libcircllhist is a C implementation of OpenHistogram log-linear histograms, providing a specialized data structure for efficient histogram computation and analysis. The library implements circular log-logarithmic histogram functionality, which enables memory-efficient storage and real-time processing of distribution statistics. This approach is particularly valuable for performance monitoring and latency measurement scenarios where traditional histogram implementations would consume excessive memory or computational resources.
The repository is written in C and requires a C compiler such as gcc or clang, along with GNU Autoconf and GNU Make for the build system. The installation process follows standard conventions, using autoconf, configure, and make commands with support for custom installation prefixes. The library can be installed system-wide or to custom directories, with particular attention paid to linker path configuration for cases where the default /usr/local prefix is not suitable.
The project provides comprehensive language bindings to extend its usability beyond C. Python bindings are included for Python 2.7 and Python 3.x, requiring setuptools and cffi for installation. Lua bindings are also provided and built by default, installing to the standard Lua 5.1 module directory. These bindings allow developers to integrate circular log-linear histogram functionality into Python and Lua applications without reimplementing the core C library.
Documentation is available through the circllhist.h header file, which contains the complete API documentation. The repository supports generating HTML documentation using Doxygen, accessible via the make docs command. Testing infrastructure is included and can be executed with make tests, ensuring code quality and correctness.
These connections suggest the library serves important use cases in distributed systems and performance monitoring contexts where precise, memory-efficient histogram collection is critical. The repository is tagged with hacktoberfest, indicating openness to community contributions.
The library's classification spans multiple domains including statistical analysis, data visualization, distributed systems, and monitoring tools. Its focus on memory efficiency, circular buffers, and real-time processing makes it particularly suited for scenarios requiring continuous performance metrics collection with bounded memory consumption. The circular log-logarithmic data structure provides high precision numerical computing capabilities while maintaining computational efficiency, making it valuable for latency measurement and performance monitoring applications where traditional histogram approaches would be impractical due to memory constraints or computational overhead.
