Not only Structured Query Language Method of Ad Request Processing
VertaMedia Company’s server provides operation of advertising exchange system between publishers (site’s owners), advertisers and intermediaries (SSP¹ and DSP² platforms). The objective of the system server is to process a request from a Publisher’s site as quickly as possible, choosing the most rel...
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України
2017
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nasplib_isofts_kiev_ua-123456789-1158592025-02-09T21:12:21Z Not only Structured Query Language Method of Ad Request Processing Nikolaiev, V.А. Konashevych, O.I. Применение методов и средств моделирования VertaMedia Company’s server provides operation of advertising exchange system between publishers (site’s owners), advertisers and intermediaries (SSP¹ and DSP² platforms). The objective of the system server is to process a request from a Publisher’s site as quickly as possible, choosing the most relevant advertisement campaign, to show it to a site’s user. The system works real-time online and as faster it makes accurate choice, the more likely that a user will see an advertisement. The obvious solution was to use relational database to compare the parameters of queries with the parameters and settings of ad campaigns, stored in this database. However, it turned out to be unsuitable as such system showed high latency. VertaMediaTM programmers have found an original way to process data, in which comparison occurred in a flat table using the hash sum and a binary tree for matching ad campaigns and another part of the request, which contained a set of keywords/tags was processed by Sphinx Search as local software solution. A method incorporates the original decision to work with database management systemless non-relational tables and use of specialized software solutions for matching keywords. It showed remarkable results in performance of a resource-intensive process, as described in detail in the article. Сервер компании VertaMedia обеспечивает работу рекламной системы обмена между издателями (владельцами сайтов), рекламодателей и посредников (SSP¹ и DSP²). Сервер системы должен обрабатывать запросы от сайтов издателей так быстро, насколько это возможно, выбирая наиболее подходящую рекламную кампанию, чтобы показать еe пользователю сайта. Система работает в реальном времени в интернете, и чем быстрее она делает точный выбор, тем больше вероятность того, что пользователь увидит рекламу. Очевидное решение состоит в использовании реляционных баз данных (БД) для сравнения параметров запросов с параметрами и настройками рекламных кампаний, которые хранятся в БД. Однако это оказалось недостаточно эффективным –– система показала высокую латентность. Программистами VertaMediaTM найден оригинальный способ обработки данных, когда сопоставления организованы в плоской таблице с помощью хеш сумм и бинарного дерева, а также локального программного решения Sphinx Search, которым обрабатываются ключевые слова и метки рекламных кампаний. Метод представляет собой оригинальное решение проблемы работы с нереляционными таблицами без системы управления БД с использованием специализированного программного решения для согласования ключевых слов. Полученные результаты свидетельствуют о значительном увеличении скорости при выполнении ресурсоемких процессов. 2017 Article Not only Structured Query Language Method of Ad Request Processing / V.А. Nikolaiev, O.I. Konashevych // Электронное моделирование. — 2017. — Т. 39, № 1. — С. 105-111. — Бібліогр.: 15 назв. — англ. 0204-3572 https://nasplib.isofts.kiev.ua/handle/123456789/115859 004.04, 004.6 en Электронное моделирование application/pdf Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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Применение методов и средств моделирования Применение методов и средств моделирования |
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Применение методов и средств моделирования Применение методов и средств моделирования Nikolaiev, V.А. Konashevych, O.I. Not only Structured Query Language Method of Ad Request Processing Электронное моделирование |
| description |
