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Circonus Contributes Histogram Technology to Open Source Community

Circonus released its powerful, patented histogram technology with open source licensing – creating a standard histogram format for the industry and solving the long-standing interoperability challenges caused by incompatible, vendor-proprietary formats. Circonus developed its mergeable and efficient histogram technology in 2011 and has both patents and pending patents on that technology. Histograms are a data structure that allows users to model the distribution of a set of samples – for example, the age of every human on earth. But instead of storing each sample as its own record, they are grouped together in “buckets” or “bins” which allows for significant data compression and thus superior economics. This compression of data allows for extraordinary metric transmission and ingestion rates, high frequency, real-time analytics, and economical long-term storage. Histograms are also particularly useful in handling the breadth and depth of metric data produced by container technologies such as Kubernetes. The key to solving the interoperability and mergeability challenges of histograms is in the definition of the bin sizes or “boundaries.” Without standard bin sizes, there is no way to merge histograms together without introducing significant error that carries into the subsequent analysis of the data. While there are a number of approaches to selecting optimal bin boundaries, the Circonus implementation has been independently tested and evaluated over the years and consistently deemed superior to other approaches in terms of performance, accuracy, correctness, and usability. Understandably, there has been hesitancy in wider adoption due to the presence of the patents. Today’s announcement addresses that concern head on by affording patent rights for the use of the technology when using the specified bin boundaries. “This is a very exciting development in the drive to create open standards for sharing telemetry data in the monitoring and observability industry,” said Ben Sigelman, CEO and co-founder of Lightstep and co-creator of the OpenTelemetry project within the CNCF. “Circonus’ log linear histograms are a time-tested, best-practice solution for representing the high frequency telemetry found in modern software deployments. The ability to easily exchange telemetry between vendor platforms is a huge win for the community.” “We are excited to leverage our technology so that end-users who are faced with the challenge of digesting and analyzing massive amounts of distribution data can rely on a consistent, interchangeable, and stable representation of that data,” said Theo Schlossnagle, founder and CTO of Circonus. “Ensuring that all data, everywhere can be merged and seamlessly exchanged between platforms without introducing error is incredibly valuable to the owner of that data and therefore invaluable to our industry. We welcome everyone in the world to leverage this technology.” Circonus’ histogram technology is ready for immediate use and has been used in production, at scale, by major clients such as Major League Baseball, Sparkpost, and SmugMug for many years.

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Circonus Contributes Histogram Technology to Open Source Community

Circonus released its powerful, patented histogram technology with open source licensing – creating a standard histogram format for the industry and solving the long-standing interoperability challenges caused by incompatible, vendor-proprietary formats. Circonus developed its mergeable and efficient histogram technology in 2011 and has both patents and pending patents on that technology. Histograms are a data structure that allows users to model the distribution of a set of samples – for example, the age of every human on earth. But instead of storing each sample as its own record, they are grouped together in “buckets” or “bins” which allows for significant data compression and thus superior economics. This compression of data allows for extraordinary metric transmission and ingestion rates, high frequency, real-time analytics, and economical long-term storage. Histograms are also particularly useful in handling the breadth and depth of metric data produced by container technologies such as Kubernetes. The key to solving the interoperability and mergeability challenges of histograms is in the definition of the bin sizes or “boundaries.” Without standard bin sizes, there is no way to merge histograms together without introducing significant error that carries into the subsequent analysis of the data. While there are a number of approaches to selecting optimal bin boundaries, the Circonus implementation has been independently tested and evaluated over the years and consistently deemed superior to other approaches in terms of performance, accuracy, correctness, and usability. Understandably, there has been hesitancy in wider adoption due to the presence of the patents. Today’s announcement addresses that concern head on by affording patent rights for the use of the technology when using the specified bin boundaries. “This is a very exciting development in the drive to create open standards for sharing telemetry data in the monitoring and observability industry,” said Ben Sigelman, CEO and co-founder of Lightstep and co-creator of the OpenTelemetry project within the CNCF. “Circonus’ log linear histograms are a time-tested, best-practice solution for representing the high frequency telemetry found in modern software deployments. The ability to easily exchange telemetry between vendor platforms is a huge win for the community.” “We are excited to leverage our technology so that end-users who are faced with the challenge of digesting and analyzing massive amounts of distribution data can rely on a consistent, interchangeable, and stable representation of that data,” said Theo Schlossnagle, founder and CTO of Circonus. “Ensuring that all data, everywhere can be merged and seamlessly exchanged between platforms without introducing error is incredibly valuable to the owner of that data and therefore invaluable to our industry. We welcome everyone in the world to leverage this technology.” Circonus’ histogram technology is ready for immediate use and has been used in production, at scale, by major clients such as Major League Baseball, Sparkpost, and SmugMug for many years.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.