This is a great new for scaling performance in the Data Centers. For routers, switches…
And this makes me think about all the Architects that are using Memcached and Redis in different Servers, in Networks of 1Gbps and makes me want to share with you what a nonsense, is often, that.
So the idea of having Memcache or Redis is just to cache the queries and unload the Database from those queries.
But 1Gbps is equivalent to 125MB (Megabytes) per second.
Local RAM Memory in Servers can perform at 24GB and more (24,000,000 Megabytes) per second, even more.
A PCIE NVMe drive at 3.5GB per second.
A local SSD drive without RAID 550 MB/s.
A SSD in the Cloud, varies a lot on the provider, number of drives, etc… but I’ve seen between 200 MB/s and 2.5GB/s aggregated in RAID.
In fact I have worked with Servers equipped with several IO Controllers, that were delivering 24GB/s of throughput writing or reading to HDD spinning drives.
If you’re in the Cloud. Instead of having 2 Load Balancers, 100 Front Web servers, with a cluster of 5 Redis with huge amount of RAM, and 1 MySQL Master and 1 Slave, all communicating at 1Gbps, probably you’ll get a better performance having the 2 LBs, and 11 Front Web with some more memory and having the Redis instance in the same machine and saving the money of that many small Front and from the 5 huge dedicated Redis.
The same applies if you’re using Docker or K8s.
Even if you just cache the queries to drive, speed will be better than sending everything through 1 Gbps.
This will matter for you if your site is really under heavy load. Most of the sites just query the MySQL Server using 1 Gbps lines, or 2 Gbps in bonding, and that’s enough.
I’ve been working for years within Data centers, with D&R strategies, and then in the middle of COVID-19, with huge demands on increments of bandwidth and compute, some DCs decided to do not allow in the Engineers of their customers.
As somebody that had my own Startup and CSP and had infrastructure in DCs and servers from customers in colocation, and has replaced Hw components at 1AM, replaced drives from broken RAIDs, and fixed systems so many times inside so many Datacenters across the world, I’m shocked about that.
I understand health reasons can be argued, but I still have Servers in Datacenters because we all believed they were the most safe place, prepared for disaster and recovery, with security, 24×7… and now, one realise that cannot enter to fix or upgrade the own machines.
Please note, still you can use the remote hands from the DC, although this is not a good idea many times, I’m not sure this will still be an available option when the lock down in those countries becomes more strict.
I’m wondering if DCs current model have any future at all.
I think most of the D&R strategies from now will be in the cloud, in different regions, with different providers, so companies can resist providers or governments letting them down.
ctop.py is an Open Source tool for Linux System Administration that I’ve written in Python3. It uses only the System (/proc), and not third party libraries, in order to get all the information required. I use only this modules, so it’s ideal to run in all the farm of Servers and Dockers:
os
sys
time
shutil (for getting the Terminal width and height)
The purpose of this tool is to help to troubleshot and to identify problems with a single view to a single tool that has all the typical indicators.
It provides in a single view information that is typically provided by many programs:
top, htop for the CPU usage, process list, memory usage
meminfo
cpuinfo
hostname
uptime
df to see the free space in / and the free inodes
iftop to see real-time bandwidth usage
ip addr list to see the main Ip for the interfaces
netstat or lsof to see the list of listening TCP Ports
uname -a to see the Kernel version
Other cool things it does is:
Identifying if you’re inside an Amazon VM, Google GCP, OpenStack VMs, Virtual Box VMs, Docker Containers or lxc.
Compatible with Raspberry Pi (tested on 3 and 4, on Raspbian and Ubuntu 20.04LTS)
Uses colors, and marks in yellow the warnings and in red the errors, problems like few disk space reaming or high CPU usage according to the available cores and CPUs.
Redraws the screen and adjust to the size of the Terminal, bigger terminal displays more information
It doesn’t use external libraries, and does not escape to shell. It reads everything from /proc /sys or /etc files.
Identifies the Linux distribution
Supports Plugins loaded on demand.
Shows the most repeated binaries, so you can identify DDoS attacks (like having 5,000 apache instances where you have normally 500 or many instances of Python)
Indicates if an interface has the cable connected or disconnected
Shows the Speed of the Network Connection (useful for Mellanox cards than can operate and 200Gbit/sec, 100, 50, 40, 25, 10…)
It displays the local time and the Linux Epoch Time, which is universal (very useful for logs and to detect when there was an issue, for example if your system restarted, your SSH Session would keep latest Epoch captured)
No root required
Displays recent errors like NFS Timed outs or Memory Read Errors.
You can enforce the output to be in a determined number of columns and rows, for data scrapping.
You can specify the number of loops (1 for scrapping, by default is infinite)
You can specify the time between screen refreshes, for long placed SSH sessions
You can specify to see the output in b/w or in color (default)
Plugins allow you to extend the functionality effortlessly, without having to learn all the code. I provide a Plugin sample for starting lights on a Raspberry Pi, depending on the CPU Load, and playing a message “The system is healthy” or “Warning. The CPU is at 80%”.
Limitations:
It only works for Linux, not for Mac or for Windows. Although the idea is to help with Server’s Linux Administration and Troubleshot, and Mac and Windows do not have /proc
The list of process of the System is read every 30 seconds, to avoid adding much overhead on the System, other info every second
It does not run in Python 2.x, requires Python 3 (tested on 3.5, 3.6, 3.7, 3.8, 3.9)
I decided to code name the version 0.7 as “Catalan Republic” to support the dreams and hopes and democratic requests of the Catalan people, to become and independent republic.
I created this tool as Open Source and if you want to help I need people to test under different versions of:
Atypical Linux distributions
If you are a Cloud Provider and want me to implement the detection of your VMs, so the tool knows that is a instance of the Amazon, Google, Azure, Cloudsigma, Digital Ocean… contact me through my LinkedIn.
Monitoring an Amazon Instance, take a look at the amount of traffic sent and received
Some of the features I’m working on are parsing the logs checking for errors, kernel panics, processed killed due to lack of memory, iscsi disconnects, nfs errors, checking the logs of mysql and Oracle databases to locate errors
Update: 2021-07-23 Ubuntu 19.04 is no longer available, so I updated the article in order to work with Ubuntu 20.04. and with PHP 7.4 and all their dependencies.
This was interesting under the point of view of dealing with elastic Ip’s, Amazon AWS Volumes, etc… but was a process basically manual. I could have generated an immutable image to start from next time, but this is another discussion, specially because that Server Instance has different base Software, including a MySql Database.
This time I want to explain, step by step, how to containerize my Server, so I can port to different platforms, and I can be independent on what the Server Operating System is. It will work always, as we defined the Operating System for the Docker Container.
So we start to use IaC (Infrastructure as Code).
So first you need to install docker.
So basically if your laptop is an Ubuntu 18.04 LTS or 20.04 LTS you have to:
sudo apt install docker.io
Start and Automate Docker
The Docker service needs to be setup to run at startup. To do so, type in each command followed by enter:
sudo systemctl start docker
sudo systemctl enable docker
Create the Dockerfile
For doing this you can use any text editor, but as we are working with IaC why not use a Code Editor?.
You can use the versatile PyCharm, that has modules for understanding Docker and so you can use Control Version like git too.
