Bigquery Sql

by gemini-cli-extensions2df10e25bbf7No licenseListed Oct 8, 2026Updated Oct 8, 2026

Provides BigQuery SQL query optimization techniques, execution best practices, and performance tuning rules for high-efficiency querying. Use when optimizing BigQuery SQL queries, reducing query costs, or designing performant SQL transformations.

Instructions onlyData & Analytics
AI-generated overview

Provides BigQuery SQL optimization rules for query performance, cost reduction, and efficient transformations.

What it does
Supplies a set of BigQuery SQL performance and efficiency guidelines covering column pruning, predicate pushdown, join optimization, and materialization strategies. It defines rules that are applied automatically, rewrites that are mandatory, and conditional changes proposed with confirmation. It also asks for a summary section listing only the optimizations applied.
When to use it
Use when optimizing BigQuery SQL queries, reducing query costs, or designing performant SQL transformations. It fits review or rewriting of existing BigQuery queries against the stated rules.
Requirements
Instructions only; no scripts are shipped. It assumes work on BigQuery SQL queries and does not itself require credentials or network access.

BigQuery SQL Optimization

Performance and efficiency guidelines for BigQuery SQL queries. Includes rules for column pruning, predicate pushdown, join optimization, and materialization strategies.

SQL Optimization Rules

[!TIP] Always include a "Summary of Optimizations" section listing only the optimizations applied.

Always Apply (Automatic)

  • Column Pruning: Remove unnecessary columns from all query stages.
  • Common Subexpression Reuse: Factor out identical expressions to avoid redundant computation.
  • Predicate Pushdown: Apply WHERE filters as early as possible.
  • Early Aggregation: Perform GROUP BY before joins when possible.
  • Intermediate Materialization: Choose VIEW vs TABLE for intermediate nodes based on efficiency.
Intermediate Node Strategy
  • VIEW: Small datasets or simple transformations.
  • TABLE: Large datasets, expensive computations, or nodes reused multiple times.

Always Rewrite (Mandatory)

  • WHERE <col> IN (SELECT ...): Replace With WHERE EXISTS (SELECT 1 FROM ...)
  • WHERE (SELECT COUNT(*) ...) > 0: Replace With WHERE EXISTS (SELECT 1 FROM ...)

Propose with Confirmation (Conditional)

  • UNION → UNION ALL: Faster (skips deduplication), but permits duplicate rows.
  • COUNT(DISTINCT) → APPROX_COUNT_DISTINCT: Faster and lower memory, but approximate.

Source and attribution

Source:gemini-cli-extensions/data-agent-kit-starter-packinskills/bigquery-sqlat commit2df10e2

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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