(allocation-by-type)(allocation-by-type opts)Groups allocations by object type from an allocation trace.
Delegates to criterium.allocation.analysis/by-type-fn. Returns data-map unchanged if allocation trace is not present (no-op behavior).
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :allocation-by-type) :trace-id - Path for source trace in input (default: [:samples :allocation-trace])
Groups allocations by object type from an allocation trace.
Delegates to criterium.allocation.analysis/by-type-fn. Returns data-map
unchanged if allocation trace is not present (no-op behavior).
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :allocation-by-type)
:trace-id - Path for source trace in input
(default: [:samples :allocation-trace])(allocation-hotspots)(allocation-hotspots opts)Identifies allocation hotspots by call-site from an allocation trace.
Delegates to criterium.allocation.analysis/hotspots-fn. Returns data-map unchanged if allocation trace is not present (no-op behavior).
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :allocation-hotspots) :trace-id - Path for source trace in input (default: [:samples :allocation-trace]) :limit - Maximum number of hotspots to return (default: 10) :order-by - Sort key, :bytes or :count (default: :bytes)
Identifies allocation hotspots by call-site from an allocation trace.
Delegates to criterium.allocation.analysis/hotspots-fn. Returns data-map
unchanged if allocation trace is not present (no-op behavior).
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :allocation-hotspots)
:trace-id - Path for source trace in input
(default: [:samples :allocation-trace])
:limit - Maximum number of hotspots to return (default: 10)
:order-by - Sort key, :bytes or :count (default: :bytes)(allocation-summary)(allocation-summary opts)Computes allocation summary statistics from an allocation trace.
Delegates to criterium.allocation.analysis/summary-fn. Returns data-map unchanged if allocation trace is not present (no-op behavior).
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :allocation-summary) :trace-id - Path for source trace in input (default: [:samples :allocation-trace])
Computes allocation summary statistics from an allocation trace.
Delegates to criterium.allocation.analysis/summary-fn. Returns data-map
unchanged if allocation trace is not present (no-op behavior).
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :allocation-summary)
:trace-id - Path for source trace in input
(default: [:samples :allocation-trace])(allocation-treemap)(allocation-treemap opts)Transforms allocation records into hierarchical treemap data.
Delegates to criterium.allocation.analysis/treemap-fn. Returns data-map unchanged if allocation trace is not present (no-op behavior).
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :allocation-treemap) :trace-id - Path for source trace in input (default: [:samples :allocation-trace]) :group-by - Hierarchy: :class→line→type or :type→class→line (default: :class→line→type) :size-by - Value sizing: :count, :bytes, :bytes-per-allocation (default: :bytes) :filter-by - Filter: :freed, :not-freed, :all (default: :all)
Transforms allocation records into hierarchical treemap data.
Delegates to criterium.allocation.analysis/treemap-fn. Returns data-map
unchanged if allocation trace is not present (no-op behavior).
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :allocation-treemap)
:trace-id - Path for source trace in input
(default: [:samples :allocation-trace])
:group-by - Hierarchy: :class→line→type or :type→class→line
(default: :class→line→type)
:size-by - Value sizing: :count, :bytes, :bytes-per-allocation
(default: :bytes)
:filter-by - Filter: :freed, :not-freed, :all (default: :all)(autocorrelation)(autocorrelation {:keys [id samples-id outlier-id metric-ids] :as _options})Computes autocorrelation function (ACF) to detect sample non-independence.
Returns a function that analyzes lag-1 and higher lag autocorrelations. Use effective-sample-size-analysis and autocorrelation-classification separately to compute ESS and pattern/classification results.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :autocorrelation) :samples-id - Key for source samples (default: :samples) :outlier-id - Key for outlier data to filter (default: nil, no filtering) :metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
When :outlier-id is nil (default), uses all samples for pattern detection before outlier removal. When :outlier-id is provided, filters outlier samples before computing ACF for more accurate effective sample size estimation.
Example: (let [analyze (autocorrelation {}) result (analyze {:samples {...}})] (get-in result [:autocorrelation :elapsed-time :acf]))
Computes autocorrelation function (ACF) to detect sample non-independence.
Returns a function that analyzes lag-1 and higher lag autocorrelations.
Use effective-sample-size-analysis and autocorrelation-classification
separately to compute ESS and pattern/classification results.
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :autocorrelation)
:samples-id - Key for source samples (default: :samples)
:outlier-id - Key for outlier data to filter
(default: nil, no filtering)
:metric-ids - Set of metric ids to analyze
(default: all quantitative)
The returned function:
- Takes a sampled data map containing samples
- Returns the map with autocorrelation analysis added under :id key
- For each metric provides:
- :acf - Map of lag -> autocorrelation coefficient for lags 1 to n/2
- :lag-1 - {:value r1 :severity <:none|:minor|:moderate|:severe>}
- :effective-sample-size - {:n-original n} (for downstream analyses)
When :outlier-id is nil (default), uses all samples for pattern detection
before outlier removal. When :outlier-id is provided, filters outlier samples
before computing ACF for more accurate effective sample size estimation.
