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/*
* package-info.java
* Written by Gil Tene of Azul Systems, and released to the public domain,
* as explained at http://creativecommons.org/publicdomain/zero/1.0/
*/
/**
* A High Dynamic Range (HDR) Histogram Package
*
* An HdrHistogram histogram supports the recording and analyzing 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.
*
*
* In contrast to traditional histograms that use linear, logarithmic, or arbitrary sized bins or buckets,
* HdrHistograms use a fixed storage internal data representation that simultaneously supports an arbitrarily high
* dynamic range and arbitrary precision throughout that dynamic range. This capability makes HdrHistograms extremely
* useful for tracking and reporting on the distribution of percentile values with high resolution and across a wide
* dynamic range -- a common need in latency behavior characterization.
*
*
* The HdrHistogram package was specifically designed with latency and performance sensitive applications in mind.
* Experimental u-benchmark measurements show value recording times as low as 3-6 nanoseconds on modern
* (circa 2012) Intel CPUs. All Histogram variants can maintain a fixed cost in both space and time. When not
* configured to auto-resize, 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.
*
* NOTE: Histograms can optionally be configured to auto-resize their dynamic range as a convenience feature.
* When configured to auto-resize, recording operations that need to expand a histogram will auto-resize its
* dynamic range to include recorded values as they are encountered. Note that recording calls that cause
* auto-resizing may take longer to execute, and that resizing incurs allocation and copying of internal data
* structures.
*
*
* The 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 value count 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.
*
*
* An HdrHistogram histogram is usually configured to maintain value count data with a resolution good enough
* to support a desired precision in post-recording analysis and reporting on the collected data. Analysis can include
* the computation and reporting of distribution by percentiles, linear or logarithmic arbitrary value buckets, mean
* and standard deviation, as well as any other computations that can supported using the various iteration techniques
* available on the collected value count data. In practice, a precision levels of 2 or 3 decimal points are most
* commonly used, as they maintain a value accuracy of +/- ~1% or +/- ~0.1% respectively for derived distribution
* statistics.
*
*
* A good example of HdrHistogram use would be tracking of latencies across a wide dynamic range. E.g. from a
* microsecond to an hour. A Histogram can be configured to track and later report on the counts of observed integer
* usec-unit latency values between 0 and 3,600,000,000 while maintaining a value precision of 3 significant digits
* across that range. 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).
*
* Code for this use example would include these basic elements:
*
*
*
* {@link com.couchbase.client.core.deps.org.HdrHistogram.Histogram} histogram = new {@link com.couchbase.client.core.deps.org.HdrHistogram.Histogram}(3600000000L, 3);
* .
* .
* .
* // Repeatedly record measured latencies:
* histogram.{@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValue(long) recordValue}(latency);
* .
* .
* .
* // Report histogram percentiles, expressed in msec units:
* histogram.{@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#outputPercentileDistribution(java.io.PrintStream, Double) outputPercentileDistribution}(histogramLog, 1000.0)};
*
*
* Specifying 3 decimal points of precision in this example guarantees that value quantization within the value range
* will be no larger than 1/1,000th (or 0.1%) of any recorded value. This example Histogram can be therefor used to
* track, analyze and report the counts of observed latencies ranging between 1 microsecond and 1 hour in magnitude,
* while maintaining a value resolution 1 microsecond (or better) 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 it's maximum tracked
* value (1 hour), it would still maintain a resolution of 3.6 seconds (or better).
* Histogram variants and internal representation
* The HdrHistogram package includes multiple implementations of the {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram} class:
*
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.Histogram}, which is the commonly used Histogram form and tracks value counts
* in
long fields.
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.IntCountsHistogram} and {@link com.couchbase.client.core.deps.org.HdrHistogram.ShortCountsHistogram}, 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 (e.g. systems where tens of thousands of in-memory histogram are
* being tracked).
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AtomicHistogram}, {@link com.couchbase.client.core.deps.org.HdrHistogram.ConcurrentHistogram}
* and {@link com.couchbase.client.core.deps.org.HdrHistogram.SynchronizedHistogram}
*
*
* Internally, data in HdrHistogram variants is maintained using a concept somewhat similar to that of floating
* point number representation: Using a an exponent a (non-normalized) mantissa to
* support a wide dynamic range at a high but varying (by exponent value) resolution.
