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Clustering keys are critical in optimizing performance for specific types of queries that filter or sort data on the designated columns. When a clustering key is defined on a table, Snowflake organizes the data in a way that allows for more efficient scanning of the data during query execution. This is particularly beneficial for queries that include filters or sorts on the column defined as a clustering key.

By minimizing the amount of data that needs to be read to satisfy these queries, clustering keys can significantly reduce query execution times. This is especially evident when the dataset is large, as it allows for better data locality, which means the relevant data can be retrieved more quickly by the query engine.

In contrast, while performance improvements are most pronounced in large datasets, clustering keys can still enhance performance to some degree on smaller datasets, although the benefits may not be as noticeable. However, they are not universally beneficial for all types of queries—only for those that specifically filter or sort based on the clustering key. This makes the understanding of how and when to use clustering keys essential for Snowflake users looking to optimize their queries.