Faster Analytics: Snowflake Improves Average Query Duration by 40%

At Snowflake, we're committed to delivering consistent, automatic performance enhancements. We work behind the scenes to make your data operations faster, more efficient and more cost effective — without any user intervention, manual configuration or scheduled downtime. Every week, we seamlessly deploy updates in the background, ensuring your workloads are always running on the latest and fastest version of Snowflake with zero disruption to your service. We know that your time is money, so we aim to make Snowflake as easy to use and as optimized as possible, right out of the box. 

This approach is all about helping you get the best price for performance with Snowflake. Plus, with our consumption-based pricing model, these performance boosts can lead to real cost savings for you.

Snowflake Performance Index results

Our dedication to your success drives us to continually measure and enhance the performance you experience with Snowflake. The Snowflake Performance Index (SPI) tracks these real-world improvements over time. Instead of using synthetic benchmarks for performance comparisons, we measure our enhancements using real customer data on production workloads. 

This means the SPI reflects genuine improvements that make a difference in your day-to-day operations. Since we launched the SPI in August 2022, the average query duration for stable workloads has now improved by 40%. In the last 12 months alone, the SPI has seen a 20% improvement.

Latest performance improvements tracked by the SPI

Over the past 12 months, we've introduced several significant improvements — mostly happening automatically, without needing any configuration or additional effort to modify code.

Query execution improvement: We’ve invested effort to continue reducing execution times and handling complex query patterns more effectively. Examples include optimizing join queries, automatically handling skew and expanding support for Top-K pruning to improve performance for queries with specific aggregation and filtering patterns. These updates help your queries run faster, even as workloads grow in complexity.

Data ingestion and replication: We reduced the time spent on metadata replication, we made cloning faster, and we optimized the ingestion of large data sets to help you bring data into Snowflake faster and more reliably, streamlining your workflows and pipelines.

Adaptive optimization: We launched a number of adaptive optimizations to make Snowflake smarter at choosing the best strategies for query execution. For instance, we've expanded Top-K pruning to include a broader range of queries and refined the optimizer's ability to make intelligent join-order decisions, benefitting you from faster, more efficient query planning.

Platform efficiency: We continued to enhance the platform's overall reliability and speed. For example, we reduced the time required for cloning operations and made compression more efficient, reducing resource consumption and enabling smoother system operations.

These are just a few examples of how we're making Snowflake faster and more efficient for you. We're committed to keeping this momentum going. By continuously investing in performance enhancements, we're dedicated to helping you get more value from Snowflake while reducing your operational costs over time.

To learn more about the latest improvements, check out our performance release notes and visit the SPI website.

*Based on internal Snowflake data, average query duration for customers’ stable workloads improved by 40% from August 25, 2022, to October 31, 2024. To calculate SPI, we identify a group of customer workloads that are stable and comparable in both amount of queries and data processed over the period presented. Reduction in query duration resulted from a combination of factors, including hardware and software improvements and customer optimizations. Improvement in query duration metrics are rounded to the nearest hundredth.