Use Case Couchbase Common Use Cases Social Gaming

Use Case Couchbase

Common Use Cases Social Gaming • Couchbase stores player and game data • Examples customers include: Zynga • Tapjoy, Ubisoft, Tencent Mobile Apps • Couchbase stores user info and app content • Examples customers include: Kobo, Playtika Ad Targeting • Couchbase stores user information for fast access • Examples customers include: AOL, Mediamind, Convertro Session store • Couchbase Server as a keyvalue store • Examples customers include: Concur, Sabre Content & Metadata Store User Profile Store • Couchbase Server as a key -value store • Examples customers include: Tunewiki High availability cache • Couchbase Server used as a cache tier replacement • Examples customers include: Orbitz • Couchbase document store with Elastic Search • Examples customers include: Mc. Graw Hill 3 rd party data aggregation • Couchbase stores social media and data feeds • Examples customers include: Sambacloud

Use Case: Content and Metadata Store Types of Data • Content metadata • Content: Articles, text • Landing pages for website • Digital content: e. Books, magazine, research material Application Requirements • 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 Why No. SQL and Couchbase • 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 Elastic. Search for full-text search • Ease of scalability ensures that the data cluster can be grown seamlessly as the amount of user and ad data grows

Mc. Graw Hill Education Labs Learning portal

Use Case: Content and metadata store Building a self-adapting, interactive learning portal with Couchbase

The Problem As learning move online in great numbers Growing need to build interactive learning environments that 0101001001 1101010 101010 Scale! Scale to millions of learners Serve MHE as well as third-party content Including open content Support learning apps Self-adapt via usage data

The Challenge Backend is an Interactive Content Delivery Cloud that must: • Allow for elastic scaling under spike periods • Ability to catalog & deliver content from many sources • Consistent low-latency for 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 + Elastic. Search integration • Available for download and further development as open source code

Couchbase 2. 0 1 Store full-text articles as well as document metadata for image, video and text content in Couchbase 2 Logs user behavior to calculate user preference statistics (e. g. video > text) + 3 4 Elasticsearch Continuously accept updates from Couchbase with new content & stats Combine user preferences statistics with custom relevancy scoring to provide personalized search results

Architecture

Thank you anil@couchbase. com @anil. kumar 1129