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Related: Pure Ruby Relational Algebra Engine

Related

Related is a Ruby relational algebra engine. Define relations, add data to them, and then perform relational algebra operations on them.

When I was learning relational algebra, I had trouble visualizing what my operations were doing, and I had no quick way of testing them out without spinning up a database. Even then, SQL didn’t map very directly to the underlying principles I was trying to learn.

Enter Related. Related was written to provide students and others with a quick scratchpad to run relational algebra operations.

Each of Codd’s Primitives ( selection , projection , cross_product , union , and projection ) are implemented, meaning you can in theory perform any operation possible. natural_join is also implemented, and I’ll work to add more over time.

Example

Define relations like this:

require 'related'  people = Relation.new do |r|   r.schema = Schema.new(name: String, age: Integer, gender: String)   r.add_tuple ['Amy', 16,  'female']   r.add_tuple ['Ben', 21,  'male']   r.add_tuple ['Cal', 33,  'male']   r.add_tuple ['Dan', 13,  'male']   r.add_tuple ['Eli', 45,  'male']   r.add_tuple ['Fay', 21,  'female']   r.add_tuple ['Gus', 24,  'male']   r.add_tuple ['Hil', 30,  'female']   r.add_tuple ['Ian', 18,  'male'] end  favorites = Relation.new do |r|   r.schema = Schema.new(name: String, pizza: String)   r.add_tuple ['Amy', 'mushroom']   r.add_tuple ['Amy', 'pepperoni']   r.add_tuple ['Ben', 'cheese']   r.add_tuple ['Ben', 'pepperoni']   r.add_tuple ['Cal', 'supreme']   r.add_tuple ['Dan', 'cheese']   r.add_tuple ['Dan', 'mushroom']   r.add_tuple ['Dan', 'pepperoni']   r.add_tuple ['Dan', 'sausage']   r.add_tuple ['Dan', 'supreme']   r.add_tuple ['Eli', 'cheese']   r.add_tuple ['Eli', 'supreme']   r.add_tuple ['Fay', 'mushroom']   r.add_tuple ['Gus', 'cheese']   r.add_tuple ['Gus', 'mushroom']   r.add_tuple ['Gus', 'supreme']   r.add_tuple ['Hil', 'cheese']   r.add_tuple ['Hil', 'supreme']   r.add_tuple ['Ian', 'pepperoni']   r.add_tuple ['Ian', 'supreme'] end  menus = Relation.new do |r|   r.schema = Schema.new(pizzeria: String, pizza: String, price: Numeric)   r.add_tuple ['Chicago Pizza', 'cheese',  7.75]   r.add_tuple ['Chicago Pizza', 'supreme', 8.5]   r.add_tuple ['Dominos', 'cheese', 9.75]   r.add_tuple ['Dominos', 'mushroom',  11]   r.add_tuple ['Little Caesars',  'cheese',  7]   r.add_tuple ['Little Caesars',  'mushroom',  9.25]   r.add_tuple ['Little Caesars',  'pepperoni', 9.75]   r.add_tuple ['Little Caesars',  'sausage', 9.5]   r.add_tuple ['New York Pizza',  'cheese',  7]   r.add_tuple ['New York Pizza',  'pepperoni', 8]   r.add_tuple ['New York Pizza',  'supreme', 8.5]   r.add_tuple ['Pizza Hut', 'cheese',  9]   r.add_tuple ['Pizza Hut', 'pepperoni', 12]   r.add_tuple ['Pizza Hut', 'sausage', 12]   r.add_tuple ['Pizza Hut', 'supreme', 12]   r.add_tuple ['Straw Hat', 'cheese',  9.25]   r.add_tuple ['Straw Hat', 'pepperoni', 8]   r.add_tuple ['Straw Hat', 'sausage', 9.75] end

Then, perform operations on them.

Say you wanted to find the names of all females that would be happy eating at Straw Hat. You would need to join all three relations, select tuples on the condition that gender = 'female' and pizzeria = 'Straw Hat' , then project name on to the resulting relation.

With Related, I could do:

joined_relations = people.natural_join(favorites).natural_join(menus)  happy_females = joined_relations.select do |t|   #Each tuple is passed to this block, and behaves like a hash   t[:gender] == 'female' && t[:pizzeria] == 'Straw Hat' end  names = happy_females.project(:name)  puts names

This’ll output:

Relation | Name | _________ | Amy  | | Hil  | 

Limitations

Be warned: not only is Related a work in progress, it’s also meant as a fun side project.

First and foremost, relations are all anonymous. Therefore, you can’t get the cross product of relations with any matching attributes. Make sure to rename those first ( natural_join takes care of that for you).

While I tried to grab any low-hanging performance fruit, performamce and efficiency hasn’t been focused on. Think twice before using this on relations thousands of tuples big.

Todo

  • More Operations
    • Theta Joins
    • Semi Joins
    • Division
    • Outer Joins?
  • Easier to Import/Export Data
    • Read/Write CSV?
  • Better DSL
    • particularly for defining Relations

Contributing

Theta-joins, Semi-joins, division, outer joins… There’s plenty left that can be implemented for convenience and illustration purposes. There’s also plenty of refactoring that can be done, as well as the limitations above that can be addressed. Feel free to open an issue if something is important to you, or submit pull requests and I’ll happily review them.

I try to stay consistent withthese style guides, but there’s always room for improvement!

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