Tag Archives: Python

Python Combat Guide published

After some work reviewing it and ensuring it has the expected quality, I finally published my book Python Combat Guide.

Is an atypical creation. Is more a Master Class to my best friend, it could be a SDM, TL leading a small Software Development department, a Coder or a Scientist wanting to join IT as programmer and to learn a lot of stuff very quickly, than rather a formal Python Book for learning. Absolutely is not for beginners.

If you want to buy it, to explore the TOC, extended description…

https://leanpub.com/pythoncombatguide

Lesson 0, learning to code in Python for non programmers

Please note: Even if I tried to make it easy, probably there are too many concepts for a non-programmer. Will try to deliver more basic previous knowledge and foundations, so people with zero knowledge don’t feel overwhelmed.

Start by installing Python 3.8 or 3.9 in your computer, and the IDE PyCharm. Install also Git, and create an account in GitLab so you can share code with other people and understand how Git works.

Here you can read the basic steps for setup PyCharm and GitLab.

Ok, so you can take a look at my video, and hopefully it makes spark your motivation to learn by yourself. :)

I’ve been asked why I used print(“”) instead of print().

Is a good question. The reason is, when we programmed in Python 2.x the native way was to print without parenthesis, like:

print "Hello World!"

Python 3.x was incompatible with that and requires to use parenthesis, like:

print("Hello World!")

Fortunately Python 2.x accepts also to print using parenthesis. In order to have compatibility within Python 2.x and Python 3.x or for future compatibility we were using always print(“Whatever”) in Python2.

However, there is one difference.

If you user print() or print(“”) in Python3 that will generate an empty line.

In Python 2 print(“”) will generate too an empty line, nevertheless print() in Python2 will print two parenthesis. We don’t want that.

This is illustrated in this screenshot:

So all the people that are at home, closed down for coronavirus, you have a chance now to start learning Python and from there get a live as programmer.

You can download the code for this lesson 0, from:

https://gitlab.com/carles.mateo/teach-unit-testing/-/blob/master/lesson0/tree.py

Capturing data from keyboard

In order to be able to do more samples, and then being a bit interesting an dynamic, I will introduce here how to get data inputted by the Keyboard.

print("Please enter your name:")
s_name = input()

This will add whatever we type, without the final Enter, to the String variable s_name.

Capturing numbers from Keyboard

How we do to capture a number, like how old are you, in years?.

The same way, and then we convert this to an Integer value. An Integer is a data type which is basically a number, not decimal. Like: 1, 2, 7, 1000 o -5.

print("Please enter your name:")
s_name = input()

print("Please enter your age:")
s_age = input()
# With int() we convert a String to an Integer, as long as it is possible.
# Wit str() we convert a Integer to a String, as long as it is possible.
i_age = int(s_age)

If you enter a number incorrectly and so that cannot be converted, you will get an Exception Error. That is something that happened in a way that was not expected. These error can be trapped, and we will see this later, in the future.

You know:

  • How to capture data from the keyboard with input()
  • How to convert data entered as String to Integer with int()
  • How to sum two numbers, like 2 + 3
  • How to subtract two numbers, like 2 – 3
  • How to multiply, like 2 * 3

So know, you should be able to solve a basic arithmetic exercise in Hacker Rank:

https://www.hackerrank.com/challenges/python-arithmetic-operators/problem

More sources with explanations

I’m teaching Unit Testing, Refactors, Quality Code and moving from Procedural to OOP to some colleagues, you can find source code for our classes here (please, be aware that there are some error made on purpose to show why and why not do things and hot to apply proper unit testing)

https://gitlab.com/carles.mateo/teach-unit-testing/-/tree/master

More resources

There are many free useful resources to learn Python:

Resources for Microservices and Business Domain Solutions for the Cloud Architect / Microservices Architect

First you have to understand that Python, Java and PHP are worlds completely different.

In Python you’ll probably use Flask, and listen to the port you want, inside Docker Container.

In PHP you’ll use a Frameworks like Laravel, or Symfony, or Catalonia Framework (my Framework) :) and a repo or many (as the idea is that the change in one microservice cannot break another it is recommended to have one git repo per Service) and split the requests with the API Gateway and Filters (so /billing/ goes to the right path in the right Server, is like rewriting URLs). You’ll rely in Software to split your microservices. Usually you’ll use Docker, but you have to add a Web Server and any other tools, as the source code is not packet with a Web Server and other Dependencies like it is in Java Spring Boot.

In Java you’ll use Spring Cloud and Spring Boot, and every Service will be auto-contained in its own JAR file, that includes Apache Tomcat and all other Dependencies and normally running inside a Docker. Tcp/Ip listening port will be set at start via command line, or through environment. You’ll have many git repositories, one per each Service.

Using many repos, one per Service, also allows to deploy only that repository and to have better security, with independent deployment tokens.

It is not unlikely that you’ll use one language for some of your Services and another for other, as well as a Database or another, as each Service is owner of their data.

In any case, you will be using CI/CD and your pipeline will be something like this:

  1. Pull the latest code for the Service from the git repository
  2. Compile the code (if needed)
  3. Run the Unit and Integration Tests
  4. Compile the service to an executable artifact (f.e. Java JAR with Tomcat server and other dependencies)
  5. Generate a Machine image with your JAR deployed (for Java. Look at Spotify Docker Plugin to Docker build from Maven), or with Apache, PHP, other dependencies, and the code. Normally will be a Docker image. This image will be immutable. You will probably use Dockerhub.
  6. Machine image will be started. Platform test are run.
  7. If platform tests pass, the service is promoted to the next environment (for example Dev -> Test -> PreProd -> Prod), the exact same machine is started in the next environment and platform tests are repeated.
  8. Before deploying to Production the new Service, I recommend running special Application Tests / Behavior-driven. By this I mean, to conduct tests that really test the functionality of everything, using a real browser and emulating the acts of a user (for example with BeHat, Cucumber or with JMeter).
    I recommend this specially because Microservices are end-points, independent of the implementation, but normally they are API that serve to a whole application. In an Application there are several components, often a change in the Front End can break the application. Imagine a change in Javascript Front End, that results in a call a bit different, for example, with an space before a name. Imagine that the Unit Tests for the Service do not test that, and that was not causing a problem in the old version of the Service and so it will crash when the new Service is deployed. Or another example, imagine that our Service for paying with Visa cards generates IDs for the Payment Gateway, and as a result of the new implementation the IDs generated are returned. With the mocked objects everything works, but when we deploy for real is when we are going to use the actual Bank Payment. This is also why is a good idea to have a PreProduction environment, with PreProduction versions of the actual Services we use (all banks or the GDS for flights/hotel reservation like Galileo or Amadeus have a Test, exactly like Production, Gateway)

If you work with Microsoft .NET, you’ll probably use Azure DevOps.

We IT Engineers, CTOs and Architects, serve the Business. We have to develop the most flexible approaches and enabling the business to release as fast as their need.

Take in count that Microservices is a tool, a pattern. We will use it to bring more flexibility and speed developing, resilience of the services, and speed and independence deploying. However this comes at a cost of complexity.