* AbstractHistogram uses exponentially increasing bucket value ranges (the parallel of
* the exponent portion of a floating point number) with each bucket containing
* a fixed number (per bucket) set of linear sub-buckets (the parallel of a non-normalized mantissa portion
* of a floating point number).
* Both dynamic range and resolution are configurable, with highestTrackableValue
* controlling dynamic range, and numberOfSignificantValueDigits controlling
* resolution.
*
* Synchronization and concurrent access
* In the interest of keeping value recording cost to a minimum, the commonly used {@link com.couchbase.client.core.deps.org.HdrHistogram.Histogram}
* class and it's {@link com.couchbase.client.core.deps.org.HdrHistogram.IntCountsHistogram} and {@link com.couchbase.client.core.deps.org.HdrHistogram.ShortCountsHistogram}
* variants are NOT internally synchronized, and do NOT use atomic variables. Callers wishing to make potentially
* concurrent, multi-threaded updates or queries against Histogram objects should either take care to externally
* synchronize and/or order their access, or use the {@link com.couchbase.client.core.deps.org.HdrHistogram.ConcurrentHistogram},
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AtomicHistogram}, or {@link com.couchbase.client.core.deps.org.HdrHistogram.SynchronizedHistogram} variants.
*
* A common pattern seen in histogram value recording involves recording values in a critical path (multi-threaded
* or not), coupled with a non-critical path reading the recorded data for summary/reporting purposes. When such
* continuous non-blocking recording operation (concurrent or not) is desired even when sampling, analyzing, or
* reporting operations are needed, consider using the {@link com.couchbase.client.core.deps.org.HdrHistogram.Recorder} and
* {@link com.couchbase.client.core.deps.org.HdrHistogram.SingleWriterRecorder} variants that were specifically designed for that purpose.
* Recorders provide a recording API similar to Histogram, and internally maintain and coordinate active/inactive
* histograms such that recording remains wait-free in the presense of accurate and stable interval sampling.
*
*
* It is worth mentioning that since Histogram objects are additive, it is common practice to use per-thread
* non-synchronized histograms or {@link com.couchbase.client.core.deps.org.HdrHistogram.SingleWriterRecorder}s, and using a summary/reporting
* thread perform histogram aggregation math across time and/or threads.
*
* Iteration
* Histograms supports multiple convenient forms of iterating through the histogram data set, including linear,
* logarithmic, and percentile iteration mechanisms, as well as means for iterating through each recorded value or
* each possible value level. The iteration mechanisms all provide {@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}
* data points along the histogram's iterated data set, and are available via the following methods:
*
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#percentiles percentiles} :
* An {@link java.lang.Iterable}{@literal <}{@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}{@literal >} through the
* histogram using a {@link com.couchbase.client.core.deps.org.HdrHistogram.PercentileIterator}
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#linearBucketValues linearBucketValues} :
* An {@link java.lang.Iterable}{@literal <}{@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}{@literal >} through
* the histogram using a {@link com.couchbase.client.core.deps.org.HdrHistogram.LinearIterator}
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#logarithmicBucketValues logarithmicBucketValues} :
* An {@link java.lang.Iterable}{@literal <}{@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}{@literal >}
* through the histogram using a {@link com.couchbase.client.core.deps.org.HdrHistogram.LogarithmicIterator}
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordedValues recordedValues} :
* An {@link java.lang.Iterable}{@literal <}{@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}{@literal >} through
* the histogram using a {@link com.couchbase.client.core.deps.org.HdrHistogram.RecordedValuesIterator}
* - {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#allValues allValues} :
* An {@link java.lang.Iterable}{@literal <}{@link com.couchbase.client.core.deps.org.HdrHistogram.HistogramIterationValue}{@literal >} through
* the histogram using a {@link com.couchbase.client.core.deps.org.HdrHistogram.AllValuesIterator}
*
*
* Iteration is typically done with a for-each loop statement. E.g.:
*
* for (HistogramIterationValue v : histogram.percentiles(percentileTicksPerHalfDistance)) {
* ...
