What feature of Amazon Redshift allows for quick query performance?

Columnar storage and data compression are key features of Amazon Redshift that significantly enhance query performance. In a traditional row-based storage system, data is stored in rows, which can be inefficient for analytical queries that often access only a few columns from a large dataset. Instead, Redshift uses columnar storage, which means data is stored in columns. This allows for more efficient retrieval of data, as only the required columns are read during a query, significantly reducing the amount of data scanned.

Moreover, Redshift leverages data compression techniques to further optimize storage and query performance. By compressing the data in each column, less disk I/O is required during query execution, leading to faster retrieval times. This combination of columnar storage and compression not only improves performance but also reduces the storage costs associated with maintaining large datasets.

In contrast, other features like row-based storage, in-memory computing, and distributed computing, while they have their own roles in database management systems, do not directly contribute as significantly to rapid query performance as columnar storage and data compression do within the context of Redshift. Row-based storage, for example, may lead to more data being read than necessary for analytical queries, while in-memory computing may refer more broadly to processing techniques outside of