Microservices is more related to giving flexibility to the Business, and developing according to the Business Domains. Normally oriented to suite an API. If you have an API that is consumed by third party you will have things like independence of Services (if one is down the others will still function), gradual degradation, being able to scale the Services that have more load only, being able to deploy a new version of a Service which is independent of the rest of the Services, etc… the complexity in the technical solution comes from all this resilience, and flexibility.

If your Dev Team is up to 10 Developers or you are writing just a CRUD Web Application, a PoC, or you are an Startup with a critical Time to Market you probably you will not want to use Microservices approach. Is like killing flies with laser cannons. You can use typical Web services approach, do everything in one single Https request, have transactions, a single Database, etc…

But if your team is 100 Developer, like a big eCommerce, you’ll have multiple Teams between 5 and 10 Developers per Business Domain, and you need independence of each Service, having less interdependence. Each Service will own their own Data. That is normally around 5 to 7 tables. Each Service will serve a Business Domain. You’ll benefit from having different technologies for the different needs, however be careful to avoid having Teams with different knowledge that can have hardly rotation and difficult to continue projects when the only 2 or 3 Devs that know that technology leave. Typical benefit scenarios can be having MySql for the Billing Services, but having NoSQL Database for the image catalog, or to store logs of account activity. With Microservices, some services will be calling other Services, often asynchronously, using Queues or Streams, you’ll have Callbacks, Databases for reading, you’ll probably want to have gradual and gracefully failure of your applications, client load balancing, caches and read only databases/in-memory databases… This complexity is in order to protect one Service from the failure of others and to bring it the necessary speed under heavy load.

Here you can find a PDF Document of the typical resources I use for Microservice Projects.

You can also download it from my github repository:

https://github.com/carlesmateo/awesome-microservices

Do you use other solutions that are not listed?. Leave a message. I’ll investigate them and update the Document, to share with the Community.

Update 2020-03-06: I found this very nice article explaining the same. Microservices are not for everybody and not the default option: https://www.theregister.co.uk/AMP/2020/03/04/microservices_last_resort/

Update 2020-03-11: Qcom with 1,600 microservices says that microservices architecture is the las resort: https://www.theregister.co.uk/AMP/2020/03/09/monzo_microservices/

A handy trick command line to get the usages of our Python Methods in the code

We all use powerful code analysis tool, but sometimes you’re presented with a problem and you have just… the terminal.

This Bash code is handy.

grep "def " /home/carles/code/gitlab/cloud/terraform/src/scale/lib/iscsi.py | tr "()" "  " | awk '{ print $2; }' |  grep -v "__init" | sort > ./function_names_iscsi.txt

So this basically will get all the methods (“def ” whatever), strip the parenthesis with tr, and get the second column with awk, so basically the method name, sort it and write it to the file.

Then I will cd to the src directory and execute the seconds part:

cd /home/carles/code/gitlab/cloud/terraform/src/
for fname in $(cat ~/function_names_iscsi.txt); do printf "%s: %s\n" "$fname" "$(grep -r $fname *|grep -v 'def ' -c)"; done > ~/functions_being_used.txt

That will produce a nice list with the number of times of the method being called, in the form of:

method_name: occurrences

That’s the equivalent to doing Find Usages is PyCharm.

It’s easy to identify dead code then, with method_name: 0.

You can also run this to your Jenkins to warn when there is Dead Code in your repository.

Curiosity Python string.strip() removes just more than white spaces

Another Python curiosity.

If you see the Official Python3 documentation for strip(), it says that strip without parameters will return the string without the leading and trailing white spaces.
Optionally you can pass a string with the characters you want to eliminate.

The official documentation for Python 2 says:

string.strip(s[, chars])

Return a copy of the string with leading and trailing characters removed. If chars is omitted or None, whitespace characters are removed. If given and not None, chars must be a string; the characters in the string will be stripped from the both ends of the string this method is called on.

Changed in version 2.2.3: The chars parameter was added. The chars parameter cannot be passed in earlier 2.2 versions.

https://docs.python.org/2/library/string.html

A white space is a white space. Is not an Enter.
But strip() without parameters will remove white spaces (space), and Enter \n and Tabs \t.

Probably you will not realize that unless you read from a file that has empty lines at the end for a reason, and you use strip().

You can see a demonstration following this small program, that runs the same for Python2 and Python3.

And the corresponding output for python2 and python3:

The [ ] characters where added to show that there are no hidden tabs or similar after.

Here I paste the code so you can try yourself:

import sys


def print_bar():
    print("-----------------------------------------------------")


def print_between_brackets(s_test):
    print("[" + s_test + "]")


s_string_with_enters = "  Testing strip not only removing white spaces, but Enter and Tabs s well\n\t\n\n"

print("Testing .strip()")
print("You are running Python " + sys.version)
print("This is the original string")
print_bar()
print_between_brackets(s_string_with_enters)
print_bar()
print("Now after strip()...")
print_bar()
print_between_brackets(s_string_with_enters.strip())
print_bar()
print("As you can see the Enters and the Tabs have been removed, not just the spaced")

I think this should be disambiguate so I decided to take action. Is very easy to blame and never contribute. Not me. I went to Python to fix that and I located a bug reporting this issue:

https://bugs.python.org/issue25433

The issue was registed and made specially interesting contributions by Dimitri Papadopoulos Orfanos.

The thread is really interesting to read. I recommend it.

At a glance:

“Python heavily relies on isspace() to detect “whitespace” characters.”

* Lib/string.py near line 23:
  whitespace = ' \t\n\r\v\f'

So all those characters will be stripped in Python2.7 if you use just string.strip()

The ticket was opened the 2015-10-18 12:15. So it’s a shame the documentation has not been updated yet, more than 3 years later. Those are the kind of things, lack of care, that I can’t understand. Not looking for the excellence.

Please, do note that Python3 supports Unicode natively and things are always a bit different than with Python2 and AscII.

Curiosity Python 2.7 and print() from Python 3.6

I lead a project where I decided to go with Python 2.7, for the wide compatibility across all the Servers around.

With RHEL now supporting Python 3 as well, it doesn’t make much sense any more, as all the major brands do support Python 3 directly.

I saw it coming so in my Coding Style Guide for my Team I explained that we will use print(“”) which is the required way to proceed with Python 3, as opposite to print “whatever” from Python 2. Noye Python 2 supports both methods.

But today something unexpected appeared in the Tests. One line of code was making a print of ().

The line of code was:

print()

And not

print("")

And the curiosity is that if you do print() in Python 2.7 it outputs ().