This is the updated Dockerfile to work with Ubuntu 20.04 LTS
FROM ubuntu:20.04
MAINTAINER Carles <carles@carlesmateo.com>
ARG DEBIAN_FRONTEND=noninteractive
#RUN echo "nameserver 8.8.8.8" > /etc/resolv.conf
RUN echo "Europe/Ireland" | tee /etc/timezone
# Note: You should install everything in a single line concatenated with
# && and finalizing with
# apt autoremove && apt clean
# In order to use the less space possible, as every command is a layer
RUN apt update && apt install -y apache2 ntpdate libapache2-mod-php7.4 mysql-server php7.4-mysql php-dev libmcrypt-dev php-pear git && apt autoremove && apt clean
RUN a2enmod rewrite
RUN mkdir -p /www
# In order to activate Debug
# RUN sed -i "s/display_errors = Off/display_errors = On/" /etc/php/7.2/apache2/php.ini
# RUN sed -i "s/error_reporting = E_ALL & ~E_DEPRECATED & ~E_STRICT/error_reporting = E_ALL/" /etc/php/7.2/apache2/php.ini
# RUN sed -i "s/display_startup_errors = Off/display_startup_errors = On/" /etc/php/7.2/apache2/php.ini
# To Debug remember to change:
# config/{production.php|preproduction.php|devel.php|docker.php}
# in order to avoid Error Reporting being set to 0.
ENV PATH_CATALONIA /www/www.cataloniaframework.com/
ENV PATH_CATALONIA_WWW /www/www.cataloniaframework.com/www/
ENV PATH_CATALONIA_CACHE /www/www.cataloniaframework.com/cache/
ENV APACHE_RUN_USER www-data
ENV APACHE_RUN_GROUP www-data
ENV APACHE_LOG_DIR /var/log/apache2
ENV APACHE_PID_FILE /var/run/apache2/apache2.pid
ENV APACHE_RUN_DIR /var/run/apache2
ENV APACHE_LOCK_DIR /var/lock/apache2
ENV APACHE_LOG_DIR /var/log/apache2
RUN mkdir -p $APACHE_RUN_DIR
RUN mkdir -p $APACHE_LOCK_DIR
RUN mkdir -p $APACHE_LOG_DIR
RUN mkdir -p $PATH_CATALONIA
RUN mkdir -p $PATH_CATALONIA_WWW
RUN mkdir -p $PATH_CATALONIA_CACHE
# Remove the default Server
RUN sed -i '/<Directory \/var\/www\/>/,/<\/Directory>/{/<\/Directory>/ s/.*/# var-www commented/; t; d}' /etc/apache2/apache2.conf
RUN rm /etc/apache2/sites-enabled/000-default.conf
COPY www.cataloniaframework.com.conf /etc/apache2/sites-available/
RUN chmod 777 $PATH_CATALONIA_CACHE
RUN chmod 777 $PATH_CATALONIA_CACHE.
RUN chown --recursive $APACHE_RUN_USER.$APACHE_RUN_GROUP $PATH_CATALONIA_CACHE
RUN ln -s /etc/apache2/sites-available/www.cataloniaframework.com.conf /etc/apache2/sites-enabled/
# Note: You should clone locally and COPY to the Docker Image
# Also you should add the .git directory to your .dockerignore file
# I made this way to show you and for simplicity, having everything
# in a single file
##RUN git clone https://github.com/cataloniaframework/cataloniaframework_v1_sample_website /www/www.cataloniaframework.com
##RUN git checkout tags/v.1.16-web-1.0
# In order to change profile to Production
# RUN sed -i "s/define('ENVIRONMENT', DOCKER)/define('ENVIRONMENT', PRODUCTION)/" /var/www/www.cataloniaframework.com/config/general.php
COPY *.php /www/www.cataloniaframework.com/www
# for debugging
#RUN apt-get install -y vim
RUN service apache2 restart
EXPOSE 80
CMD ["/usr/sbin/apache2", "-D", "FOREGROUND"]
This will copy the file www.cataloniaframework.com.conf that must be in the same directory that the Dockerfile file, to the /etc/apache2/sites-available/ folder in the container.
<VirtualHost *:80>
ServerAdmin webmaster@cataloniaframework.com# Uncomment to use a DNS name in a multiple VirtualHost Environment #ServerName www.cataloniaframework.com #ServerAlias cataloniaframework.com DocumentRoot /www/www.cataloniaframework.com/www
<Directory /www/www.cataloniaframework.com/www/>
Options -Indexes +FollowSymLinks +MultiViews AllowOverride All Order allow,deny allow from all Require all granted
</Directory>
ErrorLog ${APACHE_LOG_DIR}/www-cataloniaframework-com-error.log # Possible values include: debug, info, notice, warn, error, crit, # alert, emerg. LogLevel warn CustomLog ${APACHE_LOG_DIR}/www-cataloniaframework-com-access.log combined
</VirtualHost>
Stopping, starting the docker Service and creating the Catalonia image
service docker stop && service docker start
To build the Docker Image we will do:
docker build -t catalonia . --no-cache
I use the –no-cache so git is pulled and everything is reworked, not kept from cache.
Now we can run the Catalonia Docker, mapping the 80 port.
docker run -d -p 80:80 catalonia
If you want to check what’s going on inside the Docker, you’ll do:
docker ps
And so in this case, we will do:
docker exec -i -t distracted_wing /bin/bash
Finally I would like to check that the web page works, and I’ll use my preferred browser. In this case I will use lynx, the text browser, cause I don’t want Firefox to save things in the cache.
Nothing new, zombie computers, hackers, pirates, networks of computers… trying to abuse the system and to hack into it. Why? There could be many reasons, from storing pirate movies, trying to use your Server for sending Spam, try to phishing or to host Ransomware pages…
Most of those guys doesn’t know that is almost impossible to Spam from Amazon. Few emails per hour can come out from the Server unless you explicitly requests that update and configure everything.
But I thought it was a great opportunity to force myself to update the Operating System, core tools, versions of PHP and MySql.
Forensics / Postmortem of the incident
The task was divided in two parts:
Understanding the origin of the attack
Blocking the offending Ip addresses or disabling XMLRPC
Making the VM boot again (problems with Amazon AWS)
I didn’t know why it was not booting so.
Upgrading the OS
I disabled the access to the site while I was working using Amazon Web Services Firewall. Basically I turned access to my ip only. Example: 8.8.8.8/32
I changed 0.0.0.0/0 so the world wide mask to my_Ip/3
That way the logs were reflecting only what I was doing from my Ip.
Dealing with Snapshots and Volumes in AWS
Well the first thing was doing an Snapshot.
After, I tried to boot the original Blog Server (so I don’t stop offering service) but no way, the Server appeared to be dead.
So then I attached the Volume to a new Server with the same base OS, in order to extract (dump) the database. Later I would attach the same Volume to a new Server with the most recent OS and base Software.
Something that is a bit annoying is that the new Instances, the new generation instances, run only in VPC, not in Amazon EC2 Classic. But my static Ip addresses are created for Amazon EC2 Classic, so I could not use them in new generation instances.
I choose the option to see all the All the generations.
Upgrading the system base Software had its own challenges too.
Upgrading the OS / Base Software
My approach was to install an Ubuntu 18.04 LTS, and install the base Software clean, and add any modification I may need.
