Improving the Efficiency of Multi-site Web Search Engines

Transcription

1 Improving the Efficiency of Multi-site Web Search Engines Xiao Bai ( xbai@yahoo-inc.com) Yahoo Labs Joint work with Guillem Francès Medina, B. Barla Cambazoglu and Ricardo Baeza-Yates July 15, 2014

2 Web search is difficult 2 Size of the Web Cost of data centers Diversity of users 130+ billions pages Hardware investment Different information need Constantly changing Energy consumption Little patience

3 Multi-site Web search engine Fully replicated index Easy to implement Vertical scalability Partially replicated index Faster response Horizontal scalability 3

4 Challenges in multi-site web search Site 1 Query processor Indexer Crawler Web Query processor Indexer Crawler Crawler Indexer Query processor Site 2 Site N Distributed Web crawling Which site is the best to crawl a page? Index partitioning Which site is the best to index a page? Improve query locality Reduce query response time Query forwarding Which sites contain the best-matching pages? Index replication Which pages to replicate in each site? Distributed result caching Which site is the best to cache a query result? 4

5 System architecture Geographically distributed search sites Document-based index partition Easy to build, good load balancing, better fault tolerance One document is indexed in only one site Document language, domain, server IP, etc. 5

6 Query forwarding Improve result quality by serving documents indexed in remote sites w1 (d1, 0.87) (d2, 0.59) (d3, 0.32) Q: w1,top-2 w1 (d4, 0.25) (d5, 0.18) w1 (d10, 0.11) w1 (d7, 0.70) (d8, 0.23) (d9, 0.07) Accurate query forwarding is important False negative forwarding: decrease result quality False positive forwarding: increase query response time & system workload Threshold-based algorithms Thresholds periodically exchanged or learnt from previous query processing 6

7 Machine-learned query forwarder m m binary classifier for each pair of sites Q: w1,top-2??? A single classifier Pre-retrieval and post-retrieval classifiers: 100 decision trees Pre-retrieval confidence threshold C q Pre-retrieval classifier < F, c > Yes c > C F Pre-retrieval features Term lengths ML Term query IDFs forwarder Term scores Query language Query popularity Query performance No Local query processor Local query score F Post-retrieval classifier Post-retrieval features Local query score LP forwarder decision 7

8 Performance of machine-learned query forwarder 5 sites: 200M web pages + 5M training queries + 2M test queries Accuracy 1-FN-FP Query locality Fraction of queries without forwarding Machine-learned Machine-learned Oracle Baseline: Linear programming Baseline: Linear programming 8

9 Document replication Objective Select replicated subset of documents that maximizes the fraction of queries whose top-k bestmatching documents R(q) are all indexed in local site with a given budget b% S i Replication strategies Identical replication Global budget Individual replication Global budget Individual replication Local budget b%/4 b% b% 9

10 Document selection heuristics 0-1 knapsack problem Utility of document d D \ D i for site S i $ & u i (d) = % & ' freq(q j ) Ri(q j ) s(d), if d R(q ) j q j Q i 0, otherwise Application in different document replication strategies Utility Budget Identical Individual + Global budget Individual + Local budget u(d) = 1 i m u i (d) b % size(d) m u i (d), S i b% size(d) u i (d) b% size(d i ) 10

11 Performance of document replication Comparison of different strategies Query locality Impact on query forwarding Query locality Individual+Global Identical Individual+Local 11

12 Result caching Improve query locality by caching previously processed query results Basic assumptions Cache with unlimited size TTL-based invalidation Cache strategies: where to cache a query? Local cache state-of-the-art Mechanism A query is cached in the site it is issued to Local TTL Pros Easy to implement Cons Redundant processing for popular queries Global cache Mechanism A query is cached in all sites TTL w.r.t. the local site receiving the query Pros Highest cache hit rate Cons Redundant transmission among sites 12

13 Result caching strategies Cache strategies: where to cache a query? Partial cache Mechanism Forward cache Mechanism A query is cached in the site it is issued to A query is cached in the site it is issued to and the sites it is forwarded to A pointer to the query is cached in the site receiving the forwarded query TTL w.r.t. the local site receiving the query TTL w.r.t. the local site receiving the query Pros Reduce redundant transmission among sites Pros Further reduce redundant transmission Cons Reduce cache hit rate Cons Increase query response time Q: w1 13 Q: w1

14 Performance of result caching Comparison of different strategies Cache hit rate Impact of global cache Query locality 14

15 User experience Result quality Centralized top-10 as ground-truth Caching has no impact on result quality Query response time Estimation Processing time: size of index Transmission time: geographical distance Setting Identical replication: 8% Global cache: TTL=2h 15

16 Conclusions First study on the interplay among the key components of multi-site search engines Query forwarding Almost the same query response time as Oracle Slightly decreases result quality of Oracle Document replication Significantly reduces query response time Improves result quality Result caching Significantly reduces query response time No impact on result quality Multi-site search engine is very promising as an alternative to traditional search engines 16

17 Thank you! Improving the Efficiency of Multi-site Web Search Engines Xiao Bai 17