 

An Epic fail that are committing all the universities

Article created on: 1528997557 | 2018-06-14 18:32:15 IST

Recently a mentor of the UCC university came to visit me to my office, in order to do the following of one of the members of my Team, an intern.
Conversation was well, and then at some point he asked what courses could do the university teach to their students in order to be more prepared for working with us.
The Head of Business Development, that was in the meeting with me, mentioned something interesting:
– Make the publish their best code in github, bitbucket or similar git repository, and maintain it. It is like a CV.
He pointed that some of the students sent me their repository page, and they have not committed a thing for more than a year. And usually the code that I find there is less than a tic-tac-toe exercise.
– Obviously, to have git experience.
– Having contributed to an Open Source project

I exposed some things that would be helpful to have in the interns and grads that I hire:
– git experience
– Python programming
– C programming
– Unit Testing experience
– Networking experience, in particular iSCSI exports, tcpdump
– Programming Best practices, PEP-8 at least for Python
– Usage of Professional Tools like PyCharm, JetBrains IntelliJ, PHPStorm, Code Lion, Netbeans, Eclipse
– Linux experience. Many of them use Windows at home cause they also play video games. Really few programmers in real life use windows. So at least guys install Virtual Box or VMWare and run Linux in an Virtual Machine.
– Cloud experience. Using instances, CDNs, APIs, tools…

And as the talking advanced I gave him a hint of the Epic fail that all the universities are committing.
They teach git for a semester. They teach Python for one or two semester, the first year usually one, the second year another. And that’s it. Is gone.
When they exit the university they have not programmed in Python for 2 or 3 years, they have not used git, they have not used SQL for the same amount of time, etc…

My boss pointed that the best candidates do side projects in their spare time, and have that bright in their eyes. That sparkling in the eyes is what I call the eye of the tiger, the desire to improve, to learn. That spark.

I told the mentor of my intern that the big mistake is doing things in small parcels, isolated, one block and is gone. That the best way to proceed would be to:
Make the student start a project from the very beginning, from the first semester. Then keep making it bigger and better over time.
Let them improve it over time. Screw it in all the ways possible. Make them reach the limits of their initial architecture. Allow them to face having to redo the thing from the scratch. Allow them to do screw it, to break things, and to learn from their mistakes. Over and over.

Nobody becomes a great programmer coding average things for two semesters.
But let them realize where the problems are. Let them come back to their code of two or three months ago, before holidays, and realize how important is to make comments, to give proper names to the files and to the variables. Let them run that project over so many time, that at some point they have to change computer and they realize that what worked with windows Uppercases and Lowercase mixed files, does not work with Unix (case sensitive).
Let them grow.
Let them see their mistakes over the time.

Let them run the project for so long so they switch several times from Cloud provider, and discover the pros and the cons and the not-to-do, and things like run for your life before using sharing hostings that limit your CPU quota even that kills your MySql instances when they look at the email (true history, connecting to POP3 was raising the CPU and the provider was killing the MySQL instances, and so the queries) or that limits your queries per second, and then ask them to install a drupal and they will learn the hard way why Quality is always better than price and will make the right decisions when they work for somebody else or for their own Startup.

Even many of the supposedly Senior guys never learned from their mistakes, for example the Outsourcing guys, cause they work 6 months to a year in a project and then jump to another. Nobody explains the hell in maintenance and incidental they have left there. Nobody teach them.

Programming an small project for 6 months doesn’t make a master. Doing it for 5 years, growing it, learning from your mistakes and learning the YES and DO-NOT the hard way, the real way that works, cause makes you understand why something is better than other things, is the path.

That also remembers me why I love the MT Notation and many of the guys in Barcelona that saw it criticized the method, while my colleagues at Facebook and Dropbox actually told me that they use it, specially for Python and C/C++.

Allow them to thing about how to solve sorting a list of 1000 items by themselves. Let them think. The lazy will copy, but they will not grow.

Then let them implement a Bubble sort. Let them improve it, if they can. Allow them a week to try to improve that. Then make them sort 1,000,000 items so they see that is bloody slow. How can I improve that?. May I read the data from the drive at once, reading line by line was slow… let them think. Like if they were learning Martial Arts, and so discovering their strengths, that they have fast reflexes, allow them to grow.

Universities have to create good professional, not just machines of passing the exams. Real world demands talent, problem solving abilities, passion, ability to learn, and will to do the things well and to improve, and discipline.

After 5-6 years of programming on a daily basis, with an IDE, git, deploying to the Cloud as the basic, and growing a program and seeing the downsides of the solutions chosen, observing that the caveats where for a reason, learning that the Hardware is important, that is not the same to write to memory that to disk or to network, detecting the problems, redoing things, ending in a cul-de-sac, fixing, improving, learning, growing the project, growing himself/herself as a mind, as a programmer, as a thinker, as an expert, daily, even if it’s 30 minutes per day, then that person is prepared for some serious business.

Like piano, guitar, painting, writing… and any other activity, one require continue training in order to improve.

Students have to follow a journey in order to improve.

Let them start with Command Line, i.e. in C and files. Let’s do add later database support.

Deal with buffer overflow, file descriptor, locks and conversion types. Let them migrate to another language the entire project, using Git from the beginning.

Let them migrate again when they need to add Web support. Allow them to discover that instead of reloading all the page they can use Ajax/JSON. Let them deal with click-click that many common users do on the page buttons (so they submit twice the information). To discover SQL Injections. To use a Web Framework. To add Unit Testing. Add some improvement via Javascript Frameworks like responsive for mobiles.

Allow them to use a new Database, new Webserver or technology that is fashion and everybody on Twitter talks about, so it crashes in their face. And so they discover that they will not play or discover new technologies in actual project time in the Company of their future employers, cause shit happens, and impacts the Schedule, and the Company loses money. Universities: Teach them, let the students learn this for themselves, rather than screwing it up in several companies after university.

A simple sample to print colors in Terminal with Python (local tty or stty in a ssh)

This is a very simple code, but handy.

I love the output for the simplicity and I use to check for my programs to see what will suit best.

#!/bin/env python 
# Collection of Effects
# 1m - Bold
# 2m - Normal Dark colors
# 3m - Italic
# 4m - Underline
# 7m - Background
# 9m - Strikethrough (except 38;9m)
# 40m - Bakground Dark Grey, with the colors in foreground 1 (bold),2,31-37
# 41m - Bakground Red, with the colors in foreground 1 (bold),2,30-37
# 42m - Bakground Green, with the colors in foreground 1 (bold),2,30-37
# 43m - Bakground Yellow, with the colors in foreground 1 (bold),2,30-37
# 44m - Bakground Blue, with the colors in foreground 1 (bold),2,30-37
# 45m - Bakground Violet, with the colors in foreground 1 (bold),2,30-37
# 46m - Bakground Cyan, with the colors in foreground 1 (bold),2,30-37
# 47m - Bakground Grey, with the colors in foreground 1 (bold),2,31-37
# 49m - Normal

CLEAR = "\x1B[0m"

a_s_background = ["1", "2", "3", "4", "7", "9", "40", "41", "42", "43", "44", "45", "46", "47", "49"]
a_s_color = ["1", "2", "31", "32", "33", "34", "35", "36", "37"]

for s_effect in a_s_background:
    s_line = ""
    for s_color in a_s_color:
        s_color_text = "\x1B[" + s_color + ";" + s_effect + "m"
        s_line += s_color_text + s_color + ";" + s_effect + "m" + CLEAR + " "
    print(s_line)
    print("--------------------------------------------")

You can find the source code here:

https://gitlab.com/carles.mateo/blog.carlesmateo.com-source-code/-/blob/master/colors_in_terminal.py

Please, take in count that the colors may be different depending on the Terminal used, so if you’re creating a commercial application I recommend you to try with some of them like: ubuntu terminal, ssh, putty for windows, MobaXTerm…

View from the colors from the Console of PyCharm Community Edition

I like to write Software that does not require from external packages, but if you want to use colors and write a portable program that runs in Linux, Mac OS X and Windows, I recommend you to use colorama.

A sample way to return in Python not-to-do

Today I was checking the code, the latest push to the git repo, as I always do, and I saw something that was wrong.