I wanted to have all the supported packages and a recent version of PHP 7 and the latest Software pieces link Apache or MySQL.
Config files that before were working stopped working as the new Apache version requires the files or symlinks under /etc/apache2/sites-enabled/ to end with .conf extension.
Also some directives changed, so some websites will not able to work properly.
Those projects using my Catalonia Framework were affected, although I have this very well documented to make it easy to work with both versions of Apache Http Server, so it was a very straightforward change.
From the previous version I had to change my www.cataloniaframework.com.conf file and enable:
<Directory /www/www.cataloniaframework.com> Options Indexes FollowSymLinks MultiViews AllowOverride All Order allow,deny allow from all </Directory>
Then Open the ports for the Web Server (443 and 80).
sudo ufw allow in "Apache Full"
Then service apache restart
Catalonia Framework Web Site, which is also created with Catalonia Framework itself once restored
MySQL
The problem was to use the most updated version of the Database. I could use one of the backups I keep, from last week, but I wanted more fresh data.
I had the .db files and it should had been very straightforward to copy to /var/lib/mysql/ … if they were the same version. But they weren’t. So I launched an instance with the same base Software as the old previous machine had, installed mysql-server, stopped it, copied the .db files, started it, and then I made a dump with mysqldump –all-databases > 2019-04-29-all-databases.sql
Note, I copied the .db files using the mythical mc, which is a clone from Norton Commander.
Then I stopped that instance and I detached that volume and attached it to the new Blog Instance.
I did a Backup of my original /var/lib/mysql/ files for the purpose of faster restoring if something went wrong.
I mounted it under /mnt/blog_old and did mysql -u root -p < /mnt/blog_old/home/ubuntu/2019-04-29-all-databases.sql
That worked well I had restored the blog. But as I was watching the /var/log/mysql/error.log I noticed some columns were not where they should be. That’s because inadvertently I overwritten the MySql table as well, which in MySQL 5.7 has different structure than in MySQL 5.5. So I screwed. As I previewed this possibility I restored from the backup in seconds.
So basically then I edited my .sql files and removed all that was for the mysql database.
I started MySql, and run the mysql import procedure again. It worked, but I had to recreate the users for all the Databases and Grant them permissions.
GRANT ALL PRIVILEGES ON db_mysqlproxycache.* TO 'wp_dbuser_mysqlproxy'@'localhost' IDENTIFIED BY 'XWy$&{yS@qlC|<¡!?;:-ç';
PHP7
Some modules in my blogs where returning errors in /var/log/apache2/mysite-error.log so I checked that it was due to lack of support of latest PHP versions, and so I patched manually the code or I just disabled the offending plugin.
WordPress
As seen checking the /var/log/apache2/blog.carlesmateo.com-error.log some URLs where not located by WordPress.
For example:
The requested URL /wordpress/wp-json/ was not found on this server
I had to activate modrewrite and then restart Apache.
a2enmod rewrite; service apache2 restart
Making the site more secure
Checking at the logs of Apache, /var/log/apache2/blog.carlesmateo.com-access.log I checked for Ip’s accessing Admin areas, I looked for 404 Errors pointing to intents to exploit any unsafe WP Plugin, I checked for POST protocol as well.
I added to the Ubuntu Uncomplicated Firewall (UFW) the offending Ip’s and patched the xmlrpc.php file to exit always.
My talk in Google Developers Cork Group.
It’s about deploying an Instance in GCE and grows in complexity until Deploying a Load Balancer with AutoScaling for a group of LAMP Webservers.
Join the group at: https://www.meetup.com/GDG-Cork/
Notes on 2017-03-26 18:57 CEST – Unix time: 1490547518 :
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.
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.
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.
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?.
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.
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.
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))
GNU bash, version 4.3.11(1)-release (x86_64-pc-linux-gnu)
Conclusions and Lessons Learnt
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)
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
Oracle’s Java and OpenJDK shows no difference in performance in this test
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
JIT compilers for Python: Pypy, and for Lua: LuaJit, make them really fly. The difference is truly amazing
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
Bash is the worst language for doing the loop and inc operations in the test, lasting for more than 13 hours for the test
From command line PHP is much faster than Python, Perl and Ruby
Facebook Hip Hop Virtual Machine (HHVM) improves a lot PHP’s speed
It looks like the future of compilers is JIT.
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)
Optimizations really needs people dedicated to just optimizations and checking the speed of the newly added code for the running platforms
Node.js was a big surprise. It really performed well. It is promising. New version performs even faster
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)
Gambas 3 performed surprisingly fast. Better than PHP
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)
Perl performance really change from using a for style or another. (See Perl section below)
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
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:
Preventing overflow of the integer if growing without control
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;
}
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.
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.
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)
To 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:
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; $i_loop3++) {
i_counter++;
if (i_counter > 50) {
i_counter = 0;
}
}
}
}
var s_date_time_end = new Date();
console.log('Counter: ' + i_counter + '\n');
console.log('End: ' + s_date_time_end + '\n');
Execute with:
nodejs nested_loops.js
Phantomjs
The same code as nodejs adding to the end:
phantom.exit(0);
In the case of Phantom it performs the same in both versions 1.9.0 and 2.0.1-development compiled from sources.
PHP
The interesting thing on PHP is that you can write your own extensions in C, so you can have the easy of use of PHP and create functions that really brings fast performance in C, and invoke them from PHP.
from datetime import datetime
import time
print ("Starting at: " + str(datetime.now()))
s_unixtime_start = str(time.time())
i_counter = 0
# From 0 to 31999
for i_loop1 in range(0, 10):
for i_loop2 in range(0,32000):
for i_loop3 in range(0,32000):
i_counter += 1
if ( i_counter > 50 ) :
i_counter = 0
print ("Ending at: " + str(datetime.now()))
s_unixtime_end = str(time.time())
i_seconds = long(s_unixtime_end) - long(s_unixtime_start)
s_seconds = str(i_seconds)
print ("Total seconds:" + s_seconds)
Ruby
#!/usr/bin/ruby -w
time1 = Time.new
puts "Starting : " + time1.inspect
i_counter = 0;
for i_loop1 in 0..9
for i_loop2 in 0..31999
for i_loop3 in 0..31999
i_counter = i_counter + 1
if i_counter > 50
i_counter = 0
end
end
end
end
time1 = Time.new
puts "End : " + time1.inspect
Perl
The case of Perl was very interesting one.
This is the current code:
#!/usr/bin/env perl
print "$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:
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.
Note: 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.
This year I was invited to speak at the PHP Conference at Berlin 2014.
It was really nice, but I had to decline as I was working hard in a Start up, and I hadn’t the required time in order to prepare the nice conference I wanted and that people deserves.
However, having time, I decided to write an article about what I would had speak at the conference.
I will cover improving performance in a single server, and Scaling out multi-Server architecture, focusing on the needs of growing and Start up projects. Many of those techniques can be used to improve performance with other languages, not just with PHP.
Many of my friends are very good Developing, but know nothing about Architecture and Scaling. Hope this approach the two worlds, Development ad Operatings, into a DevOps bridge.
Improving performance on a single server
Hosting
Choose a good hosting. And if you can afford it choose a dedicated server.
