Filtersreduce

Filtersreduce is a conceptual operation used in functional programming and data processing that merges filtering and reduction into one pass over a collection. It filters elements by a predicate and applies a reducing function to accumulate results from the filtered elements.

Formally, given a sequence X, a predicate P, a reducer function F, and an initial accumulator a0, the operation iterates through X; for each element x, if P(x) is true then a := F(a, x) else a remains unchanged. The final value is a. This captures the idea of performing a filter followed by a reduction, but often in a single pass to save work and memory.

Implementation can be either two-pass (filter then reduce) or a single-pass streaming approach that evaluates the predicate as elements are observed. Single-pass implementations are common in lazy evaluation languages and streaming libraries; however, correctness typically requires that the reducer be associative and that predicate evaluation be deterministic. The main performance benefit is avoiding the creation of intermediate collections, which can reduce memory usage and increase throughput.

Common use cases include analytics pipelines, event stream processing, and log aggregation, where only elements meeting a condition contribute to the result. By combining two operations into one, filtersreduce can simplify pipeline design and improve performance in streaming or large-scale data processing.

Related concepts include filter, reduce (fold), map-reduce, and scan. The exact naming and API may vary across languages and libraries, and the term filtersreduce is not universally standardized, often described as a pattern rather than a fixed primitive.