Often Engineers can be confused by the ways different languages treat similar operations, so similarly as POSIX I try to use an standard way to program in any language that makes the code very clear and easy to understand, no matter if it’s C, Java, Python, PHP…

My code and the code of my Teams will be clear, and easy to understand. And as the good Engineers jump from language to language upon the needs, is better for all to proceed like this to avoid confusions.

In this case I want to cover a simple case that I detected. A wrong usage.

The code was returning True on success and if not simply return.

Here I show a simple demonstration that return itself will be returning return None.

# Proof of Concept for avoiding return without the type
# Author: Carles Mateo
# Creation Date: 2018-03-27
#

from pprint import pprint

def boolean_test(b_value):
if b_value is False:
return

return True

b_true = boolean_test(True)
b_false = boolean_test(False)

pprint(b_true)
pprint(b_false)

if b_false is False:
print “I detect it as False (even if it’s None)”

if b_false is True:
print “I detect it as True (even if it’s None)”

if b_false is None:
print “It is None!”

print “Be careful”

Variables use the MT Notation. I include tips like this and guidelines in programming guide for my Teams.

See: Wiki Python Programming/Data Types

Performance of several languages

Notes on 2017-03-26 18:57 CEST – Unix time: 1490547518 :

  1. As several of you have noted, it would be much better to use a random value, for example, read by disk. This will be an improvement done in the next benchmark. Good suggestion thanks.
  2. Due to my lack of time it took more than expected updating the article. I was in a long process with google, and now I’m looking for a new job.
  3. I note that most of people doesn’t read the article and comment about things that are well indicated on it. Please before posting, read, otherwise don’t be surprise if the comment is not published. I’ve to keep the blog clean of trash.
  4. I’ve left out few comments cause there were disrespectful. Mediocrity is present in the society, so simply avoid publishing comments that lack the basis of respect and good education. If a comment brings a point, under the point of view of Engineering, it is always published.

Thanks.

(This article was last updated on 2015-08-26 15:45 CEST – Unix time: 1440596711. See changelog at bottom)

One may think that Assembler is always the fastest, but is that true?.

If I write a code in Assembler in 32 bit instead of 64 bit, so it can run in 32 and 64 bit, will it be faster than the code that a dynamic compiler is optimizing in execution time to benefit from the architecture of my computer?.

What if a future JIT compiler is able to use all the cores to execute a single thread developed program?.

Are PHP, Python, or Ruby fast comparing to C++?. Does Facebook Hip Hop Virtual machine really speeds PHP execution?.

This article shows some results and shares my conclusions. It is as a base to discuss with my colleagues. Is not an end, we are always doing tests, looking for the edge, and looking at the root of the things in detail. And often things change from one version to the other. This article shows not an absolute truth, but brings some light into interesting aspects.

It could show the performance for the certain case used in the test, although generic core instructions have been selected. Many more tests are necessary, and some functions differ in the performance. But this article is a necessary starting for the discussion with my IT-extreme-lover friends and a necessary step for the next upcoming tests.

It brings very important data for Managers and Decision Makers, as choosing the adequate performance language can save millions in hardware (specially when you use the Cloud and pay per hour of use) or thousand hours in Map Reduce processes.

Acknowledgements and thanks

Credit for the great Eduard Heredia, for porting my C source code to:

  • Go
  • Ruby
  • Node.js

And for the nice discussions of the results, an on the optimizations and dynamic vs static compilers.

Thanks to Juan Carlos Moreno, CTO of ECManaged Cloud Software for suggesting adding Python and Ruby to the languages tested when we discussed my initial results.

Thanks to Joel Molins for the interesting discussions on Java performance and garbage collection.

Thanks to Cliff Click for his wonderful article on Java vs C performance that I found when I wanted to confirm some of my results and findings.

I was inspired to do my own comparisons by the benchmarks comparing different framework by techempower. It is amazing to see the results of the tests, like how C++ can serialize JSon 1,057,793 times per second and raw PHP only 180,147 (17%).

For the impatients

I present the results of the tests, and the conclusions, for those that doesn’t want to read about the details. For those that want to examine the code, and the versions of every compiler, and more in deep conclusions, this information is provided below.

Results

This image shows the results of the tests with every language and compiler.

All the tests are invoked from command line. All the tests use only one core. No tests for the web or frameworks have been made, are another scenarios worth an own article.

More seconds means a worst result. The worst is Bash, that I deleted from the graphics, as the bar was crazily high comparing to others.

* As later is discussed my initial Assembler code was outperformed by C binary because the final Assembler code that the compiler generated was better than mine.

After knowing why (later in this article is explained in detail) I could have reduced it to the same time than the C version as I understood the improvements made by the compiler.

blog-carlesmateo-com-performance-several-languages-php7-phantomjs-nodejs-java-bash-go-perl-luajit-hhvm3_9-scale_mod5

Table of times:

Seconds executing Language Compiler used Version
6 s. Java Oracle Java Java JDK 8
6 s. Java Oracle Java Java JDK 7
6 s. Java Open JDK OpenJDK 7
6 s. Java Open JDK OpenJDK 6
7 s. Go Go Go v.1.3.1 linux/amd64
7 s. Go Go Go v.1.3.3 linux/amd64
8 s. Lua LuaJit Luajit 2.0.2
10 s. C++ g++ g++ (Ubuntu 4.8.2-19ubuntu1) 4.8.2
10 s. C gcc gcc (Ubuntu 4.8.2-19ubuntu1) 4.8.2
10 s.
(* first version was 13 s. and then was optimized)
Assembler nasm NASM version 2.10.09 compiled on Dec 29 2013
10 s. Nodejs nodejs Nodejs v0.12.4
14 s. Nodejs nodejs Nodejs v0.10.25
18 s. Go Go go version xgcc (Ubuntu 4.9-20140406-0ubuntu1) 4.9.0 20140405 (experimental) [trunk revision 209157] linux/amd64
20 s. Phantomjs Phantomjs phantomjs 1.9.0
21 s. Phantomjs Phantomjs phantomjs 2.0.1-development
38 s. PHP Facebook HHVM HipHop VM 3.4.0-dev (rel)
44 s. Python Pypy Pypy 2.2.1 (Python 2.7.3 (2.2.1+dfsg-1, Nov 28 2013, 05:13:10))
52 s. PHP Facebook HHVM HipHop VM 3.9.0-dev (rel)
52 s. PHP Facebook HHVM HipHop VM 3.7.3 (rel)
128 s. PHP PHP PHP 7.0.0alpha2 (cli) (built: Jul 3 2015 15:30:23)
278 s. Lua Lua Lua 2.5.3
294 s. Gambas3 Gambas3 3.7.0
316 s. PHP PHP PHP 5.5.9-1ubuntu4.3 (cli) (built: Jul 7 2014 16:36:58)
317 s. PHP PHP PHP 5.6.10 (cli) (built: Jul 3 2015 16:13:11)
323 s. PHP PHP PHP 5.4.42 (cli) (built: Jul 3 2015 16:24:16)
436 s. Perl Perl Perl 5.18.2
523 s. Ruby Ruby ruby 1.9.3p484 (2013-11-22 revision 43786) [x86_64-linux]
694 s. Python Python Python 2.7.6
807 s. Python Python Python 3.4.0
47630 s. Bash GNU bash, version 4.3.11(1)-release (x86_64-pc-linux-gnu)

