How to Use Window Functions in PostgreSQL

How do I use window functions in PostgreSQL for running totals and rankings?

Window functions perform calculations across sets of query rows related to the current row without collapsing them into a single output.

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Description

What are PostgreSQL window functions used for?

Window functions let you compute running totals, rankings, moving averages, and other analytics while keeping each row visible. They simplify complex queries that would otherwise need subqueries or self-joins.

How does the OVER() clause work?

OVER() defines the “window”—the subset of rows the function can see. Use PARTITION BY to reset calculations per group and ORDER BY to set row order. Adding ROWS or RANGE refines the frame (e.g., last 3 rows).

Which window functions are most common?

SUM(), AVG(), COUNT(), ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE(), and NTILE() are the workhorses for analytics, reporting, and dashboards.

When should I prefer window functions over GROUP BY?

Choose window functions when you need aggregated data alongside the detail rows. GROUP BY collapses rows; window functions retain them, allowing side-by-side comparison of row-level and aggregate metrics.

How to calculate customer lifetime spend?

Partition by customer_id and sum total_amount, producing a lifetime_total column beside each order. This avoids multiple scans or joins.

How to rank products by sales?

Join Orders and OrderItems, aggregate by product, then apply RANK() OVER(ORDER BY sales DESC) to create a sales_rank for leaderboard style reports.

Best practices for window functions?

Index columns used in PARTITION BY and ORDER BY, keep frames as narrow as possible, prefer ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW for running totals, and avoid mixing GROUP BY with window functions unless necessary.

FAQ: Can window functions filter rows?

No. Use them to calculate; apply WHERE or an outer SELECT to filter on their results.

FAQ: Do window functions hurt performance?

They sort partitions, so large unindexed datasets can slow down. Proper indexing and smaller partitions mitigate this.

Why How to Use Window Functions in PostgreSQL is important

How to Use Window Functions in PostgreSQL Example Usage


-- Rank customers by total spend, keep each order row visible
SELECT o.id,
       c.name,
       o.total_amount,
       SUM(o.total_amount) OVER (PARTITION BY c.id)      AS customer_total,
       RANK()           OVER (ORDER BY SUM(o.total_amount) DESC) AS spend_rank
FROM   Customers c
JOIN   Orders    o ON o.customer_id = c.id
GROUP  BY c.id, c.name, o.id, o.total_amount;

How to Use Window Functions in PostgreSQL Syntax


SELECT column_list,
       window_function ( expression ) OVER (
            [ PARTITION BY partition_expression ]
            [ ORDER BY order_expression ]
            [ ROWS | RANGE frame_spec ]
       ) AS alias
FROM   table_name;

-- E-commerce example: running customer spend
SELECT id,
       customer_id,
       total_amount,
       SUM(total_amount) OVER (
           PARTITION BY customer_id
           ORDER BY order_date
           ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
       ) AS lifetime_total
FROM   Orders;

Common Mistakes

Using GROUP BY with the same columns as PARTITION BY, which collapses rows and defeats the purpose. Remove GROUP BY or nest queries to keep detail rows.
Forgetting ORDER BY inside OVER() when calculating running totals, leading to incorrect or random accumulation. Always ORDER BY a deterministic column like order_date or id.