The core of SQLAlchemy's query and object mapping operations are supported by database metadata, which is comprised of Python objects that describe tables and other schema-level objects. These objects can be created by explicitly naming the various components and their properties, using the Table, Column, ForeignKey, Index, and Sequence objects imported from sqlalchemy.schema
. There is also support for reflection of some entities, which means you only specify the name of the entities and they are recreated from the database automatically.
A collection of metadata entities is stored in an object aptly named MetaData
:
from sqlalchemy import * metadata = MetaData()
To represent a Table, use the Table
class:
users = Table('users', metadata, Column('user_id', Integer, primary_key = True), Column('user_name', String(16), nullable = False), Column('email_address', String(60), key='email'), Column('password', String(20), nullable = False) ) user_prefs = Table('user_prefs', metadata, Column('pref_id', Integer, primary_key=True), Column('user_id', Integer, ForeignKey("users.user_id"), nullable=False), Column('pref_name', String(40), nullable=False), Column('pref_value', String(100)) )
The specific datatypes for each Column, such as Integer, String, etc. are described in The Types System, and exist within the module sqlalchemy.types
as well as the global sqlalchemy
namespace.
Foreign keys are most easily specified by the ForeignKey
object within a Column
object. For a composite foreign key, i.e. a foreign key that contains multiple columns referencing multiple columns to a composite primary key, an explicit syntax is provided which allows the correct table CREATE statements to be generated:
# a table with a composite primary key invoices = Table('invoices', metadata, Column('invoice_id', Integer, primary_key=True), Column('ref_num', Integer, primary_key=True), Column('description', String(60), nullable=False) ) # a table with a composite foreign key referencing the parent table invoice_items = Table('invoice_items', metadata, Column('item_id', Integer, primary_key=True), Column('item_name', String(60), nullable=False), Column('invoice_id', Integer, nullable=False), Column('ref_num', Integer, nullable=False), ForeignKeyConstraint(['invoice_id', 'ref_num'], ['invoices.invoice_id', 'invoices.ref_num']) )
Above, the invoice_items
table will have ForeignKey
objects automatically added to the invoice_id
and ref_num
Column
objects as a result of the additional ForeignKeyConstraint
object.
The MetaData
object supports some handy methods, such as getting a list of Tables in the order (or reverse) of their dependency:
>>> for t in metadata.table_iterator(reverse=False): ... print t.name users user_prefs
And Table
provides an interface to the table's properties as well as that of its columns:
employees = Table('employees', metadata, Column('employee_id', Integer, primary_key=True), Column('employee_name', String(60), nullable=False, key='name'), Column('employee_dept', Integer, ForeignKey("departments.department_id")) ) # access the column "EMPLOYEE_ID": employees.columns.employee_id # or just employees.c.employee_id # via string employees.c['employee_id'] # iterate through all columns for c in employees.c: # ... # get the table's primary key columns for primary_key in employees.primary_key: # ... # get the table's foreign key objects: for fkey in employees.foreign_keys: # ... # access the table's MetaData: employees.metadata # access the table's bound Engine or Connection, if its MetaData is bound: employees.bind # access a column's name, type, nullable, primary key, foreign key employees.c.employee_id.name employees.c.employee_id.type employees.c.employee_id.nullable employees.c.employee_id.primary_key employees.c.employee_dept.foreign_key # get the "key" of a column, which defaults to its name, but can # be any user-defined string: employees.c.name.key # access a column's table: employees.c.employee_id.table is employees >>> True # get the table related by a foreign key fcolumn = employees.c.employee_dept.foreign_key.column.table
A MetaData
object can be associated with an Engine
or an individual Connection
; this process is called binding. The term used to describe "an engine or a connection" is often referred to as a connectable. Binding allows the MetaData
and the elements which it contains to perform operations against the database directly, using the connection resources to which it's bound. Common operations which are made more convenient through binding include being able to generate SQL constructs which know how to execute themselves, creating Table
objects which query the database for their column and constraint information, and issuing CREATE or DROP statements.
