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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.

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77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

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Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

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