Usage → Query

The Query tab provides detailed analysis of query patterns in your BigQuery environment. Queries are grouped by their parameterized hash, which allows you to analyze query patterns regardless of specific parameter values.

Overview

The Query usage view enables you to:

Identify the most expensive query patterns
Compare similar queries to understand performance variations
Track query frequency and resource consumption
Drill down into individual query executions

What is a Parameterized Query Hash?

A parameterized query hash groups queries that have the same structure but different literal values. For example, these two queries would have the same hash:

SELECT * FROM orders WHERE customer_id = 123
SELECT * FROM orders WHERE customer_id = 456

This grouping helps you understand how often a query pattern runs and its total cost across all executions.

Page Controls

Project Filter

Filter metrics to specific BigQuery projects.

Project & Region Selection

Select a specific project and region to view queries.

Cost Toggle

Toggle between resource metrics (slots) and cost estimates (USD).

Query Table

The main table displays query patterns with:

Column	Description
Query Parameterized Hash	Unique identifier for the query pattern
Query Type	Type of query (SELECT, INSERT, etc.)
Job Type	BigQuery job type
Query Text	Preview of the SQL query
Total Slot (ms) / Cost	Aggregate slot consumption or cost
Total Bytes Processed	Total data scanned
Last 7d Query Count	Execution frequency
Labels	BigQuery job labels

Click on any query hash to view detailed execution history.

Query Comparison

Compare two query patterns side-by-side:

Click the Compare button
Select up to 2 queries
View the SQL differences

This is useful for comparing before/after optimization or understanding why similar queries have different performance.

Query Details Page

Click on a query hash to see:

Individual query executions with job IDs, users, duration, and status
Execution time trends
Resource consumption patterns
Detailed query execution stages

Use Cases

Finding Expensive Queries

Sort by total cost to find the most expensive query patterns
Review the query text to identify optimization opportunities
Check frequency to prioritize high-impact improvements

Identifying Optimization Candidates

Look for queries with:

High bytes processed (may benefit from partitioning)
High slot consumption (may need query optimization)
High frequency (small improvements have large aggregate impact)

Debugging Performance Issues

Find the query pattern
View the execution history
Compare recent runs to identify when performance changed
NEW! Ask RADEN to help identify the reason for the slowness. RADEN offers query explanation and optimization using the context