BigQuery SQL Optimizer-AI-powered query optimization for BigQuery.

Example

Suppose a query includes a subquery that can be simplified or transformed into a more efficient join. The optimizer can rewrite the query to replace the subquery with an equivalent, more efficient join statement.

Scenario

Imagine running a query that retrieves user activity data from a massive dataset and groups it by user ID. If theBigQuery SQL Optimizer query involves multiple nested subqueries, BigQuery's SQL optimizer could rewrite these into more efficient joins, potentially reducing the overall query runtime significantly.

Example

The optimizer examines various query plans and evaluates their costs (e.g., time to execute, number of resources used). It selects the one with the lowest expected cost. If a query involves sorting a large number of rows, the optimizer might choose a different algorithm, such as a hash join, over a nested loop join to minimize resource consumption.

Scenario

In a scenario where you're querying large datasets and performing multiple joins, the optimizer might choose an execution strategy that uses partitioned tables or a distributed query plan to process data more efficiently and reduce resource utilization, improving performance.

Example

BigQuery SQL Optimizer can leverage BigQuery's distributed infrastructure to parallelize queries across multiple servers. For example, when querying a large dataset, the optimizer might split the query into smaller tasks and execute them concurrently across multiple nodes.

Scenario

When running a report that aggregates sales data across millions of rows, the optimizer can break the query into parallel processes that scan different partitions of the dataset simultaneously, significantly reducing query execution time and improving the overall performance.