Example illustrating the best practices for organizing PySpark code with extensive SQL usage:
In this example, the SQL code is stored in separate SQL files within the sql/ directory. The data_preprocessing.sql and feature_engineering.sql files encapsulate the respective SQL operations.
The feature_engineering.sql file imports common functions from common_functions.sql within the utils/ subdirectory. This approach promotes code reusability and separates the common functions from the main SQL code.
The main.py script reads the SQL files and executes the SQL code using spark.sql(). By following this approach, you maintain a clean separation between the PySpark code and SQL logic, making it easier to manage and maintain your project.
Directory Structure:
project/
├── main.py
└── sql/
├── data_preprocessing.sql
├── feature_engineering.sql
└── utils/
├── common_functions.sql
└── feature_functions.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
-- Import common functions
:load sql/utils/common_functions.sql
-- Define the SQL operations for feature engineering
CREATE OR REPLACE TEMPORARY VIEW transformed_data AS
SELECT
column1,
column2,
-- Perform feature engineering operations here
-- Example using a custom feature function
feature_function(column1) AS new_feature
FROM
cleaned_data;
common_functions.sql (in sql/utils/):
-- common_functions.sql
-- Define common SQL functions used across multiple SQL files
CREATE OR REPLACE FUNCTION feature_function(column)
RETURNS feature
BEGIN
-- Implement the feature function logic here
-- ...
RETURN feature_value;
END;
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")
# Perform further operations on transformed_data or train models, etc.