Writing Safer and Cleaner Spark SQL with PySpark’s Parameterized Queries

When working with Spark SQL queries, using regular Python string interpolation can lead to security vulnerabilities and require extra steps like creating temporary views. PySpark offers a better solution with parameterized queries, which:

1. Protect against SQL injection
2. Allow using DataFrame objects directly in queries
3. Automatically handle date formatting
4. Provide a more expressive way to write SQL queries

Let’s explore this concept with a practical example.

First, we’ll create a Spark DataFrame to work with:

import pandas as pd
from datetime import date
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

item_price_pandas = pd.DataFrame({
    "item_id": [1, 2, 3, 4],
    "price": [4, 2, 5, 1],
    "transaction_date": [
        date(2023, 1, 15),
        date(2023, 2, 1),
        date(2023, 3, 10),
        date(2023, 4, 22)
    ]
})

item_price = spark.createDataFrame(item_price_pandas)
item_price.show()

+-------+-----+----------------+
|item_id|price|transaction_date|
+-------+-----+----------------+
|      1|    4|      2023-01-15|
|      2|    2|      2023-02-01|
|      3|    5|      2023-03-10|
|      4|    1|      2023-04-22|
+-------+-----+----------------+

Now, let’s compare the traditional approach with PySpark’s parameterized queries.

Traditional approach

This approach requires creating a temporary view and manually formatting the date:

item_price.createOrReplaceTempView("item_price_view")
transaction_date = "2023-02-15"

query = f"""SELECT *
FROM item_price_view 
WHERE transaction_date > '{transaction_date}'
"""

spark.sql(query).show()

+-------+-----+----------------+
|item_id|price|transaction_date|
+-------+-----+----------------+
|      3|    5|      2023-03-10|
|      4|    1|      2023-04-22|
+-------+-----+----------------+

PySpark’s Parameterized Query Approach

This method is more secure and doesn’t require a temporary view:

query = """SELECT *
FROM {item_price} 
WHERE transaction_date > {transaction_date}
"""

spark.sql(query, item_price=item_price, transaction_date=transaction_date).show()

+-------+-----+----------------+
|item_id|price|transaction_date|
+-------+-----+----------------+
|      3|    5|      2023-03-10|
|      4|    1|      2023-04-22|
+-------+-----+----------------+

This method allows for easy parameter substitution and direct use of DataFrames, making your Spark SQL queries both safer and more convenient to write and maintain.