1. Relational Databases: Object Relational Mappers - SQLObject
BCHB524 Lecture 22
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2. Outline Object relational mappers Minimal SQLObject example
Tables as classes, rows as instances
Advantages & disadvantages
Minimal SQLObject example
Legacy databases
Exercises
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3. Relational Databases Store information in a table Rows represent items
Columns represent items' properties or attributes
Name
Continent
Region
Surface Area
Population
GNP
Brazil
South America
776739
Indonesia
Asia
Southeast Asia
84982
India
Southern and Central Asia
447114
China
Eastern Asia
982268
Pakistan
796095
61289
United States
North America
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4. ... as Objects Objects have data members or attributes.
Store objects in a list or iterable.
Abstract away details of underlying RDBMS
c1 = Country() c1.name = 'Brazil' c1.continent = 'South America' c1.region = 'South America' c1.surfaceArea =  c1.population =  c1.gnp =  # initialize c2, ..., c6 countryTable = [ c1, c2, c3, c4, c5, c6 ] for cnty in countryTable:     if cnty.population >  :         print cnty.name, cnty.population
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5. Pros and Cons Pros: Learn one language Ease of development
Simplified joins
One set of data-types
Easy storage of higher-level objects
Can apply the power of python as necessary
Abstract away RDBMS
Distribute CPU load
Cons:
Execution speed
Sometimes forced into poor strategies
Optimal SQL construct may be impossible
Tend not to take advantage of RDBMS quirks.
Can be difficult to apply to legacy databases
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6. SQLObject Just one of many object-relational mappers
Each tool makes different tradeoffs in
Table/row/attribute abstraction
How much SQL you need to know
Overhead
Ease of adapting to legacy databases
SQLObject is almost completely devoid of SQL and is almost entirely "objecty".
See
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7. Minimal SQLObject Example: Define the database model (model.py)
from sqlobject import * import os.path dbfile = 'myworld.db3' # Magic formatting for database URI conn_str = os.path.abspath(dbfile) conn_str = 'sqlite:'+ conn_str sqlhub.processConnection = connectionForURI(conn_str) class Country(SQLObject):     name = StringCol()     continent = StringCol()     region = StringCol()     surfaceArea = FloatCol()     population = IntCol()     gnp = FloatCol()
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8. Minimal SQLObject Example: Populate the database
from model import Country # Initialize the table Country.createTable() # Add some rows c = Country(name="Brazil", continent="South America",             region="South America", surfaceArea= ,             population= , gnp=776739) c = Country(name="China", continent="Asia",             region="Eastern Asia", surfaceArea= ,             population= , gnp=982268) # ...
c = Country(name="United States", continent="North America",             region="North America", surfaceArea= ,             population= , gnp= ) # Retrieve and print all countries for c in Country.select():     print c.id, c.name, c.continent, c.gnp
from model import Country
# Initialize the table
Country.createTable()
# Add some rows
c = Country(name="Brazil", continent="South America",
region="South America", surfaceArea= ,
population= , gnp=776739)
c = Country(name="Indonesia", continent="Asia",
region="Southeast Asia", surfaceArea= ,
population= , gnp=84982)
c = Country(name="India", continent="Asia",
region="Southern and Central Asia",
surfaceArea= , population= ,
gnp=447114)
c = Country(name="China", continent="Asia",
region="Eastern Asia", surfaceArea= ,
population= , gnp=982268)
c = Country(name="Pakistan", continent="Asia",
surfaceArea=796095, population= ,
gnp=61289)
c = Country(name="United States", continent="North America",
region="North America", surfaceArea= ,
population= , gnp= )
# Retrieve and print all countries
for c in Country.select():
print c.id, c.name, c.continent, c.gnp
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9. Minimal SQLObject Example: Access/Change the database
from model import Country # Change country #6 c = Country.get(6) c.name = 'United States of America' # Retrieve and print all countries for c in Country.select():     print c.id, c.name, c.continent, c.gnp
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10. Minimal SQLObject Example: Access the rows as objects
from model import Country # Select countries with more than 500,000,000 in population for c in Country.select(Country.q.population >=  ):     print "A:", c.id, c.name, c.population # Select countries that start with 'U' for c in Country.select(Country.q.name.startswith("U")):     print "B:", c.id, c.name, c.population # Lookup by id, exactly 1 country with each id c = Country.get(5) print "C:", c.id, c.name, c.population # Get exception for bad id # c = Country.get(100) # Shortcut for select, countries with continent == 'Asia' for c in Country.selectBy(continent = 'Asia'):     print "D:", c.id, c.name, c.population
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11. Legacy databases
If the legacy database is well-structured, SQLObject can figure out (most of) the definitions
If there is no id column...
Need to tell SQLObject what to use for the ID.
May need to specify the id at instantiation time.
Have to fill in MultipleJoins and ForeignKeys yourself
Need to declare which columns in two different tables should correspond.
Enables SQLObject to make relationships explicit
Enables SQLObject to turn joins into lists
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12. Legacy databases
from sqlobject import * import os.path dbfile = 'taxa.db3' conn_str = os.path.abspath(dbfile) conn_str = 'sqlite:'+ conn_str sqlhub.processConnection = connectionForURI(conn_str) class Taxonomy(SQLObject):     class sqlmeta:         idName = "tax_id"         fromDatabase = True     names = MultipleJoin('Name', joinColumn="tax_id") class Name(SQLObject):     class sqlmeta:         fromDatabase = True     taxa = ForeignKey('Taxonomy', dbName="tax_id")
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13. Legacy databases
# Set up data-model from model import * # get homo sapiens hs1 = Taxonomy.get(9606) # select the Taxonomy object # with scientific name Homo sapiens hs2 = Taxonomy.selectBy(scientificName='Homo sapiens')[0] # get the name human try:     hsname = Name.selectBy(name='human')[0] except IndexError:     print "Can't find name 'human'"     sys.exit(1) # get the Taxonomy object from the Name object # Uses the magic Foreign Key attribute hs3 = hsname.taxa # hs1, hs2, hs3 the same! print hs1 print hs2 print hs3
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14. Legacy databases
# Set up data-model from model import * # get homo sapiens hs = Taxonomy.get(9606) # find rows in the Name table with taxa the same as hs # Use ForeignKey to create condition, equality test # between objects condition = (Name.q.taxa == hs) for n in Name.select(condition):     print n # find rows in the Name table corresonding to hs # Easy shortcut, using MultipleJoin iterable for n in hs.names:     print n.name, "|", n.nameClass # More general conditions condition = Name.q.name.startswith('Da') for n in Name.select(condition):     print n.name, "|", n.nameClass
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15. Exercises Read through the SQLObject documentation
Write a python program using SQLObject to lookup the scientific name for a user-supplied organism name.
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