Shared hostings are really bad. Some of them kill your http and mysql instances if you reach certain CPU use (really few), while others share the same hardware between 100+ users serving your pages sloooooow. Others cap the amount of queries that your MySql will handle per hour at so ridiculous few amount that even Drupal or WordPress are unable to complete a request in development.
Other ISP (Internet Service Providers) have poor Internet bandwidth, and so you web will load slow to users.
Some companies invest hundreds of thousands in developing a web, and then spend 20 € a year in the hosting. Less than the cost of a dinner.
You can use a decent dedicated server from 50 to 99 €/month and you will celebrate this decision every day.
Take in count that virtualization wastes between 20% and 30% of the CPU power. And if there are several virtual machines the loss will be more because you loss the benefits of the CPU caching for optimizing parallel instructions execution and prediction. Also if the hypervisor host allows to allocate more RAM than physically available and at some point it swaps, the performance of all the VM’s will be much worst.
If you have a VM and it swaps, in most providers the swap goes over the network so there is an additional bottleneck and performance penalty.
To compare the performance of dedicated servers and instances from different Cloud Providers you can take a look at my project cmips.net
Improve your Server
If your Sever has few RAM, add more. And if your project is running slow and you can afford a better Server, do it.
Using SSD disk will incredibly improve the performance on I/O operations and on swap operations. (but please, do backups and keep them in another place)
If you use a CMS like ezpublish with http_cache enabled probably you will prefer to have a Server with faster cores, rather tan a Server with one or more CPU’s plenty of cores, but slower cores, and that last for a longer time to render the page to the http cache.
That may seem obvious but often companies invest 320 hours in optimizing the code 2%, at a cost of let’s say 50 €/h * 320 hours = 16.000 €, while hiring a better Server would had bring between a 20% to 1000% improvement at a cost of additional 50€/month only or at the cost of 100 € of increasing the RAM memory.
The point here is that the hardware is cheap, while the time of the Engineers is expensive. And good Engineers are really hard to find.
And you probably, as a CEO or PO, prefer to use the talent to warranty a nice time to market for your project, or adding more features, rather than wasting this time in refactorizing.
Even with the most optimal code in the universe, if your project is successful at certain point you’ll have to scale. So adding more Servers. To save a Server now at the cost of slowing the business has not any sense.
Upgrade you PHP version
Many projects still use PHP 5.3, and 5.4.
Latest versions of PHP bring more and more performance. If you use old versions of PHP you can have a Quick Win by just upgrading to the last PHP version.
Use OpCache (or other cache accelerator)
OpCache is shipped with PHP 5.5 by default now, so it is the recommended option. It is though to substitute APC.
To activate OpCache edit php.ini and add:
Linux/Unix:
zend_extension=/path/to/opcache.so
Windows:
zend_extension=C:\path\to\php_opcache.dll
It will greatly improve your PHP performance.
Ensure that OpCache in Production has the optimal config for Production, that will be different from Development Environment.
Note: If you plan to use it with XDebug in Development environments, load OpCache before XDebug.
Disable Profiling and xdebug in Production
In Production disable the profiling, xdebug, and if you use a Framework ensure the Development/Debug features are disabled in Production.
Ensure your logs are not full of warnings
Check that Production logs are not full of warnings.
I’ve seen systems were every seconds 200 warnings were written to logs, the same all the time, and that obviously was slowing down the system.
Typical warnings like this can be easily fixed:
Message: date() [function.date]: It is not safe to rely on the system’s timezone settings. You are required to use the date.timezone setting or the date_default_timezone_set() function. In case you used any of those methods and you are still getting this warning, you most likely misspelled the timezone identifier. We selected ‘UTC’ for ‘8.0/no DST’ instead
Profile in Development
To detect where your slow code is, profile it in Development to see where it is spent the most CPU/time.
Check the slow-queries if you use MySql.
Cache html to disk
Imagine you have a sort of craigslist and you are displaying all the categories, and the number of new messages in this landing page. To do that you are performing many queries to the database, SELECT COUNTs, etc… every time a user visits your page. That certainly will overload your database with actually few concurrent visitors.
Instead of querying the Database all the time, do cache the generated page for a while.
This can be achieved by checking if the cache html file exists, and checking the TTL, and generating a new page if needed.
A simple sample would be:
<?php
// Cache pages for 5 minutes
$i_cache_TTL = 300;
$b_generate_cache = false;
$s_cache_file = '/tmp/index.cache.html';
if (file_exists($s_cache_file)) {
// Get creation date
$i_file_timestamp = filemtime($s_cache_file);
$i_time_now = microtime(true);
if ($i_time_now > ($i_file_timestamp + $i_cache_TTL)) {
$b_generate_cache = true;
} else {
// Up to date, get from the disk
$o_fh = fopen($s_cache_file, "rb");
$s_html = stream_get_contents($o_fh);
fclose($o_fh);
// If the file was empty something went wrong (disk full?), so don't use it
if (strlen($s_html) == 0) {
$b_generate_cache = true;
} else {
// Print the page and exit
echo $s_html;
exit();
}
}
} else {
$b_generate_cache = true;
}
ob_start();
// Render your page normally here
// ....
$s_html = ob_get_clean();
if ($b_generate_cache == true) {
// Create the file with fresh contents
$o_fp = fopen($s_cache_file, 'w');
if (fwrite($o_fp, $s_html) === false) {
// Error. Impossible to write to disk
// throw new Exception('CacheCantWrite');
}
fclose($o_fp);
}
// Send the page to the browser
echo $s_html;
This sample is simple, and works for many cases, but presents problems.
Imagine for example that the page takes 5 seconds to be generated with a single request, and you have high traffic in that page, let’s say 500 requests per second.
What will happen when the cache expires is that the first user will trigger the cache generation, and the second, and the third…. so all of the 500 requests * 5 seconds will be hitting the database to generate the cache, but… if creating the page per one requests takes 5 seconds, doing this 2,500 times will not last 5 seconds… so your process will enter in a vicious state where the first queries have not ended after minutes, and more and more queries are being added to the queue until:
a) Apache runs out of childs/processes, per configuration
b) Mysql runs out of connections, per configuration
c) Linux runs out of memory, and processes crashes/are killed
Not to mention the users or the API client, waiting infinitely for the http request to complete, and other processes reading a partial file (size bigger than 0 but incomplete).
Different strategies can be used to prevent that, like:
a) using semaphores to lock access to the cache generation (only one process at time)
b) using a .lock file to indicate that the file is being generated, and so next requests serving from the cache until the cache generation process ends the task, also writing to a buffer like acachefile.buffer (to prevent incomplete content being read) and finally when is complete renaming to the final name and removing the .lock
c) using memcached, or similar, to keep an index in memory of what pages are being generated now, and why not, keeping the cached files there instead of a filesystem
d) using crons to generate the cache files, so they run hourly and you ensure only one process generates the cache files
If you use crons, a cheap way to generate the .html content is that the crons curls/wget your webpage. I don’t recommend this as has some problems, like if that web request fails for any reason, you’ll have cached an error instead of content.
