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Introducing Stig: A New Open Source, Non-relational, Distributed Graph Database Developed at Tagged

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Introducing Stig: A New Open Source, Non-relational, Distributed Graph Database Developed at Tagged

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This is a teaser talk for the "Stig: Social Graphs and Discovery at Scale" session on Wednesday. We'll give a quick introduction to the concepts behind the Stig database, such as graph traversal, scalability, and eventual consistency. Learn how Stig can work for you!

This is a teaser talk for the "Stig: Social Graphs and Discovery at Scale" session on Wednesday. We'll give a quick introduction to the concepts behind the Stig database, such as graph traversal, scalability, and eventual consistency. Learn how Stig can work for you!

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Introducing Stig: A New Open Source, Non-relational, Distributed Graph Database Developed at Tagged

  1. 1. Introducing Stig Architect of Scalable Infrastructure - Jason Lucas
  2. 2. Stig is… • A very large-scale non-SQL database, – But it speaks SQL and can emulate many aspects of an SQL-based system. • A graph-oriented data store, – But it can also look like a key-value store, a set of relational tables, or a file system. • A foundation for building general web applications, – B t it particularly excels at social apps. But ti l l l t i l • A general-purpose programming language in its own right, – But can be used from other languages like PHP, Perl, Java, and Python. • A distributed system with a shared-nothing architecture, di t ib t d t ith h d thi hit t – But offers developers a stable, consistent, easy-to-manage path to data. • A solution to the complex problem of CAP-limited storage systems, – But it empowers the developer rather than burdening him.
  3. 3. Is Your Project… • Graph-shaped? G h h d? • Really huge? R ll h ? – Representing graphs as graphs – The store scales very close to (instead of as tables or key pairs) linearly, so more data just means simplifies y p your life. more machines. – Stig graphs are fat, meaning they're – The size of the cluster doesn't really any number of simultaneous, generally doesn't affect the intersecting graphs, so go nuts. performance of individual operations. • Transactional? – Reliably atomic state transitions • Deeply analytic? also simplify your life. – Use inferences to describe relations – Asynchronous transaction y and conditions you're interested in. management makes it more – Build up arbitrarily complex libraries tolerable. of inference to extract meaning from data. • Real-time? – C Control the influence of updates f f with shared points-of-view. – Never be blocked waiting for the database to respond.
  4. 4. Come see us • Stig: Social Graphs & Discovery at Scale 10:30 – 11:20AM • Lunch ‘N Learn – Dive into the Stig query gq y language and API - 1:15 – 2:15PM • Online – www.stigdb.org – jlucas@tagged.com

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