Web Search Engines process huge amounts of data to support search but must run under strong performance requirements (to answer a query in a fraction of a second). To meet that performance they implement different optimization techniques such as caching, that may be implemented at several levels. One of these caching levels is the intersection cache, that attempts to exploit frequently occurring pairs of terms by keeping in the memory of the search node the results of intersecting the corresponding inverted lists. In this work we propose an optimization step to decide which items should be cached and which not by introducing the usage of data mining techniques. Our preliminary results show that it is possible to achieve extra cost savings in this already hyper-optimized field.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Feuerstein, Esteban

Tolosa, Gabriel Hernán

SEDICI - Repositorio de la UNLP

AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.
Using Big Data Analysis to Improve
Cache Performance in Search Engines
Gabriel Tolosa1,2 and Esteban Feuerstein1
1 University of Buenos Aires, Argentina
2 National University of Luja´n, Argentina
tolosoft@unlu.edu.ar, efeuerst@dc.uba.ar
Abstract. Web Search Engines process huge amounts of data to sup-
port search but must run under strong performance requirements (to
answer a query in a fraction of a second). To meet that performance
they implement different optimization techniques such as caching, that
may be implemented at several levels. One of these caching levels is the
intersection cache, that attempts to exploit frequently occurring pairs
of terms by keeping in the memory of the search node the results of in-
tersecting the corresponding inverted lists. In this work we propose an
optimization step to decide which items should be cached and which not
by introducing the usage of data mining techniques. Our preliminary re-
sults show that it is possible to achieve extra cost savings in this already
hyper-optimized field.
Keywords: big data, search engines, intersection caching, classification
1 Introduction
Web Search Engines (WSE) are probably one of the first examples of Big Data
technology. From their beginnings, they needed to process huge amounts of data
(i.e. web pages) to build sophisticated structures that support search. The (big)
storage, analysis, management and search challenges that they proposed lead
to the development of distributed architectures and algorithms that scale up to
many billion documents. Many crawling, mining, linking and search algorithms
form the basis of modern search technologies.
To handle the increasing scale and variety of data, WSE architectures are
organized in clusters of commodity hardware that offer the possibility to easily
add/replace machines. This kind of cluster is formed by a front-end node (broker)
and a large number of search nodes that process queries in parallel. According
to [3] a web-scale search engine cluster is normally made up of more than 15.000
commodity class PCs.
Each search node holds only a fraction of the document collection and builds
local data structures (inverted indices) that are used to achieve high query
throughput. Given a cluster of P search nodes and a document collection of
C documents, each node maintains an index with information related to only CP
documents (assuming an even document distribution among nodes). Besides, to
44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 7
AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.
meet the strong performance requirements typically imposed to WSE (shortly:
to answer a query in a fraction of a second), WSEs usually implement different
optimization techniques where one of the most important is caching [2].
Caching can be implemented at several levels in a typical achitecture. At
broker level, a results cache [7] is generally maintained. This stores the final list
of results corresponding to the most frequent or recent queries. The lowest level
is posting list caching which simply stores in a memory buffer the posting lists
of popular or valuable terms. Complementarily, an intersection cache [6] may
be implemented to obtain additional performance gains. This cache attempts
to exploit frequently occurring pairs of terms by keeping in the memory of the
search node the results of intersecting the corresponding inverted lists (saving
not only disk access time but CPU time as well).
In previous work we showed that it is possible to obtain extra savings increas-
ing the efficiency and effectiveness [5] of the intersection cache and proposed
hybrid data structures [8]. In this work we go a step ahead in that direction
introducing the usage of data mining techniques to decide which items should
be cached and which not.
2 Proposal and Preliminary Results
Given a conjunctive query q = {t1, t2, t3, ..., tn} that represents a user’s infor-
mation need (each ti is a term, and the user wants the list of documents that
contain all the terms) we adopt a query resolution strategy that first decomposes
the query in pairs of terms. Each pair is checked in the Intersection Cache and
