You’ve written your query, hit execute, and waited. And waited. And waited. Then you ask yourself: “Why is deduplicating my data taking so long?”
If you’re using SQL to remove duplicates, you probably rely on GROUP BY or DISTINCT. But here’s the truth few people admit: they are not interchangeable, and choosing the wrong one can silently kill your performance.
Let’s break down why—and how you can finally make your queries fast again.
1. DISTINCT: The Quick Fix That Isn’t Always Quick
What it does: DISTINCT removes duplicate rows from the result set. Simple, elegant, and easy to read.
Why it can slow you down: Behind the scenes, the database often has to sort or hash the entire dataset to find duplicates. On large tables, that can become a nightmare.
When to use: Small to medium datasets or when you need a quick, readable deduplication. Avoid on massive tables without proper indexing.
2. GROUP BY: More Power, More Responsibility
What it does: GROUP BY aggregates rows based on one or more columns, often paired with functions like COUNT, SUM, or MAX.
Why it can hurt performance: GROUP BY usually triggers sorting or temporary tables. Complex groupings across millions of rows can be surprisingly expensive.
When to use: When you need aggregation or you want precise control over grouping logic—but be cautious with huge datasets or multiple joins.
3. The Hidden Factors Affecting Speed
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Indexes: If the columns in DISTINCT or GROUP BY aren’t indexed, the database must scan everything.
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Data Type: Large text or JSON fields can slow down comparisons.
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Query Structure: Nested queries, joins, or subqueries can multiply the work.
4. Choosing the Right Weapon
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For pure deduplication on indexed columns: DISTINCT is usually faster.
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For deduplication plus aggregation: GROUP BY is your tool.
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For massive datasets: Consider temporary tables, window functions, or even ETL pre-processing.
5. Pro Tips from the Trenches
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Avoid SELECT *; explicitly list columns.
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Use indexed columns whenever possible.
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Test both DISTINCT and GROUP BY with EXPLAIN to see which plan is faster.
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Consider database-specific optimizations like MySQL’s “Using index for group-by” hints.
The Bottom Line
GROUP BY and DISTINCT aren’t just syntax—they’re different strategies with hidden performance costs. Understanding the trade-offs is the difference between a query that flies and one that crawls.
Next time your query feels slow, don’t just blame the database—look at how you’re deduplicating. The right approach can save minutes, hours, or even days in large-scale analytics.
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