HdrHistogram | A High Dynamic Range Histogram | Time Series Database library

HdrHistogram is a Java library typically used in Database, Time Series Database applications. HdrHistogram has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However HdrHistogram has a Non-SPDX License. You can download it from GitHub, Maven.

HdrHistogram: A High Dynamic Range (HDR) Histogram. This repository currently includes a Java implementation of HdrHistogram. C, C#/.NET, Python, Javascript, Rust, Erlang, and Go ports can be found in other repositories. All of which share common concepts and data representation capabilities. Look at repositories under the HdrHistogram organization for various implementations and useful tools. Note: The below is an excerpt from a Histogram JavaDoc. While much of it generally applies to other language implementations as well, some details may vary by implementation (e.g. iteration and synchronization), so you should consult the documentation or header information of the specific API library you intend to use. HdrHistogram supports the recording and analyzing of sampled data value counts across a configurable integer value range with configurable value precision within the range. Value precision is expressed as the number of significant digits in the value recording, and provides control over value quantization behavior across the value range and the subsequent value resolution at any given level. For example, a Histogram could be configured to track the counts of observed integer values between 0 and 3,600,000,000 while maintaining a value precision of 3 significant digits across that range. Value quantization within the range will thus be no larger than 1/1,000th (or 0.1%) of any value. This example Histogram could be used to track and analyze the counts of observed response times ranging between 1 microsecond and 1 hour in magnitude, while maintaining a value resolution of 1 microsecond up to 1 millisecond, a resolution of 1 millisecond (or better) up to one second, and a resolution of 1 second (or better) up to 1,000 seconds. At its maximum tracked value (1 hour), it would still maintain a resolution of 3.6 seconds (or better). The HdrHistogram package includes the Histogram implementation, which tracks value counts in long fields, and is expected to be the commonly used Histogram form. IntHistogram and ShortHistogram, which track value counts in int and short fields respectively, are provided for use cases where smaller count ranges are practical and smaller overall storage is beneficial. HdrHistogram is designed for recording histograms of value measurements in latency and performance sensitive applications. Measurements show value recording times as low as 3-6 nanoseconds on modern (circa 2012) Intel CPUs. AbstractHistogram maintains a fixed cost in both space and time. A Histogram's memory footprint is constant, with no allocation operations involved in recording data values or in iterating through them. The memory footprint is fixed regardless of the number of data value samples recorded, and depends solely on the dynamic range and precision chosen. The amount of work involved in recording a sample is constant, and directly computes storage index locations such that no iteration or searching is ever involved in recording data values. A combination of high dynamic range and precision is useful for collection and accurate post-recording analysis of sampled value data distribution in various forms. Whether it's calculating or plotting arbitrary percentiles, iterating through and summarizing values in various ways, or deriving mean and standard deviation values, the fact that the recorded data information is kept in high resolution allows for accurate post-recording analysis with low [and ultimately configurable] loss in accuracy when compared to performing the same analysis directly on the potentially infinite series of sourced data values samples. A common use example of HdrHistogram would be to record response times in units of microseconds across a dynamic range stretching from 1 usec to over an hour, with a good enough resolution to support later performing post-recording analysis on the collected data. Analysis can include computing, examining, and reporting of distribution by percentiles, linear or logarithmic value buckets, mean and standard deviation, or by any other means that can can be easily added by using the various iteration techniques supported by the Histogram. In order to facilitate the accuracy needed for various post-recording analysis techniques, this example can maintain a resolution of ~1 usec or better for times ranging to ~2 msec in magnitude, while at the same time maintaining a resolution of ~1 msec or better for times ranging to ~2 sec, and a resolution of ~1 second or better for values up to 2,000 seconds. This sort of example resolution can be thought of as "always accurate to 3 decimal points." Such an example Histogram would simply be created with a highestTrackableValue of 3,600,000,000, and a numberOfSignificantValueDigits of 3, and would occupy a fixed, unchanging memory footprint of around 185KB (see "Footprint estimation" below).