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N064-E1 Tier 5 · Expert · easy ecommerce · Brightlane

Return each order day, the number of `orders` placed on that day, and the running total of all `orders` placed from the earliest day through that day

Part of Running Totals and Cumulative Metrics in SQL

The problem

Scenario: Brightlane's operations team is tracking how total order volume has grown over time and needs each day's order count alongside the cumulative count from the start of the data through that day.

Task: Write a query to return each order day, the number of orders placed on that day, and the running total of all orders placed from the earliest day through that day.

Assumptions:

  • An order day is identified by its date.
  • A day's daily_orders is the count of orders placed on that day.
  • A day's cumulative_orders is the combined order count from the earliest day in the data through that day inclusive.

Output:

  • One row per order day present in the data.
  • Columns in this order: order_day, daily_orders, cumulative_orders.
  • Sorted by order_day ascending.
Schema · ecommerce 5 tables
categories
id integer
name text
parent_id? integer
products
id integer
name text
category_id integer
price numeric
stock_qty integer
attributes? jsonb
order_items
id integer
order_id integer
product_id integer
quantity integer
unit_price numeric
customers
id integer
name text
email text
city? text
country text
created_at timestamptz
is_active boolean
orders
id integer
customer_id integer
ordered_at timestamptz
status text
total_amount numeric
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Solution query
SELECT
  DATE_TRUNC('day', ordered_at) AS order_day,
  COUNT(*) AS daily_orders,
  SUM(COUNT(*)) OVER (
    ORDER BY
      DATE_TRUNC('day', ordered_at) ROWS BETWEEN UNBOUNDED PRECEDING
      AND CURRENT ROW
  ) AS cumulative_orders
FROM
  orders
GROUP BY
  DATE_TRUNC('day', ordered_at)
ORDER BY
  order_day

The shape

The two-stage shape is doing the work. DATE_TRUNC('day', ordered_at) collapses every order into a day bucket, COUNT(*) produces the per-day count, and SUM(COUNT(*)) OVER (ORDER BY ...) accumulates those daily counts from the earliest day forward. The running total is computed on the already-aggregated day rows, not on the raw orders.

Clause by clause

  • SELECT DATE_TRUNC('day', ordered_at) AS order_day, COUNT(*) AS daily_orders produces one row per order day with that day's count. The truncation gives every order on the same date the same group key.
  • SUM(COUNT(*)) OVER (ORDER BY DATE_TRUNC('day', ordered_at) ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_orders sums the per-day counts in date order. The inner COUNT(*) is the group's daily count; the outer SUM(...) OVER (...) accumulates those daily counts across the ordered output. UNBOUNDED PRECEDING anchors the frame at the earliest day, CURRENT ROW extends it through the day in hand.
  • FROM orders GROUP BY DATE_TRUNC('day', ordered_at) reads every order and groups by day so the aggregate has one row per day to feed the window function.
  • ORDER BY order_day sorts the final output chronologically. The window's own ORDER BY and the query's final ORDER BY happen to align here, but they are independent instructions.

Why this and not running totals over raw orders

A window function applied directly to the raw orders table accumulates one row at a time, producing a running total per individual order. The task wants one row per day with a day-level cumulative count. Aggregating first, then accumulating, is what produces day-level output.

You practiced layering an unbounded-preceding window over per-day counts, so each order day carries both its own count and the running total through that day.

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