View this webinar presented by Search Technologies' Chief Architect Paul Nelson on cloud search and a Wikipedia use case. Webinar given in conjunction with Amazon Cloud Search. Search Technologies provides implementation and consulting services for Amazon CloudSearch. For further information, see http://www.searchtechnologies.com/amazon-cloudsearch-services.html http://www.searchtechnologies.com/

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Searching Wikipedia with Amazon CloudSearch

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Agenda
• Project Background
• High-level Architecture
• Summary & Observations

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Project Background
• Amazon contracted with Search Technologies
to help with beta-testing, prior to the launch of
Amazon CloudSearch
• Decision to use Wikipedia as a convenient data
set for testing purposes
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High-level Architecture
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Indexing
• Wikipedia provides content in a series of large xml files
• Amazon CloudSearch ingests xml in a specified form
• Various content processing tasks to perform
• Splitting into individual documents
• Date normalization
• Metadata extraction & mapping
• Cleanup, etc.
• We used Aspire for these tasks
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Aspire in Brief
• Based on Apache Felix / OSGi
• Thread-safe, multi-threaded, distributable
• Any number of pipelines, conditional branching
• Plug-in components individually testable & upgradable
• In use with FAST ESP, FS4SP, Solr, Amazon CloudSearch, GSA.
• Tested with Elasticsearch and SP 2013
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XML Input
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Indexing
• Streaming Wikipedia Dump Files directly into
CloudSearch
• 500 docs/second achieved without much effort
• Using 4 x XL instances of CloudSearch
• 1 x XL EC2 instance for Aspire
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Searching
• Amazon CloudSearch provides a RESTful/XML
interface for search purposes
• For the Wikipedia project, we needed a UI
• Chose to use Twigkit
• Wrote a Java API for CloudSearch
• The Java API is freely downloadable (with source) at
http://www.searchtechnologies.com/java-api-amazon-
cloudsearch.html
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Searching
• Supports navigators and
relevancy customization
• E.g. a “PageRank” style link
analysis was performed
• Limits set high: E.g.
retrieve 500,000 results in a
single list, delivered in just a
few seconds
• Very useful for analysis
applications
• So, what does it look like?
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Summary & Observations
• A capable and scalable “raw” engine
• xml in, RESTful/xml out
• Easy to set up – much the same as an EC2
instance
• Elastic scalability
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Summary & Observations
• Cost effective
• From $75 per month, including management /
maintenance
• Extremely convenient
• Switch on / off at leisure
• Promotes experimentation & agility
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