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Relational DBs

Relational DBs. Basics. Formally understood. Set theoretic Originally defined with an algebra, with Selection, Projection, Join, and Union/Difference/Intersection Declarative calculus that is based on the algebra and supports large grained queries Clean implementation spec

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Relational DBs

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  1. Relational DBs • Basics

  2. Formally understood • Set theoretic • Originally defined with an algebra, with Selection, Projection, Join, and Union/Difference/Intersection • Declarative calculus that is based on the algebra and supports large grained queries • Clean implementation spec • Unambiguous optimization - with its own algebra of query parse tree transformations

  3. Structure • Schema based - we can leverage this to address volume • Tables, rows, columns, domains • Keys (PK and CK) • FKs • Triggers as a catch-all integrity constraint • Normalization for formal table minimization

  4. Semantics are in queries • Relational algebra compliant • Queries written in declarative calculus • Set-oriented • Programmers tend to follow PK/FK pairs • Query results are legal tables (Views)

  5. Also we get • Fixed size tuples for easy row-optimization • 2P transactions • Table, Row distribution • Two language based, with lowest common denominator semantics • Security • Checkpointing • Powerful query optimizers

  6. Object-relational DBs • This runs somewhat counter to NoSQL trends - we make the data types even more complex • We make domains out of type constructors • Object IDs • A row can be a tuple - or an object, with an object ID and a tuple, making all relational DBs also O-R

  7. Object-oriented DBs • No tuple rows • Blend SQL and the app language • This avoids lowest common denominator semantics • These bombed, as relational DBs were not O-O • And they are tough to optimize

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