I prefer preparing my projects to being able of rendering the content being invoked from HTTP/S or from command line. But if you use curl because is cheap and easy and time to market is important for your project, then be sure that you check that your backend code writes an Status OK in the HTML that the cron can check to ensure that the content has been properly generated. (some crons only check for http status, like 200, but if your database or a xml gateway you use fails you will likely get a 200 and won’t detect that you’re caching pages with “error I can’t connect to the database” instead)
Many Frameworks have their own cache implementation that prevent corruption that could come by several processes writing to the same file at the same time, or from PHP dying in the middle of the render.
You can see a more complex MVC implementation, with Views, from my Framework Catalonia here:
By serving .html files instead of executing PHP with logic and performing queries to the database you will be able to serve hundreds of thousands requests per day with a single machine and really fast -that’s important for SEO also-.
I’ve done this in several Start ups with wonderful results, and my Framework Catalonia also incorporates this functionality very easily to use.
Note: This is only one of the techniques to save the load of the Database Servers. Many more come later.
Cache languages to disk
If you have an application that is multi-language, or if your point for the Strings (sections, pages, campaigns..) to be edited by Marketing is the Database, there is no need to query it all the time.
Simply provide a tool to “generate language files”.
Your languages files can be Javascript files loaded by the page, or can be PHP files generated.
For example, the file common_footer_en.php could be generated reading from Database and be like that:
<php
/* Autogenerated English translations file common_footer_en.php
on 2014-08-10 02:22 from the database */
$st_translations['seconds'] = 'seconds';
$st_translations['Time'] = 'Time';
$st_translations['Vars used'] = 'Vars used in these templates';
$st_translations['Total Var replacements'] = 'Total replaced';
$st_translations['Exec time'] = 'Execution time';
$st_translations['Cached controller'] = 'Cached controller';
So the PHP file is going to be generated when someone at your organization updates the languages, and your code is including it normally like with any other PHP file.
Use the Crons
You can set cron jobs to do many operations, like map reduce, counting in the database or effectively deleting the data that the user selected to delete.
Imagine that you have classified portal, and you want to display the number of announces for that category. You can have a table NUM_ANNOUNCES to store the number of announces, and update it hourly. Then your database will only do the counting once per hour, and your application will be reading the number from the table NUM_ANNOUNCES.
The Cron can also be used to make expire old announces. That way you can avoid a user having to wait for that clean up taking process when you have a http request to PHP.
A cron file can be invoked by:
php -f cron.php
By:
./cron.php
If you give permissions of execution with chmod +x and set the first line in cron.php as:
#!/usr/bin/env php
Or you can do a trick, that is emulate a http request from bash, by invoking a url with curl or with wget. Set the .htaccess so the folder for the cron tasks can only be executed from localhost for adding security.
This last trick has the inconvenient that the calling has the same problems as any http requests: restarting Apache will kill the process, the connection can be closed by timed out (e.g. if process is taking more seconds than the max. execution time, etc…)
Use Ramdisk for PHP files
With Linux is very easy to setup a RamDisk.
You can setup a RamDisk and rsync all your web .PHP files at system boot time, and when deploying changes, and config Apache to use the Ramdisk folder for the website.
That way for every request to the web, PHP files will be served from RAM directly, saving the slow disk access. Even with OpCache active, is a great improvement.
At these times were one Gigabyte of memory is really cheap there is a huge difference from reading files from disk, and getting them from memory. (Reading and writing to RAM memory is many many many times faster than magnetic disks, and many times faster than SSD disks)
Also .js, .css, images… can be served from a Ram disk folder, depending on how big your web is.
Ramdisk for /tmp
If your project does operations on disk, like resizing images, compressing files, reading/writing large CSV files, etcetera you can greatly improve the performance by setting the /tmp folder to a Ramdisk.
If your PHP project receives file uploads they will also benefit (a bit) from storing the temporal files to RAM instead to the disk.
Use Cache Lite
Cache Lite is a Pear extension that allows you to keep data in a local cache of the Web Server.
You can cache .html pages, or you can cache Queries and their result.
<?php
require_once "Cache/Lite.php";
$options = array(
'cacheDir' => '/tmp/',
'lifeTime' => 7200,
'pearErrorMode' => CACHE_LITE_ERROR_DIE
);
$cache = new Cache_Lite($options);
if ($data = $cache->get('id_of_the_page')) {
// Cache hit !
// Content is in $data
echo $data;
} else {
// No valid cache found (you have to make and save the page)
$data = '<html><head><title>test</title></head><body><p>this is a test</p></body></html>';
echo $data;
$cache->save($data);
}
It is nice that Cache Lite handles the TTL and keeps the info stored in different sub-directories in order to keep a decent performance. (As you may know many files in the same directory slows the access much).
Use HHVM (HipHop Virtual Machine) from Facebook
Facebook Engineers are always trying to optimize what is run on the Servers.
Faster code means, less machines. Even 1% of CPU use improvement means a lot of Servers less. Less Servers to maintain, less money wasted, less space on the Data Centers…
So they created the HHVM HipHop Virtual Machine that is able to run PHP code, much much faster than PHP. And is compatible with most of the Frameworks and Open Source projects.
They also created the Hack language that is an improved PHP, with type hinting.
So you can use HHVM to make your code run faster with the same Server and without investing a single penny.
Use C extensions
You can create and use your own C extensions.
C extensions will bring really fast execution. Just to get the idea:
I built a PHP extension to compare the performance from calculating the Bernoulli number with PHP and with the .so extension created in C.
In my Core i7 times were:
PHP:
Computed in 13.872583150864 s
PHP calling the C compiled extension:
Computed in 0.038495063781738 s
That’s 360.37 times faster using the C extension. Not bad.
Use Zephir
Zephir is a an Open Source language, very similar to PHP, that allows to create and maintain easily extensions for PHP.
Use Phalcon
Phalcon is a Web MVC Framework implemented as C extension, so it offers a high performance.
Check if you’re using the correct Engine for MySql
Many Developers create the tables and never worry about that. And many are using MyIsam by default. It was the by default Engine prior to MySql 5.5.
While MyIsam can bring good performance in some certain cases, my recommendation is to use InnoDb.
Normally you’ll have a gain in performance with MyIsam if you’ve a table were you only write or only read, but in all the other cases InnoDb is expected to be much more performant and safe.
MyIsam tables also get corruption from time to time and need manually fixing and writing to disks are not so reliable than InnoDb.
As MyIsam uses table-locking for updates and deletes to any existing row, it is easy to see that if you’re in a web environment with multiple users, blocking the table -so the other operations have to wait- will make things be slow.
If you have to use Joins clearly you will benefit from using InnoDb also.
Use InMemory Engine from MySql
MySql has a very powerful Engine called InMemory.
The InMemory Engine will store things in RAM and loss the data when MySql is restarted.
However is very fast and very easy to use.
Imagine that you have a travel application that constantly looks at which country belongs the city specified by user. A Quickwin would be to INSERT all this data in the InMemory Engine of MySql when it is started, and do just one change in your code: to use that Table.
Really easy. Quick improvement.
Use curl asynchronously
If your PHP has to communicate with other systems using curl, you can do the http/s call, and instead of waiting for a response let your PHP do more things in the meantime, and then check the results.
You can also call to multiple curl calls in parallel, and so avoid doing one by one in serial.
Guess that you have a query that returns 1000 results. Then you add one by one to an array.
Probably you’re going to have substantial gain if you keep in the database a single row, with the array serialized.
