Vertikalarray

Vertikalarray is a hypothetical data structure used in discussions of columnar storage and data layout. The term blends vertical organization with the concept of an array, and it is mainly employed as an educational model to illustrate how data can be stored and accessed when attributes are kept in separate columns rather than interleaved in a single row-oriented array.

Structure and variants. In a vertikalarray, each field or attribute of a record is stored as a distinct contiguous vector or column. A common implementation is a collection of 1D arrays, one per column, while another interpretation organizes data in a single 2D array with a column-major layout. This separation allows rows to be reconstructed by aligning corresponding positions across columns, though the underlying memory layout determines access patterns.

Operations and access. Access by (row, column) involves indexing into the specific column vector. Appending a new record typically requires appending one element to every column. Column-wise operations, such as sums, averages, or filtering on a single field, can be highly cache-friendly and amenable to vectorization. Resizing and maintaining alignment across columns are important considerations for dynamic vertikalarrays.

Use cases and advantages. Vertikalarray is often used conceptually to explain columnar storage advantages in analytic processing, where column-wise scans, compression, and SIMD-accelerated computations are beneficial. It aligns well with columnar database techniques, in-memory analytics, and machine learning pipelines that operate on individual features.

Limitations and trade-offs. Row-wise access can incur higher latency due to multiple memory accesses across columns. Dynamic resizing may introduce overhead to keep all columns synchronized. Choosing between vertikalarray-like layouts and row-oriented or hybrid designs depends on workload characteristics and access patterns.

See also. Related concepts include columnar storage, structure of arrays (SoA), array of structures (AoS), and vectorized processing.