VertaMedia Company’s server provides operation of advertising exchange system between publishers (site’s owners), advertisers and intermediaries (SSP¹ and DSP² platforms). The objective of the system server is to process a request from a Publisher’s site as quickly as possible, choosing the most relevant advertisement campaign, to show it to a site’s user. The system works real-time online and as faster it makes accurate choice, the more likely that a user will see an advertisement. The obvious solution was to use relational database to compare the parameters of queries with the parameters and settings of ad campaigns, stored in this database. However, it turned out to be unsuitable as such system showed high latency. VertaMediaTM programmers have found an original way to process data, in which comparison occurred in a flat table using the hash sum and a binary tree for matching ad campaigns and another part of the request, which contained a set of keywords/tags was processed by Sphinx Search as local software solution. A method incorporates the original decision to work with database management systemless non-relational tables and use of specialized software solutions for matching keywords. It showed remarkable results in performance of a resource-intensive process, as described in detail in the article. |
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Article |
| author |
Nikolaiev, V.А. Konashevych, O.I. |
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Nikolaiev, V.А. Konashevych, O.I. |
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Nikolaiev, V.А. |
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Not only Structured Query Language Method of Ad Request Processing |
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Not only Structured Query Language Method of Ad Request Processing |
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Not only Structured Query Language Method of Ad Request Processing |
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Not only Structured Query Language Method of Ad Request Processing |
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Not only Structured Query Language Method of Ad Request Processing |
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not only structured query language method of ad request processing |
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Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України |
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2017 |
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Применение методов и средств моделирования |
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https://nasplib.isofts.kiev.ua/handle/123456789/115859 |
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Not only Structured Query Language Method of Ad Request Processing / V.А. Nikolaiev, O.I. Konashevych // Электронное моделирование. — 2017. — Т. 39, № 1. — С. 105-111. — Бібліогр.: 15 назв. — англ. |
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ÓÄÊ 004.04, 004.6
V.À. Nikolaiev
VertaMedia Company
(224 West 35th St., Suite 1102-5, New York, NY10001, USA,
basil@vertamedia.com),
O.I. Konashevych, post-graduate
Pukhov Institute for Modeling in Energy Engineering
(15, General Naumov St., Kyiv, 03164, Ukraine,
a.konashevich@gmail.com)
Not only Structured Query Language Method
of Ad Request Processing
VertaMedia Company’s server provides operation of advertising exchange system between pub-
lishers (site’s owners), advertisers and intermediaries (SSP1 and DSP2 platforms). The objective
of the system server is to process a request from a Publisher’s site as quickly as possible, choosing
the most relevant advertisement campaign, to show it to a site’s user. The system works real-time
online and as faster it makes accurate choice, the more likely that a user will see an advertisement.
The obvious solution was to use relational database to compare the parameters of queries with the
parameters and settings of ad campaigns, stored in this database. However, it turned out to be un-
suitable as such system showed high latency. VertaMediaTM programmers have found an original
way to process data, in which comparison occurred in a flat table using the hash sum and a binary
tree for matching ad campaigns and another part of the request, which contained a set of
keywords/tags was processed by Sphinx Search as local software solution. A method incorpo-
rates the original decision to work with database management systemless non-relational tables
and use of specialized software solutions for matching keywords. It showed remarkable results in
performance of a resource-intensive process, as described in detail in the article.
Ñåðâåð êîìïàíèè VertaMedia îáåñïå÷èâàåò ðàáîòó ðåêëàìíîé ñèñòåìû îáìåíà ìåæäó
èçäàòåëÿìè (âëàäåëüöàìè ñàéòîâ), ðåêëàìîäàòåëåé è ïîñðåäíèêîâ (SSP1 è DSP2). Ñåðâåð
ñèñòåìû äîëæåí îáðàáàòûâàòü çàïðîñû îò ñàéòîâ èçäàòåëåé òàê áûñòðî, íàñêîëüêî ýòî
âîçìîæíî, âûáèðàÿ íàèáîëåå ïîäõîäÿùóþ ðåêëàìíóþ êàìïàíèþ, ÷òîáû ïîêàçàòü åe ïîëü-
çîâàòåëþ ñàéòà. Ñèñòåìà ðàáîòàåò â ðåàëüíîì âðåìåíè â èíòåðíåòå, è ÷åì áûñòðåå îíà
äåëàåò òî÷íûé âûáîð, òåì áîëüøå âåðîÿòíîñòü òîãî, ÷òî ïîëüçîâàòåëü óâèäèò ðåêëàìó.
