Snowflake Query Optimization in 2025

Snowflake is renowned for its incredible performance, but as data scales into terabytes and petabytes, no platform is immune to a slow-running query. For a data engineer, mastering Snowflake query optimization is the difference between building an efficient, cost-effective data platform and one that burns through credits and frustrates users.

In 2025, the principles of query optimization remain the same, but the tools and techniques have matured. It’s no longer just about warehouse size; it’s about understanding the query execution plan, leveraging micro-partitions, and writing smarter SQL.

This guide will walk you through the essential strategies and best practices for Snowflake query optimization, moving from the foundational tools to advanced, real-world techniques.

The Golden Rule: Always Start with the Query Profile

Before you change a single line of code, your first and most important step is to analyze the Query Profile. This tool is your window into how Snowflake’s cloud services layer executes your query.

To access it, go to the History tab in the Snowsight UI, find your query, and click on the Query ID.

What to look for:

• Operator Time: Which steps in the execution plan are taking the most time? Is it a TableScan, a Join, or a Sort operation?
• Partition Pruning: How many partitions is the TableScan reading versus the total partitions in the table? If it’s scanning a high percentage, your pruning is ineffective.
• Spilling: Is Snowflake spilling data to local or remote storage? Spilling to remote storage is a major performance killer.

For a detailed walkthrough, check out Snowflake’s official documentation on Using Query Profile.

1. Right-Size Your Warehouse (But It’s Not a Silver Bullet)

It’s tempting to throw a larger warehouse at a slow query, but this isn’t always the right or most cost-effective answer.

• Scale Up (Increase Size): Do this when a single, complex query is slow due to heavy computations like large joins, sorts, or aggregations. A larger warehouse provides more memory and local SSD, which can prevent spilling.
• Scale Out (Add Clusters): Use a multi-cluster warehouse for high-concurrency scenarios (e.g., a BI dashboard). This won’t make a single query faster, but it will handle more queries at once.

Best Practice: Don’t use a larger warehouse to compensate for a poorly written query. Always try to optimize the query and data structure first.

2. Master Clustering for Effective Pruning

This is the most impactful technique for optimizing queries on large tables. A Clustering Key reorganizes your data into co-located micro-partitions based on the key you define. This allows Snowflake to prune (ignore) massive amounts of data that aren’t relevant to your query’s WHERE clause.

When to Cluster:

• On tables larger than a terabyte.
• When you frequently filter or join on a high-cardinality column (e.g., user_id, event_timestamp).

Example:

SQL

-- A query on an un-clustered 100TB table
SELECT * FROM event_logs WHERE user_id = 'a1b2-c3d4';
-- This might scan 50% of the table's partitions.

-- Cluster the table
ALTER TABLE event_logs CLUSTER BY (user_id);

-- After reclustering, the same query might scan less than 0.01% of partitions.
SELECT * FROM event_logs WHERE user_id = 'a1b2-c3d4';

The Credit Karma engineering blog has an excellent real-world case study on how they used clustering to dramatically reduce costs.

3. Avoid Spilling at All Costs

Spilling occurs when Snowflake runs out of memory for an operation and has to write intermediate results to storage. Spilling to local SSD is okay, but spilling to remote storage is a performance disaster.

How to Fix It:

1. Increase Warehouse Size: This is the most direct solution, as it provides more memory.
2. Optimize the Query: Reduce the data being processed. Filter early in your query (using WHERE clauses) and select only the columns you need.
3. Use APPROX_COUNT_DISTINCT: For large-scale distinct counts, using an approximation function can be orders of magnitude faster and use less memory than COUNT(DISTINCT ...).

4. Write Smarter SQL

Sometimes, the best optimization is simply writing a better query.

• Filter Early, Join Late: Apply your WHERE clause filters to your largest tables before you join them. You can do this with a subquery or a Common Table Expression (CTE).
• Reduce Data Movement: In joins, join on columns with the same data type. If you join a STRING to a NUMBER, Snowflake has to cast the data on the fly, which can slow things down.
• Leverage Specific Functions: Use optimized functions like MATCH_RECOGNIZE for sequential pattern matching or FLATTEN for parsing semi-structured data instead of writing complex, self-joining SQL.

5. Use Materialized Views for Repetitive Queries

If you have a complex query that runs frequently on data that doesn’t change often, a Materialized View can be a game-changer. It pre-computes and stores the query result, so subsequent queries are just reading the stored results, which is incredibly fast.

Best For:

• BI dashboards that query complex aggregations.
• Queries on large, slowly changing datasets.

The Snowflake Community offers great primers and discussions on when to best use this feature.

Conclusion

Snowflake query optimization in 2025 is a multi-faceted discipline. It starts with a deep analysis of the Query Profile and extends to making intelligent choices about your warehouse sizing, data clustering, and SQL patterns. By moving beyond brute force and adopting these strategic techniques, you can build a Snowflake environment that is not only lightning-fast but also highly cost-effective.