blocksorting

Blocksorting is a family of sorting techniques that operate on the input data in fixed-size blocks rather than sorting the entire dataset as a single stream. The standard approach divides the data into consecutive blocks of size B, sorts each block independently to produce locally ordered subsequences, and then merges the blocks to produce a globally sorted sequence. This blockwise strategy emphasizes data locality and can be parallelized by assigning different blocks to separate processing units.

In practice, blocksorting is commonly used in external sorting, where the dataset is too large to fit in memory. It underpins external merge sort: blocks are read from storage into memory, sorted, written back, and then merged across passes to form the final sorted output. The block size is chosen to balance memory usage, cache efficiency, and disk I/O, with larger blocks reducing the number of blocks but potentially increasing memory pressure.

Complexity and performance depend on data size and the chosen block size. If the dataset contains N elements and there are m = ceil(N/B) blocks, sorting within blocks costs roughly O(m B log B) comparisons, i.e., O(N log B). Merging the sorted blocks typically requires O(N log m) time. The overall performance for comparison-based sorts is thus related to O(N log m + N log B), which approaches O(N log N) for large N. In external settings, disk I/O often dominates, so blocksize decisions focus on minimizing I/O and maximizing cache hits.

Applications include external sorting for large-scale databases, cache-efficient sorting in memory-limited environments, and preprocessing steps in certain data-compression workflows where block-level organization helps expose regularities. Blocksorting is generally favored when locality, parallelism, or memory constraints are central considerations.