Simple profiling and performance monitoring for Clojure/Script
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Taoensso open-source

CHANGELOG | API | current Break Version:

[com.taoensso/tufte "1.1.1"] ; Please see CHANGELOG for details

Please consider helping to support my continued open-source Clojure/Script work?

Even small contributions can add up + make a big difference to help sustain my time writing, maintaining, and supporting Tufte and other Clojure/Script libraries. Thank you!

- Peter Taoussanis


Simple profiling and performance monitoring for Clojure/Script


Charles Joseph Minard's Carte Figurative, one of Edward Tufte's favourite examples of good data visualization.

10-second example

(require '[taoensso.tufte :as tufte :refer (defnp p profiled profile)])

;; We'll request to send `profile` stats to `println`:
(tufte/add-basic-println-handler! {})

;;; Let's define a couple dummy fns to simulate doing some expensive work
(defn get-x [] (Thread/sleep 500)             "x val")
(defn get-y [] (Thread/sleep (rand-int 1000)) "y val")

;; How do these fns perform? Let's check:

(profile ; Profile any `p` forms called during body execution
  {} ; Profiling options; we'll use the defaults for now
  (dotimes [_ 5]
    (p :get-x (get-x))
    (p :get-y (get-y))))

;; The following will be printed to *out*:
;;            pId      nCalls       Min        Max       MAD      Mean   Time% Time
;;         :get-y           5  171.68ms   940.84ms  264.82ms  541.28ms      52 2.71s
;;         :get-x           5   502.3ms   505.07ms    1.17ms   503.4ms      48 2.52s
;;     Clock Time                                                          100 5.22s
;; Accounted Time                                                          100 5.22s


  • Small, fast, cross-platform Clojure/Script codebase (<1k loc)
  • Tiny, flexible API: p, profiled, profile
  • Great compile-time elision and runtime filtering support
  • Arbitrary Clojure/Script form-level profiling
  • Full support for thread-local and multi-threaded profiling
  • Stats are just Clojure maps: aggregate, analyse, log, serialize to db, ...


Add the necessary dependency to your project:

[com.taoensso/tufte "1.1.1"]

And setup your namespace imports:

(ns my-clj-ns ; Clojure namespace
  (:require [taoensso.tufte :as tufte :refer (defnp p profiled profile)]))

(ns my-cljs-ns ; ClojureScript namespace
  (:require [taoensso.tufte :as tufte :refer-macros (defnp p profiled profile)]))

Step 1: identify forms you'd like to [sometimes] profile

Just wrap them with p and give them an id (namespaced keyword):

(defn get-customer-info []
  (let [raw-customer-map (p ::get-raw-customer (fetch-from-db))]
    (p ::enrich-raw-customer
      (do-some-work raw-customer-map))))

Tufte will record the execution times of these p forms whenever profiling is active.

NB: whether or not profiling is active, a p form always returns its normal body result.

This last point is important: you never need to worry about Tufte messing with your return values.

Step 2: activate profiling of p forms

Activate profiling with profiled or profile:

API Return value Effect
profiled [<body-result> <?stats>] None
profile <body-result> Sends <?stats> to registered handlers[1]

[1] Register handlers using (tufte/add-handler! <handler-id> <ns-pattern> <handler-fn>)

Handler ideas: save to a db, log, put! to a core.async channel, filter, aggregate, use for a realtime analytics dashboard, examine for outliers or unexpected behaviour, feed into your other performance/analytics systems, ...

  • Use profiled to handle stats yourself directly at the callsite.
  • Use profile to queue stats for handling later/elsewhere.

Between the two, you have great flexibility for a wide range of use cases in production and during development/debugging.

Conditional profiling

Tufte offers extensive facilities to control if and when profiling happens.

Both compile-time elision and runtime filtering are supported.

Method 1/3: profiling levels

Every p, profiled, and profile form can take an optional profiling level#{0 1 2 3 4 5}.

This level must be >= tufte/*min-level* for profiling to occur.

For example:

(profiled {:level 3} ...) ; Only activates profiling when (>= 3 *min-level*)
Min level Set with
Runtime tufte/set-min-level!, tufte/with-min-level
Compile-time TUFTE_MIN_LEVEL environment variable

Note that runtime filtering stacks with any compile-time elision

Method 2/3: namespace filtering

Likewise- p, profiled, and profile forms can be elided or filtered by the namespace in which they occur.

Namespace filter Set with
Runtime tufte/set-ns-pattern!, tufte-with-ns-pattern
Compile-time TUFTE_NS_PATTERN environment variable

Note that runtime filtering stacks with any compile-time elision

Some example namespace patterns:

#{"*" "some.lib.*"}
{:whitelist #{"*"} :blacklist #{"noisy.lib.*"}}

Method 3/3: arbitrary runtime conditions

Finally, profiled and profile both support an optional arbitrary test expression:

(profiled {:when my-cond?} ...) ; Only activates profiling when `my-cond?` is truthy

This can be used for a wide range of sophisticated behaviour including smart, application-aware rate limiting.

As one simpler example, we can get sampled profiling like this:

(profiled {:when (tufte/chance 0.5)} ...) ; Only activates profiling with 50% probability


Why not just use YourKit, JProfiler, or VisualVM?