Example:
(let [analyze (autocorrelation {})
result (analyze {:samples {...}})]
(get-in result [:autocorrelation :elapsed-time :acf]))(autocorrelation-classification)(autocorrelation-classification {:keys [id autocorrelation-id metric-ids]
:as _options})Computes pattern detection and classification from autocorrelation results.
Returns a function that takes autocorrelation analysis output and computes pattern detection and classification. This enables computing classification on unfiltered data independently from effective sample size.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :autocorrelation-classification) :autocorrelation-id - Key for source autocorrelation analysis (default: :autocorrelation) :metric-ids - Set of metric ids to analyze (default: all from source)
The returned function:
Example: (let [analyze (autocorrelation-classification {}) result (analyze {:autocorrelation {...}})] (get-in result [:autocorrelation-classification :elapsed-time :pattern]))
Computes pattern detection and classification from autocorrelation results.
Returns a function that takes autocorrelation analysis output and computes
pattern detection and classification. This enables computing classification
on unfiltered data independently from effective sample size.
Parameters:
opts - Optional map with keys:
:id - Key for result in output
(default: :autocorrelation-classification)
:autocorrelation-id - Key for source autocorrelation analysis
(default: :autocorrelation)
:metric-ids - Set of metric ids to analyze
(default: all from source)
The returned function:
- Takes a data map containing autocorrelation analysis results
- Returns the map with classification analysis added under :id key
- For each metric provides:
- :ljung-box - {:q-statistic Q :df h :p-value p}
- :pattern - :clean, :transient-effects, :drift, :periodic,
:severe, or :alternating-*
- :classification - :pass, :acceptable, :warning, or :fail
- :detected-period - Integer period for :periodic pattern, nil otherwise
Example:
(let [analyze (autocorrelation-classification {})
result (analyze {:autocorrelation {...}})]
(get-in result [:autocorrelation-classification :elapsed-time :pattern]))Analysis function that adds bootstrap statistics to the result.
Computes bootstrap BCa confidence intervals for mean, variance, and configurable quantiles (0.1, 0.25, 0.5, 0.75, 0.9 by default plus any additional quantiles specified in opts).
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :bootstrap-stats) :samples-id - Key for source samples (default: :samples) :outliers-id - Key for outlier analysis (default: :outliers)
:ess-id - Key for effective sample size analysis
(default: nil). When provided, CI widths
are inflated by ci-inflation-factor.
:metric-ids - Set of metric ids to analyze
(default: all quantitative)
:quantiles - Additional quantiles beyond defaults (e.g., [0.99])
:estimate-quantiles - Confidence interval bounds (e.g., [0.025 0.975])
:bootstrap-size - Number of bootstrap resamples
(default: sample count)
:min-samples - Minimum sample size threshold (default: 30)
The returned function:
When :outliers-id is provided, outliers are removed from samples before bootstrap resampling.
When :ess-id is provided and effective sample size analysis exists, CI widths are inflated by ci-inflation-factor and stored as :adjusted-estimate-quantiles.
Analysis function that adds bootstrap statistics to the result.
Computes bootstrap BCa confidence intervals for mean, variance, and
configurable quantiles (0.1, 0.25, 0.5, 0.75, 0.9 by default plus any
additional quantiles specified in opts).
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :bootstrap-stats)
:samples-id - Key for source samples (default: :samples)
:outliers-id - Key for outlier analysis (default: :outliers)
:ess-id - Key for effective sample size analysis
(default: nil). When provided, CI widths
are inflated by ci-inflation-factor.
:metric-ids - Set of metric ids to analyze
(default: all quantitative)
:quantiles - Additional quantiles beyond defaults (e.g., [0.99])
:estimate-quantiles - Confidence interval bounds (e.g., [0.025 0.975])
:bootstrap-size - Number of bootstrap resamples
(default: sample count)
:min-samples - Minimum sample size threshold (default: 30)
The returned function:
- Takes a data map containing samples (and optionally outliers)
- Returns the map with bootstrap statistics added under :id key
- For each metric, calculates bootstrapped estimates with BCa CIs for:
- mean, variance
- quantiles (0.1, 0.25, 0.5, 0.75, 0.9 plus configured)
When :outliers-id is provided, outliers are removed from samples before
bootstrap resampling.
When :ess-id is provided and effective sample size analysis exists,
CI widths are inflated by ci-inflation-factor and stored as
:adjusted-estimate-quantiles.(distribution-fit)(distribution-fit {:keys [id samples-id metric-ids distributions n-bootstrap
alpha]
:as options})Fits parametric distributions to sample data using MLE.
Returns a function that computes maximum likelihood estimates for gamma, log-normal, inverse-gaussian, and Weibull distributions, with model selection via AIC/BIC and goodness-of-fit testing.
