Data Engineering Guide

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Spark SQL 🧠 ​

Spark SQL allows you to run SQL queries on distributed data using Spark.

It provides a SQL interface over DataFrames.

What is Spark SQL? ​

Spark SQL is a module in Spark that:

  • allows SQL queries on structured data
  • integrates with DataFrame API
  • uses Catalyst optimizer for execution

Why Spark SQL? ​

Spark SQL is useful because:

  • SQL is widely understood
  • easier for analysts
  • same engine as DataFrame API
  • optimized execution under the hood

Creating a Temporary View ​

To use SQL, we must register a DataFrame as a view.

df.createOrReplaceTempView("users")

Running SQL Queries ​

Once a view is created, we can query it:

result = spark.sql("SELECT * FROM users WHERE age > 25")

result.show()

SQL vs DataFrame API ​

Both use the same execution engine.

SQL Example ​

spark.sql("SELECT name, age FROM users WHERE age > 25")

DataFrame Equivalent ​

df.filter(df.age > 25).select("name", "age")

Execution Flow ​

  1. SQL query is parsed
  2. Logical plan is created
  3. Catalyst optimizer optimizes query
  4. Physical plan is generated
  5. Execution runs on cluster

Catalyst Optimizer ​

Spark SQL uses Catalyst optimizer to:

  • optimize query execution
  • reorder operations
  • reduce computation cost

Common SQL Operations ​

SELECT ​

SELECT name, age FROM users

WHERE ​

SELECT * FROM users WHERE age > 25

GROUP BY ​

SELECT age, COUNT(*) FROM users GROUP BY age

JOIN ​

SELECT a.name, b.salary
FROM users a
JOIN salary b
ON a.id = b.id

Temporary Views vs Global Views ​

Temporary View ​

  • session-level
  • destroyed after session ends

Global View ​

  • accessible across sessions
  • shared across application

Use Cases ​

  • data exploration
  • analytics queries
  • ETL transformations
  • reporting pipelines

Performance Considerations ​

  • SQL queries use same optimizations as DataFrames
  • Catalyst optimizer improves performance automatically
  • avoid unnecessary joins and subqueries

Common Mistakes ​

  • creating too many temp views
  • writing complex nested SQL without optimization
  • ignoring partitioning strategy

Best Practices ​

  • prefer SQL for readability
  • use DataFrame API for complex logic
  • filter early in queries
  • avoid unnecessary joins

Mental Model ​

Think of Spark SQL as:

A SQL layer on top of a distributed execution engine powered by Spark.

Summary ​

Spark SQL:

  • allows SQL queries on Spark data
  • integrates with DataFrames
  • uses Catalyst optimizer
  • executes in distributed fashion