the final resolution order is given by first considering the pairs that are present
in the cache and afterwards intersecting them with the remaining ones [5]. The
intersection cache is dynamically managed using the Greedy-Dual Size (GDS)
strategy [4], and an interesting challenge is to define an access policy, i.e. to
previously decide which pairs will be allowed into the cache and which ones will
not. In this work we take up that challenge.
We model this as a classification problem with the added restriction that
we must use the minimal number of features because this decision should not
affect the overall performance of the search node, even at the expense of lower
classification ratios. Our first attempt is to identify pairs of terms that appear
only once in the query stream, and therefore caching them does not provide any
advantage. We call these singleton pairs. We consider only four features of each
pair (t1, t2):
– TFt1 and TFt2: The individual term frequencies of t1 and t2 (respectively)
in a query log used as a training set.
– DFt1 and DFt2: The document frequencies of t1 and t2 in the document
collection that the search node holds.
For our experiments we use a document collection derived from the Stanford
WebBase Project of roughly 8 million documents (appropriate for a single node)
and the well-known AOL query log. We train our classifiers with 40 million pairs
44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 8
AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.
of terms of positive and negative examples and tested several approaches. We
report here the results of two of them: Decision Trees (DT) and Random Forest
(RD) [1].
Given a trained classifier, a pair of terms (t1, t2) and the values of the ob-
served features of the pair, the decision process is quite simple:
if not (isSingleton(pair, TFt1, TFt2, DFt1, DFt2))
insert-in-cache (pair)
else
do nothing
Our baseline is the basic caching algorithm without any cache access pol-
icy (NoAP) and the ideal bound is a variation of a clairvoyant algorithm that
“knows” all the singleton pairs and avoids caching them (AP-Clair). We explore
several cache sizes and run 5 million queries over a simulation framework [5].
Figure 1 shows the results we obtain.
Fig. 1. Performance of the different policies (x-axis in log scale).
Although the classification performance was rather low (75% in the best
case) our results show that with this simple data mining approach to handle the
access to the Intersection Cache it is possible to achieve extra cost savings in
this already hyper-optimized field. The access policies achieve an improvement
of around 3% for cache sizes smaller than 8 GB (AP-Clair reduces the cost by
6% in the best case). The Decision Tree-based classifier performed slightly better
than the Random Forest method. We think this happens because, in our case,
a reduced number of features is considered. The resulting tree does not grow
enough to overfit the training set thus the advantages of the forest approach are
not significant. Presently, we are using this feedback in our algorithms to improve
the classification performance and adding new strategies to detect singleton pairs
more accurately.
44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 9
AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.
References
1. C. Aggarwal. Data Classification: Algorithms and Applications. Chapman and
Hall/CRC Press, 2014.
2. R. Baeza-Yates, A. Gionis, F. Junqueira, V. Murdock, V. Plachouras, and F. Sil-
vestri. The impact of caching on search engines. In Proc. of the 30th annual Int.
Conf. on Research and Development in Information Retrieval, SIGIR ’07, pages
183–190, USA, 2007.
3. L. A. Barroso, J. Dean, and U. Ho¨lzle. Web search for a planet: The google cluster
architecture. IEEE Micro, 23(2):22–28, Mar. 2003.
4. P. Cao and S. Irani. Cost-aware www proxy caching algorithms. In Proceedings of the
USENIX Symposium on Internet Technologies and Systems on USENIX Symposium
on Internet Technologies and Systems, USITS’97, pages 18–18, Berkeley, CA, USA,
1997. USENIX Association.
5. E. Feuerstein and G. Tolosa. Cost-aware intersection caching and processing strate-
gies for in-memory inverted indexes. In Workshop on Large-scale and Distributed
Systems for Information Retrieval (LSDS-IR 2014) at ACM International Confer-
ence on Web Search and Data Mining (WSDM’14), LSDS-IR’14, New York, NY,
USA, 2014. ACM.
6. X. Long and T. Suel. Three-level caching for efficient query processing in large web
search engines. In Proc. of the 14th Int. Conf. on World Wide Web, WWW ’05,
pages 257–266, USA, 2005.
7. E. Markatos. On caching search engine query results. Comput. Commun., 24(2):137–
143, Feb. 2001.
8. G. Tolosa, L. Becchetti, E. Feuerstein, and A. Marchetti-Spaccamela. Performance
improvements for search systems using an integrated cache of lists+intersections. In
Proceedings of 21st International Symposium of String Processing and Information
Retrieval, SPIRE’14, pages 227–235, 2014.
44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 10


