Analyze Usage

Analyze BigQuery Usage

This skill performs comprehensive analysis of BigQuery usage patterns, costs, and query performance for a given project. It identifies expensive queries, heavy users, and provides actionable optimization recommendations.

When to Use This Skill

This skill is automatically invoked by the /bigquery:analyze-usage command to perform usage analysis.

Prerequisites

• Google Cloud SDK (bq command-line tool) must be installed
• User must have BigQuery read access to the project
• User must be authenticated (gcloud auth login)
• User needs bigquery.jobs.list permission at minimum

Parameters

When invoked, this skill expects:

• Project ID: The GCP project ID to analyze (required)
• Timeframe: Time period for analysis in hours (e.g., 24, 168 for 7 days)

Analysis Workflow

1. Validate Prerequisites

First, verify the environment is ready:

• Check if bq command is available
• Verify project access
• Parse timeframe into hours

2. Collect Usage Data

Execute the following BigQuery queries against INFORMATION_SCHEMA:

Total Usage Summary

SELECT
  COUNT(*) as total_queries,
  ROUND(SUM(total_bytes_processed) / POW(10, 12), 2) as total_tb_scanned,
  ROUND(SUM(total_bytes_processed) / POW(10, 12) * 6.25, 2) as estimated_cost_usd
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
  AND job_type = 'QUERY'
  AND state = 'DONE'
  AND statement_type != 'SCRIPT'

Usage by User/Service Account

SELECT
  user_email,
  COUNT(*) as query_count,
  ROUND(SUM(total_bytes_processed) / POW(10, 12), 2) as total_tb_scanned,
  ROUND(SUM(total_bytes_processed) / POW(10, 12) * 6.25, 2) as estimated_cost_usd,
  ROUND(AVG(total_bytes_processed) / POW(10, 9), 2) as avg_gb_per_query
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
  AND job_type = 'QUERY'
  AND state = 'DONE'
  AND statement_type != 'SCRIPT'
GROUP BY user_email
ORDER BY total_tb_scanned DESC
LIMIT 20

Top Individual Queries by Cost

SELECT
  creation_time,
  user_email,
  job_id,
  ROUND(total_bytes_processed / POW(10, 12), 3) as tb_scanned,
  ROUND(total_bytes_processed / POW(10, 12) * 6.25, 2) as cost_usd,
  SUBSTR(query, 1, 200) as query_preview
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
  AND job_type = 'QUERY'
  AND state = 'DONE'
  AND statement_type != 'SCRIPT'
  AND total_bytes_processed > 0
ORDER BY total_bytes_processed DESC
LIMIT 20

Query Pattern Analysis

SELECT
  SUBSTR(query, 1, 200) as query_pattern,
  COUNT(*) as execution_count,
  ROUND(SUM(total_bytes_processed) / POW(10, 12), 3) as total_tb_scanned,
  ROUND(AVG(total_bytes_processed) / POW(10, 9), 2) as avg_gb_per_execution,
  ANY_VALUE(user_email) as sample_user
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
  AND job_type = 'QUERY'
  AND state = 'DONE'
  AND statement_type != 'SCRIPT'
  AND total_bytes_processed > 0
GROUP BY query_pattern
HAVING execution_count > 10
ORDER BY total_tb_scanned DESC
LIMIT 15

3. Per-User Deep Dive Analysis

For the top 2-3 users by data scanned, perform detailed query pattern analysis:

SELECT
  SUBSTR(query, 1, 300) as query_pattern,
  COUNT(*) as execution_count,
  ROUND(SUM(total_bytes_processed) / POW(10, 12), 3) as total_tb_scanned,
  ROUND(AVG(total_bytes_processed) / POW(10, 9), 2) as avg_gb_per_execution,
  MIN(creation_time) as first_execution,
  MAX(creation_time) as last_execution
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
  AND job_type = 'QUERY'
  AND state = 'DONE'
  AND statement_type != 'SCRIPT'
  AND user_email = @user_email
  AND total_bytes_processed > 0
GROUP BY query_pattern
ORDER BY total_tb_scanned DESC
LIMIT 20

This reveals:

• What each heavy user is querying
• Patterns in their query behavior
• Opportunities for user-specific optimizations
• Whether queries are automated (service accounts) or manual (humans)

4. Analyze Results and Identify Patterns

Look for common issues:

Query Anti-Patterns:

• SELECT * on large tables
• Full table scans without WHERE clauses
• High-frequency queries that could be cached
• Queries scanning data unnecessarily (e.g., counting rows by scanning 40GB)
• Missing partitioning filters
• Repeated identical queries

User Behavior Patterns:

• Service accounts with high query volume (automation candidates)
• Deduplication checks that scan large amounts of data
• Dashboard queries hitting raw tables instead of materialized views
• Scheduled queries running too frequently

Cost Drivers:

• Single expensive queries
• High-volume low-cost queries (death by a thousand cuts)
• Inefficient aggregations
• Missing indexes/clustering

5. Generate Optimization Recommendations

For each issue found, provide:

