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Diffstat (limited to 'lib/sqlalchemy/ext/sqlsoup.py')
| -rw-r--r-- | lib/sqlalchemy/ext/sqlsoup.py | 11 |
1 files changed, 10 insertions, 1 deletions
diff --git a/lib/sqlalchemy/ext/sqlsoup.py b/lib/sqlalchemy/ext/sqlsoup.py index f9c13d388..390a2c16d 100644 --- a/lib/sqlalchemy/ext/sqlsoup.py +++ b/lib/sqlalchemy/ext/sqlsoup.py @@ -159,6 +159,12 @@ to disambiguate columns with their table name: >>> db.with_labels(join1).c.keys() ['users_name', 'users_email', 'users_password', 'users_classname', 'users_admin', 'loans_book_id', 'loans_user_name', 'loans_loan_date'] +You can disambiguate just one table in a join by applying labels to that table, +and joining on the returned object: + >>> labeled_loans = db.with_labels(db.loans) + >>> db.join(db.users, labeled_loans, isouter=True).c.keys() + ['name', 'email', 'password', 'classname', 'admin', 'loans_book_id', 'loans_user_name', 'loans_loan_date'] + Advanced Use ============ @@ -196,7 +202,10 @@ just needs to be unique within the select, and not necessarily correspond to a >>> years_with_count.select_by(published_year='1989') [MappedBooks(published_year='1989',n=1)] -Obviously if we just wanted to get a list of counts associated with book years once, raw SQL is going to be less work. The advantage of mapping a Select is reusability, both standalone and in Joins. (And if you go to full SQLAlchemy, you can perform mappings like this directly to your object models.) +Obviously if we just wanted to get a list of counts associated with book years +once, raw SQL is going to be less work. The advantage of mapping a Select is +reusability, both standalone and in Joins. (And if you go to full SQLAlchemy, +you can perform mappings like this directly to your object models.) Raw SQL |