So an array like:
$st_places = Array(‘Barcelona’, ‘Dublin’, ‘Edinburgh’, ‘San Francisco’, ‘London’, ‘Berlin’, ‘Andorra la Vella’, ‘Prats de Lluçanès’);
Would be serialized to an string like:
a:8:{i:0;s:9:”Barcelona”;i:1;s:6:”Dublin”;i:2;s:9:”Edinburgh”;i:3;s:13:”San Francisco”;i:4;s:6:”London”;i:5;s:6:”Berlin”;i:6;s:16:”Andorra la Vella”;i:7;s:19:”Prats de Lluçanès”;}
This can be easily stored as String and unserialized later back to an array.
Note: In Internet we have a lot of encodings, Hebrew, Japanese… languages. Be careful with encodings when serializing, using JSon, XML, storing in databases without UTF support, etc…
Use Memcached to store common things
Memcached is a NoSql database in memory that can run in cluster.
The idea is to keep things there, in order to offload the load of the database. And as everything is in RAM it really runs fast.
You can use Memcached to cache Queries and their results also.
For example:
You have query SELECT * FROM translations WHERE section=’MAIN’.
Then you look if that String exists as key in the Memcached, and if it exists you fetch the results (that are serialized) and you avoid the query. If it doesn’t exist, you do normally the query to the database, serialize the array and store it in the Memcached with a TTL (Time to Live) using the Query (String) as primary key. For security you may prefer to hash the query with MD5 or SHA-1 and use the hash as key instead of using it plain.
When the TTL is reached the validity of the data would have expired and so it’s time to reinsert the contents in the next query.
Be careful, I’ve seen projects that were caching private data from users without isolating the key properly, so other users were getting the info from other users.
For example, if the key used was ‘Name’ and the value ‘Carles Mateo’ obviously the next user that fetch the key ‘Name’ would get my name and not theirs.
If you store private data of users in Memcache, it is a nice idea to append the owner of that info to the hash. E.g. using key: 10701577-FFADCEDBCCDFFFA10C
Where ‘10701577’ would be the user_id of the owner of the info, and ‘FFADCEDBCCDFFFA10C’ a hash of the query.
Before I suggested that you can keep a table of counting for the announces in a classified portal. This number can be stored in the Memcached instead.
You can store also common things, like translations, or cities like in the example before, rate of change for a currency exchanging website…
The most common way to store things there is serialized or Json encoded.
Be aware of the memory limits of Memcached and contrl the cache hitting ratio to avoid inserting data, and losing it constantly because is used few and Memcached has few memory.
Use jQuery for Production (small file) and minimized files for js
Use the Production jQuery library in Production, I mean do not use the bigger file Development jQuery library for Production.
There are product that eliminate all the necessary spaces in .js and .css files, and so are served much faster. These process is called minify.
It is important to know that in many emerging markets in the world, like Brazil, they have slow DSL lines. Many 512 Kbit/secons, and even modem connections!.
Activate compression in the Server
If you send large text files, or Jsons, you’ll benefit from activating the compression at the Server.
It consumes some CPU, but many times it brings an important improvement in speed serving the pages to the users.
Use a CDN
You can use a Content Delivery Network to offload your Servers from sending plain texts, html, images, videos, js, css…
You can delegate this to the CDN, they have very speedy Internet lines and Servers, so your Servers can concentrate into doing only BackEnd operations.
Please take attention to the documentation, a common mistake is to send Cache Headers to the CDN servers, while they’ll use this headers to set the cache TTL and ignore their web configuration parameters. (For example s-maxage, like: Cache-Control: public, s-maxage=600)
You can take a look at any website by telneting to the port 80 and doing the request manually or easily by using lynx:
lynx -mime_header http://blog.carlesmateo.com | less
Do you need a Framework?
If you’re processing only BackEnd petitions, like in the video games industry, serving API’s, RESTful, etc… you probably don’t need a Framework.
The Frameworks are generic and use much more resources than you’re really need for a fast reply.
Many times using a heavy Framework has a cost of factor times, compared to use simply PHP.
Save database connections until really needed
Many Frameworks create a connection to the Database Server by default. But certain parts of your code application do not require to connect to the database.
For example, validating the data from a form. If there are missing fields, the PHP will not operate with the Database, just return an error via JSon or refreshing the page, informing that the required field is missing.
If a not logged user is requesting the dashboard page, there is no need to open a connection to the database (unless you want to write the access try to an error log in the database).
In fact opening connections by default makes easier for attackers to do DoS attacks.
With a Singleton pattern you can easily implement a Db class that handles this transparently for you.
Scaling out / Multi Server Environment
Memcached session
When you have several Web Servers you’ll need something more flexible than the default PHP handler (that stores to a file in the Web Server).
The most common is to store the Session, serialized, in a Memcached Cluster.
Use Cassandra
Apache Cassandra is a NoSql database that allows to Scale out very easily.
The main advantage is that scales linearly. If you have 4 nodes and add 4 more, your performance will be doubled. It has no single point of failure, is also resilient to node failures, it replicates the data among the nodes, splits the load over the nodes automatically and support distributed datacenter architectures.
A easy way to split the load is to have a MySql primary Server, that handles the writes, and MySql secondary (or Slave) Servers handling the reads.
Every write sent to the Master is replicated into the Slaves. Then your application reads from the slaves.
You have to tell your code to do the writes to database to the primary Server, and the reads to the secondaries. You can have a Load Balancer so your code always ask the Load Balancer for the reads and it makes the connection to the less used server.
Do Database sharding
To shard the data consist into splitting the data according to a criteria.
For example, imagine we have 8 MySql Servers, named mysql0 to mysql7. If we want to insert or read data for user 1714, then the Server will be chosen from dividing the user_id, so 1714, between the number of Servers, and getting the MOD.
So 1714 % 8 gives 2. This means that the MySql Server to use is the mysql2.
For the user_id 16: 16 & 8 gives 0, so we would use mysql0. And so.
You can shard according to the email, or other fields as well. And you can have the same master and secondaries for the shards also.
When doing sharding in MySql you cannot do joins to data in other Servers. (but you can replicate all the data from the several shards in one big server in house, in your offices, and so query it and join if you need that for marketing purposes).
I always use my own sharding, but there is a very nice product from CodeFutures called dbshards. It handles the traffics transparently. I used it when in a video games Start up with very satisfying result.
Use Cassandra assync queries
Cassandra support asynchronous queries. That means you can send the query to the Server, and instead of waiting, do other jobs. And check for the result later, when is finished.
Consider using Hadoop + HBASE
A Cluster alternative to Cassandra.
Use a Load Balancer
You can put a Load Balancer or a Reverse Proxy in front of your Web Servers. The Load Balancer knows the state of the Web Servers, so it will remove a Web Server from the Array if it stops responding and everything will continue being served to the users transparently.
There are many ways to do Load Balancing: Round Robin, based on the load on the Web Servers, on the number of connections to each Web Server, by cookie…
To use a Cookie based Load Balancer is a very easy way to split the load for WordPress and Drupal Servers.
Imagine you have 10 Web Servers. In the .htaccess they set a rule to set a Cookie like:
SERVER_ID=WEB01
That was in the case of the first Web Server.
