To retrieve unique values from a column in SQL, I use the DISTINCT keyword with the SELECT statement. The DISTINCT keyword ensures that duplicate values are filtered out and only unique records are returned.
For example, if I have a customers table and I want to find all the unique cities where customers are located, I’d write:
SELECT DISTINCT city
FROM customers;
This will return each city only once, even if multiple customers belong to the same city.
In a real-world scenario, I used DISTINCT while generating a marketing report where the client wanted to know how many unique customer regions we had across different product categories. Initially, the report was showing repetitive entries because of multiple transactions by the same customer. Using DISTINCT helped us clean the dataset before aggregation.
A challenge I faced was that using DISTINCT on multiple columns — for example:
SELECT DISTINCT city, country FROM customers;
— sometimes produced unexpected results because DISTINCT considers the combination of all selected columns. So, even if the city was the same but the country differed (due to data entry issues), it would count as unique. To fix that, I had to clean or normalize the data first.
A limitation of DISTINCT is performance — on large tables, it can be slower since it needs to compare and eliminate duplicates across potentially millions of rows.
As an alternative, in cases where I just need unique identifiers or better performance, I sometimes use GROUP BY instead, which achieves the same effect and also allows for aggregation. For example:
SELECT city FROM customers GROUP BY city;
This works similarly to DISTINCT and sometimes performs better depending on the database engine and indexing.
So overall, SELECT DISTINCT is the simplest and most readable way to retrieve unique values, but it’s important to be mindful of data quality and performance when using it on large datasets.