To bind MetaData
to an Engine
, use the bind
attribute:
engine = create_engine('sqlite://', **kwargs) # create MetaData meta = MetaData() # bind to an engine meta.bind = engine
Once this is done, the MetaData
and its contained Table
objects can access the database directly:
meta.create_all() # issue CREATE statements for all tables # describe a table called 'users', query the database for its columns users_table = Table('users', meta, autoload=True) # generate a SELECT statement and execute result = users_table.select().execute()
Note that the feature of binding engines is completely optional. All of the operations which take advantage of "bound" MetaData
also can be given an Engine
or Connection
explicitly with which to perform the operation. The equivalent "non-bound" of the above would be:
meta.create_all(engine) # issue CREATE statements for all tables # describe a table called 'users', query the database for its columns users_table = Table('users', meta, autoload=True, autoload_with=engine) # generate a SELECT statement and execute result = engine.execute(users_table.select())
A Table
object can be created without specifying any of its contained attributes, using the argument autoload=True
in conjunction with the table's name and possibly its schema (if not the databases "default" schema). (You can also specify a list or set of column names to autoload as the kwarg include_columns, if you only want to load a subset of the columns in the actual database.) This will issue the appropriate queries to the database in order to locate all properties of the table required for SQLAlchemy to use it effectively, including its column names and datatypes, foreign and primary key constraints, and in some cases its default-value generating attributes. To use autoload=True
, the table's MetaData
object need be bound to an Engine
or Connection
, or alternatively the autoload_with=<some connectable>
argument can be passed. Below we illustrate autoloading a table and then iterating through the names of its columns:
>>> messages = Table('messages', meta, autoload=True) >>> [c.name for c in messages.columns] ['message_id', 'message_name', 'date']
Note that if a reflected table has a foreign key referencing another table, the related Table
object will be automatically created within the MetaData
object if it does not exist already. Below, suppose table shopping_cart_items
references a table shopping_carts
. After reflecting, the shopping carts
table is present:
>>> shopping_cart_items = Table('shopping_cart_items', meta, autoload=True) >>> 'shopping_carts' in meta.tables: True
To get direct access to 'shopping_carts', simply instantiate it via the Table
constructor. Table
uses a special contructor that will return the already created Table
instance if its already present:
shopping_carts = Table('shopping_carts', meta)
Of course, its a good idea to use autoload=True
with the above table regardless. This is so that if it hadn't been loaded already, the operation will load the table. The autoload operation only occurs for the table if it hasn't already been loaded; once loaded, new calls to Table
will not re-issue any reflection queries.
Individual columns can be overridden with explicit values when reflecting tables; this is handy for specifying custom datatypes, constraints such as primary keys that may not be configured within the database, etc.
>>> mytable = Table('mytable', meta, ... Column('id', Integer, primary_key=True), # override reflected 'id' to have primary key ... Column('mydata', Unicode(50)), # override reflected 'mydata' to be Unicode ... autoload=True)
Some databases support the concept of multiple schemas. A Table
can reference this by specifying the schema
keyword argument:
financial_info = Table('financial_info', meta, Column('id', Integer, primary_key=True), Column('value', String(100), nullable=False), schema='remote_banks' )
Within the MetaData
collection, this table will be identified by the combination of financial_info
and remote_banks
. If another table called financial_info
is referenced without the remote_banks
schema, it will refer to a different Table
. ForeignKey
objects can reference columns in this table using the form remote_banks.financial_info.id
.
ON UPDATE
and ON DELETE
clauses to a table create are specified within the ForeignKeyConstraint
object, using the onupdate
and ondelete
keyword arguments:
foobar = Table('foobar', meta, Column('id', Integer, primary_key=True), Column('lala', String(40)), ForeignKeyConstraint(['lala'],['hoho.lala'], onupdate="CASCADE", ondelete="CASCADE"))
Note that these clauses are not supported on SQLite, and require InnoDB
tables when used with MySQL. They may also not be supported on other databases.
