Tweaking Open Source

Tweaking Open-Source
a case-study

 Nelson Gomes (nelson.gomes@telecom.pt)
Team Leader
11th of November 2011

Talk Index

Introduction
OpenX Components
Improvements Introduced
Overall Architecture
Report Server
Problems Found
Links
Q&A

Introduction – Online Advertisement

Online advertisement coupes with delivering ads;
Placing ads in sites is a complex process:
Obtain all ads electable for a placeholder;
Exclude ads with business limitations like capping;
Assure that the ads are beying presented to the target
audience;
Assure the advertiser goals are being met;
Account the ads delivered;
Deliver the right ad format;

Introduction – Online Advertisement
Examples

Introduction – OpenX

Open Source advertising server;
Licensed under GNU General Public License;
Project forked from phpAds developed by Tobias Ratschiller
in 1998;
Was called phpAdsNew, OpenAds and finally OpenX;
Features:
Has a web based GUI;
Extendable plugins architecture;
Serves ads throught JS and Iframes calls mainly;

Introduction – OpenX

Support technologies:
PHP;
MySQL;
Web Server (Apache, Nginx);
Optional memcached usage;
Filesystem to serve ad content;

Introduction – OpenDisplay

Starting from OpenX 1.8.5 version, SAPO OpenDisplay
project began;
A four-person team started in April 2010 to analyse and
improve OpenX capabilities to ensure entire SAPO's ad
serving network;
In August 2010, OpenDisplay started to serve a major
website, while development was undergoing;
In February 2011, SAPO began migrating it's ad serving
network in a process that took about 3 months to complete;
Today OpenDisplay serves the entire SAPO's ad network;

Introduction – OpenDisplay

OpenDisplay serves ads for several media:
Internet;
Mobile Internet;
Mobile Applications;
TV set-top boxes;
Connected TV's;
In the near future we'll be serving bulk campaigns for other
media;
I'll try to tell in this presentation this endeavour steps and
quirks;

OpenDisplay Components – Frontend

This component is responsible to serve all ad formats;
No data processing is done here due to performance besides
adserving itself;
The adserving is done using munged PHP scripts for
performance;
Plugins are included in a on demand basis;
Database queries are cached;
So it's all about ad serving decision making;

OpenDisplay Components – Backend

Comprises data feature processing;
Web based GUI for campaign and ad management;
Ad serving statistics;
Reporting;
Batch processing of ad delivery data for use by the
frontends;

OpenDisplay Components – Tasks

Maintenance Priority Engine (MPE)
Determines witch campaigns to serve given their
priorities;
Calculates ad serving probabilites given it's probabilities
and corrects them when underperforming or
overperforming;
Maintenance Statistics Engine (MSE)
Processes ad serving numbers;
Starts and stops campaigns;

Improvements Introduced - General

Added reusable segmentation rules;
This way a rule can be reused in several campaigns;
Added compound segmentation rules;
Segmentation rules engine was rewritten, cause the
previous segmentation system was inadequate;
Added the concept of Orders;
Sometimes a customer has several goals to different sites;
The concept of order allows to place several campaigns
with different goals in a single customer order;


Added Zone Groups;
Instead of selecting placeholders one a at a time we can
associate several at once;
Imagine that a Run of Network (RON) campaign for all
MREC (300x250) placeholders would need to be
associated to all placeholders one by one;
Added revenue-share acounting;
For ads served on pages with third-party content;
This way, revenue can be shared with third-party content
providers;


OpenDisplay went through a security audit by SAPO's
security team and several issues were solved;
Backoffice:
UI session cookies are now only delivered over SSL;
Session id generation function wasn't good enought and
could be easily guessed. This correction minimized
session hijacking;
New user profiles were added, and entity access was
reviewed;
Some user profiles were changed to read-only, like
advertisers and sites;


Ads uploaded into the ad server are stored in a folder and
served upon;
At first look there is no problem with this, but over time
in some systems this can cause inode exhaustion;
So to prevent this, and speed up file retrieval we
improved upload component to distribute the files in a
two-level folder hierarchy;
OpenX can use a content farm to deliver ads, so we use this
feature from the start;


Traffic forecast:
OpenX doesn't have a traffic forecast engine, instead it
uses an average of ads served;
We developed two alternative forecast algorithms using
Python;
This forecast is critical for a couple of reasons:
Inventory selling;
Correct impression allocation for campaigns,
specially due to targetting rules;


Traffic forecast example:


