MediumWindow Functions

Day-over-Day Account Balance Net Delta

Frequently Asked By:StripeCoinbaseBloomberg

Interactive In-Browser Workspace

Practice this problem on live schema tables in PostgreSQL 16 WASM with instant row verification and visual EXPLAIN plans.

Solve in Workbench

Problem Statement

For each record in `daily_account_snapshots`, use `LAG()` to find the previous recorded balance for that account. Compute `daily_net_change` as `(closing_balance - prev_closing_balance)`. Return: 1. `account_id` 2. `snapshot_date` 3. `closing_balance` 4. `daily_net_change` Order results by `account_id` ascending, then by `snapshot_date` ascending.

Why Technical Interviewers Ask This

Treasury risk operations track daily closing cash positions across merchant accounts, computing the net dollar change compared to the previous day's closing balance.

Common Pitfalls & Interview Traps

Forgetting required sorting and deterministic tie-breaking.

Not accounting for NULL or zero-value denominators during calculations.

Using inefficient quadratic subqueries instead of single-pass window functions constructs.

Canonical Modern SQL Solution

PostgreSQL 16 / ANSI SQL

SELECT 
        account_id,
        snapshot_date,
        closing_balance,
        (closing_balance - LAG(closing_balance) OVER (PARTITION BY account_id ORDER BY snapshot_date ASC)) AS daily_net_change
      FROM daily_account_snapshots
      ORDER BY account_id ASC, snapshot_date ASC;

Approach Breakdown

This query addresses the problem requirements by filtering and structuring data in Window Functions. It meets the target criteria: Daily closing balances with day-over-day net cash variance.

Edge Cases to Verbally Clarify with Your Interviewer

The dataset contains zero matching records (must return valid empty result without crashing).

Multiple records tie on sorting criteria (must evaluate tie-breaking order deterministically).

Foreign keys or secondary attributes have NULL values.

Frequently Asked Follow-Up Questions

What makes this question a favorite among Stripe and Coinbase interviewers?

Interviewers use this problem to test whether candidates write defensive, scalable Window Functions queries that run efficiently on multi-million row production databases without excessive memory usage.

How does the query optimizer execute this query?

Using single-pass table scans with index lookups or sort buffers. Running EXPLAIN (ANALYZE, BUFFERS) in the DB Exercises workbench visualizes the execution nodes and buffer hit cache.