{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Records and analyzes high-precision latency and performance distributions using HDR Histogram, a data structure optimized for capturing value ranges with controlled precision loss.","skillfed_tags":["performance-monitoring","latency-analysis","metrics"],"use_cases":["Track API response latencies in production and analyze tail percentiles (p99, p99.9) without storing every request.","Measure and compare performance distributions across multiple services or time windows by encoding histograms as portable blobs.","Correct for coordinated omission bias when benchmarking systems under load, ensuring accurate latency reporting.","Generate percentile distribution tables in .hgrm format for plotting and reporting performance characteristics.","Aggregate histograms from distributed systems into a central histogram for unified performance analysis."],"what_it_does":"hdrhistogram is a Python port of the Java HDR Histogram library, designed to record and analyze latency and performance distributions with high precision across a wide dynamic range. It solves the problem of accurately capturing percentile-based performance metrics without the memory overhead of storing every individual measurement; instead, it uses a bucketing strategy that trades a small, controlled amount of precision for dramatic space efficiency.\n\nThe package supports recording values with optional correction for coordinated omission (a common measurement bias in latency testing), querying percentiles and statistical summaries, iterating over recorded values in multiple ways, and serializing histograms to portable formats for storage or cross-platform analysis. It includes a command-line tool (dump_hdrh) for inspecting encoded histograms and supports 16-bit, 32-bit, and 64-bit counters. Runtime dependencies are minimal (pbr and setuptools for build support).","worth_installing":"Yes. hdrhistogram is actively maintained, has no known vulnerabilities, and fills a specific need for efficient, precise latency distribution tracking. Install it if you need percentile-based performance analysis or are porting HDR Histogram code from Java or C. The prebuilt wheels make installation straightforward on common platforms; only consider friction if you're on an unsupported architecture."},"id":"hdrhistogram","links":{"html":"https://skillfed.io/packages/hdrhistogram","md":"https://skillfed.io/packages/hdrhistogram.md","pypi":"https://pypi.org/project/hdrhistogram/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-09","license_spdx":null,"license_treatment":"permissive","name":"hdrhistogram","python_support":"supports_current","summary":"High Dynamic Range histogram in native python"},"popularity":{"monthly_downloads":551525,"position":6048,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.7"}