PySpark Example Code

A datasource connection has been created and bound to a queue on the DLI management console.

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.

1. Import dependency packages.

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   from __future__ import print_function from pyspark.sql.types import StructType, StructField, IntegerType, StringType from pyspark.sql import SparkSession

2. Create a session.

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

1. Configure datasource connection parameters.

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   url = "jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306" dbtable = "test.customer" user = "root" password = "######" driver = "com.mysql.jdbc.Driver"

   For details about the parameters, see Table 1.

2. Set data.

   1	dataList = sparkSession.sparkContext.parallelize([(123, "Katie", 19)])

3. Configure the schema.

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   schema = StructType([StructField("id", IntegerType(), False),\ StructField("name", StringType(), False),\ StructField("age", IntegerType(), False)])

4. Create a DataFrame.

   1	dataFrame = sparkSession.createDataFrame(dataList, schema)

5. Save data to RDS.

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   dataFrame.write \ .format("jdbc") \ .option("url", url) \ .option("dbtable", dbtable) \ .option("user", user) \ .option("password", password) \ .option("driver", driver) \ .mode("Append") \ .save()

   

   The value of mode can be one of the following:

   • ErrorIfExis: If the data already exists, the system throws an exception.
   • Overwrite: If the data already exists, the original data will be overwritten.
   • Append: If the data already exists, the system saves the new data.
   • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
6. Read data from RDS.

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   jdbcDF = sparkSession.read \ .format("jdbc") \ .option("url", url) \ .option("dbtable", dbtable) \ .option("user", user) \ .option("password", password) \ .option("driver", driver) \ .load() jdbcDF.show()

7. View the operation result.

   

1. Create a table to connect to an RDS data source and set connection parameters.

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   sparkSession.sql( "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (\ 'url'='jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306',\ 'dbtable'='test.customer',\ 'user'='root',\ 'password'='######',\ 'driver'='com.mysql.jdbc.Driver')")

   For details about the parameters for creating a table, see Table 1.

2. Insert data.

   1	sparkSession.sql("insert into dli_to_rds values(3,'John',24)")

3. Query data.

   1 2	jdbcDF_after = sparkSession.sql("select * from dli_to_rds") jdbcDF_after.show()

4. View the operation result.

   

1. Upload the Python code file to DLI.

2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

3. After the Spark job is created, click Execute in the upper right corner of the console to submit the job. If the message "Spark job submitted successfully." is displayed, the Spark job is successfully submitted. You can view the status and logs of the submitted job on the Spark Jobs page.
   

   • The queue you select for creating a Spark job is the one bound when you create the datasource connection.
   • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.rds when you submit a job.
   • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

     spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

     spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
from pyspark.sql import SparkSession
if __name__ == "__main__":
  # Create a SparkSession session.    
  sparkSession = SparkSession.builder.appName("datasource-rds").getOrCreate()

  # Set cross-source connection parameters.   
  url = "jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306"
  dbtable = "test.customer"
  user = "root"
  password = "######"
  driver = "com.mysql.jdbc.Driver"

  # Create a DataFrame and initialize the DataFrame data.  
  dataList = sparkSession.sparkContext.parallelize([(123, "Katie", 19)])

  # Setting schema   
  schema = StructType([StructField("id", IntegerType(), False),\          
                       StructField("name", StringType(), False),\               
                       StructField("age", IntegerType(), False)])

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

  # Write data to the RDS.
  dataFrame.write \    
      .format("jdbc") \     
      .option("url", url) \     
      .option("dbtable", dbtable) \   
      .option("user", user) \    
      .option("password", password) \     
      .option("driver", driver) \     
      .mode("Append") \      
      .save()

  # Read data  
  jdbcDF = sparkSession.read \    
      .format("jdbc") \      
      .option("url", url) \   
      .option("dbtable", dbtable) \   
      .option("user", user) \   
      .option("password", password) \    
      .option("driver", driver) \  
      .load()  
  jdbcDF.show()

  # close session  
  sparkSession.stop()

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

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

  # Create a data table for DLI - associated RDS    
  sparkSession.sql(
       "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (\
       'url'='jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306',\
       'dbtable'='test.customer',\
       'user'='root',\
       'password'='######',\
       'driver'='com.mysql.jdbc.Driver')")

  # Insert data into the DLI data table   
  sparkSession.sql("insert into dli_to_rds values(3,'John',24)")

  # Read data from DLI data table    
  jdbcDF = sparkSession.sql("select * from dli_to_rds")   
  jdbcDF.show() 

  # close session  
  sparkSession.stop()