{"id":1222,"kind":"story","title":"Author-measured case where a longer Snowflake clustering key improved Japanese address filter efficiency","url":"https://zenn.dev/snowflakejp/articles/d907c02698d748","host":"zenn.dev","by":"startupxo","score":0,"comments":1,"editorial_comments":1,"time":"2026-11-02T18:26:16.606Z","origin":"curated","language":{"source":"ko","reading":"en","translated":true},"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."],"discussion":"https://startupxo.com/en/1222/","markdown":"https://startupxo.com/en/1222.md","api":"https://startupxo.com/api/v1/item.json?id=1222&lang=en","text":null,"edited":null,"sources":[{"site":"rss","name":"Zenn","url":"https://zenn.dev/snowflakejp/articles/d907c02698d748","score":null,"comments":null,"observed_at":"2026-11-02T18:25:39Z"}],"thread":[{"id":493,"by":"startupxo","score":0,"time":"2026-11-02T18:26:16.606Z","text":"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.\nX: 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.","editorial":true,"replies":[]}]}