I am new to sqlAlchemy and I wonder if there is a way create a class that would be mapped to the existing table in DB without specifying any columns of the table (but all columns could be accessed as attributes of the object)?
This is how I generated models for flask-sqlalchemy which use MS SQL.
flask-sqlacodegen --flask mssql+pymssql://<username>:<password>#<server>/<database> --tables <table_names>> db_name.py
you have to install flask-sqlacodegen and pymssql though.
Have recently came across this issue. Try steps below:
from sqlalchemy import create_engine, MetaData, Table
from sqlalchemy.orm import mapper, sessionmaker
class User(object): # class which can can act as ORM class
pass
dbPath = 'places.sqlite'
engine = create_engine('sqlite:///%s' % dbPath, echo=True) # create engine
metadata = MetaData(engine)
user_table= Table('user_existing_class', metadata, autoload=True) # create a Table object
mapper(User, user_table) # map Table to ORM class
Session = sessionmaker(bind=engine)
session = Session()
res = session.query(User).all()
res[1].name
Related
I am working with SQLAlchemy and I am getting confused between Engine, Metadata, Base, and Sessions.
I did find some useful links: here, here and here, but I think it would help to know why the code below is not working.
import sqlalchemy as sqlalc
from sqlalchemy import Column, INTEGER, Integer,ForeignKey, Table, VARCHAR, TIMESTAMP, MetaData, create_engine, inspect, cast, select, SmallInteger
from sqlalchemy.orm import relationship, backref, sessionmaker
from sqlalchemy.ext.declarative import declarative_base
url = 'mysql://info#127.0.0.1/info'
engine = create_engine(url, echo=True)
metadata = MetaData()
metadata.bind = engine
metadata.create_all(engine)
connection = engine.connect()
Base = declarative_base()
Base.metadata.create_all()
Session = sessionmaker(bind=engine)
session = Session()
When I execute, I get the following error:
sqlalchemy.exc.UnboundExecutionError: MetaData object is not bound to an Engine or Connection. Execution can not proceed without a database to execute against.
Definitions
Engine: an abstraction for a database connection, or connection pool
Metadata: an object that knows about database objects, primarily tables
Base: the base class for ORM models
Session: an object that keeps track of new, removed and changed ORM model instances while they are in use; it sends changes to the database (Session.commit()) or reverts them (Session.rollback())
Code
This code
metadata = MetaData()
metadata.bind = engine
metadata.create_all(engine)
"works" because you have bound (or associated) an engine with your MetaData instance: the engine enables connection to a database to create any tables that have been registered with the MetaData instance. However you should be aware that binding like this is deprecated in SQLAlchemy v1.4. The preferred approach is to pass the engine to create_all like this:
metadata = MetaData()
metadata.create_all(engine)
This code
Base = declarative_base()
Base.metadata.create_all()
does not work because there is no engine associated with Base.metadata. As before, the preferred approach is to pass an engine to create_all:
Base.metadata.create_all(engine)
In both cases, executing create_all will do nothing unless tables have been registered with the metadata instances. For core, this usually happens through table definitions:
metadata = MetaData()
t = Table(
't',
metadata, # <- this will associate the table with the metadata
Column(...),
)
In the ORM, (using declarative mapping), the association is made by inheriting from Base:
Base = orm.declarative_base()
class MyModel(Base): # <- this will register mytable with Base's metadata
__tablename__ = 'mytable'
...
What I'm trying to do:
I have a general huge set of models in my Django (v2.3.3) application in PostgreSQL DB, but now for a specific task I need to create a rather complicated aggregation of DB objects. And it would be much easier to work with them if I could only in that thread/process that handles this specific web-request create an in-memory SQLite DB, define a new set of model-classes there (without foreign keys to the global set of models of course), create some objects in that DB, do my calculations and kill that DB upon the response serving. For consistency I would like to use Django models for this small DB as well.
Is this possible? Or do you have some better ideas how to approach this?
Eventually I've ended up with SQLAlchemy + in-memory SQLite solution:
from sqlalchemy import Column, Integer, String, ForeignKey, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, sessionmaker, backref
Base = declarative_base()
engine = create_engine('sqlite:///:memory:')
Session = sessionmaker(bind=engine)
session = Session()
class Node(Base):
"""A node entity in sankey diagram."""
__tablename__ = 'node'
id = Column(Integer, primary_key=True)
name = Column(String) # unique - might include parts from event.extra_data
event = Column(String) # not unique - totally equeals to event.event
Base.metadata.create_all(engine)
I create a very simple database with sqlalchemy as follows:
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
Base = declarative_base()
class Person(Base):
__tablename__ = 'person'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
engine = create_engine('sqlite:///sqlalchemy_example.db')
# Create all tables in the engine. This is equivalent to "Create Table"
# statements in raw SQL.
Base.metadata.create_all(engine)
Base.metadata.bind = engine
DBSession = sessionmaker(bind=engine)
session = DBSession()
# Insert a Person in the person table
new_person = Person(name='new person')
session.add(new_person)
session.commit()
and then I tried to read it using pyDAL reference.
from pydal import DAL, Field
db = DAL('sqlite://sqlalchemy_example.db', auto_import=True)
db.tables
>> []
db.define_table('person', Field('name'))
>> OperationalError: table "person" already exists
How do I access the table using pyDAL?
thank you
First, do not set auto_import=True, as that is only relevant if pyDAL *.table migration metadata files exist for the tables, which will not be the case here.
