The Power of Clustering Keys: Reduce Scan Costs by 70%

The Power of Clustering Keys: Reduce Scan Costs by 70%

Properly configured clustering keys can dramatically reduce the amount of data scanned per query. Discover which tables benefit most from clustering.

How Clustering Works

Snowflake automatically clusters data as it's loaded, but you can define clustering keys to optimize how data is organized. Queries that filter on clustering key columns will scan significantly fewer micro-partitions.

Which Tables Benefit Most

• Large tables (multi-TB)
• Tables with selective filter predicates
• Tables with time-series data
• Tables queried frequently with consistent filters

Implementation

ALTER TABLE <table_name> CLUSTER BY (<column1>, <column2>);

Choosing the Right Clustering Keys

1. Analyze your most expensive queries
2. Identify columns frequently used in WHERE clauses
3. Consider columns with high cardinality
4. Prioritize time-based columns for time-series data

Best Practices

• Use 1-4 columns maximum (usually 1-2 is optimal)
• Order matters: put the most selective column first
• Monitor clustering depth regularly
• Re-cluster when depth exceeds 4-5

Real-World Example

A financial services company with a 10TB transaction table was scanning 2-3TB per query. After adding a clustering key on (transaction_date, account_id), query scans dropped to 200-300GB, reducing query costs by 85% and improving performance 10x.

Key Takeaways

• Clustering keys can reduce data scanned by 70%+ on large tables
• Focus on columns used in WHERE clauses
• Monitor clustering depth and re-cluster as needed
• Balance clustering costs against query cost savings