English

Using Big Data Analysis to Improve Cache Performance in Search Engines

Servicio de Difusión de la Creación Intelectual

AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.Using Big Data Analysis to ImproveCache Performance in Search EnginesGabriel Tolosa1,2 and Esteban Feuerstein11 University of Buenos Aires, Argentina2 National University of Luja´n, Argentinatolosoft@unlu.edu.ar, efeuerst@dc.uba.arAbstract. Web Search Engines process huge amounts of data to sup-port search but must run under strong performance requirements (toanswer a query in a fraction of a second). To meet that performancethey implement diﬀerent optimization techniques such as caching, thatmay be implemented at several levels. One of these caching levels is theintersection cache, that attempts to exploit frequently occurring pairsof terms by keeping in the memory of the search node the results of in-tersecting the corresponding inverted lists. In this work we propose anoptimization step to decide which items should be cached and which notby introducing the usage of data mining techniques. Our preliminary re-sults show that it is possible to achieve extra cost savings in this alreadyhyper-optimized ﬁeld.Keywords: big data, search engines, intersection caching, classiﬁcation1 IntroductionWeb Search Engines (WSE) are probably one of the ﬁrst examples of Big Datatechnology. From their beginnings, they needed to process huge amounts of data(i.e. web pages) to build sophisticated structures that support search. The (big)storage, analysis, management and search challenges that they proposed leadto the development of distributed architectures and algorithms that scale up tomany billion documents. Many crawling, mining, linking and search algorithmsform the basis of modern search technologies.To handle the increasing scale and variety of data, WSE architectures areorganized in clusters of commodity hardware that oﬀer the possibility to easilyadd/replace machines. This kind of cluster is formed by a front-end node (broker)and a large number of search nodes that process queries in parallel. Accordingto [3] a web-scale search engine cluster is normally made up of more than 15.000commodity class PCs.Each search node holds only a fraction of the document collection and buildslocal data structures (inverted indices) that are used to achieve high querythroughput. Given a cluster of P search nodes and a document collection ofC documents, each node maintains an index with information related to only CPdocuments (assuming an even document distribution among nodes). Besides, to44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 7AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.meet the strong performance requirements typically imposed to WSE (shortly:to answer a query in a fraction of a second), WSEs usually implement diﬀerentoptimization techniques where one of the most important is caching [2].Caching can be implemented at several levels in a typical achitecture. Atbroker level, a results cache [7] is generally maintained. This stores the ﬁnal listof results corresponding to the most frequent or recent queries. The lowest levelis posting list caching which simply stores in a memory buﬀer the posting listsof popular or valuable terms. Complementarily, an intersection cache [6] maybe implemented to obtain additional performance gains. This cache attemptsto exploit frequently occurring pairs of terms by keeping in the memory of thesearch node the results of intersecting the corresponding inverted lists (savingnot only disk access time but CPU time as well).In previous work we showed that it is possible to obtain extra savings increas-ing the eﬃciency and eﬀectiveness [5] of the intersection cache and proposedhybrid data structures [8]. In this work we go a step ahead in that directionintroducing the usage of data mining techniques to decide which items shouldbe cached and which not.2 Proposal and Preliminary ResultsGiven a conjunctive query q = {t1, t2, t3, ..., tn} that represents a user’s infor-mation need (each ti is a term, and the user wants the list of documents thatcontain all the terms) we adopt a query resolution strategy that ﬁrst decomposesthe query in pairs of terms. Each pair is checked in the Intersection Cache andthe ﬁnal resolution order is given by ﬁrst considering the pairs that are presentin the cache and afterwards intersecting them with the remaining ones [5]. Theintersection cache is dynamically managed using the Greedy-Dual Size (GDS)strategy [4], and an interesting challenge is to deﬁne an access policy, i.e. topreviously decide which pairs will be allowed into the cache and which ones willnot. In this work we take up that challenge.We model this as a classiﬁcation problem with the added restriction thatwe must use the minimal number of features because this decision should notaﬀect the overall performance of the search node, even at the expense of lowerclassiﬁcation ratios. Our ﬁrst attempt is to identify pairs of terms that appearonly once in the query stream, and therefore caching them does not provide anyadvantage. We call these singleton pairs. We consider only four features of eachpair (t1, t2):– TFt1 and TFt2: The individual term frequencies of t1 and t2 (respectively)in a query log used as a training set.