Benchmarking library for clojure
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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


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)
(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. For functions that are very fast, or return a lot of data, you may need to supply a function to reduce the results to prevent excessive memory allocation. The default for :reduce-with adds the hash codes of the results.

(bench (rand) :reduce-with +)


API Documentation

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.


The library can be installed through Leiningen or through maven.


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


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Licensed under EPL