Parameters: opts - Optional map with keys: :id - Key for fit results in output (default: :distribution-fit) :samples-id - Key for source samples (default: :samples) :outliers-id - Key for outlier analysis to filter (default: :outliers) :metric-ids - Set of metric ids to analyze (default: all quantitative) :distributions - Vector of distributions to fit (default: all four) Options: :gamma, :lognormal, :inverse-gaussian, :weibull :n-bootstrap - Bootstrap samples for parameter CIs (default: 200) :alpha - Significance level for CIs (default: 0.05)
The returned function:
Uses moment-matching prefilter to skip distributions with invalid initial parameter estimates. Bootstraps parameter CIs only for the best model by AIC.
Example: (let [analyze (distribution-fit {:distributions [:gamma :lognormal]}) result (analyze {:samples {...} :outliers {...}})] (get-in result [:distribution-fit :elapsed-time :best-model])) ;; Returns :gamma or :lognormal
Fits parametric distributions to sample data using MLE.
Returns a function that computes maximum likelihood estimates for
gamma, log-normal, inverse-gaussian, and Weibull distributions,
with model selection via AIC/BIC and goodness-of-fit testing.
Parameters:
opts - Optional map with keys:
:id - Key for fit results in output (default: :distribution-fit)
:samples-id - Key for source samples (default: :samples)
:outliers-id - Key for outlier analysis to filter (default: :outliers)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
:distributions - Vector of distributions to fit (default: all four)
Options: :gamma, :lognormal, :inverse-gaussian, :weibull
:n-bootstrap - Bootstrap samples for parameter CIs (default: 200)
:alpha - Significance level for CIs (default: 0.05)
The returned function:
- Takes a sampled data map containing samples
- Returns the map with distribution fits added under :id key
- For each metric provides:
- :n - sample size
- :warning - :small-sample if n < 30
- :distributions - map of distribution to fit results:
- :params - fitted parameters
- :log-likelihood - maximized log-likelihood
- :aic, :bic, :aicc - information criteria
- :delta-aic - difference from best model's AIC
- :ks-test, :cvm-test - goodness-of-fit test results
- Or :error/:skipped if fitting failed
- :best-model - distribution with lowest AIC
- :parameter-cis - bootstrap CIs for best model parameters
Uses moment-matching prefilter to skip distributions with invalid
initial parameter estimates. Bootstraps parameter CIs only for
the best model by AIC.
Example:
(let [analyze (distribution-fit {:distributions [:gamma :lognormal]})
result (analyze {:samples {...} :outliers {...}})]
(get-in result [:distribution-fit :elapsed-time :best-model]))
;; Returns :gamma or :lognormal(effective-sample-size-analysis)(effective-sample-size-analysis {:keys [id autocorrelation-id metric-ids]
:as _options})Computes effective sample size and CI inflation from autocorrelation results.
Returns a function that takes autocorrelation analysis output and computes effective sample size statistics. This enables computing effective sample size on filtered data independently from pattern detection.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :effective-sample-size) :autocorrelation-id - Key for source autocorrelation analysis (default: :autocorrelation) :metric-ids - Set of metric ids to analyze (default: all from source)
The returned function:
Example: (let [analyze (effective-sample-size-analysis {}) result (analyze {:autocorrelation {...}})] (get-in result [:effective-sample-size :elapsed-time :ci-inflation-factor]))
Computes effective sample size and CI inflation from autocorrelation results.
Returns a function that takes autocorrelation analysis output and computes
effective sample size statistics. This enables computing effective sample size
on filtered data independently from pattern detection.
Parameters:
opts - Optional map with keys:
:id - Key for result in output
(default: :effective-sample-size)
:autocorrelation-id - Key for source autocorrelation analysis
(default: :autocorrelation)
:metric-ids - Set of metric ids to analyze
(default: all from source)
The returned function:
- Takes a data map containing autocorrelation analysis results
- Returns the map with effective sample size analysis added under :id key
- For each metric provides:
- :effective-sample-size - {:n-original n :n-effective n_eff :ratio ratio}
- :ci-inflation-factor - Multiplier for confidence interval widths
Example:
(let [analyze (effective-sample-size-analysis {})
result (analyze {:autocorrelation {...}})]
(get-in
result
[:effective-sample-size :elapsed-time :ci-inflation-factor]))(event-stats)(event-stats {:keys [id samples-id metric-ids] :as analysis})Calculates statistics for discrete events captured during sampling.
Returns a function that aggregates event metrics like JIT compilation, garbage collection, and class loading events that occur during benchmarking.
Parameters: opts - Optional map with keys: :id - Key for event stats in output (default: :event-stats) :samples-id - Key for source samples (default: :samples) :metric-ids - Set of event metric ids to analyze (default: all events)
The returned function:
Example: (let [analyze (event-stats) result (analyze {:samples {...} :metrics-defs {...}})] (:event-stats result)) ;; Returns {:compilation {:time-ms 8 :sample-count 2} ...}
Calculates statistics for discrete events captured during sampling.
Returns a function that aggregates event metrics like JIT compilation,
garbage collection, and class loading events that occur during benchmarking.
