and keytab files, see Completing Configurations for Enabling Kerberos Authentication.

NOTE:

For details about parameters in the table, see Table 1.

Import data to HBase.

sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")

Read data from HBase.

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

Connecting to data sources through DataFrame APIs

Create a table to connect to an HBase data source.

        
           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')")

NOTE:

For details about the ZKHost, RowKey, and Cols parameters, see Table 1.
TableName: Name of a table in the CloudTable file. If no table name exists, the system automatically creates one.

Construct a schema.

        
           schema = StructType([StructField("id", StringType()),\                     
                     StructField("location", StringType()),\                     
                     StructField("city", StringType()),\                     
                     StructField("booleanf", BooleanType()),\                     
                     StructField("shortf", ShortType()),\                     
                     StructField("intf", IntegerType()),\                     
                     StructField("longf", LongType()),\                     
                     StructField("floatf", FloatType()),\                     
                     StructField("doublef", DoubleType())])

Set data.

        
           dataList = sparkSession.sparkContext.parallelize([("11111", "aaa", "aaa", False, 4, 3, 23, 2.3, 2.34)])

Create a DataFrame.

        
           dataFrame = sparkSession.createDataFrame(dataList, schema)

Import data to HBase.

        
           dataFrame.write.insertInto("test_hbase")

Read data from HBase.

        
         
           
           // Set cross-source connection parameters
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"
ZKHost = "cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
          cloudtable-cf82-zk1- WY09px9l.cloudtable.com:2181"

// select
jdbcDF = sparkSession.read.schema(schema)\
                 .format("hbase")\
                 .option("ZKHost",ZKHost)\
                 .option("TableName",TableName)\
                 .option("RowKey",RowKey)\
                 .option("Cols",Cols)\
                 .load()
jdbcDF.filter("id = '12333' or id='11111'").show()

          

        
       

NOTE:

The length of id, location, and city parameter is limited. When inserting data, you must set the data values based on the required length. Otherwise, an encoding format error occurs during query.

View the operation result.

Submitting a Spark job
1. Upload the Python code file to the OBS bucket.
2. (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 shows how to add the files.
  Figure 1 Adding dependency files
3. 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, specify the 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

Connecting to MRS HBase through SQL APIs

Sample code when Kerberos authentication is disabled

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
from pyspark.sql import SparkSession

if __name__ == "__main__":
  # Create a SparkSession session.    
  sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()

  sparkSession.sql(
    "CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
    'ZKHost' = '192.168.0.189:2181',\
    'TableName' = 'hbtest',\
    'RowKey' = 'id:5',\
    'Cols' = 'location:info.location,city:detail.city')")


  sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")

  sparkSession.sql("select * from testhbase").show()
  # close session    
  sparkSession.stop()

Sample code when Kerberos authentication is enabled

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark import SparkFiles
from pyspark.sql import SparkSession
import shutil
import time
import os

if __name__ == "__main__":
    # Create a SparkSession session.
    sparkSession = SparkSession.builder.appName("Test_HBase_SparkSql_Kerberos").getOrCreate()
    sc = sparkSession.sparkContext
    time.sleep(10)

    krb5_startfile = SparkFiles.get("krb5.conf")
    keytab_startfile = SparkFiles.get("user.keytab")
    path_user = os.getcwd()
    krb5_endfile = path_user + "/" + "krb5.conf"
    keytab_endfile = path_user + "/" + "user.keytab"
    shutil.copy(krb5_startfile, krb5_endfile)
    shutil.copy(keytab_startfile, keytab_endfile)
    time.sleep(20)

    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') ")

      sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")

    sparkSession.sql("select * from testhbase").show()
    # close session
    sparkSession.stop()

Connecting to HBase through DataFrame APIs

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
from pyspark.sql import SparkSession

if __name__ == "__main__":
  # Create a SparkSession session.    
  sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()

  # Createa data table for DLI-associated ct    
  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')")

  # Create a DataFrame and initialize the DataFrame data.    
  dataList = sparkSession.sparkContext.parallelize([("11111", "aaa", "aaa", False, 4, 3, 23, 2.3, 2.34)])

  # Setting schema    
  schema = StructType([StructField("id", StringType()), 
                       StructField("location", StringType()), 
                       StructField("city", StringType()),                         
                       StructField("booleanf", BooleanType()),                        
                       StructField("shortf", ShortType()),                     
                       StructField("intf", IntegerType()),                 
                       StructField("longf", LongType()),                   
                       StructField("floatf", FloatType()),              
                       StructField("doublef", DoubleType())])

  # Create a DataFrame from RDD and schema    
  dataFrame = sparkSession.createDataFrame(dataList, schema)

  # Write data to the cloudtable-hbase    
  dataFrame.write.insertInto("test_hbase")

  # Set cross-source connection parameters    
  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"
  ZKHost = "cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
            cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
  # Read data on CloudTable-HBase    
  jdbcDF = sparkSession.read.schema(schema)\
                       .format("hbase")\
                       .option("ZKHost", ZKHost)\
                       .option("TableName",TableName)\
                       .option("RowKey", RowKey)\
                       .option("Cols", Cols)\
                       .load()    
  jdbcDF.filter("id = '12333' or id='11111'").show()

  # close session    
  sparkSession.stop()

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