countwi

CountWi is a software framework and data structure designed for counting occurrences of discrete items within large data streams or static corpora, with support for weighted counting. The system provides exact and approximate counting modes and offers interfaces for both streaming and batch processing, making it suitable for corpus linguistics, event tracking, and analytics workflows.

Origin and use cases

CountWi originated in theoretical and applied data processing discussions as a method for tracking item frequencies while incorporating per-item weights. It is used to reflect varying importance, confidence, or source reliability in counts, enabling analysts to prioritize certain items or to combine data from heterogeneous sources without losing the weighting information.

Design and implementation

The core data model consists of a map from keys to numeric counts, with configurable precision and optional floating-point representation. It supports incremental updates, serialization, and transactional-like semantics for consistency in multi-step pipelines. An optional approximate counting mode leverages data structures such as Count-Min Sketch to bound errors in exchange for reduced memory usage. The framework provides multi-language bindings, with APIs in Python, Java, and Rust, plus a common interface for increments, merges, and queries. A query capability allows retrieving counts, ranking keys by weight, applying time windows, and exporting results to CSV or JSON.

Features and limitations

Key features include weighted counts, streaming and batch interfaces, windowed counting, persistence and snapshotting, and cross-collection merging. The design emphasizes memory efficiency and fault tolerance, with concurrency-safe operations. In approximate mode, accuracy is traded for speed and reduced memory footprint, and decay or time-based invalidation can be configured for long-running analyses.

See also

Count-Min Sketch, weighted counting, streaming analytics.