Î÷åâèäíîå ðåøåíèå ñîñòîèò â èñïîëüçîâàíèè ðåëÿöèîííûõ áàç äàííûõ (ÁÄ) äëÿ ñðàâ-
íåíèÿ ïàðàìåòðîâ çàïðîñîâ ñ ïàðàìåòðàìè è íàñòðîéêàìè ðåêëàìíûõ êàìïàíèé, êîòîðûå
õðàíÿòñÿ â ÁÄ. Îäíàêî ýòî îêàçàëîñü íåäîñòàòî÷íî ýôôåêòèâíûì –– ñèñòåìà ïîêàçàëà
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2017. Ò. 39. ¹ 1 105
� V.À. Nikolaiev, O.I. Konashevych, 2016
1 Supply side platform.
2 Demand side platform.
âûñîêóþ ëàòåíòíîñòü. Ïðîãðàììèñòàìè VertaMediaTM íàéäåí îðèãèíàëüíûé ñïîñîá îáðà-
áîòêè äàííûõ, êîãäà ñîïîñòàâëåíèÿ îðãàíèçîâàíû â ïëîñêîé òàáëèöå ñ ïîìîùüþ õåø-
ñóìì è áèíàðíîãî äåðåâà, à òàêæå ëîêàëüíîãî ïðîãðàììíîãî ðåøåíèÿ Sphinx Search, êî-
òîðûì îáðàáàòûâàþòñÿ êëþ÷åâûå ñëîâà è ìåòêè ðåêëàìíûõ êàìïàíèé. Ìåòîä ïðåäñòàâëÿåò
ñîáîé îðèãèíàëüíîå ðåøåíèå ïðîáëåìû ðàáîòû ñ íåðåëÿöèîííûìè òàáëèöàìè áåç ñèñòåìû
óïðàâëåíèÿ ÁÄ ñ èñïîëüçîâàíèåì ñïåöèàëèçèðîâàííîãî ïðîãðàììíîãî ðåøåíèÿ äëÿ ñîãëàñî-
âàíèÿ êëþ÷åâûõ ñëîâ. Ïîëó÷åííûå ðåçóëüòàòû ñâèäåòåëüñòâóþò î çíà÷èòåëüíîì óâåëè÷åíèè
ñêîðîñòè ïðè âûïîëíåíèè ðåñóðñîåìêèõ ïðîöåññîâ.
K e y w o r d s: not only structured query language (noSQL), statistics, information technologies,
big data, statistical process control.
The problem. VertaMediaTM company provides services of online advertising
placement. Its main customers are so-called Publishers that manage their web-
sites on the Internet, mobile applications, online games etc (let us call it
‘content’). Publishers place advertisement (usually video clips) near their content
[1] through specific protocols and algorithms within ad network [2]. Ad network
provides interaction with ad operators, advertising owners and intermediaries.
When a user consumes Publisher’s content, Publisher’s software sends re-
quest to VertaMediaTM company. Then the company makes search in its avail-
able advertising campaigns database and finds the one or a few that matches best
to Publisher’s request parameters and provides a command to forward this re-
quest to the server of the chosen ad operator/owner. In case of a positive re-
sponse from the operator’s server, ad unit is playbacked to a user while he/she
browses the site or application.
The main point on the technical side is query processing speed. A user of a
site will not wait until a Publisher chooses ads. The problem is compounded
when owners deny requests at their own discretion, including fraud traffic con-
trol services that Advertisers use to check requests. Advertisers do not announce
most of their parameters of the selection. Therefore, in order not to waste time on
requesting all ad campaigns one by one from a list, it is very important to find
exact matching within the known parameters.
Body section. Publishers’ requests consist of dataset transmitted by a given
protocol. A request contains fields with parameters which are required by
VertaMediaTM to make matching with the list of ad campaigns, as schematically
shown in Fig.1. Generally, an amount of advertising campaigns is more than
thousand. The task of the server is to produce the most accurate choice in the
shortest possible time with minimal system resources.
Decisions are taken automatically with a given algorithm that has been de-
veloped by VertaMediaTM, when it faced the fact that the speed of the relational
database [3] processing using MySQL [4] was not high enough, and the need for
resources was redundant. Therefore, it has been hypothesized to use database
management system (DBMS) less approach to search and match data in a
non-relational database [5], excluding matching keywords.
V.À. Nikolaiev, O.I. Konashevych
106 ISSN 0204–3572. Electronic Modeling. 2017. V. 39. ¹ 1
Keywords comparison in various combinations appeared, as well, rather re-
source-intensive task for such ordinary solution as MySQL and unreasonably
complex to create its own solution. It required a customized application. Full-
text search engine “Sphinx Search” [6], was found as a the most suitable solu-
tion. That will be discussed hereinafter.
