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Columnar databases provide differentiated data storage that is particularly advantageous for analytical applications. By storing data in columns, query performance can be significantly improved, and focusing on specific attributes allows for deeper analyses.
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A columnar database stores values from each column together and reads only the columns needed by a query.
The approach grew from analytical database and data-warehouse systems, where large tables are often queried through only a few columns. The C-Store paper by Abadi, Madden, and Hachem is an early research foundation for modern column-oriented systems.
With row storage, one record stays together; with column storage, all values of an attribute stay together. Reading many records but fewer columns reduces I/O and often improves compression.
A jointly stored range of values for one attribute.
Limiting a query to the columns it actually needs.
Compact encoding of similar values within a column.
Columnar storage is especially effective for analytical scans and aggregations over large tables. Frequent single-row updates and access to nearly every column may fit row-oriented storage better.
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