 

Conclusions and Lessons Learnt

  1. There are languages that will execute faster than a native Assembler program, thanks to the JIT Compiler and to the ability to optimize the program at runtime for the architecture of the computer running the program (even if there is a small initial penalty of around two seconds from JIT when running the program, as it is being analysed, is it more than worth in our example)
  2. Modern Java can be really fast in certain operations, it is the fastest in this test, thanks to the use of JIT Compiler technology and a very good implementation in it
  3. Oracle’s Java and OpenJDK shows no difference in performance in this test
  4. Script languages really sucks in performance. Python, Perl and Ruby are terribly slow. That costs a lot of money if you Scale as you need more Server in the Cloud
  5. JIT compilers for Python: Pypy, and for Lua: LuaJit, make them really fly. The difference is truly amazing
  6. The same language can offer a very different performance using one version or another, for example the go that comes from Ubuntu packets and the last version from official page that is faster, or Python 3.4 is much slower than Python 2.7 in this test
  7. Bash is the worst language for doing the loop and inc operations in the test, lasting for more than 13 hours for the test
  8. From command line PHP is much faster than Python, Perl and Ruby
  9. Facebook Hip Hop Virtual Machine (HHVM) improves a lot PHP’s speed
  10. It looks like the future of compilers is JIT.
  11. Assembler is not always the fastest when executed. If you write a generic Assembler program with the purpose of being able to run in many platforms you’ll not use the most powerful instructions specific of an architecture, and so a JIT compiler can outperform your code. An static compiler can also outperform your code with very clever optimizations. People that write the compilers are really good. Unless you’re really brilliant with Assembler probably a C/C++ code beats the performance of your code. Even if you’re fantastic with Assembler it could happen that a JIT compiler notices that some executions can be avoided (like code not really used) and bring magnificent runtime optimizations. (for example a near JMP is much more less costly than a far JMP Assembler instruction. Avoiding dead code could result in a far JMP being executed as near JMP, saving many cycles per loop)
  12. Optimizations really needs people dedicated to just optimizations and checking the speed of the newly added code for the running platforms
  13. Node.js was a big surprise. It really performed well. It is promising. New version performs even faster
  14. go is promising. Similar to C, but performance is much better thanks to deciding at runtime if the architecture of the computer is 32 or 64 bit, a very quick compilation at launch time, and it compiling to very good assembler (that uses the 64 bit instructions efficiently, for example)
  15. Gambas 3 performed surprisingly fast. Better than PHP
  16. You should be careful when using C/C++ optimization -O3 (and -O2) as sometimes it doesn’t work well (bugs) or as you may expect, for example by completely removing blocks of code if the compiler believes that has no utility (like loops)
  17. Perl performance really change from using a for style or another. (See Perl section below)
  18. Modern CPUs change the frequency to save energy. To run the tests is strictly recommended to use a dedicated machine, disabling the CPU governor and setting a frequency for all the cores, booting with a text only live system, without background services, not mounting disks, no swap, no network

(Please, before commenting read completely the article )

Explanations in details

Obviously an statically compiled language binary should be faster than an interpreted language.

C or C++ are much faster than PHP. And good code machine is much faster of course.

But there are also other languages that are not compiled as binary and have really fast execution.

For example, good Web Java Application Servers generate compiled code after the first request. Then it really flies.

For web C# or .NET in general, does the same, the IIS Application Server creates a native DLL after the first call to the script. And after this, as is compiled, the page is really fast.

With C statically linked you could generate binary code for a particular processor, but then it won’t work in other processors, so normally we write code that will work in all the processors at the cost of not using all the performance of the different CPUs or use another approach and we provide a set of different binaries for the different architectures. A set of directives doing one thing or other depending on the platform detected can also be done, but is hard, long and tedious job with a lot of special cases treatment. There is another approach that is dynamic linking, where certain things will be decided at run time and optimized for the computer that is running the program by the JIT (Just-in-time) Compiler.

Java, with JIT is able to offer optimizations for the CPU that is running the code with awesome results. And it is able to optimize loops and mathematics operations and outperform C/C++ and Assembler code in some cases (like in our tests) or to be really near in others. It sounds crazy but nowadays the JIT is able to know the result of several times executed blocks of code and to optimize that with several strategies, speeding the things incredible and to outperform a code written in Assembler. Demonstrations with code is provided later.

A new generation has grown knowing only how to program for the Web. Many of them never saw Assembler, neither or barely programmed in C++.

None of my Senior friends would assert that a technology is better than another without doing many investigations before. We are serious. There is so much to take in count, so much to learn always, that one has to be sure that is not missing things before affirming such things categorically. If you want to be taken seriously, you have to take many things in count.

Environment for the tests

Hardware and OS

Intel(R) Core(TM) i7-4770S CPU @ 3.10GHz with 32 GB RAM and SSD Disk.

Ubuntu Desktop 14.04 LTS 64 bit

Software base and compilers

PHP versions

Shipped with my Ubuntu distribution:

php -v
PHP 5.5.9-1ubuntu4.3 (cli) (built: Jul  7 2014 16:36:58)
Copyright (c) 1997-2014 The PHP Group
Zend Engine v2.5.0, Copyright (c) 1998-2014 Zend Technologies
with Zend OPcache v7.0.3, Copyright (c) 1999-2014, by Zend Technologies

Compiled from sources:

PHP 5.6.10 (cli) (built: Jul  3 2015 16:13:11)
Copyright (c) 1997-2015 The PHP Group
Zend Engine v2.6.0, Copyright (c) 1998-2015 Zend Technologies
PHP 5.4.42 (cli) (built: Jul  3 2015 16:24:16)
Copyright (c) 1997-2014 The PHP Group
Zend Engine v2.4.0, Copyright (c) 1998-2014 Zend Technologies

 

Java 8 version

java -showversion
java version "1.8.0_05"
Java(TM) SE Runtime Environment (build 1.8.0_05-b13)
Java HotSpot(TM) 64-Bit Server VM (build 25.5-b02, mixed mode)

C++ version

g++ -v
Using built-in specs.
COLLECT_GCC=g++
COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/4.8/lto-wrapper
Target: x86_64-linux-gnu
Configured with: ../src/configure -v --with-pkgversion='Ubuntu 4.8.2-19ubuntu1' --with-bugurl=file:///usr/share/doc/gcc-4.8/README.Bugs --enable-languages=c,c++,java,go,d,fortran,objc,obj-c++ --prefix=/usr --program-suffix=-4.8 --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --with-gxx-include-dir=/usr/include/c++/4.8 --libdir=/usr/lib --enable-nls --with-sysroot=/ --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-gnu-unique-object --disable-libmudflap --enable-plugin --with-system-zlib --disable-browser-plugin --enable-java-awt=gtk --enable-gtk-cairo --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-4.8-amd64/jre --enable-java-home --with-jvm-root-dir=/usr/lib/jvm/java-1.5.0-gcj-4.8-amd64 --with-jvm-jar-dir=/usr/lib/jvm-exports/java-1.5.0-gcj-4.8-amd64 --with-arch-directory=amd64 --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --enable-objc-gc --enable-multiarch --disable-werror --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --with-tune=generic --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu
Thread model: posix
gcc version 4.8.2 (Ubuntu 4.8.2-19ubuntu1)