Let us consider first of all what solutions turned out unsuitable and why. It is
known several types of data models [7]. MySQL belongs to relational database
data model [8]. Structured query language (SQL) together with Document-ori-
ented [9], Key-value [10], and Information Retrieval Systems [11] could not
provide high performance for VertaMediaTM system tasks, as it is empirically
reasoned by experience of company’s researchers. Latency was high and hin-
dered reaching the required 0.02-0.90 m per request with reasonable resources.
Finally Row-based [12] model was approached with originally developed solu-
tion. But firstly, it was tested ready-for-use software application Sphinx Search
for processing the entire ad request data.
Sphinx Search appeared unsuitable as a standalone solution for all issues in
ad request, still it could work standalone [13]. It was good enough to process full
text search. However, it is not required often, because the share of ad campaign
with tags/keywords fields is not more than 10 percent among all campaigns. The
Not only Structured Query Language Method of Ad Request Processing
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2017. Ò. 39. ¹ 1 107
VertaMedia
TM
Ads
list
Traffic type
Bid
Country (optional)
Region (optional)
City (optional)
Keywords (optional)
Sphinx search
Publisher
ad request
User IP address
Traffic type
Keyword/tags
Min bid (optional)
server
Fig. 1. Publisher request processing
application appeared to be unable to keep the burden of the entire flow of re-
quests at a desired speed.
To resolve the issue the researchers built their own code using Java.
Row-oriented flat table was created that included ad campaign list with all possi-
ble combinations of parameters initially stored and received from relational da-
tabase. An example is illustrated in Table, where null values are replaced with
ZZ, and the column Campaign ID shows advertising campaigns named by num-
bers which are repeated in proposed example with different options. In accor-
dance with Table, Campaign 2 contains two requirements with traffic of the se-
cond type:
1) show advertising in the United States in any city of California;
2) show advertising in the United States in the UK in any region, in any city.
The above mentioned example shows that each campaign can have multiple
options.
Then, HashMap [14] uses composed keys as concatenation of fields:
TrafficType, Country, Region and City. After that a tree has been put which con-
sists of Bid and advertisements list:
HashMap [
Hash(TrafficType+Country+Region+City)
�
TreeMap[bid � List[Ad]
]
V.À. Nikolaiev, O.I. Konashevych
108 ISSN 0204–3572. Electronic Modeling. 2017. V. 39. ¹ 1
Publisher's ad request Campaign #3
Country: US
Region: CA
City: ZZ
Traffic type: 2
Keywords:
baseball
Ask > S7
Country: US
Region: CA
City: ZZ
Traffic type: 2
Keywords:
baseball
Bid = S8
HASH:
04c085d07e161d2b
3d2589119bd3df05
HASH:
04c085d07e161d2b
3d2589119bd3df05
Fig. 2. Request matching
Campaign ID Country Region City Traffic type
1 US ZZ ZZ 1
2 US CA ZZ 2
2 GB ZZ ZZ 2
The set of input parameters of hash function does not contain a campaign
number. This approach allows matching hashes in database and ad request elimi-
nated campaign number.
Hash and bid’s options allow one to get an advertisement list of relevant
matches in two iterations:
1) hashes matching;
2) bids matching. When equal hashes and fee conformity is found, Verta-
MediaTM server redirects request to the server of Advertiser who is the owner of
the best matched ad. If the request has keywords (tags), then after matching ap-
propriate ads, the request is processed by Sphinx Search. Finally application
creates a list of best matched ad campaigns as schematically shown in Fig. 2.
