FullText Search in P2P Networks

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Full-Text Search in P2P Networks

• Christof Leng
• Databases and Distributed Systems Group
• TU Darmstadt

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Content

• Short Intro to full-text search
• Full-Text search on DHTs
• Performance Comparison
• Conclusion / Outlook

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What is full-text search?

• Searching for documents containing all of a list
  of specified words
• Search for QuaP2P ? Darmstadt ? Research
• Very common operation
• Google
• Filesharing
• Wikis
• Source Code
• Document / Knowledge Management
• Can be extended to phrase search
• Search for TU Darmstadt ? Christof Leng

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Inverted Index

• Full-text search is normally solved with inverted
  indexes
• Query result is intersection of all searched word
  entries
• Stemming can reduce the number of word entries

doc1 New P2P system could provide speed
increase.
?
doc2 Similarity searches accelerate P2P
downloads by 30-70 percent.
?
doc3 I fail to see how this will make downloads
faster.
?
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Overlay Types and Full-Text Search
Inverted index on central server
Inverted index on each (super-)node
Distributed inverted index
? Challenge
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Naïve Approach

• Map inverted index to DHT
• Key Lookup for every word
• Intersect result lists at client
• Pro
• Simple
• Short latency
• Con
• Result lists may be extremely large!
• Result list sizes may vary extremely!

Darmstadt
QuaP2P
Research
Search for QuaP2P ? Darmstadt ? Research
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Zipf Distributions in Natural Text

• Some words are extremely common
• Most words are extremely uncommon
• Largest word frequency is proportional to number
  of distinct words
• ? Avoid transfering result lists before
  intersection!

Word Occurences
Rank
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Intersecting on the way

• Query least common word first
• Forward result list to next word
• Intersect on the way
• Pro
• Reduces traffic
• Con
• High latency
• Knowledge about word frequencies required
• Search for the and who (7.2 and 2.4 billion
  hits on Google each)

Darmstadt
QuaP2P
Research
Search for QuaP2P ? Darmstadt ? Research
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Using Bloom Filters

• Bloom Filters reduce result list size
• Forward Bloom Filters and return result list
  recursively
• Pro
• Reduces traffic even more (up to factor 50x)
• Con
• Even higher latency
• Getting complicated

Darmstadt
QuaP2P
Research
Search for QuaP2P ? Darmstadt ? Research
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Zipf Distributions in Query Terms, too

• Query popularity obeys Zipf Law (déjà vu!)
• This puts high load on nodes with the most
  popular keys
• Even worse, this load scales linearly with the
  network size and user activity
• The responsible nodes are randomly assigned
  (could be a modem user)
• ? Hotspots will occur

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Caching and Precomputation

• Caching
• Keep lists received for intersection
• Keep answers to popular queries
• Traffic reduction 38
• But How to ensure coherence?
• Precomputation
• Inverted index for pairs or tupels of words
• Only feasible for the most popular words
• (but most effective there anyway)
• Traffic reduction 50

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Further Optimizations

• Compression of result lists
• Adaptive Set Intersection
• Gap Compression
• Clustering of keys
• Incremental Results
• Do not return all results at once
• Should be used in conjunction with ranking
  algorithm

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Comparison of different approaches

• Yang et al compared
• DHT with Bloom Filters
• Supernode with exhaustive flooding
• Unstructured Random Walk w/o replication
• Network size 1000
• Random data set from WWW
• All approaches have strengths

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Feasibility of P2P Web Search Engine

• Li et al calculated the bandwidth usage of a
  P2P-based web search engine
• 3 billion documents (10KB each)
• 60,000 peers
• Basic DHT was 100x worse than basic Gnutella
• DHT Optimizations (e.g. Bloom Filters) made it
  competitive
• No index creation or maintenance cost included
  (60TB)
• No replica maintenance cost included

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Conclusion

• Distributed Inverted Indexes are challenging
• Implementation requires a lot of tricks
• Performance is not outstanding
• No comparison to state-of-the-art unstructured
  systems available
• Maybe even more tricks from information retrieval
  research will help
• Modeling the correct workload is really important
  for system design

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Outlook

• Examine robustness of full-text search under Zipf
  query workloads
• Implement DHT full-text search in simulator
• Compare state-of-the-art unstructured and
  structured full-text search overlays
• Improve consistency and coherence in DHT
  full-text search systems
• Implement wiki and source code management with
  full-text search for Scenario B
• Phrase search is even more challenging

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Recommended Reading

• Performance Comparison
• Li et al. On the Feasibility of Peer-to-Peer Web
  Indexing and Search. IPTPS 2003.
• Yang et al. Performance of Full Text Search in
  Structured and Unstructured Peer-to-Peer Systems.
  INFOCOM 2006.
• DHT Full-Text Search
• P. Reynolds and A. Vahdat. Efficient Peer-to-Peer
  Keyword Searching. IMC 2003.
• O. Gnawali. A Keyword Set Search System for
  Peer-to-Peer Networks. Msc. Thesis, MIT, 2002.
• Workload Modeling
• Breslau et al. Web Caching and Zipf-like
  Distributions Evidence and Implications. INFOCOM
  1999.
• Gummadi et al. Measurement, Modeling and Analysis
  of a Peer-to-Peer File-Sharing Workload. SOSP
  2003.