Gambas 3

gbr3 --version
3.7.0

Go (downloaded from google)

go version
go version go1.3.1 linux/amd64

Go (Ubuntu packages)

go version
go version xgcc (Ubuntu 4.9-20140406-0ubuntu1) 4.9.0 20140405 (experimental) [trunk revision 209157] linux/amd64

Nasm

nasm -v
NASM version 2.10.09 compiled on Dec 29 2013

Lua

lua -v
Lua 5.2.3  Copyright (C) 1994-2013 Lua.org, PUC-Rio

Luajit

luajit -v
LuaJIT 2.0.2 -- Copyright (C) 2005-2013 Mike Pall. http://luajit.org/

Nodejs

Installed with apt-get install nodejs:

nodejs --version
v0.10.25

Installed by compiling the sources:

node --version
v0.12.4

Phantomjs

Installed with apt-get install phantomjs:

phantomjs --version
1.9.0

Compiled from sources:

/path/phantomjs --version
2.0.1-development

Python 2.7

python --version
Python 2.7.6

Python 3

python3 --version
Python 3.4.0

Perl

perl -version
This is perl 5, version 18, subversion 2 (v5.18.2) built for x86_64-linux-gnu-thread-multi
(with 41 registered patches, see perl -V for more detail)

Bash

bash --version
GNU bash, version 4.3.11(1)-release (x86_64-pc-linux-gnu)
Copyright (C) 2013 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>

This is free software; you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.

Test: Time required for nested loops

This is the first sample. It is an easy-one.

The main idea is to generate a set of nested loops, with a simple counter inside.

When the counter reaches 51 it is set to 0.

This is done for:

  1. Preventing overflow of the integer if growing without control
  2. Preventing the compiler from optimizing the code (clever compilers like Java or gcc with -O3 flag for optimization, if it sees that the var is never used, it will see that the whole block is unnecessary and simply never execute it)

Doing only loops, the increment of a variable and an if, provides us with basic structures of the language that are easily transformed to Assembler. We want to avoid System calls also.

This is the base for the metrics on my Cloud Analysis of Performance cmips.net project.

Here I present the times for each language, later I analyze the details and the code.

Take in count that this code only executes in one thread / core.

C++

C++ result, it takes 10 seconds.

Code for the C++:

/*
* File:   main.cpp
* Author: Carles Mateo
*
* Created on August 27, 2014, 1:53 PM
*/

#include <cstdlib>
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include <ctime>

using namespace std;

typedef unsigned long long timestamp_t;

static timestamp_t get_timestamp()
{
    struct timeval now;
    gettimeofday (&now, NULL);
    return  now.tv_usec + (timestamp_t)now.tv_sec * 1000000;
}

int main(int argc, char** argv) {

    timestamp_t t0 = get_timestamp();

    // current date/time based on current system
    time_t now = time(0);

    // convert now to string form
    char* dt_now = ctime(&now);

    printf("Starting at %s\n", dt_now);

    int i_loop1 = 0;
    int i_loop2 = 0;
    int i_loop3 = 0;

    

    for (i_loop1 = 0; i_loop1 < 10; i_loop1++) {
        for (i_loop2 = 0; i_loop2 < 32000; i_loop2++) {
            for (i_loop3 = 0; i_loop3 < 32000; i_loop3++) {
                i_counter++;

                if (i_counter > 50) {
                    i_counter = 0;
                }
            }
            // If you want to test how the compiler optimizes that, remove the comment
            //i_counter = 0;
         }
     }

    // This is another trick to avoid compiler's optimization. To use the var somewhere
    printf("Counter: %i\n", i_counter);

    timestamp_t t1 = get_timestamp();
    double secs = (t1 - t0) / 1000000.0L;
    time_t now_end = time(0);

    // convert now to string form
    char* dt_now_end = ctime(&now_end);

    printf("End time: %s\n", dt_now_end);

    return 0;
}

blog-carlesmateo-com-test-nested-loops-cpp-netbeans-10seconds

You can try to remove the part of code that makes the checks:

                /* if (i_counter > 50) {
                    i_counter = 0;
                }*/

And the use of the var, later:

    //printf("Counter: %i\n", i_counter);

Note: And adding a i_counter = 0; at the beginning of the loop to make sure that the counter doesn’t overflows. Then the C or C++ compiler will notice that this result is never used and so it will eliminate the code from the program, having as result and execution time of 0.0 seconds.

Java

The code in Java:

package cpu;

/**
 *
 * @author carles.mateo
 */
public class Cpu {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
        
        int i_loop1 = 0;
        //int i_loop_main = 0;
        int i_loop2 = 0;
        int i_loop3 = 0;
        int i_counter = 0;
        
        String s_version = System.getProperty("java.version");
        
        System.out.println("Java Version: " + s_version);

        System.out.println("Starting cpu.java...");
        
        for (i_loop1 = 0; i_loop1 < 10; i_loop1++) {            
                for (i_loop2 = 0; i_loop2 < 32000; i_loop2++) {
                    for (i_loop3 = 0; i_loop3 < 32000; i_loop3++) {
                        i_counter++;
                        
                        if (i_counter > 50) { 
                            i_counter = 0;
                        }
                    }
                }
        }
        
        System.out.println(i_counter);
        System.out.println("End");
    }
    
}

It is really interesting how Java, with JIT outperforms C++ and Assembler.

It takes only 6 seconds.

Netbeans with Java IDE executing with OpenJDK 1.6 in 6 seconds

Go

The case of Go is interesting because I saw a big difference from the go shipped with Ubuntu, and the the go I downloaded from http://golang.org/dl/. I downloaded 1.3.1 and 1.3.3 offering the same performance. 7 seconds.

blog-carlesmateo-com-go1-3-3-linux-amd64-performance-37Source code for nested_loops.go

package main

import ("fmt"
        "time")

func main() {
   fmt.Printf("Starting: %s", time.Now().Local())
   var i_counter = 0;
   for i_loop1 := 0; i_loop1 < 10; i_loop1++ {
       for i_loop2 := 0; i_loop2 < 32000; i_loop2++ {
           for i_loop3 := 0; i_loop3 < 32000; i_loop3++ {
               i_counter++;
               if i_counter > 50 {
                   i_counter = 0;
               }
           }
       }
    }

   fmt.Printf("\nCounter: %#v", i_counter)
   fmt.Printf("\nEnd: %s\n", time.Now().Local())
}

Assembler

Here is the Assembler for Linux code, with SASM, that I created initially (bellow is optimized).