The traditional recommendation for Clojure profiling has usually been to use a standard JVM profiling tool like one of the above.

And they can certainly do the job, but they also tend to be a little hairy: requiring special effort to use, and often producing gobs of information that can be difficult or time-consuming to meaningfully interpret.

In contrast, Tufte offers some interesting benefits:

  • Arbitrary application-aware, form-level profiling; measure [just] what you care about
  • Simple thread-local or multi-threaded semantics
  • During dev/debug: check performance right from within your REPL
  • During production: ongoing, application-aware conditional profiling, logging, and analysis (stats are just Clojure maps)
  • A cross-platform API that works seamlessly between your server (Clj) and client (Cljs) applications

Note that JVM profiling tools can still be very handy. Tufte doesn't offer memory profiling for example, and it's not well suited to forensic or very low-level profiling.

If you want to know clojure.lang.Numbers$, Number) or memory stats, you'll want a JVM tool. If you want to know my-fn stats, or you want ongoing stats in production - Tufte could be a good fit.

How does Tufte compare to the profiling in @ptaoussanis/Timbre?

Actually, I developed Tufte one weekend while refactoring Timbre's profiling. It's basically a refinement of the ideas from there.

Decided that I could make some worthwhile improvements with some breaking API changes and a new set of dedicated docs. Tufte's implementation is cross-platform, considerably faster, and its API more flexible.

With the release of Tufte, I'll be deprecating Timbre's profiling features.

Note that Tufte's a feature superset of Timbre's profiling, so porting should be straightforward:

API Timbre Tufte
p [id & body] [id & body], [opts & body]
profile [level id & body] [opts & body]
profiled [level id & body] [opts & body]
profile Output -> log Output -> arbitrary handler-fn [1]

[1] See tufte.timbre/add-timbre-logging-handler! for directing Tufte's profile output to Timbre.

How does Tufte compare to @hugoduncan/Criterium?

Basically, they serve different use cases: benchmarking for Criterium, and profiling for Tufte.

Library Measures Use for During Emphasis
Criterium 1 Clojure form Benchmarking Dev Accuracy
Tufte >=1 Clojure/Script forms Profiling, basic benchmarking Dev, prod Flexibility, speed

So Criterium produces very accurate stats for a single Clojure expression.

Tufte produces combined stats for an arbitrary number of Clojure/Script expressions with less emphasis on decimal-digit-level accuracy.

For example:

  • Use Criterium to measure and compare the performance of two libraries.
  • Use Tufte to measure or monitor the performance of various parts of your system and how they relate.

How's the performance in production?

For thread-local profiling: very good. For dynamic profiling: good.

Tufte's designed specifically to support ongoing use in production, and is highly optimized: so it's about as fast as it gets in both cases.

I'd think of it this way: if something's at all worth profiling, the overhead that Tufte introduces will be vanishingly insignificant.

Also, keep in mind that Tufte's conditional profiling gives you complete control over if and when you do pay (however little) for profiling.

What's the difference between thread-local and dynamic (multi-threaded) profiling?

If you don't already know the difference, you probably want thread-local profiling (the default). It's faster and conceptually simpler: it literally just profiles what happens sequentially on the current thread.

Work being done concurrently in futures and agents will be ignored.

In contrast, dynamic profiling works across thread boundaries using Clojure's standard ^:dynamic binding conveyance as in:

(def ^:dynamic *my-dynamic-var* nil)
(binding [*my-dynamic-var* "foobar!"] ; This val will be available across Clojure threads
  (future (println [:thread1 *my-dynamic-var*]))
  (future (println [:thread2 *my-dynamic-var*]))
  (println [:thread3 *my-dynamic-var*])
  (Thread/sleep 100))

;; %> "foobar!", "foobar!", "foobar!"

How do I get dynamic (multi-threaded) profiling?

profiled and profile have a :dynamic? option:

(profiled {:dynamic? true} ...) ; Activates dynamic (multi-threaded) profiling

This works through Clojure's standard ^:dynamic binding conveyance.

If you really want to get fancy, you can also do manual multi-threaded profiling using tufte/stats-accumulator.

What's the difference between Clock Time and Accounted Time?

This question refers to the values reported by the format-stats util

Clock time is just the total real-world time that elapsed between the start and end of a profiled or profile call. This is the amount of time that you'd have seen pass on a stopwatch in your hand.

Accounted time is the total execution time tracked by all p forms during the same period. It can be:

Outcome Meaning
(< accounted clock-time) Some work was done that wasn't tracked by any p forms
(> accounted clock-time) Nested p forms, and/or multi-threaded profiling[1]

[1] For example: if you're doing concurrent work on 6 threads, then you can do 6ms of work for each 1ms of clock time.

Contacting me / contributions

Please use the project's GitHub issues page for all questions, ideas, etc. Pull requests welcome. See the project's GitHub contributors page for a list of contributors.

Otherwise, you can reach me at Happy hacking!

- Peter Taoussanis


Distributed under the EPL v1.0 (same as Clojure).
Copyright © 2016 Peter Taoussanis.