/contrib/famzah

Enthusiasm never stops


49 Comments

C++ vs. Python vs. PHP vs. Java vs. Others performance benchmark (2016 Q3)

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, dynamic arrays with numbers, basic math operations.

This is an improved redo of the tests done in previous years. You are strongly encouraged to read the additional information about the tests in the article.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 0.899 0.053 0.951 g++ 6.1.1 link
Rust 0.898 0.129 1.026 7% 7% 1.12.0 link
Java 8 (non-std lib) 1.090 0.006 1.096 15% 6% 1.8.0_102 link
Python 2.7 + PyPy 1.376 0.120 1.496 57% 36% PyPy 5.4.1 link
C# .NET Core Linux 1.583 0.112 1.695 78% 13% 1.0.0-preview2 link
Javascript (nodejs) 1.371 0.466 1.837 93% 8% 4.3.1 link
Go 2.622 0.083 2.705 184% 47% 1.7.1 link
C++ (not optimized) 2.921 0.054 2.975 212% 9% g++ 6.1.1 link
PHP 7.0 6.447 0.178 6.624 596% 122% 7.0.11 link
Java 8 (see notes) 12.064 0.080 12.144 1176% 83% 1.8.0_102 link
Ruby 12.742 0.230 12.972 1263% 6% 2.3.1 link
Python 3.5 17.950 0.126 18.077 1800% 39% 3.5.2 link
Perl 25.054 0.014 25.068 2535% 38% 5.24.1 link
Python 2.7 25.219 0.114 25.333 2562% 1% 2.7.12 link

The big difference this time is that we use a slightly modified benchmark method. Programs are no longer limited to just 10 loops. Instead they run for 90 wall-clock seconds, and then we divide and normalize their performance as if they were running for only 10 loops. This way we can compare with the previous results. The benefit of doing the tests like this is that the startup and shutdown times of the interpreters should make almost no difference now. It turned out that the new method doesn’t significantly change the outcome compared to the previous benchmark runs, which is good as the old way of benchmarks seems also correct.

For the curious readers, the raw results also show the maximum used memory (RSS).

Brief analysis of the results:

  • Rust, which we benchmark for the first time, is very fast. 🙂
  • C# .NET Core on Linux, which we also benchmark for the first time, performs very well by being as fast as NodeJS and only 78% slower than C++. Memory usage peak was at 230 MB which is the same as Python 3.5 and PHP 7.0, and two times less than Java 8 and NodeJS.
  • NodeJS version 4.3.x got much slower than the previous major version 4.2.x. This is the only surprise. It turned out to be a minor glitch in the parser which was easy to fix. NodeJS 4.3.x is performing the same as 4.2.x.
  • Python and Perl seem a bit slower than before but this is probably due to the fact that C++ performed even better because of the new benchmark method.
  • Java 8 didn’t perform much faster as we expected. Maybe it gets slower as more and more loops are done, which also allocated more RAM.
  • Also review the analysis in the old 2016 tests for more information.

The tests were run on a Debian Linux 64-bit machine.

You can download the source codes, raw results, and the benchmark batch script at:
https://github.com/famzah/langs-performance

Update @ 2016-10-15: Added the Rust implementation. The minor versions of some languages were updated as well.
Update @ 2016-10-19: A redo which includes the NodeJS fix.
Update @ 2016-11-04: Added the C# .NET Core implementation.


35 Comments

C++ vs. Python vs. Perl vs. PHP performance benchmark (2016)

There are newer benchmarks: C++ vs. Python vs. PHP vs. Java vs. Others performance benchmark (2016 Q3)

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, dynamic arrays with numbers, basic math operations.

This is a redo of the tests done in previous years. You are strongly encouraged to read the additional information about the tests in the article.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 0.952 0.172 1.124 g++ 5.3.1 link
Java 8 (non-std lib) 1.332 0.096 1.428 27% 27% 1.8.0_72 link
Python 2.7 + PyPy 1.560 0.160 1.720 53% 20% PyPy 4.0.1 link
Javascript (nodejs) 1.524 0.516 2.040 81% 19% 4.2.6 link
C++ (not optimized) 2.988 0.168 3.156 181% 55% g++ 5.3.1 link
PHP 7.0 6.524 0.184 6.708 497% 113% 7.0.2 link
Java 8 14.616 0.908 15.524 1281% 131% 1.8.0_72 link
Python 3.5 18.656 0.348 19.004 1591% 22% 3.5.1 link
Python 2.7 20.776 0.336 21.112 1778% 11% 2.7.11 link
Perl 25.044 0.236 25.280 2149% 20% 5.22.1 link
PHP 5.6 66.444 2.340 68.784 6020% 172% 5.6.17 link

The clear winner among the script languages is… PHP 7. 🙂

Yes, that’s not a mistake. Apparently the PHP team did a great job! The rumor that PHP 7 is really fast confirmed for this particular benchmark test. You can also review the PHP 7 infographic by the Zend Performance Team.

