Grouped LIMIT in PostgreSQL: Show the first N rows for each group?

sql

prompt-engineering

performance

best-practices

byAnton Shumikhin·Sep 21, 2024

Retain the top N records per group in PostgreSQL using the ROW_NUMBER() window function within a subquery, specify the group and order, then filter for rn <= N in the outer query.

SELECT * FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY group_column ORDER BY order_column) as rn
    FROM table_name
) AS ranked
WHERE ranked.rn <= N;

Swap group_column with the column to group, order_column for ordering within groups, table_name with your database's name, and N with the required record count per group.

Delving into the details

Leveraging the ROW_NUMBER() function which was introduced in PostgreSQL 8.4 and later, assigns a unique number to each row within each partition of the result. By partitioning on the group_column with the partition keyword, we can ensure each group’s records are assigned row numbers independently. The ORDER BY clause specifies the order of these numbers within each group. Finally, we employ a filter of WHERE rn <= N to retrieve the maximum N records per group.

What about efficiency?

For optimized performance, we ensure the ORDER BY clause is applied before the LIMIT in subqueries. Furthermore, we create indexes on your group_column and order_column to accelerate query execution. The indexing enables PostgreSQL to use these indexes effectively to minimize sorting time hence speeding up data retrieval.

Large datasets demand nested queries and a well-thought-out WHERE clause to minimize the number of scanned rows thereby enhancing performance. If performance is sluggish, consider upgrading PostgreSQL to leverage new optimizers.

Dealing with sparse data

Sparse groups (having fewer than N records) must be handled carefully to avoid null values. The COALESCE function or a LEFT JOIN with the original table can handle such situations gracefully by treating nulls systematically and representing sparse data correctly.

When simple limits just won't do - enter `LATERAL` joins

Correlated subqueries and complex queries are where a LATERAL join shines. It's a powerful tool enabling efficient row limiting within groups.

SELECT *
FROM table_name t,
LATERAL (
    SELECT *
    FROM other_table -- Say this is the "VIP list"
    WHERE other_table.group_column = t.group_column
    ORDER BY other_table.order_column -- We party in order
    LIMIT N -- Can't invite everyone though!
) as lateral_subquery;

No clones please - distinct results

There might be times when you want unique group identifiers in your results. Utilize DISTINCT ON along with ORDER BY for fetching the first row per group based on the defined order.

SELECT DISTINCT ON (section_id) *
FROM your_table
ORDER BY section_id, custom_order_column;

Warning! DISTINCT ON might not be performant on large datasets - stick with ROW_NUMBER().

Organizing group results

For a better readability, you might want to lay out your results by their identifiers.

SELECT section_id, array_agg(name ORDER BY name) as names
FROM (
    SELECT section_id, name
    FROM (
        SELECT section_id, name, ROW_NUMBER() OVER (PARTITION BY section_id ORDER BY name) as row_num
        FROM students
    ) subquery
    WHERE row_num <= N -- Only the cool kids allowed
) AS subquery_grouped
GROUP BY section_id; -- Keep it tidy