Added data logging and analysis:
We started to summarize delivery properties to allow us
to calculate precise segmentation delivery probabilies;
Using these numbers in combination with traffic forecast
we can estimate the inventory for each campaign and it's
overall probability of delivery;
Also, this information is useful to commercial purposes:
Knowing the market is a very valuable information;
We are currently migrating some of this data to Hbase
that reduces data, making it usable;


Restructured VAST 1.0 system and upgraded it to 2.0;
Video Ad Serving Template (VAST) standard from
Interactive Advertising Bureau;
Delivers video ads (pre, mid and postrolls);
Delivers overlays;
We also added a new type of ad that allows us to serve
SAPO text ads has images;
This virtual ad type works has a proxy to a different ad
system, combining two different ad systems;
Probably the first time an ad system combined them;


<?xml version="1.0" encoding="UTF8" standalone="no"?>
<VAST version="2.0" (...)>
<Ad id="30324">
  <InLine>
   <AdSystem>OpenDisplay</AdSystem>
   <AdTitle><![CDATA[Teste Vast Video]]></AdTitle>
   <Description><![CDATA[VAST Ad]]></Description>
   <Impression id="OpenDisplay"><![CDATA[http://pub.sapo.pt/lg.php?(...)]]></Impression>
   <Creatives>
    <Creative id="30324">
     <Linear>
      <TrackingEvents>
       <Tracking event="creativeView"><![CDATA[http://pub.sapo.pt/(...)&vast_event=creativeview]]></Tracking>
(...)
      </TrackingEvents>
      <MediaFiles><MediaFile (...) type="video/xflv">http://(...)/video.flv</MediaFile></MediaFiles>
     </Linear>
    </Creative>
   </Creatives>
  </InLine>
</Ad>
</VAST>


Flash ads are a major problem in some systems that don't
support Flash;
iPhones and iPads for example;
To assure these ads are at all times visible we added
automatic Flash ad image generation to ads upload via
Backend;
This way, even if a Flash ad doesn't have a fallback image,
we generate one automatically;
This was accomplished using GNU's gnash in combination
with xvfb-run that provides a virtual X Window System
for gnash to run;


Future developments will include bulk campaigns;
These campaigns differ from regular campaigns cause we
know the characteristics of the audience in advance;
Splitting audiance in sets with the same features we can
process an entire set within the LP solver at once
minimizing the number of variables;
So we can optimize the revenue using linear programming
solutions;
We will use GLPK (GNU Linear Programming Kit) has a
solver to obtain an optimal solution;
This way we can provide a solution that maximizes a
campaign's revenue;


GLPK sample problem:
# Giapetto's problem, maximizing Giapetto's profit
var x1 >=0;  /* soldier worths 3€ */
var x2 >=0;  /* train worths 2€ */

/* Objective function */
maximize z: 3*x1 + 2*x2; // maximize Giapetto's profit

/* Constraints */
s.t. Finishing : 2*x1 + x2 <= 100; // only 100 hours per week
s.t. Carpentry : x1 + x2 <= 80; // only 80 hours per week
s.t. Demand    : x1 <= 40; // demand of soldiers per week
End;

Improvements Introduced - Frontend

Database write operations were removed. Database access
now is read-only;
Delivery scripts were analysed using xdebug, and major
performance issues were tuned:
User agent regexp's used by PHPSniff were taking 25% of
the entire request time. Using memchache as user agent
cache we saved 97% of this time!
All ad serving counters are done in memcache and
persisted at every minute, soon we'll migrate this to
broker queues;
Improved ad caching system, to store and retrieve
EVERYTHING in a single operation;


Using xdebug output has an input to KCachegrind it is very
easy to analyse any PHP script: just run it!
Files generated by xdebug are read and analysed by
KCachegrind that shows for instance:
How many times a function has been called;
Total time each function used;
Where request time is use;
Making very easy to detect and improve any long running
script;


KCachegrind printscreen


Instead of using an Apache web server we decided to use
Nginx with PHP-FPM:
Nginx scales almost linearly;
PHP-FPM behaved very fast in our tests;
PHP-FPM is a FastCGI implementation, now blunded with
PHP 5.3.3;
Instead of using PHP output compression, we used Nginx
compression, witch is faster;
Of course, we used a PHP accelerator: eAccelerator with
shared memory witch is adequate to PHP-FPM multi-
process architecture;


Even adding new features, we still were able to reduce
delivery times:


Introduced a cookie abstraction API to allow storing all
cookie and session information server-side:
OpenX by default stored session information in cookies
what was insufficient to keep an entire ad network
running due to cookie size limit (~4k);
This was a critical issue for long serving campaigns that
used capping or conversion data;
Less cookies means less bandwidth usage and faster
responses;


