{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Crick provides approximate and streaming algorithms optimized for performance, enabling efficient computation on large data streams without storing all data in memory.","skillfed_tags":["streaming-algorithms","approximate-computing","data-structures"],"use_cases":["Computing approximate quantiles or percentiles on streaming data without storing all values","Estimating cardinality (distinct element count) in large datasets with bounded memory","Aggregating statistics over time-series or event streams in real-time systems","Reducing memory overhead in data pipelines that process high-volume continuous data"],"what_it_does":"Crick is a library of approximate and streaming algorithms designed for high-performance computation on data streams. It allows you to compute statistics and aggregations on large datasets without loading all data into memory at once, using techniques that trade exact answers for speed and memory efficiency.\n\nThe library is distributed as pre-compiled wheels for Python 3.10+ across Windows, macOS, and Linux platforms. It has no runtime dependencies, making installation straightforward. The project originated in 2016 and received its last update in December 2024, placing it in aging maintenance\u2014suitable for stable use cases but not actively developed.","worth_installing":"Yes, if you need approximate or streaming algorithms for memory-constrained or real-time scenarios. The library is stable, has no vulnerabilities, and covers a specific niche well. However, verify that the specific algorithms you need are implemented, and be aware that active development has slowed\u2014suitable for production use of existing features but not for packages expecting ongoing feature additions."},"id":"crick","links":{"html":"https://skillfed.io/packages/crick","md":"https://skillfed.io/packages/crick.md","pypi":"https://pypi.org/project/crick/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-12-23","license_spdx":null,"license_treatment":"permissive","name":"crick","python_support":"supports_current","summary":"High performance approximate and streaming algorithms"},"popularity":{"monthly_downloads":330780,"position":7530,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.8"}