%include "io.inc"

section .text
global CMAIN
CMAIN:
    ;mov rbp, rsp; for correct debugging
    ; Set to 0, the faster way
    xor     esi, esi

DO_LOOP1:
    mov ecx, 10
LOOP1:
    mov ebx, ecx
    jmp DO_LOOP2
LOOP1_CONTINUE:
    mov ecx, ebx
    
    loop LOOP1
    jmp QUIT

DO_LOOP2:
    mov ecx, 32000
LOOP2:
    mov eax, ecx
    ;call DO_LOOP3
    jmp DO_LOOP3
LOOP2_CONTINUE:
    mov ecx, eax
        
    loop LOOP2
    jmp LOOP1_CONTINUE

DO_LOOP3:
    ; Set to 32000 loops    
    MOV ecx, 32000 
LOOP3:
    inc     esi
    cmp     esi, 50
    jg      COUNTER_TO_0
LOOP3_CONTINUE:

    loop LOOP3
    ;ret
    jmp LOOP2_CONTINUE
    
COUNTER_TO_0:
    ; Set to 0
    xor     esi, esi
    
    jmp LOOP3_CONTINUE
    
;    jmp QUIT

QUIT:
    xor eax, eax
    ret

It took 13 seconds to complete.

One interesting explanation on why binary C or C++ code is faster than Assembler, is because the C compiler generates better Assembler/binary code at the end. For example, the use of JMP is expensive in terms of CPU cycles and the compiler can apply other optimizations and tricks that I’m not aware of, like using faster registers, while in my code I use ebx, ecx, esi, etc… (for example, imagine that using cx is cheaper than using ecx or rcx and I’m not aware but the guys that created the Gnu C compiler are)

blog-carlesmateo-com-sasm-assembler-linux-64-bits-code-12-13-secondsTo be sure of what’s going on I switched in the LOOP3 the JE and the JMP of the code, for groups of 50 instructions, INC ESI, one after the other and the time was reduced to 1 second.

(In C also was reduced even a bit more when doing the same)

To know what’s the translation of the C code into Assembler when compiled, you can do:

objdump --disassemble nested_loops

Look for the section main and you’ll get something like:

0000000000400470 <main>:
400470:    bf 0a 00 00 00           mov    $0xa,%edi
400475:    31 c9                    xor    %ecx,%ecx
400477:    be 00 7d 00 00           mov    $0x7d00,%esi
40047c:    0f 1f 40 00              nopl   0x0(%rax)
400480:    b8 00 7d 00 00           mov    $0x7d00,%eax
400485:    0f 1f 00                 nopl   (%rax)
400488:    83 c2 01                 add    $0x1,%edx
40048b:    83 fa 33                 cmp    $0x33,%edx
40048e:    0f 4d d1                 cmovge %ecx,%edx
400491:    83 e8 01                 sub    $0x1,%eax
400494:    75 f2                    jne    400488 <main+0x18>
400496:    83 ee 01                 sub    $0x1,%esi
400499:    75 e5                    jne    400480 <main+0x10>
40049b:    83 ef 01                 sub    $0x1,%edi
40049e:    75 d7                    jne    400477 <main+0x7>
4004a0:    48 83 ec 08              sub    $0x8,%rsp
4004a4:    be 34 06 40 00           mov    $0x400634,%esi
4004a9:    bf 01 00 00 00           mov    $0x1,%edi
4004ae:    31 c0                    xor    %eax,%eax
4004b0:    e8 ab ff ff ff           callq  400460 <__printf_chk@plt>
4004b5:    31 c0                    xor    %eax,%eax
4004b7:    48 83 c4 08              add    $0x8,%rsp
4004bb:    c3                       retq

Note: this is in the AT&T syntax and not in the Intel. That means that add $0x1,%edx is adding 1 to EDX registerg (origin, destination).

As you can see the C compiler has created a very differed Assembler version respect what I created.
For example at 400470 it uses EDI register to store 10, so to control the number of the outer loop.
It uses ESI to store 32000 (Hexadecimal 0x7D00), so the second loop.
And EAX for the inner loop, at 400480.
It uses EDX for the counter, and compares to 50 (Hexa 0x33) at 40048B.
In 40048E it uses the CMOVGE (Mov if Greater or Equal), that is an instruction that was introduced with the P6 family processors, to move the contents of ECX to EDX if it was (in the CMP) greater or equal to 50. As in 400475 a XOR ECX, ECX was performed, EXC contained 0.
And it cleverly used SUB and JNE (JNE means Jump if not equal and it jumps if ZF = 0, it is equivalent to JNZ Jump if not Zero).
It uses between 4 and 16 clocks, and the jump must be -128 to +127 bytes of the next instruction. As you see Jump is very costly.

Looks like the biggest improvement comes from the use of CMOVGE, so it saves two jumps that my original Assembler code was performing.
Those two jumps multiplied per 32000 x 32000 x 10 times, are a lot of Cpu clocks.

So, with this in mind, as this Assembler code takes 10 seconds, I updated the graph from 13 seconds to 10 seconds.

Lua

This is the initial code:

local i_counter = 0

local i_time_start = os.clock()

for i_loop1=0,9 do
    for i_loop2=0,31999 do
        for i_loop3=0,31999 do
            i_counter = i_counter + 1
            if i_counter > 50 then
                i_counter = 0
            end
        end
    end
end

local i_time_end = os.clock()
print(string.format("Counter: %i\n", i_counter))
print(string.format("Total seconds: %.2f\n", i_time_end - i_time_start))

In the case of Lua theoretically one could take great advantage of the use of local inside a loop, so I tried the benchmark with modifications to the loop:

for i_loop1=0,9 do
    for i_loop2=0,31999 do
        local l_i_counter = i_counter
        for i_loop3=0,31999 do
             l_i_counter = l_i_counter + 1
             if l_i_counter > 50 then
                 l_i_counter = 0
             end
        end
        i_counter = l_i_counter
    end
end

I ran it with LuaJit and saw no improvements on the performance.

Node.js

var s_date_time = new Date();
console.log('Starting: ' + s_date_time);

var i_counter = 0;

for (var $i_loop1 = 0; $i_loop1 < 10; $i_loop1++) {
   for (var $i_loop2 = 0; $i_loop2 < 32000; $i_loop2++) {
       for (var $i_loop3 = 0; $i_loop3 < 32000; $i_loop3++) {
           i_counter++;
           if (i_counter > 50) {
               i_counter = 0;
           }
       }
   } 
}

var s_date_time_end = new Date();

console.log('Counter: ' + i_counter + '\n');

console.log('End: ' + s_date_time_end + '\n');

Execute with:

nodejs nested_loops.js

Phantomjs

The same code as nodejs adding to the end:

phantom.exit(0);

In the case of Phantom it performs the same in both versions 1.9.0 and 2.0.1-development compiled from sources.

PHP

The interesting thing on PHP is that you can write your own extensions in C, so you can have the easy of use of PHP and create functions that really brings fast performance in C, and invoke them from PHP.

<?php

$s_date_time = date('Y-m-d H:i:s');

echo 'Starting: '.$s_date_time."\n";

$i_counter = 0;

for ($i_loop1 = 0; $i_loop1 < 10; $i_loop1++) {
   for ($i_loop2 = 0; $i_loop2 < 32000; $i_loop2++) {
       for ($i_loop3 = 0; $i_loop3 < 32000; $i_loop3++) {
           $i_counter++;
           if ($i_counter > 50) {
               $i_counter = 0;
           }
       }
   } 
}

$s_date_time_end = date('Y-m-d H:i:s');

echo 'End: '.$s_date_time_end."\n";

Facebook’s Hip Hop Virtual Machine is a very powerful alternative, that is JIT powered.

