How to Use Window Functions in BigQuery

How do I use window functions in BigQuery?

Window functions return aggregated values while keeping individual rows, enabling advanced analytics such as running totals, rankings, and moving averages.

Welcome to the Galaxy, Guardian!

Oops! Something went wrong while submitting the form.

Description

What are window functions in BigQuery?

Window functions perform calculations across a set of rows related to the current row—called the window—without reducing the result set. They make running totals, rankings, and moving averages straightforward.

Why choose window functions over GROUP BY?

GROUP BY collapses rows into one per group, losing detail.Window functions keep every row visible while adding the computed metric, letting you combine granular data with aggregates in the same query.

How do I write a window function in BigQuery?

Attach OVER() to an aggregate or analytic function. Define PARTITION BY to group rows logically, ORDER BY to set row order, and an optional frame clause for row ranges.The window only affects the function, not the entire SELECT.

Syntax breakdown

The syntax below shows the full grammar, then a practical ecommerce variant.

Practical example with ecommerce data

Need the total spend per customer and that customer’s rank by spend? Use SUM(total_amount) OVER(PARTITION BY customer_id) and RANK() OVER(ORDER BY customer_spend DESC).

When should I use window functions?

Use them for running totals, percentiles, de-duplication via ROW_NUMBER(), funnel analysis, cohort retention, or whenever you need aggregates alongside row-level detail.

What are common mistakes with window functions?

Misdefining PARTITION BY often produces incorrect results.Forgetting ORDER BY can lead to nondeterministic ranking. Both issues are easy to fix by explicitly declaring the intended partitions and ordering.

Best practices for performant window queries

1) Partition on low-cardinality columns. 2) Order on indexed or sorted fields. 3) Select only needed columns to reduce I/O. 4) Cache intermediate results with WITH clauses for complex pipelines.

How do I debug window function output?

Start with a limited dataset using WHERE or LIMIT.Add columns showing the partition and order keys next to the computed metric to verify logic row by row.

Can I combine multiple window functions?

Yes. You can call several window functions in the same SELECT, each with its own OVER() clause, or reuse the same window spec via WINDOW window_name AS (...).

Next steps

Experiment in Galaxy’s SQL editor, use AI Copilot to refactor legacy GROUP BY queries into window functions, and share endorsed queries with your team.

Why How to Use Window Functions in BigQuery is important

How to Use Window Functions in BigQuery Example Usage


-- Running 7-day moving average of product sales quantity
WITH daily_sales AS (
  SELECT
    oi.product_id,
    DATE(o.order_date) AS sales_date,
    SUM(oi.quantity)   AS daily_qty
  FROM Orders      AS o
  JOIN OrderItems  AS oi ON oi.order_id = o.id
  GROUP BY product_id, sales_date
)
SELECT
  p.name,
  ds.sales_date,
  ds.daily_qty,
  AVG(ds.daily_qty) OVER (
        PARTITION BY ds.product_id
        ORDER BY ds.sales_date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS qty_7d_ma
FROM daily_sales AS ds
JOIN Products AS p ON p.id = ds.product_id
ORDER BY p.name, ds.sales_date;

How to Use Window Functions in BigQuery Syntax


-- Generic syntax
SELECT
  column_list,
  window_function (expression)
    OVER (
      [PARTITION BY partition_expression, ...]
      [ORDER BY order_expression [ASC|DESC], ...]
      [ROWS|RANGE BETWEEN frame_start AND frame_end]
    ) AS alias
FROM source_table;

-- Ecommerce example: total spend per customer and rank across all customers
SELECT
  c.id,
  c.name,
  SUM(o.total_amount) OVER (PARTITION BY c.id)         AS customer_spend,
  RANK()             OVER (ORDER BY SUM(o.total_amount) DESC) AS spend_rank
FROM Customers AS c
JOIN Orders AS o ON o.customer_id = c.id;

Common Mistakes

Omitting ORDER BY in ranking functions like ROW_NUMBER() causes nondeterministic results. Always specify ORDER BY to guarantee consistent ordering.
Partitioning by a high-cardinality column explodes memory usage. Partition by the lowest-cardinality set that still meets business logic to improve performance.