The new session storage mechanism added new issues;
The requests had to be sequential to allow correct session
retrieval and storage;
This required a lock mechanism to obtain session info in
an ordered fashion;
This was accomplished using memcache atomic
increments to lock session access;
All sessions are stored in memcache and the complete
process of locking, retrieving, storing and unlocking of
the session is done in a few ms (<3ms), from remote
servers!;


We can see in this chart outbound traffic dropped
significantly:


We introduced zone capping, a feature that wasn't available
in OpenX;
This feature is very useful with video ads, to avoid user
flooding with video ads;
Using zone capping we can say that a user will see one or
more ads and then will not see any more ads during a
given period of time;
This feature is managed by placeholder, independently of
the campaign settings;


Added new delivery endpoints to accomodate new formats:
Mobile:
Json
Xml
iPhonePlist
TV
VAST
Also we developed a SDK to help mobile ads integration:
Mobile ads are placed server-side, so client information
has to be passed to ad server (client IP, session id, user-
agent);


Frontend delivery algorithm was changed to support:
New segmentation rules system;
Changed election algorithm;
Zone capping;
Server-side storage of information instead of cookies;
Increased performance;
New endpoints to provide new types of ads;
No write operations into database;
Gather user properties for analysis;


Some eye opening numbers:
More than 4.000.000.000 web requests per month;
9 frontend servers using 36Mbits outbound and 25Mbits
inbound, in a total of 61Mbits throughput!
Aproximately 2,200 ad requests per second and the twice
of web requests (4,400/s);
95% of the web requests replied under 18ms;
PHP power at work... :-)

Improvements Introduced - Backend

Statistics component was changed to read information from
a database replica due large number of accesses;
Backoffice changed to support some filters and results
paging;
All user generated delete operations were removed, why?
Removal of a user, due to table relations could delete all
campaigns and statistics, and compromise forecast
results;
Deleting of a campaign, could loose all campaign data,
required for billing;
So all delete operations are done in maintenance tasks;


We also added new targetting rules and improved others:
Geographical: country, district;
Mobile Devices Model, OS, Version;
Browser Family;
Internet Service Provider;
Organization;
Day of week;


MPE was changed for a couple of reasons:
Become faster;
Decrease memory usage;
Changes in algorithm;
Optimizations;
MPE was reading ALL campaigns from database even
finished ones, so memory comsuption was increasing
linearly;
All services are now redundant;

Report Server

OpenX only generates csv reports;
A more reliable product required more reliable, comercial-
style reports;
This need lead us to try out JasperReports, an open-source
Java reports generator;
Thanks to iReport for Jasper, a Crystal-Reports style report
designer as a tool for creating reports, the reports can be
easily edited and tested;

Report Server

iReports for Jasper

Report Server

So, starting with JasperReports we generated a cloud style
report generation farm, how?
Combining it with SAPO Broker, a message passing system
and a flexible layered architecture;
Given this, a report request is a simple message delivered to
a SAPO Broker queue;
Every server generating reports can consume a report
request, allowing this architecture to scale almost linearly;

Report Server

We developed this report server in a layered style:
What report to generate;
Report parameters;
Datasource to use;
Outputs formats (HTML, XLS, Word, PDF,...);
Delivery channels (Email, FTP, SSH, …);
Report completion notification (HTTP, DB);
This layered style architecture allows us to extend any of the
layers with new features;
Will become available has open-source soon...

Report Server
Layer 1: what to generate
Report & parameters

Layer 2: data source
Data to use on report

Layer 3: output formats
Xls, pdf, doc, rtf...

Layer 4: delivery channels
Http, db, email

Layer 5: completion notification
Url, db

Problems Found

Unable to scale;
Some queries would read an entire database table if
existed long-running campaigns;
Changed this and acumulated totals in each banner what
is easier to sum;
Some internal data is still passed on using temporary
tables, but not for long...
Not fast enough, of course OpenX is good enought for small
site advertising, but not for an entire ad network;
Some entities were not working properly or were missing
due to business requirements;

Problems Found

But in retrospective OpenX gave us a good starting point...
Tweaking open-source code allowed us to:
From an existing open-source solution obtain a good base
to develop a better solution;
Save some costs if we had started for scratch;
Gain knowledge about advertisement concepts;
Customize new features according to specific needs;
So tweaking open-source is a great idea has a base to create
good solutions!!!

Links

http://www.openx.com
http://php-fpm.org
http://jasperforge.org/projects/jasperreports
http://jasperforge.org/project/ireport
http://softwarelivre.sapo.pt/broker
http://www.php.net
http://nginx.net
http://www.gnu.org/s/gnash
http://www.gnu.org/s/glpk

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