columnoriented

Columnoriented refers to a data storage and processing approach where data is stored and accessed primarily by columns rather than by rows. In a column-oriented (or columnar) system, each column's values are stored contiguously on disk or in memory, and queries read only the columns required for a result. This contrasts with row-oriented storage, where all fields of a row are stored together. Columnar designs are widely used for analytical processing and data warehousing, where queries typically perform scans, aggregates, and projections over large datasets rather than many random row updates.

Key features include columnar compression and encoding, which exploit redundancy within a column to achieve high compression ratios and reduce I/O. Common techniques include dictionary encoding, run-length encoding, delta encoding, and bit-packing. The columnar layout also enables vectorized execution and better CPU cache utilization, as operations can process data in bulk on contiguous memory regions.

Trade-offs exist: writes and updates can be more expensive in columnar stores, and some operations that require accessing many columns or row-level predicates may be less efficient. For transactional workloads with frequent random updates, row-oriented databases or hybrid approaches may be preferable. In practice, columnar storage is often used in data warehouses, business intelligence, and large-scale analytics, sometimes in combination with columnar file formats like Parquet or ORC, and in columnar database systems such as ClickHouse, Amazon Redshift, Snowflake, or Vertica, as well as hybrid engines like Kudu.

Overall, columnoriented design emphasizes read-optimized storage, high compression, and fast projection/aggregation over large datasets, at the cost of write complexity and row-level mutability.