BigQuery SQL Optimizer-BigQuery SQL query optimizer tool

Example

For a complex SQL query with multiple JOINs, the optimizer may suggest reordering JOINs, reducing the columns selected, or filtering rows early in the query to reduce data volume.

Scenario

In an e-commerce dataset, a user might JOIN tables for orders, customers, and products. The optimizer would reorder the JOINs starting with the largest table first and recommend filtering out rows in each table before the JOIN operation to reduce data volume.

Example

The optimizer helps minimize the data processed by using specific column queries instead of SELECT * and avoiding unneeded partitions.

Scenario

If a user queries a table with thousands of columns but only needs a few for analysis, the optimizer suggests explicitly selecting those columns and using partitioned columns, reducing data processed and cost.

Example

For queries combining multiple tables, it suggests aggregating data first before performing a JOIN to minimize the data read.

Scenario

In a social media dataset, where a user wants to count likes by user, the optimizer suggests aggregating likes per post first before performing a user-join to speed up execution.

Example

Suggests limiting query results or materializing intermediate results in a destination table to improve performance when dealing with large datasets.

Scenario

A marketing dataset includes millions of records, and a user wants a sorted list of the top campaigns by revenue. The optimizer recommends adding a LIMIT clause to avoid processing excessive data and to avoid errors like 'Resources exceeded.'

Example

Identifies query anti-patterns like excessive use of wildcard tables, self-joins, or lack of partitioning, and recommends improvements.

Scenario

A developer working with large-scale logs data might unknowingly use SELECT * or repeatedly apply unnecessary transformations. The optimizer detects this inefficiency and suggests targeted improvements such as using SELECT EXCEPT or materializing intermediate results.