| Path: | doc/cheat_sheet.rdoc | 
| Last Update: | Sat Oct 02 06:22:54 -0600 2010 | 
  require 'rubygems'
  require 'sequel'
  DB = Sequel.sqlite('my_blog.db')
  DB = Sequel.connect('postgres://user:password@localhost/my_db')
  DB = Sequel.postgres('my_db', :user => 'user', :password => 'password', :host => 'localhost')
  DB = Sequel.ado('mydb')
Without a filename argument, the sqlite adapter will setup a new sqlite database in memory.
DB = Sequel.sqlite
require 'logger' DB = Sequel.sqlite '', :loggers => [Logger.new($stdout)] # or DB.loggers << Logger.new(...)
  DB.run "CREATE TABLE users (name VARCHAR(255) NOT NULL, age INT(3) NOT NULL)"
  dataset = DB["SELECT age FROM users WHERE name = ?", name]
  dataset.map(:age)
  DB.fetch("SELECT name FROM users") do |row|
    p r[:name]
  end
dataset = DB[:items] dataset = DB.from(:items)
dataset = DB[:managers].where(:salary => 5000..10000).order(:name, :department)
dataset.insert(:name => 'Sharon', :grade => 50)
  dataset.each{|r| p r}
  dataset.all #=> [{...}, {...}, ...]
  dataset.first
  dataset.filter(~:active).delete
  dataset.filter('price < ?', 100).update(:active => true)
  dataset.map{|r| r[:name]}
  dataset.map(:name) # same as above
  dataset.inject(0){|sum, r| sum + r[:value]}
  dataset.sum(:value) # same as above
  dataset.filter(:name => 'abc')
  dataset.filter('name = ?', 'abc')
  dataset.filter{|o| o.value > 100}
  dataset.exclude{|o| o.value <= 100}
  dataset.filter(:value => 50..100)
  dataset.where{|o| (o.value >= 50) & (o.value <= 100)}
  dataset.where('value IN ?', [50,75,100])
  dataset.where(:value=>[50,75,100])
  dataset.filter(:name => 'abc').first
  dataset[:name => 'abc']
  dataset.where('price > (SELECT avg(price) + 100 FROM table)')
  dataset.filter{|o| o.price > dataset.select(o.avg(price) + 100)}
  DB[:items].filter{|o| o.price < 100}.sql
  #=> "SELECT * FROM items WHERE (price < 100)"
  DB[:items].filter(:name.like('AL%')).sql
  #=> "SELECT * FROM items WHERE (name LIKE 'AL%')"
There‘s support for nested expressions with AND, OR and NOT:
  DB[:items].filter{|o| (o.x > 5) & (o.y > 10)}.sql
  #=> "SELECT * FROM items WHERE ((x > 5) AND (y > 10))"
  DB[:items].filter({:x => 1, :y => 2}.sql_or & ~{:z => 3}).sql
  #=> "SELECT * FROM items WHERE (((x = 1) OR (y = 2)) AND (z != 3))"
You can use arithmetic operators and specify SQL functions:
  DB[:items].filter((:x + :y) > :z).sql
  #=> "SELECT * FROM items WHERE ((x + y) > z)"
  DB[:items].filter{|o| :price - 100 < o.avg(:price)}.sql
  #=> "SELECT * FROM items WHERE ((price - 100) < avg(price))"
dataset.order(:kind) dataset.reverse_order(:kind) dataset.order(:kind.desc, :name)
dataset.limit(30) # LIMIT 30 dataset.limit(30, 10) # LIMIT 30 OFFSET 10
DB[:items].left_outer_join(:categories, :id => :category_id).sql #=> "SELECT * FROM items LEFT OUTER JOIN categories ON categories.id = items.category_id" DB[:items].join(:categories, :id => :category_id).join(:groups, :id => :items__group_id) #=> "SELECT * FROM items INNER JOIN categories ON categories.id = items.category_id INNER JOIN groups ON groups.id = items.group_id"
dataset.count #=> record count dataset.max(:price) dataset.min(:price) dataset.avg(:price) dataset.sum(:stock) dataset.group(:category).select(:category, :AVG.sql_function(:price))
  dataset.update(:updated_at => :NOW.sql_function)
  dataset.update(:updated_at => 'NOW()'.lit)
  dataset.update(:updated_at => "DateValue('1/1/2001')".lit)
  dataset.update(:updated_at => :DateValue.sql_function('1/1/2001'))
  DB.create_table :items do
    primary_key :id
    String :name, :unique => true, :null => false
    boolean :active, :default => true
    foreign_key :category_id, :categories
    Time :created_at
    index :grade
  end
  DB.drop_table :items
  DB.create_table :test do
    String :zipcode
    enum :system, :elements => ['mac', 'linux', 'windows']
  end
DB[:items].select(:name.as(:item_name)) DB[:items].select(:name___item_name) DB[:items___items_table].select(:items_table__name___item_name) # => "SELECT items_table.name AS item_name FROM items AS items_table"
  DB.transaction do
    dataset.insert(:first_name => 'Inigo', :last_name => 'Montoya')
    dataset.insert(:first_name => 'Farm', :last_name => 'Boy')
  end # Either both are inserted or neither are inserted
Database#transaction is re-entrant:
  DB.transaction do # BEGIN issued only here
    DB.transaction
      dataset << {:first_name => 'Inigo', :last_name => 'Montoya'}
    end
  end # COMMIT issued only here
Transactions are aborted if an error is raised:
  DB.transaction do
    raise "some error occurred"
  end # ROLLBACK issued and the error is re-raised
Transactions can also be aborted by raising Sequel::Rollback:
  DB.transaction do
    raise(Sequel::Rollback) if something_bad_happened
  end # ROLLBACK issued and no error raised
Savepoints can be used if the database supports it:
  DB.transaction do
    dataset << {:first_name => 'Farm', :last_name => 'Boy'} # Inserted
    DB.transaction(:savepoint=>true) # This savepoint is rolled back
      dataset << {:first_name => 'Inigo', :last_name => 'Montoya'} # Not inserted
      raise(Sequel::Rollback) if something_bad_happened
    end
    dataset << {:first_name => 'Prince', :last_name => 'Humperdink'} # Inserted
  end
  dataset.sql #=> "SELECT * FROM items"
  dataset.delete_sql #=> "DELETE FROM items"
  dataset.where(:name => 'sequel').exists #=> "EXISTS ( SELECT * FROM items WHERE name = 'sequel' )"
  dataset.columns #=> array of columns in the result set, does a SELECT
  DB.schema(:items) => [[:id, {:type=>:integer, ...}], [:name, {:type=>:string, ...}], ...]