Redshift queries commonly filter by time range and customer ID.
Design sorting and distribution so that scans read as few blocks as possible.
Which design principle is most appropriate?
Choose a SORTKEY from query predicates and data correlation to improve range scans and block skipping.
Detailed explanation
CloudTrail is a service and not a column-design choice for a Redshift table.
CloudTrail is a service and not a column-design choice for a Redshift table.
Random order prevents the table from using correlation for efficient range scans.
Random order prevents the table from using correlation for efficient range scans.
Sorting on a useful range column can improve block skipping and reduce data read for common filters.
Sorting on a useful range column can improve block skipping and reduce data read for common filters.
S3 object lifecycle management does not change the physical order of a Redshift table.
S3 object lifecycle management does not change the physical order of a Redshift table.
Check official documentation
Compare the following points with the official documentation.
Amazon RedshiftのSORTKEY、データソート、ブロックプルーニングの公式説明を確認する。
What to confirm
S3の保存管理とRedshiftテーブル物理設計の違いを説明できる。
Key points
- Redshift SORTKEY
- Range predicate
- Block pruning
- Query pattern
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Physical design
Sort and distribution choices should follow access patterns, selectivity, and update behavior.
Sort maintenance
Load patterns and automatic optimization determine whether the table remains effectively sorted.