* }
*
* or
*
* for (HistogramIterationValue v : histogram.linearBucketValues(valueUnitsPerBucket)) {
* ...
* }
*
*
* The iterators associated with each iteration method are resettable, such that a caller that would like to avoid
* allocating a new iterator object for each iteration loop can re-use an iterator to repeatedly iterate through the
* histogram. This iterator re-use usually takes the form of a traditional for loop using the Iterator's
* hasNext() and next() methods:
*
* to avoid allocating a new iterator object for each iteration loop:
*
*
*
* PercentileIterator iter = histogram.percentiles().iterator(percentileTicksPerHalfDistance);
* ...
* iter.reset(percentileTicksPerHalfDistance);
* for (iter.hasNext() {
* HistogramIterationValue v = iter.next();
* ...
* }
*
*
* Equivalent Values and value ranges
*
* Due to the finite (and configurable) resolution of the histogram, multiple adjacent integer data values can
* be "equivalent". Two values are considered "equivalent" if samples recorded for both are always counted in a
* common total count due to the histogram's resolution level. Histogram provides methods for determining the
* lowest and highest equivalent values for any given value, as we as determining whether two values are equivalent,
* and for finding the next non-equivalent value for a given value (useful when looping through values, in order
* to avoid double-counting count).
*
* Raw vs. corrected recording
*
* Regular, raw value data recording into an HdrHistogram is achieved with the
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValue(long) recordValue()} method.
*
* Histogram variants also provide an auto-correcting
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValueWithExpectedInterval(long, long) recordValueWithExpectedInterval()}
* form in support of a common use case found when histogram values are used to track response time
* distribution in the presence of Coordinated Omission - an extremely common phenomenon found in latency recording
* systems.
* This correcting form is useful in [e.g. load generator] scenarios where measured response times may exceed the
* expected interval between issuing requests, leading to the "omission" of response time measurements that would
* typically correlate with "bad" results. This coordinated (non random) omission of source data, if left uncorrected,
* will then dramatically skew any overall latency stats computed on the recorded information, as the recorded data set
* itself will be significantly skewed towards good results.
*
*
* When a value recorded in the histogram exceeds the
* expectedIntervalBetweenValueSamples parameter, recorded histogram data will
* reflect an appropriate number of additional values, linearly decreasing in steps of
* expectedIntervalBetweenValueSamples, down to the last value
* that would still be higher than expectedIntervalBetweenValueSamples).
*
*
* To illustrate why this corrective behavior is critically needed in order to accurately represent value
* distribution when large value measurements may lead to missed samples, imagine a system for which response
* times samples are taken once every 10 msec to characterize response time distribution.
* The hypothetical system behaves "perfectly" for 100 seconds (10,000 recorded samples), with each sample
* showing a 1msec response time value. At each sample for 100 seconds (10,000 logged samples
* at 1msec each). The hypothetical system then encounters a 100 sec pause during which only a single sample is
* recorded (with a 100 second value).
* An normally recorded (uncorrected) data histogram collected for such a hypothetical system (over the 200 second
* scenario above) would show ~99.99% of results at 1msec or below, which is obviously "not right". In contrast, a
* histogram that records the same data using the auto-correcting
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValueWithExpectedInterval(long, long) recordValueWithExpectedInterval()}
* method with the knowledge of an expectedIntervalBetweenValueSamples of 10msec will correctly represent the
* real world response time distribution of this hypothetical system. Only ~50% of results will be at 1msec or below,
* with the remaining 50% coming from the auto-generated value records covering the missing increments spread between
* 10msec and 100 sec.
*
*
* Data sets recorded with and with
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValue(long) recordValue()}
* and with
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValueWithExpectedInterval(long, long) recordValueWithExpectedInterval()}
* will differ only if at least one value recorded was greater than it's
* associated expectedIntervalBetweenValueSamples parameter.
* Data sets recorded with
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValueWithExpectedInterval(long, long) recordValueWithExpectedInterval()}
* parameter will be identical to ones recorded with
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#recordValue(long) recordValue()}
* it if all values recorded via the recordValue calls were smaller
* than their associated expectedIntervalBetweenValueSamples parameters.
