| SQLAlchemy | |
|---|---|
 Abbreviated SQLAlchemy Logo | |
| Original author | Michael Bayer[1] |
| Initial release | February 14, 2006; 20 years ago (2006-02-14)[2] |
| Stable release | |
| Written in | Python |
| Operating system | Cross-platform |
| Type | Object-relational mapping |
| License | MIT License[4] |
| Website | www |
| Repository | |
SQLAlchemy is anopen-sourcePython library that provides anSQL toolkit (called "SQLAlchemy Core") and anobject–relational mapper (ORM) for database interactions. It allows developers to work with databases using Python objects, enabling efficient and flexible database access.
SQLAlchemy offers tools fordatabase schema generation, querying, and object-relational mapping. Key features include:
SQLAlchemy was first released in February 2006. It has evolved to include a wide range of features for database interaction and has gained popularity among Python developers. Notable versions include:
The following example represents an n-to-1 relationship between movies and their directors. It is shown how user-defined Python classes create corresponding database tables, how instances with relationships are created from either side of the relationship, and finally how the data can be queried — illustrating automatically generated SQL queries for bothlazy and eager loading.
Creating two Python classes and corresponding database tables in the DBMS:
fromsqlalchemyimport*fromsqlalchemy.ext.declarativeimportdeclarative_basefromsqlalchemy.ormimportrelation,sessionmakerBase=declarative_base()classMovie(Base):__tablename__="movies"id=Column(Integer,primary_key=True)title=Column(String(255),nullable=False)year=Column(Integer)directed_by=Column(Integer,ForeignKey("directors.id"))director=relation("Director",backref="movies",lazy=False)def__init__(self,title=None,year=None):self.title=titleself.year=yeardef__repr__(self):returnf"Movie({self.title},{self.year},{self.director})"classDirector(Base):__tablename__="directors"id=Column(Integer,primary_key=True)name=Column(String(50),nullable=False,unique=True)def__init__(self,name=None):self.name=namedef__repr__(self):returnf"Director({self.name})"engine=create_engine("dbms://user:pwd@host/dbname")Base.metadata.create_all(engine)
One can insert a director-movie relationship via either entity:
Session=sessionmaker(bind=engine)session=Session()m1=Movie("Robocop",1987)m1.director=Director("Paul Verhoeven")d2=Director("George Lucas")d2.movies=[Movie("Star Wars",1977),Movie("THX 1138",1971)]try:session.add(m1)session.add(d2)session.commit()except:session.rollback()
alldata=session.query(Movie).all()forsomedatainalldata:print(somedata)
SQLAlchemy issues the following query to the DBMS (omitting aliases):
SELECTmovies.id,movies.title,movies.year,movies.directed_by,directors.id,directors.nameFROMmoviesLEFTOUTERJOINdirectorsONdirectors.id=movies.directed_by
The output:
Movie('Robocop',1987L,Director('Paul Verhoeven'))Movie('Star Wars',1977L,Director('George Lucas'))Movie('THX 1138',1971L,Director('George Lucas'))
Settinglazy=True (default) instead, SQLAlchemy would first issue a query to get the list of movies and only when needed (lazy) for each director a query to get the name of the corresponding director:
SELECTmovies.id,movies.title,movies.year,movies.directed_byFROMmoviesSELECTdirectors.id,directors.nameFROMdirectorsWHEREdirectors.id=%s
