{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Estimates the cardinality (unique count) of very large datasets using the HyperLogLog algorithm, implemented in C for memory efficiency and speed.","skillfed_tags":["cardinality-estimation","probabilistic-data-structures","streaming-algorithms"],"use_cases":["Estimate unique visitor counts or unique IDs in high-volume streaming logs without storing all values","Merge cardinality estimates from multiple data sources to approximate total unique items across distributed systems","Estimate set intersection size (e.g., overlapping users between two datasets) using intersection_cardinality()","Profile memory usage of large datasets by approximating cardinality with minimal RAM overhead","Bulk-insert sequential integers efficiently using add_range() to avoid Python-C call overhead"],"what_it_does":"HLL is a C-based Python module implementing the 64-bit HyperLogLog algorithm for cardinality estimation. It trades accuracy for memory efficiency, allowing you to estimate how many unique elements exist in a dataset without storing all of them\u2014useful when datasets are too large to fit in memory or when you need fast approximate counts in streaming contexts.\n\nThe package uses a Murmur64A hash and stores registers in a hybrid sparse-dense representation: sparse when few registers are set (using a sorted dynamic array), switching to dense when memory would be wasted. Version 3.0.0 adds intersection cardinality estimation via Ertl's JMLE method, bulk insertion via add_range(), and fixes memory leaks and type errors from earlier releases. Requires Python >= 3.9 and a C compiler.","worth_installing":"Yes, if you need cardinality estimation and can tolerate a build-time dependency. The package is actively maintained, production-stable, has no known vulnerabilities, and solves a specific algorithmic problem well. Install friction is real (requires C compilation and dev headers), but that is inherent to the algorithm's performance. Not worth installing if you need exact counts or cannot set up a build environment."},"id":"hll","links":{"html":"https://skillfed.io/packages/hll","md":"https://skillfed.io/packages/hll.md","pypi":"https://pypi.org/project/hll/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-25","license_spdx":null,"license_treatment":"permissive","name":"HLL","python_support":"supports_current","summary":"Fast HyperLogLog for Python"},"popularity":{"monthly_downloads":118735,"position":12104,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.0"}