Using Window Functions in Spark SQL: Aggregation, Ranking, and Partitioning

1. Overview The article explains that window functions allow calculations across a set of rows related to the current row without collapsing the result set, unlike traditional aggregation functions that require GROUP BY.

2. Preparation It shows how to start Spark Shell, define a Scala case class Score(name: String, clazz: Int, score: Int), create an RDD of sample data, convert it to a DataFrame, register a temporary view, and display the data.

3. Aggregation Window Functions Demonstrates using count(name) OVER() to compute a total count for each row and count(name) OVER(PARTITION BY class) to compute counts per class, showing the resulting tables.

4. Sorting Window Functions

4.1 ROW_NUMBER() – assigns a unique sequential number within each partition ordered by score. Example query:

SELECT name, class, score, ROW_NUMBER() OVER(PARTITION BY class ORDER BY score) AS rank FROM scores

.

4.2 RANK() – provides ranking with gaps for ties. Example query:

SELECT name, class, score, RANK() OVER(ORDER BY score) AS rank FROM scores

.

4.3 DENSE_RANK() – provides ranking without gaps for ties. Example query:

SELECT name, class, score, DENSE_RANK() OVER(ORDER BY score) AS rank FROM scores

.

4.4 NTILE(6) – divides rows into six ordered groups. Example query:

SELECT name, class, score, NTILE(6) OVER(ORDER BY score) AS rank FROM scores

. The article also shows how to combine PARTITION BY with these functions.

Throughout the tutorial, the author includes the exact Spark SQL commands and the expected output tables, illustrating how window functions can be used for advanced analytics on big‑data platforms.