Second Server would have in the .htaccess to set a Cookie like:
SERVER_ID=WEB02
Etcetera
When for first time an user connects to the Load Balancer it sends the user to one of the 10 Web Servers. Then the Web Server sends its cookie to the browser of the Client. E.g. WEB07
After that, in the next requests from the client it will be redirected to the server by the Load Balancer to the Server that set the Cookie, so in this example WEB07.
The nice thing of this way of splitting the traffic is that you don’t have to change your code, nor handling the Sessions different.
If you use two Load Balancers you can have a heartbeat process in them and a Virtual Ip, and so in case your main Load Balancer become irresponsible the Virtual Ip will be mapping to the second Load Balancer in milliseconds. That provides HA.
Use http accelerators
Nginx, varnish, squid… to serve static content and offload the PHP Web Servers.
Auto-Scale in the Cloud
If you use the Cloud you can easily set Auto-Scaling for different parts of your core.
A quick win is to Scale the Web Servers.
As in the Cloud you pay per hour using a computer, you will benefit from cost reduction in you stop using the servers when you don’t need them, and you add more Servers when more users are coming to your sites.
Video game companies are a good example of hours of plenty use and valleys with few users, although as users come from all the planet it is most and most diluted.
Actually the Performance of the Google Cloud to Scale without any precedent is great.
Opposite to other Clouds that are based on instances, Google Cloud offers the platform, that will spawn your code across so many servers as needed, transparently to you. It’s a black box.
Schedule operations with RabbitMQ
Or other Queue Manager.
The idea is to send the jobs to the Queue Manager, the PHP will continue working, and the jobs will be performed asynchronously and notify the end.
RabbitMQ is cool also because it can work in cluster and HA.
Use GlusterFs for NAS
GlusterFs (and other products) allow you to have a Distributed File System, that splits the load and the data across the Servers, and resist node failures.
If you have to have a shared folder for the user’s uploads, for example for the profile pictures, to have the PHP and general files locally in the Servers and the Shared folder in a GlusterFs is a nice option.
Avoid NFS for PHP files and config files
As told before try to have the PHP files in a RAM disk, or in the local disk (Linux caches well and also OpCache), and try to not write code that reads files from disk for determining config setup.
I remember a Start up incubator that had a very nice Server, but the PHP files were read from a mounted NFS folder.
That meant that on every request, the Server had to go over the network to fetch the files.
Sadly for the project’s performance the PHP was reading a file called ENVIRONMENT that contained “PROD” or “DEVEL”. And this was done in every single request.
Even worst, I discovered that the switch connecting the Web Server and the NFS Server was a cheap 10 Mbit one. So all the traffic was going at 10 Mbit/s. Nice bottleneck.
Improve your network architecture
You can use 10 GbE (10 Gigabit Ethernet) to connect the Servers. The Web Servers to the Databases, Memcached Cluster, Load Balancers, Storage, etc…
You will need 10 GbE cards and 10 GbE switchs supporting bonding.
Use bonding to aggregate 10 + 10 so having 20 Gigabit.
You can also use Fibre Channel, for example 10 Gb and aggregate them, like 10 + 10 so 20 Gbit for the connection between the Servers and the Storage.
The performance improvements that your infrastructure will experiment are amazing.
Last Update: 2022-08-03 I added the Enterprises, as more and more services have been provided by Amazon and the other major Cloud Providers and they offer a suitable solution for Enterprises requiring high availability in services managed by Amazon, without the need to configure and maintain their own setups manually.
The Cloud is for Startups, and for Scaling. Nothing more.
In the future will be used by phone operators, to re-dimension their infrastructure and bandwidth in real time according to demand, but nowadays the Cloud is for Startups.
Examine the prices in my post in cmips, take a look, examine the performance also of the different CPU. You see that according to CMIPS v.1.03 a Desktop Processor Intel i7-4770S, worth USD $300, performs better than an Amazon M2 High Memory Quadruple Extra Large and than a Rackspace First gen. 30 GB RAM 8 Cores?.
Today the public cost of an Amazon M2 High Memory Quadruple Extra Large running for a month is USD $1,180.80 so USD $1.64 per hour and the Rackspace First Generation 30 GB RAM 8 Cores 1200 GB of disk costs is USD $1,425.60 so USD $1.98 per hour running.
And that’s the key, the cost per hour.
Because the greatness, the majesty of the Cloud is that you pay per hour, you pay as you need, or as you go. No attaching contracts. All on demand.
I had my company at a time where the hosting companies and the Data Centers were forcing customers to sign yearly contracts. What if a company only needs to host their Servers for three months? What if they have to close?. No options. You take it or you leave it.
Even renting a dedicated hosting was for at least a month or more, and what if the latency was not good? What if the bandwidth of the provider was not enough?.
Amazon irrupted in the market with strength. I really like that company because they grew the best eCommerce company for buying books, they did a system that really worked, and was able to recommend very useful computer books, and the delivery, logistics was so good, also post-sales service. They simply started to rent the same infrastructure they were using to attend their millions of customers and was a total success.
And for a while few people knew about Amazon deep technologies and functionalities, but later became a fashion.
Now people is using Amazon or whatever provider/Service that contains the word “Cloud” because the Cloud is in the mouth of everyone. Magazines and newspapers speak about the Cloud, so many many companies use it simply because everyone is talking about the Cloud. And those ISP that didn’t had a Cloud have invested heavily to create a Cloud, just because they didn’t want to be the ones without a Cloud, since everyone was asking for it and all the ISP companies were offering their “Clouds”.
Every company claims to have “Cloud” where the only many of them have is Vmware servers, Xen servers, Open Stack… running the tenants or instances of the customers always on the same host servers. No real Cloud, professional Cloud, abstract layered in a Professional way like Amazon, only the traditional “shared hosting” with another name, sharing CPU and RAM and Disk storage using virtual machines called instances.
So, Cloud fashion has become a confusing craziness where no one knows why they are in the Cloud but they believe they have to be in.
But do companies need the Cloud?. Cloud instances?
It depends. The best would be to ask that companies Why you choose the Cloud?.
If you compare the cost of having an instance in the Cloud, is much much more expensive than having a dedicated server. And for that high cost you don’t get more performance.
Virtualization is always slower and disk speed is always an issue in Cloud providers, where all the data travels via network from the disk cabins NAS to the Host servers running the guest instances. Data cannot be at local disks, since every time you start an instance, the resources like CPU and RAM are provisioned, and your instance run in totally different hardware. Only your data remain in the NAS (Network Attached Storage).
So unless you run your in-the-Cloud instance in a special provider that offers local disks, like DigitalOcean that offers SSD but monthly paying, (and so you pay the price by losing the hardware abstraction capability because you’re attached to the CPU that has the disk connected, and also you loss the flexibility of paying per hour of use, as you go), then you’ll face a bottleneck that is the hard disk performance (that for real takes all the data from NAS, where is stored, through the local network).
So what are the motivations to use the Cloud?. I try to put some examples, out of these it has no much sense, I think. You can send me your happy-in-Cloud scenarios if you found other good uses.
Example A) Saving initial costs, avoid contract attachment and grow easily own-made
Imagine a Developer that start its own project. May be it works, may be not, but instead of having a monthly contract for a dedicated server, he starts with an Amazon Free Tier (better not, use Small instance at least) and runs a web. If it does not work, simply stop the instance and pay no more. If the project works and has more and more users he can re-dimension the server with a click. Just stop the instance, change the type of instance, start it again with more RAM and more CPU power. Fast.
