an example of how you can modularize PySpark code that involves SQL operations using SQL files:
Create a directory structure for your project:
project/
├── main.py
└── sql/
├── data_preprocessing.sql
└── feature_engineering.sql
data_preprocessing.sql:
-- data_preprocessing.sql
-- Define the SQL operations for data preprocessing
CREATE OR REPLACE TEMPORARY VIEW cleaned_data AS
SELECT
column1,
column2,
-- Perform data preprocessing operations here
FROM
input_data;
feature_engineering.sql:
-- feature_engineering.sql
-- Define the SQL operations for feature engineering
CREATE OR REPLACE TEMPORARY VIEW transformed_data AS
SELECT
column1,
column2,
-- Perform feature engineering operations here
FROM
cleaned_data;
main.py:
from pyspark.sql import SparkSession
# Create SparkSession
spark = SparkSession.builder.getOrCreate()
# Load input data
input_data = spark.read.csv("data.csv", header=True, inferSchema=True)
input_data.createOrReplaceTempView("input_data")
# Load and execute SQL scripts
with open("sql/data_preprocessing.sql") as f:
data_preprocessing_sql = f.read()
spark.sql(data_preprocessing_sql)
with open("sql/feature_engineering.sql") as f:
feature_engineering_sql = f.read()
spark.sql(feature_engineering_sql)
# Retrieve the transformed data
transformed_data = spark.table("transformed_data")
In this example, the SQL code for data preprocessing and feature engineering is stored in separate SQL files (data_preprocessing.sql and feature_engineering.sql). The main.py script reads these SQL files, executes the SQL code using spark.sql(), and creates temporary views for intermediate data.
You can organize and structure your SQL files based on the operations you want to perform, and then include them in your PySpark code as needed. This approach separates the SQL logic from the Python code, making it easier to manage and maintain complex SQL operations within your PySpark project.