Brief analysis of the results:

  • NodeJS got almost 2x faster.
  • Java 8 seems almost 2x slower.
  • Python has no significant change in the performance. Every new release is a little bit faster but overall Python is steadily 15x slower than C++.
  • Perl has the same trend as Python and is steadily 22x slower than C++.
  • PHP 5.x is the slowest with results between 47x to 60x behind C++.
  • PHP 7 made the big surprise. It is about 10x faster than PHP 5.x, and about 3x faster than Python which is the next fastest script language.

The tests were run on a Debian Linux 64-bit machine.

You can download the source codes, an Excel results sheet, and the benchmark batch script at:
https://github.com/famzah/langs-performance

 


Leave a comment

Convert human-readable sizes back to raw numbers

Ever needed to convert lots of lines with 1M or 1G to their raw number representation?

Here is a sample:

$ cat sample
26140   132K   1.9G   1.5G     ?K     0K     8K     0K   5% mysqld
26140   132K   1.9G   1.5G     ?K     4K     8K     0K   5% mysqld
26140   132K   1.9G   1.5G     ?K     0K     0K     0K   5% mysqld
26140   132K   1.9G   1.5G     ?K    -8K     0K     0K   5% mysqld
26140   132K   1.9G   1.6G     ?K     0K    20K     0K   5% mysqld
26140   132K   1.9G   1.6G     ?K     0K    56K     0K   5% mysqld
26140   132K   1.9G   1.7G     ?K    -4K     4K     0K   5% mysqld
26140   132K   1.9G   1.7G     ?K     0K    16K     0K   5% mysqld
26140   132K   1.9G   1.8G     ?K     0K     0K     0K   5% mysqld

The following Perl one-liner comes to the rescue:

perl -Mstrict -Mwarnings -n -e 'my %p=( K=>3, M=>6, G=>9, T=>12); s/(\d+(?:\.\d+)?)([KMGT])/$1*10**$p{$2}/ge; print'

In the end you get:

$ cat sample | perl -Mstrict -Mwarnings -n -e 'my %p=( K=>3, M=>6, G=>9, T=>12); s/(\d+(?:\.\d+)?)([KMGT])/$1*10**$p{$2}/ge; print'
26140   132000   1900000000   1500000000     ?K     0     8000     0   5% mysqld
26140   132000   1900000000   1500000000     ?K     4000     8000     0   5% mysqld
26140   132000   1900000000   1500000000     ?K     0     0     0   5% mysqld
26140   132000   1900000000   1500000000     ?K    -8000     0     0   5% mysqld
26140   132000   1900000000   1600000000     ?K     0    20000     0   5% mysqld
26140   132000   1900000000   1600000000     ?K     0    56000     0   5% mysqld
26140   132000   1900000000   1700000000     ?K    -4000     4000     0   5% mysqld
26140   132000   1900000000   1700000000     ?K     0    16000     0   5% mysqld
26140   132000   1900000000   1800000000     ?K     0     0     0   5% mysqld

You can now paste this output to Excel, for example, in order to create a nice chart of it.


Leave a comment

Perl Net::Ping not working properly with ICMP by default

If you tried to ping a host with Perl Net::Ping using the ICMP protocol and that failed, even though the “ping” command-line utility can ping the host, you’re not alone 🙂 I had the same problem and it turned out to be due to the fact that Net::Ping by default sends no DATA in the ICMP request and thus its requests are rather short and non-standard. Here are some tcpdump examples:

  • Net::Ping with ICMP protocol, everything else is defaults: “$p = new Net::Ping(‘icmp’)“, no replies from remote host, note that the length is just 8 bytes:
    12:29:02.898083 IP source-addr > source-addr: ICMP echo request, id 2194, seq 41, length 8
    12:29:03.711595 IP source-addr > dest-addr: ICMP echo request, id 2194, seq 42, length 8
    
  • Linux “ping” command-line utility, remote host replies accordingly, the length is 64 bytes total:
    12:30:18.278865 IP source-addr > dest-addr: ICMP echo request, id 2488, seq 1, length 64
    12:30:18.289922 IP dest-addr > source-addr: ICMP echo reply, id 2488, seq 1, length 64
    12:30:18.790610 IP source-addr > dest-addr: ICMP echo request, id 2488, seq 2, length 64
    12:30:18.811029 IP dest-addr > source-addr: ICMP echo reply, id 2488, seq 2, length 64
    
  • Net::Ping with ICMP protocol with user-defined length, “$p = new Net::Ping(‘icmp’, 1, 56)“, remote host replies accordingly, the length is 64 bytes total:
    12:30:48.377496 IP source-addr > dest-addr: ICMP echo request, id 2488, seq 6, length 64
    12:30:48.433690 IP dest-addr > source-addr: ICMP echo reply, id 2488, seq 6, length 64
    12:30:48.934310 IP source-addr > dest-addr: ICMP echo request, id 2488, seq 7, length 64
    12:30:48.946152 IP dest-addr > source-addr: ICMP echo reply, id 2488, seq 7, length 64
    

Bottom line is that if you are going to use Net::Ping with ICMP, specify 56 for the “bytes” parameter when creating an instance of the Net::Ping object. This way you will be sending standard ICMP requests with total length of 64 bytes.