Parameters:
opts - Optional map with keys:
:id - Key for event stats in output (default: :event-stats)
:samples-id - Key for source samples (default: :samples)
:metric-ids - Set of event metric ids to analyze (default: all events)
The returned function:
- Takes a sampled data map containing samples and metrics configs
- Returns the map with event statistics added under :id key
- For each event metric collects:
- Total counts/durations
- Number of samples containing events
- Metric-specific aggregations (e.g., loaded/unloaded classes)
- Only processes metrics of type :event
Example:
(let [analyze (event-stats)
result (analyze {:samples {...} :metrics-defs {...}})]
(:event-stats result))
;; Returns {:compilation {:time-ms 8 :sample-count 2} ...}Analysis function that filters the call graph and stores the result.
Applies filter-call-tree to the source call tree and stores the filtered result under a new identifier. Multiple filter-calls can be chained to create different filtered views of the same call tree.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :filtered) :call-tree-id - Key for source call tree (default: :call-tree) :exclude-packages - Set of package prefixes to exclude entirely :stop-at-packages - Set of package prefixes where traversal stops :max-depth - Maximum depth to include (1 = root only)
The returned function:
Result structure: {:type :criterium/filtered-call-tree :source-id :call-tree :filter-opts {...} :call-tree <filtered-tree>}
Analysis function that filters the call graph and stores the result.
Applies filter-call-tree to the source call tree and stores the filtered
result under a new identifier. Multiple filter-calls can be chained to
create different filtered views of the same call tree.
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :filtered)
:call-tree-id - Key for source call tree (default: :call-tree)
:exclude-packages - Set of package prefixes to exclude entirely
:stop-at-packages - Set of package prefixes where traversal stops
:max-depth - Maximum depth to include (1 = root only)
The returned function:
- Takes a data-map containing :call-tree (or custom :call-tree-id)
- Returns the data-map with filtered tree added under :id key
- Returns data-map unchanged if call-tree is not present
Result structure:
{:type :criterium/filtered-call-tree
:source-id :call-tree
:filter-opts {...}
:call-tree <filtered-tree>}(histogram)(histogram {:keys [id samples-id quantiles-id outliers-id metric-ids]
:as analysis})Calculate a histogram for sample measurements.
Returns a function that computes histograms for quantitative metrics.
Parameters: opts - Optional map with keys: :id - Key for histograms in output (default: :histograms) :samples-id - Key for source samples (default: :samples) :quantiles-id - Key for quantile analysis (default: :quantiles) :outliers-id - Key for outlier analysis if available (default: :outliers) :metric-ids - Set of metric ids to analyze (default: all quantitative) :method - Binning method: - :freedman-diaconis (default) - IQR-based bin width - :knuth - Bayesian optimal bin count selection :max-bins - Maximum bins to search (only for :knuth, default: 50)
The returned function:
Example: (let [analyze (histogram) result (analyze {:samples {...} :outliers {...}})] (get-in result [:histograms :elapsed-time]))
Example with Knuth binning: (let [analyze (histogram {:method :knuth}) result (analyze {:samples {...} :outliers {...}})] (:optimal-bins (get-in result [:histograms :elapsed-time])))
Calculate a histogram for sample measurements.
Returns a function that computes histograms for quantitative metrics.
Parameters:
opts - Optional map with keys:
:id - Key for histograms in output (default: :histograms)
:samples-id - Key for source samples (default: :samples)
:quantiles-id - Key for quantile analysis (default: :quantiles)
:outliers-id - Key for outlier analysis if available (default: :outliers)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
:method - Binning method:
- :freedman-diaconis (default) - IQR-based bin width
- :knuth - Bayesian optimal bin count selection
:max-bins - Maximum bins to search (only for :knuth, default: 50)
The returned function:
- Takes a sampled data map containing samples
- Returns the map with histograms added under :id key
- Preserves data transforms for correct scaling
- For :knuth method, histogram includes :optimal-bins and :log-posterior
Example:
(let [analyze (histogram)
result (analyze {:samples {...} :outliers {...}})]
(get-in result [:histograms :elapsed-time]))
Example with Knuth binning:
(let [analyze (histogram {:method :knuth})
result (analyze {:samples {...} :outliers {...}})]
(:optimal-bins (get-in result [:histograms :elapsed-time])))(kde)(kde {:keys [id samples-id outliers-id metric-ids n-points n-bootstrap alpha]
:as _options})Calculate kernel density estimation for sample measurements.
Returns a function that computes KDE for quantitative metrics, providing density estimates, bandwidth selection, and confidence bands.
Parameters: opts - Optional map with keys: :id - Key for KDE results in output (default: :kde) :samples-id - Key for source samples (default: :log-samples) :outliers-id - Key for outlier analysis if available (default: :outliers) :metric-ids - Set of metric ids to analyze (default: all quantitative) :n-points - Grid size for density evaluation (default: 512) :n-bootstrap - Bootstrap samples for confidence bands (default: 200) :alpha - Confidence level (default: 0.05)
The returned function:
Note: Mode detection is now a separate analysis step. Use modes function
after KDE to compute statistically validated modes with Silverman's test.
