CommencerCommencer gratuitement

Running sums using window function SQL

A window function is like an aggregate function, except that it gives an output for every row in the dataset instead of a single row per group.

You can do aggregation along with window functions. A running sum using a window function is simpler than what is required using joins. The query duration can also be much faster.

A table called schedule, having columns train_id, station, time, and diff_min is provided for you. The diff_min column gives the elapsed time between the current station and the next station on the line.

Cet exercice fait partie du cours

Introduction to Spark SQL in Python

Afficher le cours

Instructions

  • Run a query that adds an additional column to the records in this dataset called running_total. The column running_total SUM()s the difference between station time given by the diff_min column.
  • Run the query and display the result.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Add col running_total that sums diff_min col in each group
query = """
SELECT train_id, station, time, diff_min,
____(____) OVER (PARTITION BY ____ ORDER BY ____) AS running_total
FROM schedule
"""

# Run the query and display the result
spark.____(query).show()
Modifier et exécuter le code