Second, pyDAL does not know the table already exists, and because migrations are enabled by default, it attempts to create the table. To prevent this, you can simply disable migrations:
# Applies to all tables.
db = DAL('sqlite://sqlalchemy_example.db', migrate_enabled=False)
or:
# Applies to this table only.
db.define_table('person', Field('name'), migrate=False)
If you would like pyDAL to take over migrations for future changes to this table, then you should run a "fake migration", which will cause pyDAL to generate a *.table migration metadata file for this table without actually running the migration. To do this, temporarily make the following change:
db.define_table('person', Field('name'), fake_migrate=True)
After leaving the above in place for a single request, the *.table file will be generated, and you can remove the fake_migrate=True argument.
Finally, note that pyDAL expects the id field to be an auto-incrementing integer primary key field.
I reflected an existing sqlalchemy table using:
import sqlalchemy as sa
db_engine = sa.create_engine('postgres+psycopq2://postgres:pwrd#localhost/test')
meta = sa.MetaData()
records = sa.Table('records', meta, autoload=True, autoload_with=db_engine)
now when I try to add data into it via
from sqlalchemy.orm insert sessionmaker
Session = sessionmaker(bind=db_engine)
session = Session()
new_record = records(Col1='sdf', Col2='sdsfdadf')
session.add(new_record)
session.commit()
I get an error with
TypeError: 'Table' object is not callable
isn't a reflected table usable in the same way that a declared table is?
You have to declare your own model class first, as Ilja Everilä suggested:
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class Records(Base):
__table__ = sa.Table('records', meta, autoload=True, autoload_with=db_engine)
Then you may use the new model class Records to operate database:
new_record = Records(Col1='sdf', Col2='sdsfdadf')
session.add(new_record)
session.commit()
I am using SQLAlchemy as ORM for a python project. I have created few models/schema and it is working fine. Now I need to query a existing MySQL database, no insert/update just the select statement.
How can I create a wrapper around the tables of this existing database? I have briefly gone through the sqlalchemy docs and SO but couldn't find anything relevant. All suggest execute method, where I need to write the raw sql queries, while I want to use the SQLAlchemy query method in same way as I am using with the SA models.
For example if the existing db has table name User then I want to query it using the dbsession ( only the select operation, probably with join)
You seem to have an impression that SQLAlchemy can only work with a database structure created by SQLAlchemy (probably using MetaData.create_all()) - this is not correct. SQLAlchemy can work perfectly with a pre-existing database, you just need to define your models to match database tables. One way to do that is to use reflection, as Ilja Everilä suggests:
from sqlalchemy import Table
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class MyClass(Base):
__table__ = Table('mytable', Base.metadata,
autoload=True, autoload_with=some_engine)
(which, in my opinion, would be totally fine for one-off scripts but may lead to incredibly frustrating bugs in a "real" application if there's a potential that the database structure may change over time)
Another way is to simply define your models as usual taking care to define your models to match the database tables, which is not that difficult. The benefit of this approach is that you can map only a subset of database tables to you models and even only a subset of table columns to your model's fields. Suppose you have 10 tables in the database but only interested in users table from where you only need id, name and email fields:
import sqlalchemy as sa
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class User(Base):
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String)
email = sa.Column(sa.String)
(note how we didn't need to define some details which are only needed to emit correct DDL, such as the length of the String fields or the fact that the email field has an index)
SQLAlchemy will not emit INSERT/UPDATE queries unless you create or modify models in your code. If you want to ensure that your queries are read-only you may create a special user in the database and grant that user SELECT privileges only. Alternatively/in addition, you may also experiment with rolling back the transaction in your application code.
You can access an existing table using the automap extension:
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
Base = automap_base()
Base.prepare(engine, reflect=True)
Users = Base.classes.users
session = Session(engine)
res = session.query(Users).first()
Create a table with autoload enabled that will inspect it. Some example code:
from sqlalchemy.sql import select
from sqlalchemy import create_engine, MetaData, Table
CONN_STR = '…'
engine = create_engine(CONN_STR, echo=True)
metadata = MetaData()
cookies = Table('cookies', metadata, autoload=True,
autoload_with=engine)
cols = cookies.c
with engine.connect() as conn:
query = (
select([cols.created_at, cols.name])
.order_by(cols.created_at)
.limit(1)
)
for row in conn.execute(query):
print(row)
Other answers don't mention what to do if you have a table with no primary key, so I thought I would address this. Assuming a table called Customers that has columns for CustomerId, CustomerName, CustomerLocation you could do;
from sqlalchemy.ext.automap import automap_base
from sqlalchemy import create_engine, MetaData, Column, String, Table
from sqlalchemy.orm import Session
Base = automap_base()
conn_str = '...'
engine = create_engine(conn_str)
metadata = MetaData()
# you only need to define which column is the primary key. It can automap the rest of the columns.
customers = Table('Customers',metadata, Column('CustomerId', String, primary_key=true), autoload=True, autoload_with=engine)
Base.prepare()
Customers= Base.classes.Customers
session = Session(engine)
customer1 = session.query(Customers).first()
print(customer1.CustomerName)
Assume we have a Postgresql database named accounts. And we already have a table named users.
import sqlalchemy as sa
psw = "verysecret"
db = "accounts"
# create an engine
pengine = sa.create_engine('postgresql+psycopg2://postgres:' + psw +'#localhost/' + db)
from sqlalchemy.ext.declarative import declarative_base
# define declarative base
Base = declarative_base()
# reflect current database engine to metadata
metadata = sa.MetaData(pengine)
metadata.reflect()
# build your User class on existing `users` table
class User(Base):
__table__ = sa.Table("users", metadata)
# call the session maker factory
Session = sa.orm.sessionmaker(pengine)
session = Session()
# filter a record
session.query(User).filter(User.id==1).first()
Warning: Your table should have a Primary Key defined. Otherwise, Sqlalchemy won't like it.