Hiring a dedicated server implies at least monthly contracts, average of USD $100 per month, and is not easy to move to a bigger server, not fast and is expensive as it requires the ISP tech guys to move the data, to migrate from a Server to another.
Also the available bandwidth is to be taken in consideration. Bandwidth is expensive and Amazon can offer 150 Mbit to smaller machines. Not all the Internet Service Providers can offer that bandwidth even with most advanced packets.
If the project still grows, with a click, in seconds, 20 instances with a lot of bandwidth can be deployed and serving traffic to your customers very quick.
You save the init costs of buying Servers, and the time to deal with hardware, bandwidth limitations and avoid contracts, but you pay an hourly rate a lot more expensive. So in the long run is much much expensive using Amazon and less powerful than having dedicated servers. That happened to Zynga, that was paying $63M annually to Amazon and decided to step back from Amazon to their own Data Centers again. (another fortune tech link)
The limited CPU power was also a deal breaker for many companies that needed really powerful CPU and gigs of RAM for their Database Servers. Now this situation is much better with the introduction of the new Servers.
This developer can benefit from doing bacups with a click, cloning, starting instances from an image, having more static ip’s with a click, deploying built-in (from the Cloud provider) load balancers, using monitoring services like CloudWatch, creating Volumes and attaching to the servers for additional space…
Example B) An Startup with fluctuating number of users and hopes of growing
Imagine an Startup with a wonderful Facebook Application.
During 80% of the day has few visits, may be only need 3 Servers, but during 20% of the hours of the day from 10:00 to 15:00 users connect like hell, so they need 20 servers to attend this traffic and workload, and may be tomorrow needs 30 servers.
With the Cloud they pay for 3 servers 24 hours per day and for the other 17 servers only pay the hours they are on, that’s 5 hours per day. Doing that they save money and they have an unlimited * amount of power. (* There are limits for real, you have to specially request authorisation to run more than default max. servers for the zone, that is normally 20 instances for Amazon. Also it can happen theoretically that when you request new instances the Zone has no instances available).
So well, for an Startup growing, avoiding hiring 20 dedicated servers and instead running into the Cloud as many as they need, for just the time they need, Auto-Scaling up and down, and can use the servers NOW and pay the next month with Visa card, all of that can make a difference for a growing Startup.
If the servers chosen are not powerful enough that is solved with a click, changing instance type. So fast. A minute.
It’s only a matter of money.
Example C) e-Learning companies and online universities
e-Learning platforms also get benefits from the Auto-Scaling for the full occupation hours.
The built-in functionalities of the Cloud to clone instances is very useful to deploy new web servers, or new environments for students doing practices, in the case of teaching Information Technology subjects, where the users need to practice against a real server (Linux or windows).
Those servers can be created and destroyed, cloned from the main -ready to go- template. And also servers can be scheduled to stop at a certain hour and to start also, so saving the money from the hours not needed.
Example D) Digital agencies, sports and other events
When there is an Special event, like motorcycle running, when a Football Team scores, when there is an spot in tv announcing a product…
At those moments the traffic to the site can multiply, so more servers and more bandwidth have to be deployed instantly. That cannot be done with physical servers, hardware, but is very easy to provision instances from the Cloud.
Mass mailing email campaigns can also benefit from creating new Servers when needed.
Example E) Proximity and SEO
Cloud providers have Data Centres everywhere. If you want to have servers in Asia, or static content to be deployed faster, or in South-America, or in Europe… the Cloud providers have plenty of Data Centers all over the world.
Example F) Game aficionado and friends sharing contents
People that loves cooperative games can find the needed hungry bandwidth and at a moderate price. If they run their private server few hours, at night, from 22:00 to 01:00 as example, they will benefit from a great bandwidth from the big Cloud provider and pay only 3 hours per day (the exceed of traffic uses to be paid in most providers, but price of additional GB uses to be really really competitive).
Friends sharing contents in an Ftp also, can benefit from this Cloud servers, but probably they will find more easy to use services like Dropbox.
Example G) Startup serving contents
An Startup serving videos, images, or books, can benefit not only from the great bandwidth of big Cloud providers (this has been covered before), but for a very cheap price for exceeding Gigabyte transferred.
Local ISP can’t offer 150 Mbit for an instance of USD $20 and USD $0.12 per additional GB transferred.
Many Cloud providers also allow unlimited incoming traffic from the Internet, and from Server to Server through private ip’s.
Example H) Enterprises that want to outsource the solutions and having better availability
Since I wrote this article in 2013, many CSP have been incorporation more and more powerful services to their catalalog.
For instance Amazon has service like DynamoDB, RDS, Load Balancers able to balance servers in different availability zones, with Auto-Scaling, SQS for Message Queues, ElastiCache, Lambda, S3…
Many Enterprise find convenient to have all their services offloaded to Amazon, Google Cloud, Microsoft Azure… so they require less Operations and System Administration Engineers.
Other cases
For other cases Dedicated Servers are much more Powerful, faster and cheaper, at the price of being “static” in the sense of attached, not layer abstracted, but all the aspects of your Project have to be taken in count before deciding stepping into or out of the Cloud.
In general terms I would say that the Cloud is for Scaling, but in 2022 it’s also to outsource Operation Services, to fully managed services by the CSPs.
When I first tried some years ago Amazon t1.micro Free Tier, I was really disappointed.
I moved one of my blogs there, from a vmware instance that I was running in a physical server of my own, hosted at Colt Telecom, and my deception was huge.
That instance frozen often! According to Amazon EC2 web it was Ok, but it was totally KO. No ping, unable to log by ssh.
The only solution then was to shutdown the instance from the EC2 console, what it takes some time, because in fact, machine was down but the console didn’t know.
Once down, then start again. Reassign the static Ip address (Amazon calls it Elastic Ip).
And once or twice per week I had one of those frozen.
I tried to found a pattern of why instance freezes, but no luck. It simply happen.
I was about to leave Amazon forever when I decided to try the next instance size, just in case, the Small, and problems disappeared forever.
Now, that I have started my project CMIPS to measure and compare the Cloud performance (different providers and instance types), I see how bad it was t1.micro.
Even without the freezes, my not-so-modern ultra-portable laptop performs several times faster (x9.39 times faster), so any project hosted in a t1.micro will suffer a pathetic performance.
Later I discovered that sometimes the Amazon servers where your instances are running have a hardware problem or die. A reboot doesn’t help, since when you reboot, you don’t change the server that hosts your instance (guest).
Only poweroff makes Amazon to assign you another physical server.
And as later I learned, from time to time, no matter how good your instances are -but with expensive instances it happens much less-, without a reason one of your instances can freeze. So if your Startup has 30 instances, monitor them, because one can freeze some day. It just happens. I guess hardware crashes. And it could be one of the databases that gets knock down.
If you’re lucky you’ll have no corruption in the network storage, if not you will loss data. So backup.
It has occurred to me twice with my private instances in Amazon (not counting the nightmare t1.micro), but we suffered it from time to time when I worked for a videogames company where we had many always-on servers and where we were creating on the fly, scaling the number of instances, according to the increasingly or decreasingly number of users.