*
*
* In addition to at-recording-time correction option, Histrogram variants also provide the post-recording correction
* methods
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#copyCorrectedForCoordinatedOmission(long) copyCorrectedForCoordinatedOmission()}
* and
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#addWhileCorrectingForCoordinatedOmission(AbstractHistogram, long) addWhileCorrectingForCoordinatedOmission()}.
* These methods can be used for post-recording correction, and are useful when the
* expectedIntervalBetweenValueSamples parameter is estimated to be the same for all recorded
* values. However, for obvious reasons, it is important to note that only one correction method (during or post
* recording) should be be used on a given histogram data set.
*
*
* When used for response time characterization, the recording with the optional
* expectedIntervalBetweenValueSamples parameter will tend to produce data sets that would
* much more accurately reflect the response time distribution that a random, uncoordinated request would have
* experienced.
*
* Floating point values and DoubleHistogram variants
* The above discussion relates to integer value histograms (the various subclasses of
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram} and their related supporting classes). HdrHistogram supports floating
* point value recording and reporting with a similar set of classes, including the
* {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleHistogram}, {@link com.couchbase.client.core.deps.org.HdrHistogram.ConcurrentDoubleHistogram} and
* {@link com.couchbase.client.core.deps.org.HdrHistogram.SynchronizedDoubleHistogram} histogram classes. Support for floating point value
* iteration is provided with {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleHistogramIterationValue} and related iterator classes (
* {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleLinearIterator}, {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleLogarithmicIterator},
* {@link com.couchbase.client.core.deps.org.HdrHistogram.DoublePercentileIterator}, {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleRecordedValuesIterator},
* {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleAllValuesIterator}). Support for interval recording is provided with
* {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleRecorder} and
* {@link com.couchbase.client.core.deps.org.HdrHistogram.SingleWriterDoubleRecorder}.
* Auto-ranging in floating point histograms
* Unlike integer value based histograms, the specific value range tracked by a {@link
* com.couchbase.client.core.deps.org.HdrHistogram.DoubleHistogram} (and variants) is not specified upfront. Only the dynamic range of values
* that the histogram can cover is (optionally) specified. E.g. When a {@link com.couchbase.client.core.deps.org.HdrHistogram.DoubleHistogram}
* is created to track a dynamic range of 3600000000000 (enough to track values from a nanosecond to an hour),
* values could be recorded into into it in any consistent unit of time as long as the ratio between the highest
* and lowest non-zero values stays within the specified dynamic range, so recording in units of nanoseconds
* (1.0 thru 3600000000000.0), milliseconds (0.000001 thru 3600000.0) seconds (0.000000001 thru 3600.0), hours
* (1/3.6E12 thru 1.0) will all work just as well.
* Footprint estimation
* Due to it's dynamic range representation, Histogram is relatively efficient in memory space requirements given
* the accuracy and dynamic range it covers. Still, it is useful to be able to estimate the memory footprint involved
* for a given highestTrackableValue and numberOfSignificantValueDigits
* combination. Beyond a relatively small fixed-size footprint used for internal fields and stats (which can be
* estimated as "fixed at well less than 1KB"), the bulk of a Histogram's storage is taken up by it's data value
* recording counts array. The total footprint can be conservatively estimated by:
*
* largestValueWithSingleUnitResolution = 2 * (10 ^ numberOfSignificantValueDigits);
* subBucketSize = roundedUpToNearestPowerOf2(largestValueWithSingleUnitResolution);
* expectedHistogramFootprintInBytes = 512 +
* ({primitive type size} / 2) *
* (log2RoundedUp((highestTrackableValue) / subBucketSize) + 2) *
* subBucketSize
*
*
* A conservative (high) estimate of a Histogram's footprint in bytes is available via the
* {@link com.couchbase.client.core.deps.org.HdrHistogram.AbstractHistogram#getEstimatedFootprintInBytes() getEstimatedFootprintInBytes()} method.
*/
package com.couchbase.client.core.deps.org.HdrHistogram;