How to profile methods in Scala?

问题:

What is a standard way of profiling Scala method calls?

What I need are hooks around a method, using which I can use to start and stop Timers.

In Java I use aspect programming, aspectJ, to define the methods to be profiled and inject bytecode to achieve the same.

Is there a more natural way in Scala, where I can define a bunch of functions to be called before and after a function without losing any static typing in the process?

回答1:

Do you want to do this without changing the code that you want to measure timings for? If you don't mind changing the code, then you could do something like this:

def time[R](block: => R): R = {
    val t0 = System.nanoTime()
    val result = block    // call-by-name
    val t1 = System.nanoTime()
    println("Elapsed time: " + (t1 - t0) + "ns")
    result
}

// Now wrap your method calls, for example change this...
val result = 1 to 1000 sum

// ... into this
val result = time { 1 to 1000 sum }

回答2:

In addition to Jesper's answer, you can automatically wrap method invocations in the REPL:

scala> def time[R](block: => R): R = {
   | val t0 = System.nanoTime()
   | val result = block
   | println("Elapsed time: " + (System.nanoTime - t0) + "ns")
   | result
   | }
time: [R](block: => R)R

Now - let's wrap anything in this

scala> :wrap time
wrap: no such command.  Type :help for help.

OK - we need to be in power mode

scala> :power
** Power User mode enabled - BEEP BOOP SPIZ **
** :phase has been set to 'typer'.          **
** scala.tools.nsc._ has been imported      **
** global._ and definitions._ also imported **
** Try  :help,  vals.<tab>,  power.<tab>    **

Wrap away

scala> :wrap time
Set wrapper to 'time'

scala> BigDecimal("1.456")
Elapsed time: 950874ns
Elapsed time: 870589ns
Elapsed time: 902654ns
Elapsed time: 898372ns
Elapsed time: 1690250ns
res0: scala.math.BigDecimal = 1.456

I have no idea why that printed stuff out 5 times

Update as of 2.12.2:

scala> :pa
// Entering paste mode (ctrl-D to finish)

package wrappers { object wrap { def apply[A](a: => A): A = { println("running...") ; a } }}

// Exiting paste mode, now interpreting.


scala> $intp.setExecutionWrapper("wrappers.wrap")

scala> 42
running...
res2: Int = 42

回答3:

There are three benchmarking libraries for Scala that you can avail of.

Since the URLs on the linked site are likely to change, I am pasting the relevant content below.

1. SPerformance - Performance Testing framework aimed at automagically comparing performance tests and working inside Simple Build Tool.

2. scala-benchmarking-template - SBT template project for creating Scala (micro-)benchmarks based on Caliper.

3. Metrics - Capturing JVM- and application-level metrics. So you know what's going on

回答4:

This what I use:

import System.nanoTime
def profile[R](code: => R, t: Long = nanoTime) = (code, nanoTime - t)

// usage:
val (result, time) = profile { 
  /* block of code to be profiled*/ 
}

val (result2, time2) = profile methodToBeProfiled(foo)

回答5:

testing.Benchmark might be useful.

scala> def testMethod {Thread.sleep(100)}
testMethod: Unit

scala> object Test extends testing.Benchmark {
     |   def run = testMethod
     | }
defined module Test

scala> Test.main(Array("5"))
$line16.$read$$iw$$iw$Test$     100     100     100     100     100

回答6:

I took the solution from Jesper and added some aggregation to it on multiple run of the same code

def time[R](block: => R) = {
    def print_result(s: String, ns: Long) = {
      val formatter = java.text.NumberFormat.getIntegerInstance
      println("%-16s".format(s) + formatter.format(ns) + " ns")
    }

    var t0 = System.nanoTime()
    var result = block    // call-by-name
    var t1 = System.nanoTime()

    print_result("First Run", (t1 - t0))

    var lst = for (i <- 1 to 10) yield {
      t0 = System.nanoTime()
      result = block    // call-by-name
      t1 = System.nanoTime()
      print_result("Run #" + i, (t1 - t0))
      (t1 - t0).toLong
    }

    print_result("Max", lst.max)
    print_result("Min", lst.min)
    print_result("Avg", (lst.sum / lst.length))
}

Suppose you want to time two functions counter_new and counter_old, the following is the usage:

scala> time {counter_new(lst)}
First Run       2,963,261,456 ns
Run #1          1,486,928,576 ns
Run #2          1,321,499,030 ns
Run #3          1,461,277,950 ns
Run #4          1,299,298,316 ns
Run #5          1,459,163,587 ns
Run #6          1,318,305,378 ns
Run #7          1,473,063,405 ns
Run #8          1,482,330,042 ns
Run #9          1,318,320,459 ns
Run #10         1,453,722,468 ns
Max             1,486,928,576 ns
Min             1,299,298,316 ns
Avg             1,407,390,921 ns

scala> time {counter_old(lst)}
First Run       444,795,051 ns
Run #1          1,455,528,106 ns
Run #2          586,305,699 ns
Run #3          2,085,802,554 ns
Run #4          579,028,408 ns
Run #5          582,701,806 ns
Run #6          403,933,518 ns
Run #7          562,429,973 ns
Run #8          572,927,876 ns
Run #9          570,280,691 ns
Run #10         580,869,246 ns
Max             2,085,802,554 ns
Min             403,933,518 ns
Avg             797,980,787 ns

Hopefully this is helpful

回答7:

I use a technique that's easy to move around in code blocks. The crux is that the same exact line starts and ends the timer - so it is really a simple copy and paste. The other nice thing is that you get to define what the timing means to you as a string, all in that same line.

