@Rocky I am designing a StarRocks table and considering a compound Sort Key structured as

(datetime_column, transaction_id)

. Given that

transaction_id

has very high cardinality, is rarely used in

WHERE

filters, but is heavily used in

JOIN

operations, does placing it as the second column in the Sort Key improve JOIN performance regardless of whether the datetime column is filtered or not? Or, for optimal JOIN performance, is it a better practice to remove it from the Sort Key entirely and use it exclusively as the Hash Distribution Key (

DISTRIBUTED BY HASH(transaction_id)

)?

Based on StarRocks table design principles and query execution mechanics, here is the breakdown of how your proposed configuration affects performance: 1. Does the Sort Key improve JOIN performance? No, not significantly. StarRocks primarily uses Hash Join as its core join operator. In a Hash Join, the engine reads data from the source and builds a hash table in memory. The physical sort order of the data on disk does not directly optimize the join's hash-building or probing phases. * Prefix Index Limitation: Since

transaction_id

is the second column in your Sort Key

(datetime_column, transaction_id)

, the Prefix Index for

transaction_id

only works if the first column (

datetime_column

) is also present as an equality filter in your

WHERE

clause. * Sequential Scans: While sorting by

transaction_id

might theoretically provide slightly better sequential access during the scan stage, this benefit is negligible compared to the overhead of joining. 2. Is Distribution the better practice for JOINs? Yes, absolutely. For optimal JOIN performance, the Hash Distribution Key is the most important factor. * Colocate Join: If you distribute both joining tables by

transaction_id

and place them in the same Colocation Group (

PROPERTIES("colocate_with" = "group_name")

), StarRocks can perform a Colocate Join. This is the fastest join method because it avoids all network traffic; the data is joined locally on each node. * Bucket Shuffle Join: Even if only the larger table is distributed by

transaction_id

, StarRocks can perform a Bucket Shuffle Join, which significantly reduces network overhead by shuffling only the smaller table to the nodes where the larger table's buckets reside. Recommendations * Distribution Key: Use

DISTRIBUTED BY HASH(transaction_id)

. This is the single most effective way to optimize joins on high-cardinality keys. * Sort Key: Keep the Sort Key focused on your filtering patterns. Since

transaction_id

is rarely used in

WHERE

filters, it should likely be removed from the Sort Key or placed last. Focus the Sort Key on

datetime_column

(and any other frequently filtered columns) to enable effective Zone Map pruning and Prefix Indexing. * Compound Key Order: If you do keep

transaction_id

in the Sort Key, remember that it only helps with scanning if

datetime_column

is filtered first. If you often query

transaction_id

without filtering by time, it would need to be the first column to provide any indexing benefit. Summary: To optimize for your "heavily joined" requirement, use

transaction_id

as your Distribution Key and potentially a Colocation Group. Use the Sort Key exclusively for columns that appear in your

WHERE

clauses. References • best_practices/table_clustering.md • knowledge/usage/best-practice.md