Example: (let [analyze (kde {:n-points 256}) result (analyze {:log-samples {...} :outliers {...}})] (get-in result [:kde :elapsed-time]))
Calculate kernel density estimation for sample measurements.
Returns a function that computes KDE for quantitative metrics, providing
density estimates, bandwidth selection, and confidence bands.
Parameters:
opts - Optional map with keys:
:id - Key for KDE results in output (default: :kde)
:samples-id - Key for source samples (default: :log-samples)
:outliers-id - Key for outlier analysis if available (default: :outliers)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
:n-points - Grid size for density evaluation (default: 512)
:n-bootstrap - Bootstrap samples for confidence bands (default: 200)
:alpha - Confidence level (default: 0.05)
The returned function:
- Takes a sampled data map containing samples
- Returns the map with KDE analysis added under :id key
- Returns data-map unchanged if samples unavailable (e.g., digest-based)
- For each metric provides:
- bandwidth: ISJ-selected bandwidth
- grid/density: evaluation points and density values
- lower-band/upper-band: bootstrap confidence bands
Note: Mode detection is now a separate analysis step. Use `modes` function
after KDE to compute statistically validated modes with Silverman's test.
Example:
(let [analyze (kde {:n-points 256})
result (analyze {:log-samples {...} :outliers {...}})]
(get-in result [:kde :elapsed-time]))(kde-stats)(kde-stats {:keys [id kde-id metric-ids]})Calculates descriptive statistics derived from KDE density estimates.
Returns a function that computes statistics by integrating over the KDE density function. This provides a smoothed view of the underlying distribution compared to sample-based statistics.
Parameters: opts - Optional map with keys: :id - Key for stats in output (default: :kde-stats) :kde-id - Key for source KDE data (default: :kde) :metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
Example: (let [analyze (kde-stats) result (analyze {:kde {...}})] (get-in result [:kde-stats :elapsed-time])) ;; Returns {:mean 100.0 :variance 16.0 ...}
Calculates descriptive statistics derived from KDE density estimates.
Returns a function that computes statistics by integrating over the KDE
density function. This provides a smoothed view of the underlying distribution
compared to sample-based statistics.
Parameters:
opts - Optional map with keys:
:id - Key for stats in output (default: :kde-stats)
:kde-id - Key for source KDE data (default: :kde)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
- Takes a data map containing KDE analysis results
- Returns the map with statistics added under :id key
- Returns data-map unchanged if KDE data unavailable
- For each metric, calculates:
- mean: density-weighted mean ∫ x·f(x) dx
- variance: density-weighted variance ∫ (x-μ)²·f(x) dx
- min-val/max-val: grid bounds (effective support)
- mean ±3σ bounds
- n: sample count from KDE metadata
Example:
(let [analyze (kde-stats)
result (analyze {:kde {...}})]
(get-in result [:kde-stats :elapsed-time]))
;; Returns {:mean 100.0 :variance 16.0 ...}(modes)(modes {:keys [id kde-id samples-id outliers-id metric-ids max-modes n-bootstrap
alpha method mode-method]
:as _options})Calculate statistically validated mode analysis for KDE output.
Returns a function that computes modes with multimodality testing for significance. Tests from k=1 up to max-modes to determine the statistically supported number of modes.
Parameters: opts - Optional map with keys: :id - Key for modes results in output (default: :modes) :kde-id - Key for source KDE data (default: :kde) :samples-id - Key for raw samples (default: :log-samples) :outliers-id - Key for outlier data (default: :outliers) :metric-ids - Set of metric ids to analyze (default: all from KDE) :max-modes - Maximum modes to test (default: 5) :n-bootstrap - Bootstrap samples for CIs and test (default: 200) :alpha - Significance level (default: 0.05) :method - Test method, :acr (default) or :silverman :acr uses excess mass statistic (better calibrated) :silverman uses bootstrap mode count :mode-method - Mode finding method (default: :isj) :isj - find modes from KDE density at ISJ bandwidth :critical - find modes at critical bandwidth for validated k When :critical, output includes :antimodes and :mode-bandwidth
The returned function:
Example: (let [analyze (comp (modes) (kde)) result (analyze {:log-samples {...}})] (get-in result [:modes :elapsed-time :n-modes]))
Calculate statistically validated mode analysis for KDE output.
Returns a function that computes modes with multimodality testing for
significance. Tests from k=1 up to max-modes to determine the
statistically supported number of modes.
