Built with Tutify.io

    AIStudyLabs

    PySpark Read and Write SQL Server Table

    PySpark Read and Write SQL Server Table

    Can we connect to SQL Server (mssql) from PySpark and read the table into PySpark DataFrame and write the DataFrame to the SQL table?

    In order to connect to SQL-server (mssql) from PySpark, you would need the following. Make sure you have these details before you read or write to the SQL server. The driver I am going to use in this article is com.microsoft.sqlserver.jdbc.spark

    Steps to connect PySpark to SQL Server and Read and write Table.

    <a id="h-1-pyspark-connector-for-sql-server-mssql"></a>

    1. PySpark Connector for SQL Server (mssql)

    PySpark connector for SQL server works with both

    Following are the available connectors, based on the PySpark version you are using, you should choose the appropriate version.

    | Spark Version | Maven dependency | | --- | --- | | Spark 2.4.x | groupeId : com.microsoft.azure artifactId : spark-mssql-connector version : 1.0.2 | | Spark 3.0.x | groupeId : com.microsoft.azure artifactId : spark-mssql-connector_2.12 version : 1.1.0 | | Spark 3.1.x | groupeId : com.microsoft.azure spark-mssql-connector_2.12 version : 1.2.0 |

    You can find the all available PySpark Microsoft SQL (Mssql) connectors at https://search.maven.org/search?q=spark-mssql-connector

    Download the version as per your need and keep it in the current directory.

    3. Create SparkSession & DataFrame

    Creating a SparkSession is a basic step to work with PySpark hence, first, let's create a SparkSession and construct a sample DataFrame with columns id, name, age and gender. In the below sections, I will use this DataFrame to write into SQL Server table and read from it.

    
# Imports
from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder
           .appName('AIStudyLabs.com')
           .config("spark.jars", "spark-mssql-connector_2.12.jar")
           .getOrCreate()

# Create DataFrame 
columns = ["id", "name","age","gender"]
data = [(1, "James",30,"M"), (2, "Ann",40,"F"),
    (3, "Jeff",41,"M"),(4, "Jennifer",20,"F")]

sampleDF = spark.sparkContext.parallelize(data).toDF(columns)

    4. Write PySpark DataFrame to SQL Server Table

    You would be required to provide the SQL Server details while writing PySpark DataFrame to SQL table. By using the format() specify the driver, this driver is defined in the connector dependency.

    Some points to note while writing.

    
# Write to SQL Table
sampleDF.write \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .mode("overwrite") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "employee") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .save()

    Here, used mode("overwrite") means if the table already exists with rows, overwrite the table with the rows from the DataFrame. The overwrite mode first drops the table if it already exists in the database.

    5. Read SQL Server Table to PySpark DataFrame

    Similar to writing, with read() also you need to provide the driver and the SQL connection details. In the below example, I am reading a table employee from the database emp to the DataFrame.

    
# Read from SQL Table
df = spark.read \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "employee") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .load()

df.show()

    Yields below output.

    6. Select Specific Columns to Read

    In the above example, it reads the entire table into PySpark DataFrame. Sometimes you may not be required to select the entire table, so to select the specific columns, specify the query you wanted to select with dbtable option.

    
# Read from SQL Table
df = spark.read \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "select id,age from employee where gender='M'") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .load()

df.show()

    7. Append Table

    Use spark.write.mode("append") to append the rows to the existing SQL Server table.

    
# Write to SQL Table
sampleDF.write \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .mode("append") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "employee") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .save()

    8. Complete Example

    Following is the complete example of how to connect to SQL server or Azure SQL from PySpark and read/write the table.

    
# Imports
from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder
           .appName('AIStudyLabs.com')
           .config("spark.jars", "spark-mssql-connector_2.12.jar")
           .getOrCreate()

# Create DataFrame 
columns = ["id", "name","age","gender"]
data = [(1, "James",30,"M"), (2, "Ann",40,"F"),
    (3, "Jeff",41,"M"),(4, "Jennifer",20,"F")]

sampleDF = spark.sparkContext.parallelize(data).toDF(columns)

# Write to SQL Table
sampleDF.write \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .mode("overwrite") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "employee") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .save()

# Read from SQL Table
df = spark.read \
  .format("com.microsoft.sqlserver.jdbc.spark") \
  .option("url", "jdbc:sqlserver://{SERVER_ADDR};databaseName=emp;") \
  .option("dbtable", "employee") \
  .option("user", "replace_user_name") \
  .option("password", "replace_password") \
  .load()

df.show()

    Conclusion

    In this article, you have learned how to connect to an SQL server from PySpark and write the DataFrame to SQL table and read the table into DataFrame with examples.

    Related Articles

    References