Example usage:

Timelog("timer name/description")
//code to time
Timelog("timer name/description")

The code:

object Timelog {

  val timers = scala.collection.mutable.Map.empty[String, Long]

  //
  // Usage: call once to start the timer, and once to stop it, using the same timer name parameter
  //
  def timer(timerName:String) = {
    if (timers contains timerName) {
      val output = s"$timerName took ${(System.nanoTime() - timers(timerName)) / 1000 / 1000} milliseconds"
      println(output) // or log, or send off to some performance db for analytics
    }
    else timers(timerName) = System.nanoTime()
  }

Pros:

• no need to wrap code as a block or manipulate within lines
• can easily move the start and end of the timer among code lines when being exploratory

Cons:

• less shiny for utterly functional code
• obviously this object leaks map entries if you do not "close" timers, e.g. if your code doesn't get to the second invocation for a given timer start.

回答8:

I like the simplicity of @wrick's answer, but also wanted:

• the profiler handles looping (for consistency and convenience)

• more accurate timing (using nanoTime)

• time per iteration (not total time of all iterations)

• just return ns/iteration - not a tuple

This is achieved here:

def profile[R] (repeat :Int)(code: => R, t: Long = System.nanoTime) = { 
  (1 to repeat).foreach(i => code)
  (System.nanoTime - t)/repeat
}

For even more accuracy, a simple modification allows a JVM Hotspot warmup loop (not timed) for timing small snippets:

def profile[R] (repeat :Int)(code: => R) = {  
  (1 to 10000).foreach(i => code)   // warmup
  val start = System.nanoTime
  (1 to repeat).foreach(i => code)
  (System.nanoTime - start)/repeat
}

回答9:

ScalaMeter is a nice library to perform benchmarking in Scala

Below is a simple example

import org.scalameter._

def sumSegment(i: Long, j: Long): Long = (i to j) sum

val (a, b) = (1, 1000000000)

val execution_time = measure { sumSegment(a, b) }

If you execute above code snippet in Scala Worksheet you get the running time in milliseconds

execution_time: org.scalameter.Quantity[Double] = 0.260325 ms

回答10:

While standing on the shoulders of giants...

A solid 3rd-party library would be more ideal, but if you need something quick and std-library based, following variant provides:

• Repetitions
• Last result wins for multiple repetitions
• Total time and average time for multiple repetitions
• Removes the need for time/instant provider as a param

.

import scala.concurrent.duration._
import scala.language.{postfixOps, implicitConversions}

package object profile {

  def profile[R](code: => R): R = profileR(1)(code)

  def profileR[R](repeat: Int)(code: => R): R = {
    require(repeat > 0, "Profile: at least 1 repetition required")

    val start = Deadline.now

    val result = (1 until repeat).foldLeft(code) { (_: R, _: Int) => code }

    val end = Deadline.now

    val elapsed = ((end - start) / repeat)

    if (repeat > 1) {
      println(s"Elapsed time: $elapsed averaged over $repeat repetitions; Total elapsed time")

      val totalElapsed = (end - start)

      println(s"Total elapsed time: $totalElapsed")
    }
    else println(s"Elapsed time: $elapsed")

    result
  }
}

Also worth noting you can use the Duration.toCoarsest method to convert to the biggest time unit possible, although I am not sure how friendly this is with minor time difference between runs e.g.

Welcome to Scala version 2.11.7 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_60).
Type in expressions to have them evaluated.
Type :help for more information.

scala> import scala.concurrent.duration._
import scala.concurrent.duration._

scala> import scala.language.{postfixOps, implicitConversions}
import scala.language.{postfixOps, implicitConversions}

scala> 1000.millis
res0: scala.concurrent.duration.FiniteDuration = 1000 milliseconds

scala> 1000.millis.toCoarsest
res1: scala.concurrent.duration.Duration = 1 second

scala> 1001.millis.toCoarsest
res2: scala.concurrent.duration.Duration = 1001 milliseconds

scala> 

回答11:

You can use System.currentTimeMillis:

def time[R](block: => R): R = {
    val t0 = System.currentTimeMillis()
    val result = block    // call-by-name
    val t1 = System.currentTimeMillis()
    println("Elapsed time: " + (t1 - t0) + "ms")
    result
}

Usage:

time{
    //execute somethings here, like methods, or some codes.
}  

nanoTime will show you ns, so it will hard to see. So I suggest that you can use currentTimeMillis instead of it.