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Code Keys/PySpark Academy/Module 31/Lesson 254
PYSPARK • LESSON 254

Complex Window Analytics in Spark SQL

How can we execute partition ranking and cumulative aggregations using standard ANSI SQL window specifications?

Advanced⏱ 3 Minutes⭐ 830 XP
🤔 THE QUESTION

How can we execute partition ranking and cumulative aggregations using standard ANSI SQL window specifications?

💡 WHAT IS IT?

The OVER (PARTITION BY ... ORDER BY ... ROWS BETWEEN ...) SQL syntax executes full window analytics inside spark.sql().

🎯 WHAT IS IT USED FOR?

Replicating enterprise Oracle or Snowflake analytical stored procedures directly on Apache Spark.

💻 EXAMPLE
df_window_sql = spark.sql("SELECT employee_id, department, salary, AVG(salary) OVER (PARTITION BY department) AS dept_avg_salary, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dept_salary_rank FROM employees")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Complex Window Analytics in Spark SQL.

  • ✓ ANSI SQL OVER clause in Spark
  • ✓ PARTITION BY and ORDER BY
  • ✓ SQL window metric calculation
🚀 Start Mission
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