1. What: Describe the issue clearly
2. Why: Explain why it's expensive
3. How: Provide specific fix instructions
4. Savings: Estimate potential cost reduction
5. Difficulty: Rate implementation effort (easy/medium/hard)
6. Priority: Based on impact and ease

Prioritization Framework:

• Priority 1: High impact, easy wins (>$5/day saved, easy implementation)
• Priority 2: Medium impact (>$2/day saved)
• Priority 3: Architectural improvements (long-term benefits)

6. Format Comprehensive Report

Structure the output as:

# BigQuery Usage Analysis Report
**Project:** <project-id>
**Analysis Period:** Last <timeframe>
**Generated:** <timestamp>

## Executive Summary
- Total Queries Executed: <count>
- Total Data Scanned: <TB>
- Estimated Cost: $<amount>

### Key Findings
1. <Top issue with data/cost>
2. <Second major issue>
3. <Third issue>

## Usage by User/Service Account
<Table with top 10-20 users>

## Top Query Patterns
<Detailed breakdown of top patterns with recommendations>

## Per-User Analysis

### Top User 1: <user_email> (<TB> scanned, $<cost>)
<Summary of what this user does>

**Primary Query Types:**
1. <Pattern description> (<data scanned>)
   - Execution count
   - Average per query
   - Specific optimization recommendation

### Top User 2: <user_email> (<TB> scanned, $<cost>)
<Summary of what this user does>

**Primary Query Types:**
1. <Pattern description> (<data scanned>)
   - Execution count
   - Average per query
   - Specific optimization recommendation

## Top Individual Queries
<Table of most expensive queries>

## Optimization Recommendations

### Priority 1: High Impact, Easy Wins
1. **<Recommendation title>**
   - Issue: <description>
   - Fix: <specific steps>
   - Estimated Savings: <$/day or %>
   - Difficulty: Easy/Medium/Hard

### Priority 2: Medium Impact
...

### Priority 3: Architectural Improvements
...

## Cost Breakdown Summary
- Service Accounts: $<amount> (<percentage>)
- Human Users: $<amount> (<percentage>)

7. Offer to Save Report

After presenting the analysis, ask the user if they want to save it to a markdown file:

• Suggest filename: bigquery-usage-<project-id>-<YYYYMMDD>.md
• Include all analysis details
• Format with proper markdown tables and sections

Error Handling

Handle common issues by displaying a clear error message and suggesting a fix:

"bq command not found"

• Provide installation instructions for user's platform
• macOS: brew install google-cloud-sdk
• Linux: Point to cloud.google.com/sdk
• Verify PATH configuration

"Access Denied" errors

• Guide user through gcloud auth login
• Verify project access: bq ls --project_id=<project-id>
• Check IAM permissions (need BigQuery Job User role minimum)

"Invalid project ID"

• Verify project ID vs project name
• List available projects: gcloud projects list
• Check for typos

No data returned

• Verify queries have run in the specified timeframe
• Check region (try region-us, US, EU, etc.)
• Ensure querying correct project

Query execution errors

• Check INFORMATION_SCHEMA availability
• Verify region-specific schema locations
• Adjust queries for project's BigQuery setup

Implementation Notes

Cost Calculation

• Use on-demand pricing: $6.25/TB (as of 2024-2025)
• Note in report if project may have flat-rate pricing
• Savings estimates are based on on-demand pricing

Region Handling

• Default to region-us INFORMATION_SCHEMA
• If queries fail, try US or EU
• Consider making region configurable in future

Performance Considerations

• All analysis queries are read-only
• Use INFORMATION_SCHEMA (metadata only, very efficient)
• Queries should complete in seconds
• Longer timeframes (30 days) may take longer but still fast

Query Optimization

• Use --format=json for easy parsing
• Use --use_legacy_sql=false for standard SQL
• Limit result sets to 20 rows unless the user requests more
• Filter to completed queries only (state = 'DONE')

Example Output Insights

Good User Analysis Example:

### openshift-ci-data-writer (4.07 TB, $25.41)
This service account runs test run analysis and backend disruption monitoring.

**Primary Query Types:**
1. **TestRuns_Summary_Last200Runs** (3.02 TB - 74% of usage)
   - 96 executions using SELECT *
   - 31.41 GB per query
   - **Recommendation:** Replace SELECT * with specific columns.
     Estimated savings: $9-15/day

2. **BackendDisruption Lookups** (0.68 TB - 17% of usage)
   - 4,165 queries checking for job run names
   - **Recommendation:** Add clustering on JobName + JobRunStartTime.
     Implement result caching.

This level of detail helps users understand exactly what's driving their costs and how to fix it.

Success Criteria

A successful analysis should:

1. Identify all queries scanning >100GB per execution
2. Find high-frequency query patterns (>100 executions)
3. Provide at least 3 actionable optimization recommendations
4. Include cost estimates for top recommendations
5. Offer clear next steps for the user
6. Be formatted clearly and professionally

Future Enhancements

Add:

• Trend analysis (compare current period to previous periods)
• Query performance metrics (execution time, slot usage)
• Automatic detection of partitioning opportunities
• Cost anomaly detection (unusual spikes)
• Integration with BigQuery Reservations data
• Historical cost tracking over time