Leave a comment

Getting “500 Line too long (limit is 4096)” error in Perl

The error may also be “500 Line too long (limit is 8192)” but the problem is still the same – LWP or SOAP::Lite return this error when you try to POST or GET something very long.

The one to blame is actually Net::HTTP::Methods, included somewhere by something.

It took me a few hours to get this resolved:

use LWP::Protocol::http; # to suppress the warning "possible typo" in the next statement
push(@LWP::Protocol::http::EXTRA_SOCK_OPTS, MaxLineLength => 0); # to remove the limit

Put the above code in your Perl HTTP client and you’re good to go!

References:


6 Comments

C++ vs. Python vs. Perl vs. PHP performance benchmark (part #2)

This time we will focus on the startup time. The process start time is important if your processes are not persistent. If you are using FastCGI, mod_perl, mod_php, or mod_python, then these statistics are not so important to you. However, if you are spawning many processes which do something small and live for a very short time, then you should consider the CPU resources which get wasted while the script interpreter is being initialized.

The benchmarked scripts do only one thing – say “Hello, world” on the standard output. They do not include any additional modules in their source code – this may, or may not be your use-case. Though, very often the scripting languages have pretty many built-in functions, and for simple tasks you never need to include other modules.

Here are the benchmark results:

Language CPU time Slower than
User System Total C++ previous
C++ (with or w/o optimization) 2.568 3.536 6.051
Perl 12.561 6.096 18.723 209% 209%
PHP (w/o php.ini) 20.473 13.877 34.918 477% 86%
Python 27.014 11.881 39.318 550% 13%
Python + Psyco 32.986 14.845 48.132 695% 22%

The clear winner among the script languages this time is… Perl. 🙂

All scripts were invoked 3000 times using the following Bash loop:

time ( i=3000 ; while [ “$i” -gt 0 ]; do $CMD >/dev/null ; i=$(($i-1)); done )

All tests were done on a Kubuntu Lucid box. The versions of the used software packages follow:

  • g++ (GNU project C and C++ compiler) 4.4.3
  • Python 2.6.5
  • Python Psyco 1.6 (1ubuntu2)
  • Perl 5.10.1
  • PHP 5.3.2 (1ubuntu4.2 with Suhosin-Patch), Zend Engine 2.3.0

The C++ implementation follows, click “show source” below to see the full source:

#include <iostream>
using namespace std;

int main() {
	cout << "Hello, world!\n";
	return 0;
}

The Perl implementation follows, click “show source” below to see the full source:

use strict;
use warnings;

print "Hello, world!\n";

The PHP implementation follows, click “show source” below to see the full source:

<?php
echo "Hello, world!\n";

The Python implementation follows, click “show source” below to see the full source:

#import psyco
#psyco.full()

print 'Hello, world!'

Update (Jan/14/2012): Copied the used test environment info here.


Leave a comment

Speed up RRDtool database manipulations via RRDs (Perl)

Use case
You are doing a lot of data operations on your RRD files (create, update, fetch, last), and every update is done by a separate Perl process which lives a very short time – the process is launched, it updates or reads the data, does something else, and then exits.

The problem
If you are using RRDtool and Perl as described, you surely have noticed that running many of these processes wastes a lot of CPU resources. The question is – can we do some performance optimizations, and lessen the performance hit of loading the RRDs library into Perl? We know that launching often Perl itself is quite expensive, but after all, if we chose to work with Perl, this is a price we should be ready to pay.

The RRDtool shared library is a monolithic piece of code which provides ALL functions of the RRDtool suite – data manipulation, graphics and import/export tools. The last two components bring huge dependencies in regards to other shared libraries. The library from RRDtool version 1.4.4 depends on 34 other libraries on my Linux box! This must add up to the loading time of the RRDtool library into Perl.

Resolution and benchmarks
In order to prove my theory (actually, it was more a theory of zImage, and I just followed, enhanced and tried it), I commented out the implementation of the “graphics” and “import/export tools” modules from the source code of RRDtool. Then I re-compiled the library and did some performance benchmarks. I also re-implemented the RRDs.pm module by replacing the DynaLoader module with the XSLoader one. This made no difference in performance whatsoever. The re-compiled RRD library depends on only 4 other libraries – linux-gate.so.1, libm.so.6, libc.so.6, and /lib/ld-linux.so.2. I think this is the most we can cut down. 🙂

So here are the benchmark results. They show the accumulated time for 1000 invocations of the Perl interpreter with three different configurations:

  • Only Perl (baseline): 5.454s.
  • With RRDs, no graphics or import/export functions: 9.744s (+4.290s) +78%.
  • With standard RRDs: 11.647s (+6.192s) +113%.

As you can see, you can make Perl + RRDs start 35% faster. The speed up for RRDs itself is 44%.


Here are the commands I used for the benchmarks:

  • Only Perl (baseline): time ( i=1000 ; while [ “$i” -gt 0 ]; do perl -Mwarnings -Mstrict -e ” ; i=$(($i-1)); done )
  • Perl + RRDs: time ( i=1000 ; while [ “$i” -gt 0 ]; do perl -Mwarnings -Mstrict -MRRDs -e ” ; i=$(($i-1)); done )