Snowflake Query Performance Analysis & Optimization
Analyze and optimize Snowflake queries for peak warehouse performance
What You Can Do
You can analyze Snowflake query execution plans to identify performance bottlenecks and recommend targeted optimizations. This skill provides data-driven recommendations for warehouse configuration, data clustering, and SQL rewrites that reduce both execution time and credit costs.
Features
breaks down query stages, identifies full table scans and shuffle operations
recommends compute tier based on query complexity and concurrent workload
suggests clustering keys and micro-partition strategies to reduce scan cost
identifies inefficient predicates, joins, and subqueries with optimized alternatives
estimates credit savings (per query, daily, monthly) from each optimization
identifies candidates for views to replace expensive subqueries
detects resource contention and queueing delays
documents baseline metrics, optimization gains, and validation checksums
Example Output
Execution Plan Analysis:
- Current: Full table scan on 1.2B rows (45s, 120 credits)
- Root cause: No predicate pushdown on date column
- Optimization: Add clustering key on (customer_id, created_date)
- Result: Partition pruning to 48M rows (8s, 18 credits) — 82% speedup, 85% cost reduction
Warehouse Configuration Report:
- Current: X-Large (8 cr/hr) with 10 concurrent queries
- Issue: Queue wait time avg 12s, total throughput 2 queries/min
- Recommendation: Medium (2 cr/hr) with query queueing → 98% cost reduction, acceptable SLA
SQL Rewrite:
-- BEFORE: Full outer join causing redistribute
SELECT * FROM orders o FULL OUTER JOIN users u ON o.user_id = u.id WHERE o.created_date > CURRENT_DATE - 30
-- AFTER: Inner join with filtered subquery (80% faster)
SELECT o.*, u.name FROM orders o INNER JOIN users u ON o.user_id = u.id WHERE o.created_date > CURRENT_DATE - 30
What's Included
- SKILL.md: Complete Snowflake optimization framework with decision trees and analysis workflows
- Query audit checklist: Systematic execution plan review process and issue prioritization
- Warehouse sizing worksheet: Credit calculator and tier recommendation matrix
- Clustering strategy guide: Key selection patterns and partition micro-optimization
- SQL optimization patterns: 15+ common rewrites (correlated subqueries, window functions, etc.)
- Cost estimation template: Monthly credit projection and savings validation checklist
- Performance testing workbook: Benchmark capture, validation, and regression detection procedures
Who It's For
- Data engineers — Optimize ETL pipelines, warehouse loads, and merge operations
- Database administrators — Manage compute costs and enforce performance standards
- Analytics engineers — Improve dashboard query performance and reduce refresh times
- Data analysts — Debug slow exploratory queries and optimize report generation
- Cloud architects — Design cost-efficient Snowflake infrastructure and governance policies
Best For
- Analyzing slow queries using execution plans and identifying root causes
- Right-sizing Snowflake warehouse compute for workload, SLA, and budget constraints
- Optimizing table clustering and partition strategies to reduce data scans
- Generating SQL rewrites that eliminate sorts, shuffles, and redistributes
- Quantifying credit cost savings from each optimization recommendation
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