openhistogram.io

What It Is

OpenHistogram is a vendor-neutral log-linear histogram technology donated by Circonus to the open-source community. It is not positioned as a full monitoring platform, but as a low-level data structure for compressing, merging, and analyzing telemetry data. It is well suited to compressing large volumes of high-frequency measurements into statistical, mergeable distribution models, such as latency, I/O, API call duration, and connection inter-arrival times.

Core Capabilities

Its core value lies in “universal bucketing” and mergeability. The FAQ explains why time-series histograms must be mergeable across different time windows and signals without introducing additional error. OpenHistogram uses Circonus’s log-linear binning/circllhist: it supports both positive and negative values, with a range from 10^-128 to 10^127; the maximum relative error is about 4.7%, and in practice is usually below 0.1%. The main text states that recording a sample has a CPU cost of under 10ns, and it can be used in environments without floating-point operations. Language implementations include C/C++, Lua, Python, JavaScript, Go, and C# .NET, making it easy to embed into multi-stack systems.

Pricing and Openness

The official website clearly states that OpenHistogram is 100% free and open source, under the OSI-approved Apache 2.0 license. The main text does not mention a commercial edition, hosted version, enterprise support, or SLA, so it is more like an open-source foundational library/specification than a paid SaaS tool. The patent-related notes say Circonus makes the relevant histogram patents available to users who follow its binning format, but enterprises should still evaluate this through their own compliance process before adoption.

Pros and Cons

The advantages are a clear algorithmic goal, strong mergeability, and relatively broad cross-language implementations, making it suitable as a foundation for observability, monitoring, and time-series analysis. Its vendor-neutral design also helps with data portability. The downsides are that the official content is more theoretical and FAQ-oriented; there is no obvious full API reference, example tutorial, version roadmap, or support channel. If a team needs out-of-the-box visualization, alerting, storage, and permission management, it will still need to integrate with other systems.

Who It’s For and Access from China

It is suitable for observability platform engineers, infrastructure teams, performance analysis tool developers, and teams that need to control storage costs while preserving distribution information for high-frequency data. The main text does not provide information about access from China, so actual connectivity, GitHub code access, and dependency downloads should be verified independently. Payment is largely irrelevant because the project is free and open source. Alternative directions may include other histogram/quantile summary structures or built-in solutions in existing monitoring systems, but the main text does not list specific competitors.