id: window_default_frame_ties
category: windows
difficulty: hard
probes: >
  The single most-missed detail in Spark windows. With an ORDER BY and no
  explicit frame, the default is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT
  ROW -- a *value* range, so tied ordering keys all see each other's rows.
  Writing rowsBetween(Window.unboundedPreceding, 0) gives a different, wrong
  answer on ties. Both look identical in a code review.
tags: [window, frame, range_vs_rows, ties]

prompt: |
  For each row in `events`, compute a running total of value within each user,
  ordered by ts, using the SQL default window frame (RANGE BETWEEN UNBOUNDED
  PRECEDING AND CURRENT ROW). Rows that share the same ts within a user must
  therefore share the same running total.

  Return columns: user, ts, value, running.

fixtures:
  - name: events
    schema: user STRING, ts INT, value INT
    rows:
      - ["a", 1, 10]
      - ["a", 2, 20]
      - ["a", 2, 30]
      - ["a", 3, 40]
      - ["b", 1, 5]
      - ["b", 1, 7]

solution: |
  from pyspark.sql import functions as F
  from pyspark.sql.window import Window

  def solve(spark, events):
      # No rowsBetween/rangeBetween call: this is the SQL default frame,
      # which is RANGE-based. The two ts=2 rows for user 'a' both get 60.
      w = Window.partitionBy("user").orderBy("ts")
      return events.withColumn("running", F.sum("value").over(w))

compare:
  mode: rows