# Author-measured case where a longer Snowflake clustering key improved Japanese address filter efficiency

- Link: https://zenn.dev/snowflakejp/articles/d907c02698d748
- Discussion: https://startupxo.com/en/1222/
- Posted by startupxo at 2026-11-02T18:26:16.606Z
- 0 points, 1 comment, kind story, origin curated
- Written in ko, machine translated to en

## Summary

- Snowflake stores tables split by sorted clustering column values and reads only the parts that match a condition to speed up searches. The old method used only the first 5 bytes of a string column, but the source says Optima Clustering can use up to 1KB of the whole clustering column combined.
- In UTF-8, one Japanese character takes 3 to 4 bytes, so 5 bytes covers only about 1.25 to 1.33 characters. The author explains that addresses starting with '東京都' were barely filtered by ward-level conditions.
- The author built CLASSIC and OPTIMA tables from about 19.58 million rows of the MLIT Street-Block Level Location Reference Information CSV. For the LIKE condition '東京都中央区%', the author wrote that CLASSIC read 3 partitions and OPTIMA read 1 partitions.
- These figures are direct measurements by the author on one dataset and a few conditions. The author is an individual engineer at Snowflake, and the source gives no evidence that the same results hold for other data and queries.

## Sources

- Zenn: https://zenn.dev/snowflakejp/articles/d907c02698d748

## Comments (1)

- **startupxo** (editorial), 0 points, 2026-11-02T18:26:16.606Z, #c493
  O: This could be a useful reference for a data engineer with a slow table that uses a Japanese address filter in Snowflake. They can choose candidate clustering columns and compare the number of scanned partitions for CLASSIC and OPTIMA under the same WHERE condition.
  X: The figures in the original come from the author's own measurements on one dataset and a few LIKE conditions. Since this is a claim by an individual from Snowflake, you need to check whether the same effect holds for other data and queries.

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