How would you combine multiple functions and operators to clean, transform, and aggregate data efficiently in one query?

Combining Functions and Operators for Data Cleaning, Transformation, and Aggregation in MySQL

MySQL allows combining multiple functions and operators in a single query to clean, transform, and aggregate data efficiently. This enables complex transformations without multiple query passes.

1. Data Cleaning

1
Use string functions (TRIM, REPLACE, UPPER/LOWER) to standardize text.
2
Use COALESCE() or IFNULL() to handle NULL values.
3
Example: Remove extra spaces and standardize case: UPPER(TRIM(name)).

2. Data Transformation

1
Apply arithmetic and date functions to derive new values.
2
Example: Adjust salary with bonus: salary + IFNULL(bonus, 0).
3
Convert string dates to proper date type: STR_TO_DATE(order_date_str, '%d-%m-%Y').

3. Aggregation

1
Use aggregate functions like SUM(), AVG(), COUNT() combined with transformations.
2
Example: Compute total adjusted salary per department: SUM(salary + IFNULL(bonus,0)).
3
GROUP BY can be combined with transformed expressions to create meaningful summaries.

Example Query Combining Multiple Functions and Operators

4. Optimization Tips

1
Prefer deterministic functions for computed columns or indexes to improve performance.
2
Avoid wrapping indexed columns in functions in WHERE or JOIN clauses to allow index usage.
3
Consider generated columns for frequently computed transformations.

In summary: Combining string, arithmetic, and date functions with operators in a single query allows efficient cleaning, transformation, and aggregation of data. Careful use of deterministic functions, indexing strategies, and generated columns ensures both correctness and performance.

Difficulty: 6/10

Topics: data cleaning, SQL functions, aggregation

Scenario Questions

0-2 years experience

1
We have an orders table with columns order_id, order_date, amount, and a nullable discount. Write a single MySQL query that treats null discounts as 0, calculates net_amount = amount - discount, and returns total net_amount per month.
2
How would you use COALESCE together with DATE_FORMAT to group sales by month while handling rows where order_date is NULL?
3
Given a users table with first_name and last_name, write a query that concatenates them into full_name and counts distinct users in one step.

2-5 years experience

1
Our legacy orders table stores dates as strings in mixed formats and amounts as strings with a '$' prefix. How would you write a single query that cleans the dates, strips the currency symbol, converts amounts to numbers, and returns total revenue per day?
2
A new status column can be NULL, 'completed', or 'canceled'. You need a report that treats NULL as 'unknown' and aggregates counts per status, but your CASE expression is giving wrong totals. How would you debug and fix it?
3
We need a leaderboard that ranks users by total purchase amount, excluding rows where is_test = 1. Show how you would combine functions and a window function in one query to compute rank and total, and mention any performance concerns.

5-8 years experience

1
Our nightly analytics job runs a massive MySQL query that uses IFNULL, DATE_TRUNC, JSON_EXTRACT, and multiple aggregations, and it now takes over an hour. How would you redesign the query or underlying schema to cut runtime while keeping a single‑query approach?
2
We are moving from MySQL 5.7 to 8.0 and want to replace several nested subqueries with CTEs and window functions for cleaning and aggregation. What pitfalls should we watch for, and how would you ensure the new query is both correct and performant?
3
Design a reusable view or stored procedure that encapsulates common cleaning, transformation, and aggregation logic used by multiple reports. What considerations around maintainability, security, and query planning would you address?

8+ years experience

1
dozens of services need cleaned and aggregated data from a shared MySQL instance. How would you architect a central data‑access layer or service that consolidates these transformations, minimizes duplication, and scales with traffic?
2
We plan to deprecate a legacy MySQL schema that contains messy data and replace it with a clean data warehouse over several years. Describe how you would orchestrate the migration so existing queries that combine functions and operators continue to work or are safely transitioned.
3
When exposing complex cleaning and aggregation logic as a public API, what strategies would you use to version, test, and monitor the underlying MySQL queries to avoid breaking downstream consumers?

Follow-up Questions

❓Which indexes would you add to make that query run faster?
❓How would the query change if the table grew tenfold?
❓Why did you pick COALESCE instead of IFNULL in this case?