– DFt1 and DFt2: The document frequencies of t1 and t2 in the documentcollection that the search node holds.For our experiments we use a document collection derived from the StanfordWebBase Project of roughly 8 million documents (appropriate for a single node)and the well-known AOL query log. We train our classiﬁers with 40 million pairs44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 8AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.of terms of positive and negative examples and tested several approaches. Wereport here the results of two of them: Decision Trees (DT) and Random Forest(RD) [1].Given a trained classiﬁer, a pair of terms (t1, t2) and the values of the ob-served features of the pair, the decision process is quite simple:if not (isSingleton(pair, TFt1, TFt2, DFt1, DFt2))insert-in-cache (pair)elsedo nothingOur baseline is the basic caching algorithm without any cache access pol-icy (NoAP) and the ideal bound is a variation of a clairvoyant algorithm that“knows” all the singleton pairs and avoids caching them (AP-Clair). We exploreseveral cache sizes and run 5 million queries over a simulation framework [5].Figure 1 shows the results we obtain.Fig. 1. Performance of the diﬀerent policies (x-axis in log scale).Although the classiﬁcation performance was rather low (75% in the bestcase) our results show that with this simple data mining approach to handle theaccess to the Intersection Cache it is possible to achieve extra cost savings inthis already hyper-optimized ﬁeld. The access policies achieve an improvementof around 3% for cache sizes smaller than 8 GB (AP-Clair reduces the cost by6% in the best case). The Decision Tree-based classiﬁer performed slightly betterthan the Random Forest method. We think this happens because, in our case,a reduced number of features is considered. The resulting tree does not growenough to overﬁt the training set thus the advantages of the forest approach arenot signiﬁcant. Presently, we are using this feedback in our algorithms to improvethe classiﬁcation performance and adding new strategies to detect singleton pairsmore accurately.44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 9AGRANDA 2015, 1º Simposio Argentino de Grandes Datos.References1. C. Aggarwal. Data Classiﬁcation: Algorithms and Applications. Chapman andHall/CRC Press, 2014.2. R. Baeza-Yates, A. Gionis, F. Junqueira, V. Murdock, V. Plachouras, and F. Sil-vestri. The impact of caching on search engines. In Proc. of the 30th annual Int.Conf. on Research and Development in Information Retrieval, SIGIR ’07, pages183–190, USA, 2007.3. L. A. Barroso, J. Dean, and U. Ho¨lzle. Web search for a planet: The google clusterarchitecture. IEEE Micro, 23(2):22–28, Mar. 2003.4. P. Cao and S. Irani. Cost-aware www proxy caching algorithms. In Proceedings of theUSENIX Symposium on Internet Technologies and Systems on USENIX Symposiumon Internet Technologies and Systems, USITS’97, pages 18–18, Berkeley, CA, USA,1997. USENIX Association.5. E. Feuerstein and G. Tolosa. Cost-aware intersection caching and processing strate-gies for in-memory inverted indexes. In Workshop on Large-scale and DistributedSystems for Information Retrieval (LSDS-IR 2014) at ACM International Confer-ence on Web Search and Data Mining (WSDM’14), LSDS-IR’14, New York, NY,USA, 2014. ACM.6. X. Long and T. Suel. Three-level caching for eﬃcient query processing in large websearch engines. In Proc. of the 14th Int. Conf. on World Wide Web, WWW ’05,pages 257–266, USA, 2005.7. E. Markatos. On caching search engine query results. Comput. Commun., 24(2):137–143, Feb. 2001.8. G. Tolosa, L. Becchetti, E. Feuerstein, and A. Marchetti-Spaccamela. Performanceimprovements for search systems using an integrated cache of lists+intersections. InProceedings of 21st International Symposium of String Processing and InformationRetrieval, SPIRE’14, pages 227–235, 2014.44JAIIO - AGRANDA 2015 - ISSN: 2451-7569 10

http://sedici.unlp.edu.ar/bitstream/handle/10915/51952/Documento_completo.pdf?sequence=1

Using Big Data Analysis to Improve Cache Performance in Search Engines

Abstract

Similar works

Full text

Available Versions

SEDICI - Repositorio de la UNLP

Servicio de Difusión de la Creación Intelectual