Designed and implemented VertaMediaTM solution has been tested and
compared to popular alternatives. The testing results are as follows:
Not only Structured Query Language Method of Ad Request Processing
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2017. Ò. 39. ¹ 1 109
benchmark
=============== Verta Media ===============
wrk-t4-c400-d180s‘http://localhost:9090/handmade?group=260&ip=216.58.214.206&n= 4&min_
bid= 0.002‘
Running 3m test @ 'http://localhost9090/handmade?group=260&ip=216.58.214.206&n=4&min_
bid= 0.002
4 threads and 400 connections
Thread Stats Avg Stdev Max ± Stdev
Latency 614.23us 1.54ms 189.30ms 92.66%
Req/Sec 3.24k 1.83k 11.52k 67.52%
580257 requests in 3.00m, 324.83MB read
Socket errors: connect 0, read 4844645, write 0, timeout 0
Requests/sec: 3222.88
Transfer/sec: 1.60MB
================= Lucene =================
wrk -t4 -c400 -d 180s ‘http://localhost:9090/Iucene?group=260&ip=216.58.214.206&n= 4&min_
bid= 0.002‘
Running 3m test @ http://locaIhost:9090/Iucene?group=260&ip=216.58.214.206&n=4&min
_bid=0.002
4 threads and 400 connections
Thread Stats Avg Stdev Max ± Stdev
Latency 1.90ms 3.00ms 287.51ms 91.85%
Req/Sec 1.05k 537.28 2.96k 68.19%
188082 requests in 3.00m, 105,29MB read
Socket errors: connect 0, read 4423595, write 0, timeout 0
Requests/sec: 1044.43
Transferee: 593.71KB
The results of testing showed high performance of the proposed and imple-
mented method compared to Lucene and Sphinx. VertaMediaTM solution is al-
most ten times faster than Sphinx and three times more efficient thàn Lucene:
3222.88 to 335.68 and to 1044.43 request per second, respectively. The average
latency is reduced to 0.61 ms compared to 1.90 ms (Lucene) and 5.94 (Sphinx).
Ñonclusion. The method proposed by VetaMediaTM showed better results
in system performance, than it was expected [15]. Testing results confirmed that
flat table allows reaching low latency which gave advantages to VertaMediaTM
in competitions with other ad operators of the market.
There is a HashMap generated on the upper level of the index. The system
filters lists with the HashMap by matching the part of parameters. Then bids of
matched campaigns and requests are cut with TreeMap. The last iteration is
matching keywords with Sphinx which gave the final list of ad campaigns.
Searching via tree is more resource-intensive and slow task, unlike HashMap.
SQL solutions practically perform lower results, as it was estimated by researchers
of VertaMediaTM. It is 3-10 times more in case of matching with SQL the whole
bundle of ad request parameters. The third ‘layer’ of the request processing optimi-
zation was to use specialized solution to match keywords and tags contained in ad
request by using Sphinx application, which suits better for full text search.
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V.À. Nikolaiev, O.I. Konashevych
110 ISSN 0204–3572. Electronic Modeling. 2017. V. 39. ¹ 1
================= Sphinx =================
wrk -t4 -c400 -d180s'http//localhost:9090/sphinx?qroup=260&ip=216.58.214.206&n=4& min
bid=0.002‘
Running 3m test @'http://locaIhost:9090/sphinx?group=260&ip=216.58.214.206&n=4&min_
bid= 0.002
4 threads and 400 connections
Thread Stats Avg Stdev Max ± Stdev
Latency 5.94ms7.53ms 762.50ms 93.55%
Req/Sec 338.46 117.75 820.00 73.92%
60274 requests in 3.00m, 33.74MB read
Socket errors: connect 0, read 4616561, write 0, timeout 0
Requests/sec: 334.68
Transfer/sec: 191.85KB
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Received 02.08.16
NIKOLAIEV Vasyl’ Anatoliyovych is a Chief Technical Officer, VertaMedia Company, USA, gradu-
ated from the National Aviation University, Computer Engineering, 2008. The field of research: sys-
tems design, systems performance optimization, high load systems.
KONASHEVYCH Oleksii Ihorovych is a post-graduate student of the Pukhov Institute for Modeling in
Energy Engineering of NAS of Ukraine; graduated from the National Aviation University in 2005; in
2011 he graduated from Kyiv National Trade and Economic University, Advanced Training Institute.
The field of research: blockchain technology.
Not only Structured Query Language Method of Ad Request Processing
ISSN 0204–3572. Ýëåêòðîí. ìîäåëèðîâàíèå. 2017. Ò. 39. ¹ 1 111
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