Parameters:
opts - Optional map with keys:
:id - Key for modes results in output (default: :modes)
:kde-id - Key for source KDE data (default: :kde)
:samples-id - Key for raw samples (default: :log-samples)
:outliers-id - Key for outlier data (default: :outliers)
:metric-ids - Set of metric ids to analyze (default: all from KDE)
:max-modes - Maximum modes to test (default: 5)
:n-bootstrap - Bootstrap samples for CIs and test (default: 200)
:alpha - Significance level (default: 0.05)
:method - Test method, :acr (default) or :silverman
:acr uses excess mass statistic (better calibrated)
:silverman uses bootstrap mode count
:mode-method - Mode finding method (default: :isj)
:isj - find modes from KDE density at ISJ bandwidth
:critical - find modes at critical bandwidth for
validated k
When :critical, output includes
:antimodes and :mode-bandwidth
The returned function:
- Takes a data map containing KDE and samples
- Returns the map with mode analysis added under :id key
- For each metric provides:
- modes: detected peaks with CIs and significance flags
- n-modes: statistically validated mode count
- test-results: p-values, critical bandwidths, and method used
- When :mode-method is :critical:
- antimodes: local minima between modes
- mode-bandwidth: critical bandwidth used for mode finding
Example:
(let [analyze (comp (modes) (kde))
result (analyze {:log-samples {...}})]
(get-in result [:modes :elapsed-time :n-modes]))Analysis function that identifies the most frequently called methods.
Flattens the call tree and aggregates by class+method, returning a sorted vector of the top N methods by total call count.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :most-called) :call-tree-id - Key for source call tree (default: :call-tree) :limit - Maximum methods to return (default: 20)
The returned function:
Result structure: {:type :criterium/most-called :most-called [{:class "com.example.Foo" :method "bar" :file "Foo.java" :line 42 :total-calls 1500} ...]}
Analysis function that identifies the most frequently called methods.
Flattens the call tree and aggregates by class+method, returning a sorted
vector of the top N methods by total call count.
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :most-called)
:call-tree-id - Key for source call tree (default: :call-tree)
:limit - Maximum methods to return (default: 20)
The returned function:
- Takes a data-map containing :call-tree
- Returns the data-map with :most-called added
- Returns data-map unchanged if call-tree is not present
Result structure:
{:type :criterium/most-called
:most-called [{:class "com.example.Foo"
:method "bar"
:file "Foo.java"
:line 42
:total-calls 1500}
...]}(outlier-effect significance)Return a keyword describing the effect of outliers on a point estimate.
Return a keyword describing the effect of outliers on a point estimate.
(outlier-significance)(outlier-significance {:keys [id outliers-id stats-id metric-ids]
:as _analysis})Analyzes the statistical significance of detected outliers.
Returns a function that calculates how much outliers affect the sample distribution by comparing actual variance to an idealized gaussian model.
Parameters: opts - Optional map with keys: :id - Key for significance results (default: :outlier-significance) :outliers-id - Key for outlier analysis (default: :outliers) :stats-id - Key for statistical analysis (default: :stats) :metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
Effects are classified as:
Example: (let [analyze (outlier-significance) result (analyze {:outliers {...} :stats {...}})] (get-in result [:outlier-significance :elapsed-time])) ;; Returns {:significance 0.25 :effect :moderate}
Analyzes the statistical significance of detected outliers.
Returns a function that calculates how much outliers affect the sample
distribution by comparing actual variance to an idealized gaussian model.
Parameters:
opts - Optional map with keys:
:id - Key for significance results
(default: :outlier-significance)
:outliers-id - Key for outlier analysis (default: :outliers)
:stats-id - Key for statistical analysis (default: :stats)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
- Takes a sampled data map containing outlier analysis and statistics
- Returns the map with significance analysis added under :id key
- For each metric provides:
- significance: A value between 0-1 indicating outlier impact
- effect: Keyword describing impact
(:unaffected, :slight, :moderate, :severe)
- Requires prior outlier and statistical analysis in input data
Effects are classified as:
- :unaffected - significance < 0.01
- :slight - significance < 0.1
- :moderate - significance < 0.5
- :severe - significance >= 0.5
Example:
(let [analyze (outlier-significance)
result (analyze {:outliers {...}
:stats {...}})]
(get-in result [:outlier-significance :elapsed-time]))
;; Returns {:significance 0.25 :effect :moderate}(outlier-significance* mean variance batch-size)Calculate statistical significance of outliers using gaussian fit analysis.
Determines how well a gaussian distribution describes the sample statistics by comparing the sample variance to the variance of a fitted gaussian model. A high significance indicates the outliers substantially affect the distribution.
Based on the methodology described in: http://www.ellipticgroup.com/misc/article_supplement.pdf, p17
Parameters: mean - Sample mean (must be non-zero) variance - Sample variance batch-size - Number of measurements per sample (must be >= 16)
Returns: A value between 0 and 1 representing outlier significance:
Throws: AssertionError if preconditions on inputs are not met
Example: (outlier-significance* 100.0 16.0 67108864) ;; => 0.25
Calculate statistical significance of outliers using gaussian fit analysis. Determines how well a gaussian distribution describes the sample statistics by comparing the sample variance to the variance of a fitted gaussian model. A high significance indicates the outliers substantially affect the distribution. Based on the methodology described in: http://www.ellipticgroup.com/misc/article_supplement.pdf, p17 Parameters: mean - Sample mean (must be non-zero) variance - Sample variance batch-size - Number of measurements per sample (must be >= 16) Returns: A value between 0 and 1 representing outlier significance: - 0: No significant effect from outliers - 1: Outliers heavily influence the distribution Throws: AssertionError if preconditions on inputs are not met Example: (outlier-significance* 100.0 16.0 67108864) ;; => 0.25
(outliers)(outliers {:keys [id samples-id quantiles-id metric-ids outlier-method]})Detects statistical outliers in sample measurements using boxplot criteria.
