Dynamic SQL Generation in PySpark

[dylanwan]

• Introduction
• Symptoms
• Diagnosis
• Solution
• Read the rule table
• Dynamic SQL generation

Introduction

In data processing, you may encounter situations where you need to implement user-defined business rules.   PySpark is a powerful tool for generating SQL dynamically, allowing you to adapt to these evolving requirements.

Symptoms

• You want to create flexible and adaptable data processing pipelines
• Business users would like to define their own business logic, including how a metric is calculated or how the data will be filtered
• You would like to make the program reflect the user defined logic without constantly changing your code
• You need to generate SQL queries dynamically based on external configurations

Diagnosis

Instead of relying only on static Spark SQL to define the queries in an Incorta materialized view, you can leverage PySpark to dynamically construct SQL statements based on user defined rules or configurations.

Solution

Read the rule table

User-defined rules can be stored in various formats, such as database tables, CSV files, JSON files, or configuration files.

For simplicity, let's assume you have a "rules" table containing columns like column_name, formula, and source_table..

## Assume the data is available as a Spark DataFrame.  
rule_table = [("col1", "age * 2", "my_table"),
        ("col2", "abs(age)", "my_table")]
rules_df = spark.createDataFrame(rule_table, ["column_name", "formula", "source_table"])
## read the rule table 
# rule_df = read("ConfigSchema.rule_table")

# Convert DataFrame to list of tuples
rules_tuples = [tuple(row) for row in rules_df.collect()]

Dynamic SQL generation

The selectExpr method allows you to specify expressions directly within a DataFrame's select operation.

input_df = spark.createDataFrame([
    (2, "Alice"), (5, "Bob")], schema=["age", "name"])

expressions = [row.formula + " AS " + row.column_name for row in rules_df.collect()]

output_df.selectExpr(*expressions)
output_df.show()

+---------+--------+
|col1     |col2    |
+---------+--------+
|        4|       2|
|       10|       5|
+---------+--------+

The formula of columns can be read into a Python List and you can use the list comprehension to generate the input expressions in the selectExpr function.