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When considering the need for Clustering Keys on a table within Snowflake, one key indicator is the performance of queries executed against that table. If queries are running slower than anticipated, it suggests that the data may not be organized optimally for efficient querying.

Clustering Keys help to optimize the arrangement of data within micro-partitions, which can significantly improve query performance, particularly for large tables with specific query patterns. The performance degradation could stem from how the data is physically stored and accessed. If the data is not clustered in a way that aligns with commonly executed queries, this leads to increased scan times and poorer performance.

In contrast, if all queries execute quickly, there’s typically no need to implement Clustering Keys, as the current setup is already efficient. A table with only one micro-partition would not benefit from clustering because partitioning itself would not be complex enough to warrant clustering measures. Lastly, while table size is a consideration in performance tuning, having a table smaller than 1 TB does not inherently necessitate Clustering Keys unless query performance indicates it is needed.