Returns a function that identifies outliers based on the interquartile range (IQR) method, classifying them as mild or severe deviations.
Parameters: opts - Optional map with keys: :id - Key for outlier results (default: :outliers) :samples-id - Key for source samples (default: :samples) :quantiles-id - Key for required quantile analysis (default: :quantiles) :metric-ids - Set of metric ids to analyze (default: all quantitative) :outlier-method - Method for computing outlier thresholds: :medcouple (default) - adjusted boxplot for skewed data using medcouple statistic :tukey - Tukey's symmetric 1.5×IQR whiskers Note: digest-based samples always use :tukey regardless of this setting (medcouple requires individual sample values).
The returned function:
Outlier Classification:
Example: (let [analyze (outliers) result (analyze {:samples {...} :metrics-defs {...} :quantiles {...}})] (get-in result [:outliers :elapsed-time])) ;; Returns {:thresholds [...] :outliers {...} :outlier-counts {...}}
Detects statistical outliers in sample measurements using boxplot criteria.
Returns a function that identifies outliers based on the interquartile range
(IQR) method, classifying them as mild or severe deviations.
Parameters:
opts - Optional map with keys:
:id - Key for outlier results (default: :outliers)
:samples-id - Key for source samples (default: :samples)
:quantiles-id - Key for required quantile analysis (default: :quantiles)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
:outlier-method - Method for computing outlier thresholds:
:medcouple (default) - adjusted boxplot for skewed data
using medcouple statistic
:tukey - Tukey's symmetric 1.5×IQR whiskers
Note: digest-based samples always use :tukey
regardless of this setting (medcouple requires
individual sample values).
The returned function:
- Takes a sampled data map containing samples, metrics config and quantiles
- Returns the map with outlier analysis added under :id key
- For each metric provides:
- outlier thresholds (IQR boundaries)
- identified outliers with indices
- counts by severity (low/high, mild/severe)
- Requires prior quantile analysis in input data
Outlier Classification:
- Mild: between 1.5 and 3.0 IQR from quartiles
- Severe: beyond 3.0 IQR from quartiles
Example:
(let [analyze (outliers)
result (analyze {:samples {...}
:metrics-defs {...}
:quantiles {...}})]
(get-in result [:outliers :elapsed-time]))
;; Returns {:thresholds [...] :outliers {...} :outlier-counts {...}}(quantiles)(quantiles {:keys [id samples-id metric-ids] :as analysis})Calculates statistical quantiles for quantitative sample measurements.
Returns a function that computes sample quantiles (including quartiles and custom percentiles) for each quantitative metric in the input data.
Parameters: opts - Optional map with keys: :id - Key for quantile results in output (default: :quantiles) :samples-id - Key for source samples in input (default: :samples) :metric-ids - Set of metric ids to analyze (default: all quantitative) :quantiles - Vector of quantile values to calculate [0-1] (default: [0.25 0.5 0.75])
The returned function:
Example: (let [analyze (quantiles {:quantiles [0.05 0.95]}) result (analyze {:samples {...} :metrics-defs {...}})] (get-in result [:quantiles :elapsed-time])) ;; Returns map of quantiles for elapsed time metric
Calculates statistical quantiles for quantitative sample measurements.
Returns a function that computes sample quantiles (including quartiles and
custom percentiles) for each quantitative metric in the input data.
Parameters:
opts - Optional map with keys:
:id - Key for quantile results in output (default: :quantiles)
:samples-id - Key for source samples in input (default: :samples)
:metric-ids - Set of metric ids to analyze (default: all quantitative)
:quantiles - Vector of quantile values to calculate [0-1]
(default: [0.25 0.5 0.75])
The returned function:
- Takes a sampled data map containing samples and metric configs
- Returns the map with quantile analysis added under :id key
- Preserves source data transforms for correct value scaling
Example:
(let [analyze (quantiles {:quantiles [0.05 0.95]})
result (analyze {:samples {...} :metrics-defs {...}})]
(get-in result [:quantiles :elapsed-time]))
;; Returns map of quantiles for elapsed time metric(stats)(stats {:keys [id samples-id outliers-id metric-ids] :as analysis})Calculates comprehensive descriptive statistics for sample measurements.
Returns a function that computes key statistics including mean, variance, standard deviation bounds (±3σ), and min/max values for quantitative metrics.
Parameters: opts - Optional map with keys: :id - Key for stats in output (default: :stats) :samples-id - Key for source samples (default: :samples) :outliers-id - Key for outlier analysis if available :metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
Example: (let [analyze (stats) result (analyze {:samples {...} :metrics-defs {...}})] (get-in result [:stats :elapsed-time])) ;; Returns {:mean 100.0 :variance 16.0 ...}
Calculates comprehensive descriptive statistics for sample measurements.
