bigquery-optimization

BigQuery Optimization Workflow

Prerequisites & Environment Setup

Before executing optimization analyses, evaluating editions, or applying DDL modifications:

1. Google Cloud SDK: Ensure the Google Cloud SDK is installed and configured.

2. Project Selection: Set the active Google Cloud project:

   gcloud config set project {project_id}
   

3. API Enablement: Ensure BigQuery and BigQuery Reservation APIs are enabled:

   gcloud services enable \
       bigquery.googleapis.com bigqueryreservation.googleapis.com
   

4. Authentication: Authenticate the environment:

   • CLI tools and bq commands: gcloud auth login
   • SDKs and automation: gcloud auth application-default login
   • Service accounts: Set GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"

5. Billing & IAM Roles:

   • Verify an active Google Cloud Billing account is attached to {project_id}.
   • Ensure appropriate IAM roles:
     • roles/bigquery.admin or roles/bigquery.resourceAdmin: Reservation and capacity commitment management.
     • roles/bigquery.dataEditor or roles/bigquery.admin: Modifying table schemas, partitioning, clustering, and storage billing models.
     • roles/bigquery.jobUser: Running evaluation queries.

6. Companion Skills Installation: This skill is part of a 3-pillar operations suite (bigquery-observability, bigquery-optimization, bigquery-troubleshooting). If any companion skill is not yet installed in your environment, install the full suite:

   npx skills add google/skills --skill bigquery-observability --skill bigquery-optimization --skill bigquery-troubleshooting
   

   (If bigquery-observability is not installed, use the self-contained baseline formulas and query templates provided directly in the reference sections below).

Workflows

Determine the optimization focus of the user's request and follow the relevant workflow:

• Telemetry & Observability Baseline: For direct raw usage telemetry, INFORMATION_SCHEMA queries, and baseline metric calculations, consult bigquery-observability (bigquery_observability). If the bigquery-observability companion skill is not available in the active environment, all optimization guidelines, DDL templates, and decision models across this skill and its reference guides are fully self-contained.
• Capacity & Editions Modeling: Evaluate the cost-efficiency of migrating workloads from On-Demand to Editions, as well as rightsizing active Edition reservations, baseline commitments, and autoscaling caps.
  • Instructions: Read references/capacity_planning_editions.md to provide deep links to BigQuery's built-in recommendation UIs (e.g., Slot Estimator) and guide the user through UI navigation: 1. navigate to the Slot Estimator tab, 2. select 'On-Demand' as the source to analyze historical query volume, and 3. review the Cost-Optimized Recommendations and Slot Usage Chart.
• Table & Storage Optimization: Optimize storage costs from a billing model, physical layout, and lifecycle perspective.
  • Billing Architecture: Read references/storage_billing_models.md for guidance on evaluating aggregate compression ratios (e.g. >2:1 threshold in US) to recommend Physical vs. Logical billing, noting that the break-even ratio depends on specific regional rates and custom enterprise contracts. When providing TABLE_STORAGE queries, always scope with WHERE table_schema = '{dataset_id}', use the regional dataset view, and warn that 0 rows indicates a region mismatch or lack of native tables rather than zero billable usage.
  • Partitioning & Clustering Strategy: Read references/table_partitioning_clustering.md to generate production DDL templates (CREATE TABLE, CTAS migrations for unpartitioned tables, and modifying clustering specifications), enforce pruning with require_partition_filter = true, and manage partition limits (up to 10,000 partitions/table).
  • Lifecycle Management: Read references/storage_lifecycle_management.md to pinpoint inactive data and define precise Time-to-Live (TTL) partition expirations, dataset expirations, and Time Travel window reductions.
• SQL Optimization: Optimize individual SQL queries to reduce slot-time and the amount of data read.
  • Instructions: Follow the instructions in references/sql_optimization.md to provide recommendations to the user on how to rewrite their SQL query to reduce slot-time and the amount of data read.

Execution Guardrails

• Terminology & Cost Framing: Never promise or guarantee "cost-reduction" or "reducing expenditure." Always frame recommendations using the terminology "optimizing your bill" or "improving cost-efficiency."
• Explicit Scope Framing & Region Resolution: Always state the target project_id and region at the very top of your response so the user immediately knows the exact scope being evaluated. Follow this 3-tier resolution hierarchy:
  1. Explicit Region: Use the region specified in the user's prompt (e.g., europe-west1).
  2. Contextual Region: Resolve the region from the specific dataset or resource mentioned in the context.
  3. Unspecified Fallback: Default to us / region-us, explicitly state that us was assumed as the default, and instruct the user to substitute their region if their resources reside elsewhere. Region Formatting: In Cloud Console deep links, use the region identifier directly (e.g., region=us, region=europe-west1). In SQL queries against INFORMATION_SCHEMA, use the regional dataset qualifier (e.g., region-us, region-europe-west1).
• Zero-Row Result Guard: If querying TABLE_STORAGE with WHERE table_schema = '{dataset_id}' returns 0 rows, do not proceed with an empty or zero-usage evaluation. Treat this as an indicator that the dataset may reside in a different region or have no native tables; stop and prompt the user to confirm the dataset's regional location.
• Populate Concrete Parameters: When generating URLs and SQL queries, always substitute known project_id and region values directly into the code and links. Never leave literal {project_id} or {location} placeholders for the user to manually edit.
• No Autonomous Purchasing or Financial Mutations: Never provide the user with executable scripts (e.g., gcloud or bq shell commands like bq update --storage_billing_model=...) designed to autonomously purchase annual commitments, alter edition tier bindings, or mutate storage billing models. Always guide the user to execute commitment purchases, reservation changes, and storage billing model updates manually via the Cloud Console UI.