Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007
DOI: 10.1145/1277741.1277775
|View full text |Cite
|
Sign up to set email alerts
|

The impact of caching on search engines

Abstract: In this paper we study the trade-offs in designing efficient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. dynamic caching, and caching query results vs. caching posting lists. Using a query log spanning a whole year we explore the limitations of caching and we demonstrate that caching posting lists can achieve higher hit rates than caching query answers. We propose a new algorithm for static caching of posting lists, which outperforms previous method… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

5
172
0

Year Published

2009
2009
2014
2014

Publication Types

Select...
3
3
2

Relationship

0
8

Authors

Journals

citations
Cited by 176 publications
(177 citation statements)
references
References 16 publications
5
172
0
Order By: Relevance
“…Performance can be increased significantly by caching search results. The effectiveness of exploiting usage data to boost performance by caching for centralised search engines has been shown previously (Fagni et al, 2006;Baeza-Yates et al, 2007b;Lempel and Moran, 2003). Skobeltsyn and Aberer (2006) use a distributed hash table to keep track of peers that have cached relevant search results for specific terms.…”
Section: Involving Fewer Peers During Index Look-ups By Global Replicmentioning
confidence: 90%
“…Performance can be increased significantly by caching search results. The effectiveness of exploiting usage data to boost performance by caching for centralised search engines has been shown previously (Fagni et al, 2006;Baeza-Yates et al, 2007b;Lempel and Moran, 2003). Skobeltsyn and Aberer (2006) use a distributed hash table to keep track of peers that have cached relevant search results for specific terms.…”
Section: Involving Fewer Peers During Index Look-ups By Global Replicmentioning
confidence: 90%
“…The partitioner determines which reducer will be responsible for processing a particular key, and the execution framework uses this information to copy the data to the right location during the shuffle and sort phase. 13 Therefore, a complete MapReduce job consists of code for the mapper, reducer, combiner, and partitioner, along with job configuration parameters. The execution framework handles everything else.…”
Section: Partitioners and Combiners 29mentioning
confidence: 99%
“…13 Whenever the reducer encounters a new join key, it is guaranteed that the associated value will be the relevant tuple from S. The reducer can hold this tuple in memory and then proceed to cross it with tuples from T in subsequent steps (until a new join key is encountered). Since the MapReduce execution framework performs the sorting, there is no need to buffer tuples (other than the single one from S).…”
Section: Relational Joins 65mentioning
confidence: 99%
See 1 more Smart Citation
“…For instance "access-ordered indices" [7] are where the documents which are more likely to be returned at higher ranks are placed before those that are not likely to be returned at higher ranks. Another example, is the caching of queries [1], in web search engines, where results pages are cached in response to popular queries in order to facilitate efficient access.…”
Section: Accessibility In Information Retrievalmentioning
confidence: 99%