Columnar lists

clist[T] makes data-oriented performance accessible through ordinary Plenty code. Declare a class normally, then choose clist[YourClass] when a large collection of its records is usually processed one or two fields at a time. You still append ordinary objects and refer to their named fields. The collection stores each field in its own packed, contiguous column.

For example, averaging temperatures does not need the station identifier from every reading. Keeping temperatures together lets a scan bring more useful values into the CPU cache with each memory access. Column pipelines make this kind of bulk calculation natural: select a field, then use the familiar fluent operations. The storage choice belongs to this collection; it does not change how the class is stored everywhere else.

This is useful as a representation as well as a container: it makes efficient execution easier to express for the programmer and easier to recognize for the compiler. Start with the access pattern you need, rather than expecting every operation on a columnar list to become faster. Gathering a complete row requires reading separate columns. The current implementation accepts only primitive fields; use ordinary lists for strings, nested objects, or borrowed references to whole rows.

For analytical expression plans, grouping and Parquet datasets, consider dataframes. Their optional Polars engine has runtime schemas and native query semantics; clist keeps a lightweight mutable collection and Plenty’s own arithmetic and type checking.

• Build and scan columnar records: reserve capacity, append records, scan a column, and update or copy a row.
• Count matching integer values: express a filter/count calculation that the compiler can accelerate with SIMD.
• Remove rows and reuse storage: take individual records or clear a batch while retaining capacity.