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Use Case Couchbase. Common Use Cases. Social Gaming Couchbase stores player and game data Examples customers include: Zynga Tapjoy , Ubisoft , Tencent. Ad Targeting Couchbase stores user information for fast access Examples customers include: AOL, Mediamind , Convertro.
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Common Use Cases • Social Gaming • Couchbase stores player and game data • Examples customers include: Zynga • Tapjoy, Ubisoft, Tencent • Ad Targeting • Couchbase stores user information for fast access • Examples customers include: AOL, Mediamind, Convertro • Session store • Couchbase Server as a key-value store • Examples customers include: Concur, Sabre • Content & Metadata Store • Couchbase document store with Elastic Search • Examples customers include: McGraw Hill • User Profile Store • Couchbase Server as a key-value store • Examples customers include: Tunewiki • Mobile Apps • Couchbase stores user info and app content • Examples customers include: Kobo, Playtika • 3rd party data aggregation • Couchbase stores social media and data feeds • Examples customers include: Sambacloud • High availability cache • Couchbase Server used as a cache tier replacement • Examples customers include: Orbitz
Use Case: Content and Metadata Store • Content metadata • Content: Articles, text • Landing pages for website • Digital content: eBooks, magazine, research material • Content and Metadata Store • Flexibility to store any kind of content • Fast access to content metadata (most accessed objects) and content • Full-text Search across data set • Scales horizontally as more content gets added to the system • Fast access to metadata and content via object-managed cache • JSON provides schema flexibility to store all types of content and metadata • Indexing and querying provides real-time analytics capabilities across dataset • Integration with ElasticSearchfor full-text search • Ease of scalability ensures that the data cluster can be grown seamlessly as the amount of user and ad data grows • Types of Data • Application Requirements • Why NoSQL and Couchbase
Use Case: Content and metadata store Building a self-adapting, interactive learning portal with Couchbase
Scale! 010100100111010101010101001010101010 Serve MHE as well as third-party content Scale to millions of learners Including open content Support learning apps Self-adapt via usage data The Problem As learning move online in great numbers Growing need to build interactive learning environments that
The Challenge Backend is an Interactive Content Delivery Cloud that must: • Allow for elastic scalingunder spike periods • Ability to catalog & deliver content from many sources • Consistent low-latencyfor metadata and stats access • Require full-text search support for content discovery • Offer tunable content ranking & recommendation functions Experimented with a combination of: XML Databases In-memory Data Grids SQL/MR Engines Enterprise Search Servers
The Learning Portal • Designed and built as a collaboration between MHE Labs and Couchbase • Serves as proof-of-concept and testing harness for Couchbase + ElasticSearch integration • Available for download and further development as open source code
Couchbase 2.0 + Elasticsearch 3 1 Storefull-text articlesas well as document metadatafor image, video and text content in Couchbase Continuouslyaccept updates from Couchbase with new content & stats Combine user preferences statistics withcustom relevancy scoringto providepersonalized search results 4 2 Logsuser behaviorto calculate user preference statistics (e.g. video > text)
