How to Optimize Queries in BigQuery

How do I optimize queries in BigQuery?

Reduce BigQuery runtime and cost by partitioning, clustering, materialized views, and efficient SQL patterns.

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Description

Why do my BigQuery queries run slowly?

Unpartitioned tables, missing clustering, and inefficient SQL patterns force BigQuery to scan more bytes than necessary, consume extra slots, and lengthen execution time.

What table-design features speed up queries?

Partition large fact tables

Partition Orders by DATE(order_date) so BigQuery prunes irrelevant partitions when you filter by date, shrinking scanned bytes.

Cluster frequent filters

Cluster Orders on customer_id to keep related rows together. Filters on the clustered column read only relevant blocks.

Which SQL patterns cut cost?

Filter early

Place WHERE conditions on partition and cluster keys before JOINs to reduce intermediate rows.

Select required columns

Avoid SELECT *; list needed fields to limit I/O and network transfer.

Aggregate before joining

Summarize OrderItems by order_id before joining to Orders to shrink join inputs.

How can materialized views help?

Create materialized views for repeated aggregates (e.g., daily revenue). BigQuery stores pre-computed results and refreshes only updated partitions.

When should I use approximate functions?

Use APPROX_COUNT_DISTINCT() for large distinct counts; it uses fewer resources than COUNT(DISTINCT) with minimal accuracy loss.

Best-practice checklist

Partition fact tables, add clustering, avoid SELECT *, preview bytes, use materialized views, and leverage query caching.

Why How to Optimize Queries in BigQuery is important

How to Optimize Queries in BigQuery Example Usage


-- Compute January spend for a single customer with minimal scanned bytes
SELECT
  o.customer_id,
  SUM(oi.quantity * p.price) AS customer_spend
FROM `project.dataset.OrderItems` AS oi
JOIN `project.dataset.Products`  AS p USING (product_id)
JOIN `project.dataset.Orders`    AS o USING (order_id)
WHERE o.order_date BETWEEN '2024-01-01' AND '2024-01-31'  -- partition filter
  AND o.customer_id = 123                                    -- cluster filter
GROUP BY o.customer_id;

How to Optimize Queries in BigQuery Syntax


-- Create a partitioned & clustered Orders table
CREATE TABLE `project.dataset.Orders`
PARTITION BY DATE(order_date)
CLUSTER BY customer_id AS
SELECT *
FROM `raw_dataset.orders_raw`;

-- Build a materialized view for daily revenue
CREATE MATERIALIZED VIEW `project.dataset.daily_revenue`
PARTITION BY order_date
CLUSTER BY customer_id AS
SELECT order_date,
       customer_id,
       SUM(total_amount) AS daily_total
FROM   `project.dataset.Orders`
GROUP  BY order_date, customer_id;

-- Optimized query using partition & cluster filters
SELECT customer_id,
       SUM(total_amount) AS month_revenue
FROM   `project.dataset.Orders`
WHERE  order_date BETWEEN '2024-01-01' AND '2024-01-31'
  AND  customer_id = 123
GROUP  BY customer_id;

Common Mistakes

Ignoring partition filters: Running a date filter on a non-partitioned column forces a full scan. Fix by partitioning on order_date and referencing it in WHERE.
Using SELECT * for analytics: Scans every column even if unused. Fix by explicitly selecting needed columns or using a column list shortcut in your editor.

Frequently Asked Questions (FAQs)

Does clustering add storage cost?

No extra storage cost applies. Initial clustering uses some slots, and automatic reclustering is free.

Can I add partitioning after table creation?

You must create a new partitioned table with CREATE TABLE ... PARTITION BY AS SELECT or copy the data; existing tables cannot be repartitioned in place.

When should I choose a materialized view over cached results?

Use materialized views when many users or dashboards reuse the same aggregation and the source data updates incrementally. BigQuery keeps the view fresh automatically.