Downloading the code and compiling it is just easy, just:

git clone https://github.com/facebook/hhvm.git
cd hhvm
rm -r third-party
git submodule update --init --recursive
./configure
make

Or just grab precompiled packages from https://github.com/facebook/hhvm/wiki/Prebuilt%20Packages%20for%20HHVM

Python

from datetime import datetime
import time

print ("Starting at: " + str(datetime.now()))
s_unixtime_start = str(time.time())

i_counter = 0

# From 0 to 31999
for i_loop1 in range(0, 10):
    for i_loop2 in range(0,32000):
         for i_loop3 in range(0,32000):
             i_counter += 1
             if ( i_counter > 50 ) :
                 i_counter = 0

print ("Ending at: " + str(datetime.now()))
s_unixtime_end = str(time.time())

i_seconds = long(s_unixtime_end) - long(s_unixtime_start)
s_seconds = str(i_seconds)

print ("Total seconds:" + s_seconds)

Ruby

#!/usr/bin/ruby -w

time1 = Time.new

puts "Starting : " + time1.inspect

i_counter = 0;

for i_loop1 in 0..9
    for i_loop2 in 0..31999
        for i_loop3 in 0..31999
            i_counter = i_counter + 1
            if i_counter > 50
                i_counter = 0
            end
        end
    end
end

time1 = Time.new

puts "End : " + time1.inspect

Perl

The case of Perl was very interesting one.

This is the current code:

#!/usr/bin/env perl

print "$s_datetime Starting calculations...\n";
$i_counter=0;

$i_unixtime_start=time();

for my $i_loop1 (0 .. 9) {
    for my $i_loop2 (0 .. 31999) {
        for my $i_loop3 (0 .. 31999) {
            $i_counter++;
            if ($i_counter > 50) {
                $i_counter = 0;
            }
        }
    }
}

$i_unixtime_end=time();

$i_seconds=$i_unixtime_end-$i_unixtime_start;

print "Counter: $i_counter\n";
print "Total seconds: $i_seconds";

But before I created one, slightly different, with the for loops like in the C style:

#!/usr/bin/env perl

$i_counter=0;

$i_unixtime_start=time();

for (my $i_loop1=0; $i_loop1 < 10; $i_loop1++) {
    for (my $i_loop2=0; $i_loop2 < 32000; $i_loop2++) {
        for (my $i_loop3=0; $i_loop3 < 32000; $i_loop3++) {
            $i_counter++;
            if ($i_counter > 50) {
                $i_counter = 0;
            }
        }
    }
}

$i_unixtime_end=time();

$i_seconds=$i_unixtime_end-$i_unixtime_start;

print "Total seconds: $i_seconds";

I repeated this test, with the same version of Perl, due to the comment of a reader (thanks mpapec) that told:

In this particular case perl style loops are about 45% faster than original code (v5.20)

And effectively and surprisingly the time passed from 796 seconds to 436 seconds.

So graphics are updated to reflect the result of 436 seconds.

Bash

#!/bin/bash
echo "Bash version ${BASH_VERSION}..."
date

let "s_time_start=$(date +%s)"
let "i_counter=0"

for i_loop1 in {0..9}
do
     echo "."
     date
     for i_loop2 in {0..31999}
     do
         for i_loop3 in {0..31999}
         do
             ((i_counter++))
             if [[ $i_counter > 50 ]]
             then
                 let "i_counter=0"
             fi
         done
#((var+=1))
#((var=var+1))
#((var++))
#let "var=var+1"
#let "var+=1"
#let "var++"
     done
done

let "s_time_end=$(date +%2)"

let "s_seconds = s_time_end - s_time_start"
echo "Total seconds: $s_seconds"

# Just in case it overflows
date

Gambas 3

Gambas is a language and an IDE to create GUI applications for Linux.
It is very similar to Visual Basic, but better, and it is not a clone.

I created a command line application and it performed better than PHP. There has been done an excellent job with the compiler.

blog-carlesmateo-com-gbr3-gambas-performanceNote: in the screenshot the first test ran for few seconds more than in the second. This was because I deliberately put the machine under some load and I/O during the tests. The valid value for the test, confirmed with more iterations is the second one, done under the same conditions (no load) than the previous tests.

' Gambas module file MMain.module

Public Sub Main()

    ' @author Carles Mateo http://blog.carlesmateo.com
    
    Dim i_loop1 As Integer
    Dim i_loop2 As Integer
    Dim i_loop3 As Integer
    Dim i_counter As Integer
    Dim s_version As String
    
    i_loop1 = 0
    i_loop2 = 0
    i_loop3 = 0
    i_counter = 0
    
    s_version = System.Version
    
    Print "Performance Test by Carles Mateo blog.carlesmateo.com"    
    Print "Gambas Version: " & s_version

    Print "Starting..." & Now()
    
    For i_loop1 = 0 To 9
        For i_loop2 = 0 To 31999
            For i_loop3 = 0 To 31999
                i_counter = i_counter + 1
                
                If (i_counter > 50) Then
                    i_counter = 0
                Endif
            Next
        Next
    Next
    
    Print i_counter
    Print "End " & Now()

End

Changelog

2015-08-26 15:45

Thanks to the comment of a reader, thanks Daniel, pointing a mistake. The phrase I mentioned was on conclusions, point 14, and was inaccurate. The original phrase told “go is promising. Similar to C, but performance is much better thanks to the use of JIT“. The allusion to JIT is incorrect and has been replaced by this: “thanks to deciding at runtime if the architecture of the computer is 32 or 64 bit, a very quick compilation at launch time, and it compiling to very good assembler (that uses the 64 bit instructions efficiently, for example)”

2015-07-17 17:46

Benchmarked Facebook HHVM 3.9 (dev., the release date is August 3 2015) and HHVM 3.7.3, they take 52 seconds.

Re-benchmarked Facebook HHVM 3.4, before it was 72 seconds, it takes now 38 seconds. I checked the screen captures from 2014 to discard an human error. Looks like a turbo frequency issue on the tests computer, with the CPU governor making it work bellow the optimal speed or a CPU-hungry/IO process that triggered during the tests and I didn’t detect it. Thinking about forcing a fixed CPU speed for all the cores for the tests, like 2.4 Ghz and booting a live only text system without disk access and network to prevent Ubuntu launching processes in the background.

2015-07-05 13:16

Added performance of Phantomjs 1.9.0 installed via apt-get install phantomjs in Ubuntu, and Phantomjs 2.0.1-development.

Added performance of nodejs 0.12.04 (compiled).

Added bash to the graphic. It has so bad performance that I had to edit the graphic to fit in (color pink) in order prevent breaking the scale.

2015-07-03 18:32

Added benchmarks for PHP 7 alpha 2, PHP 5.6.10 and PHP 5.4.42.

2015-07-03 15:13
Thanks to the contribution of a reader (thanks mpapec!) I tried with Perl for style, resulting in passing from 796 seconds to 436 seconds.
(I used the same Perl version: Perl 5.18.2)
Updated test value for Perl.
Added new graphics showing the updated value.

Thanks to the contribution of a reader (thanks junk0xc0de!) added some additional warnings and explanations about the dangers of using -O3 (and -O2) if C/C++.

Updated the Lua code, to print i_counter and do the if i_counter > 50
This makes it take a bit longer, few cents, but passing from 7.8 to 8.2 seconds.
Updated graphics.