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Criterium measures the computation time of an expression. It is designed to address some of the pitfalls of benchmarking, and benchmarking on the JVM in particular.

This includes:

  • statistical processing of multiple evaluations
  • inclusion of a warm-up period, designed to allow the JIT compiler to optimise its code
  • purging of gc before testing, to isolate timings from GC state prior to testing
  • a final forced GC after testing to estimate impact of cleanup on the timing results



Add the following to your :dependencies:

[criterium "0.4.6"]




The top level interface is in criterium.core.

(use 'criterium.core)

Use bench to run a benchmark in a simple manner.

(bench (Thread/sleep 1000))
                   Execution time mean : 1.000803 sec
          Execution time std-deviation : 328.501853 us
         Execution time lower quantile : 1.000068 sec ( 2.5%)
         Execution time upper quantile : 1.001186 sec (97.5%)

By default bench is quiet about its progress. Run with-progress-reporting to get progress information on *out*.

(with-progress-reporting (bench (Thread/sleep 1000) :verbose))
(with-progress-reporting (quick-bench (Thread/sleep 1000) :verbose))

Lower level functions are available, that separate benchmark statistic generation and reporting.

(report-result (benchmark (Thread/sleep 1000) {:verbose true}))
(report-result (quick-benchmark (Thread/sleep 1000)))

Note that results are returned to the user to prevent JIT from recognising that the results are not used.

Measurement Overhead Estimation

Criterium will automatically estimate a time for its measurement overhead. The estimate is normally made once per session, and is available in the criterium.core/estimated-overhead-cache var.

If the estimation is made while there is a lot of other processing going on, then benchmarking quick functions may report small negative times. You can force a recalculation of the overhead by calling criterium.core/estimated-overhead!.

If you want consistency across JVM processes, it might be prudent to explicitly set criterium.core/estimated-overhead! to a constant value.


API Documentation Annotated Source

See Elliptic Group for a Java benchmarking library. The accompanying article describes many of the JVM benchmarking pitfalls.

See Criterion for a Haskell benchmarking library that applies many of the same statistical techniques.


Serial correlation detection. Multimodal distribution detection. Use kernel density estimators?


To release, run the script. This requires that you have git-flow enabled your git repository with git flow init, and that you have configured your credentials for clojars.


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Licensed under EPL