Multidarrlike

Multidarrlike is a term used in computer science and data processing to describe data structures and interfaces that present an array-like, multi-dimensional indexing surface while accommodating irregular shapes, dynamic sizing, or heterogeneous element types. It generalizes the idea of a fixed-shape multi-dimensional array to cover a family of structures that resemble arrays in their API but relax rigidity in shape or type uniformity.

Most multidarrlike implementations combine nested arrays or lists, or store data in a flat buffer with a separate index map. Access is via a coordinate tuple, and subsetting can produce views or new multidarrlike objects with adjusted shape. Some designs maintain dense storage for homogeneous subregions and use masks, offsets, or offset maps to handle missing elements. In dynamically typed languages, elements may vary in type; in statically typed contexts, they are typically constrained by a common supertype or union.

Use cases include ragged arrays, irregular grids, datasets with variable-length records, and scientific or engineering data where dimensionality is conceptually multi-dimensional but not uniformly shaped. The concept overlaps with but is distinct from tensors, matrices, and traditional multi-dimensional arrays; it is often realized in libraries that emphasize flexible labeling and alignment, such as xarray or awkward-array in Python, or similar patterns in other ecosystems.

Limitations include higher indexing overhead, potential memory fragmentation, and more complex optimization compared with fixed-shape arrays. Multidarrlike structures can be harder to interoperate with libraries that assume uniform dimensions. When performance or memory predictability is critical, developers may fall back to fixed arrays or specialized sparse/tensor formats, reserving multidarrlike for flexible data modeling and expressive APIs.

See also: array, tensor, ragged array, jagged array, awkward-array, xarray.