Data filtering

To restrict result groups based on aggregated values.

When you need to filter groups after GROUP BY, e.g., selecting departments with more than 10 employees, or products with an average price above a threshold.

SQL databases, data analysis scripts, BI tools, any SQL-based querying.

HAVING agg_func(column) > threshold

HAVING ...

agg_func: aggregate function name (e.g., COUNT, SUM, AVG), column: column or expression, operator: comparison operator (=, !=, >, <, >=, <=, LIKE, etc.), threshold: literal value or expression.

• GROUP BY
• aggregate functions
• WHERE clause

• Using WHERE before GROUP BY for row-level filters
• using subquery or window function

Using HAVING without GROUP BY (treats whole set as one group); confusing HAVING with WHERE; referencing non-aggregated columns in HAVING without aggregating them

WHERE clause (filters rows before aggregation) vs HAVING (filters groups after aggregation)

Coming from procedural languages: thinking HAVING works like an if statement after loops; from other SQL dialects: syntax differences (e.g., MySQL allows aliases in HAVING)

• GROUP BY
• aggregate functions
• WHERE
• HAVING
• window functions

Some databases allow aliases defined in SELECT to be used in HAVING; performance: HAVING is evaluated after grouping, can be expensive if not indexed; avoid using HAVING for filters that could be done in WHERE for efficiency.

Ensures that only groups meeting aggregate criteria are returned, making summary reports meaningful.

Think 'HAVING' as 'having a condition on the group's aggregate'.

HAVING filters groups after GROUP BY, so any column referenced must be either aggregated or appear in the GROUP BY clause, otherwise the query will raise an error.

Using nested subqueries or window functions for more complex filtering.