Features
SQL COUNT is an aggregate function that returns the number of rows that match a condition. Use COUNT(*) for all rows, COUNT(column) for non-NULL values in that column, and COUNT(DISTINCT column) for unique values. Combine with GROUP BY, HAVING, and window functions to derive insights quickly.
SQL COUNT is an aggregate function that returns the number of rows that meet a given condition. COUNT(*) counts every row, even when columns contain NULL.
The database engine scans the qualifying rows and increments an internal counter, then returns the final total as a single scalar value.
COUNT(*) is best when you need every row. COUNT(column_name) ignores NULLs, useful for counting filled-in values in an optional column.
Use COUNT(DISTINCT column) to return the number of unique, non-NULL values in that column. This is slower than COUNT(*) because it requires deduplication.
Yes. GROUP BY partitions the result set, and COUNT returns a row count for each group. This is essential for summaries like orders per customer.
Use HAVING after GROUP BY to filter groups by their COUNT. Example: HAVING COUNT(*) > 5 returns only groups with more than five rows.
COUNT() OVER (PARTITION BY ...) preserves individual rows while adding a window_count column that repeats the total per partition.
Prefer COUNT(*) for clarity and performance on indexed tables. Use column-specific COUNT only when NULL filtering is needed. Always index filter columns to speed counts.
Teams use COUNT to monitor active users, validate data completeness, and power dashboards. In Galaxy’s modern SQL editor, AI Copilot autocompletes COUNT patterns and optimizes filters.
SQL COUNT quickly answers “how many?” questions. Choose the right variant, combine with GROUP BY and HAVING, and lean on tools like Galaxy to streamline analysis.
COUNT(*) counts every row regardless of NULLs; only COUNT(column) filters them out.
Modern databases treat COUNT(*) and COUNT(1) identically in execution plans, so performance is the same.
Not directly. Use separate COUNT expressions or wrap them in conditional SUM/CASE statements.
Add indexes on filter columns, use approximate_count_distinct functions if available, or rely on materialized views for static data.
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