id: null_sum_all_null_group
category: nulls_types
difficulty: medium
probes: >
  sum() over a group where every value is NULL returns NULL, not 0. The fix is
  coalesce after the aggregate, not before -- coalescing before changes the
  average and the count. Models very often emit sum(coalesce(x,0)) which is a
  different query that happens to agree here but not on avg.
tags: [null_semantics, sum, coalesce]

prompt: |
  For each region in `readings`, return the sum of `value` as `total`, but
  report 0 rather than null when the region has no non-null values at all.
  `total` must be a LONG (BIGINT) column.

  Return columns: region, total.

fixtures:
  - name: readings
    schema: region STRING, value INT
    rows:
      - ["north", 5]
      - ["north", null]
      - ["north", 7]
      - ["south", null]
      - ["south", null]
      - ["east", 3]

solution: |
  from pyspark.sql import functions as F

  def solve(spark, readings):
      # sum() ignores NULLs; an all-NULL group aggregates to NULL, so the
      # coalesce has to sit outside the aggregate.
      return (
          readings
          .groupBy("region")
          .agg(F.coalesce(F.sum("value"), F.lit(0)).cast("long").alias("total"))
      )

compare:
  mode: rows