Returns a function that computes key statistics including mean, variance,
standard deviation bounds (±3σ), and min/max values for quantitative metrics.
Parameters:
opts - Optional map with keys:
:id - Key for stats in output (default: :stats)
:samples-id - Key for source samples (default: :samples)
:outliers-id - Key for outlier analysis if available
:metric-ids - Set of metric ids to analyze (default: all quantitative)
The returned function:
- Takes a sampled data map containing samples and metric configs
- Returns the map with statistics added under :id key
- For each metric, calculates:
- mean, variance
- mean ±3σ bounds
- min/max values
- Preserves data transforms for correct scaling
Example:
(let [analyze (stats)
result (analyze {:samples {...} :metrics-defs {...}})]
(get-in result [:stats :elapsed-time]))
;; Returns {:mean 100.0 :variance 16.0 ...}(tail-analysis)(tail-analysis {:keys [id samples-id metric-ids] :as options})Computes tail statistics for extreme value analysis.
Returns a function that performs tail analysis including Hill estimator, GPD fitting, mean residual life, tail ratios, and high quantile estimation. Uses raw samples WITHOUT outlier filtering - tail analysis requires the extreme values that would normally be considered outliers.
Parameters: opts - Optional map with keys: :id - Key for result in output (default: :tail-analysis) :samples-id - Key for source samples (default: :samples) :metric-ids - Set of metric ids to analyze (default: all quantitative) :threshold - Explicit threshold value for POT analysis :threshold-quantile - Quantile to use as threshold (default: 0.9) :k-range - Range of k values for Hill estimator :high-quantiles - Quantiles to estimate via GPD (default: [0.99 0.999 0.9999])
The returned function:
Note: Unlike other analyses, tail analysis does NOT filter outliers because extreme values ARE the tail we want to analyze.
Example: (let [analyze (tail-analysis {:threshold-quantile 0.95}) result (analyze {:samples {...}})] (get-in result [:tail-analysis :elapsed-time :gpd :xi]))
Computes tail statistics for extreme value analysis.
Returns a function that performs tail analysis including Hill estimator,
GPD fitting, mean residual life, tail ratios, and high quantile estimation.
Uses raw samples WITHOUT outlier filtering - tail analysis requires the
extreme values that would normally be considered outliers.
Parameters:
opts - Optional map with keys:
:id - Key for result in output (default: :tail-analysis)
:samples-id - Key for source samples (default: :samples)
:metric-ids - Set of metric ids to analyze
(default: all quantitative)
:threshold - Explicit threshold value for POT analysis
:threshold-quantile - Quantile to use as threshold (default: 0.9)
:k-range - Range of k values for Hill estimator
:high-quantiles - Quantiles to estimate via GPD
(default: [0.99 0.999 0.9999])
The returned function:
- Takes a sampled data map containing samples
- Returns the map with tail analysis added under :id key
- For each metric provides:
- :tail-ratios - p99/p95, p999/p99 ratios indicating tail heaviness
- :hill - Hill estimator results with k-range, estimates, and
stable-estimate
- :gpd - GPD fit parameters (xi, sigma) for exceedances over threshold
- :mrl - Mean residual life values for threshold selection guidance
- :high-quantiles - Extreme quantile estimates using GPD extrapolation
- :empirical-quantiles - Raw percentiles (p95, p99, p999)
Note: Unlike other analyses, tail analysis does NOT filter outliers because
extreme values ARE the tail we want to analyze.
Example:
(let [analyze (tail-analysis {:threshold-quantile 0.95})
result (analyze {:samples {...}})]
(get-in result [:tail-analysis :elapsed-time :gpd :xi]))(transform-log)(transform-log {:keys [id samples-id metric-ids] :as options})Performs logarithmic transformation on time-based samples.
Returns a function that takes a sampled data map and adds log-transformed samples under a new key. Only transforms samples with :time dimension from quantitative metrics.
Parameters: opts - Optional map with keys:
:id - Key for transformed samples in result
(default: :log-samples)
:samples-id - Key for source samples in input (default: :samples)
:metric-ids - Set of metric ids to transform (default: all quantitative)
The returned function:
Example: (let [transform (transform-log {:id :my-logs}) result (transform {:samples {...} :metrics-defs {...}})] (:my-logs result)) ;; Contains log-transformed values
Performs logarithmic transformation on time-based samples.
Returns a function that takes a sampled data map and adds
log-transformed samples under a new key. Only transforms samples
with :time dimension from quantitative metrics.
Parameters:
opts - Optional map with keys:
:id - Key for transformed samples in result
(default: :log-samples)
:samples-id - Key for source samples in input (default: :samples)
:metric-ids - Set of metric ids to transform (default: all quantitative)
The returned function:
- Takes a sampled data map containing samples and metric configs
- Returns the map with transformed samples added under :id key
- Preserves original samples and adds transform metadata
Example:
(let [transform (transform-log {:id :my-logs})
result (transform {:samples {...} :metrics-defs {...}})]
(:my-logs result)) ;; Contains log-transformed valuescljdoc builds & hosts documentation for Clojure/Script libraries
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