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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.

Diese Übung ist Teil des Kurses

Introduction to Spark SQL in Python

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Anleitung zur Übung

  • 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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# 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()
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