Tables
may support database-specific options, such as MySQL's engine
option that can specify "MyISAM", "InnoDB", and other backends for the table:
addresses = Table('engine_email_addresses', meta, Column('address_id', Integer, primary_key = True), Column('remote_user_id', Integer, ForeignKey(users.c.user_id)), Column('email_address', String(20)), mysql_engine='InnoDB' )
Creating and dropping individual tables can be done via the create()
and drop()
methods of Table
; these methods take an optional bind
parameter which references an Engine
or a Connection
. If not supplied, the Engine
bound to the MetaData
will be used, else an error is raised:
meta = MetaData() meta.bind = 'sqlite:///:memory:' employees = Table('employees', meta, Column('employee_id', Integer, primary_key=True), Column('employee_name', String(60), nullable=False, key='name'), Column('employee_dept', Integer, ForeignKey("departments.department_id")) ) sqlemployees.create()
drop()
method:
sqlemployees.drop(bind=e)
The create()
and drop()
methods also support an optional keyword argument checkfirst
which will issue the database's appropriate pragma statements to check if the table exists before creating or dropping:
employees.create(bind=e, checkfirst=True) employees.drop(checkfirst=False)
Entire groups of Tables can be created and dropped directly from the MetaData
object with create_all()
and drop_all()
. These methods always check for the existence of each table before creating or dropping. Each method takes an optional bind
keyword argument which can reference an Engine
or a Connection
. If no engine is specified, the underlying bound Engine
, if any, is used:
engine = create_engine('sqlite:///:memory:') metadata = MetaData() users = Table('users', metadata, Column('user_id', Integer, primary_key = True), Column('user_name', String(16), nullable = False), Column('email_address', String(60), key='email'), Column('password', String(20), nullable = False) ) user_prefs = Table('user_prefs', metadata, Column('pref_id', Integer, primary_key=True), Column('user_id', Integer, ForeignKey("users.user_id"), nullable=False), Column('pref_name', String(40), nullable=False), Column('pref_value', String(100)) ) sqlmetadata.create_all(bind=engine)
SQLAlchemy includes several constructs which provide default values provided during INSERT and UPDATE statements. The defaults may be provided as Python constants, Python functions, or SQL expressions, and the SQL expressions themselves may be "pre-executed", executed inline within the insert/update statement itself, or can be created as a SQL level "default" placed on the table definition itself. A "default" value by definition is only invoked if no explicit value is passed into the INSERT or UPDATE statement.
The "default" keyword argument on Column can reference a Python value or callable which is invoked at the time of an insert:
# a function which counts upwards i = 0 def mydefault(): global i i += 1 return i t = Table("mytable", meta, # function-based default Column('id', Integer, primary_key=True, default=mydefault), # a scalar default Column('key', String(10), default="default") )
Similarly, the "onupdate" keyword does the same thing for update statements:
import datetime t = Table("mytable", meta, Column('id', Integer, primary_key=True), # define 'last_updated' to be populated with datetime.now() Column('last_updated', DateTime, onupdate=datetime.now), )
The "default" and "onupdate" keywords may also be passed SQL expressions, including select statements or direct function calls:
t = Table("mytable", meta, Column('id', Integer, primary_key=True), # define 'create_date' to default to now() Column('create_date', DateTime, default=func.now()), # define 'key' to pull its default from the 'keyvalues' table Column('key', String(20), default=keyvalues.select(keyvalues.c.type='type1', limit=1)) # define 'last_modified' to use the current_timestamp SQL function on update Column('last_modified', DateTime, onupdate=func.current_timestamp()) )
The above SQL functions are usually executed "inline" with the INSERT or UPDATE statement being executed. In some cases, the function is "pre-executed" and its result pre-fetched explicitly. This happens under the following circumstances:
the column is a primary key column
the database dialect does not support a usable cursor.lastrowid
accessor (or equivalent); this currently includes Postgres, Oracle, and Firebird.
the statement is a single execution, i.e. only supplies one set of parameters and doesn't use "executemany" behavior
the inline=True
flag is not set on the Insert()
or Update()
construct.
For a statement which executes with inline=False
and is not an executemany execution, the returned ResultProxy
will contain a collection accessible via result.postfetch_cols()
which contains a list of all Column
objects which had an inline-executed default. Similarly, all parameters which were bound to the statement, including all Python and SQL expressions which were pre-executed, are present in the last_inserted_params()
or last_updated_params()
collections on ResultProxy
. The last_inserted_ids()
collection contains a list of primary key values for the row inserted.
A variant on a SQL expression default is the PassiveDefault
, which gets placed in the CREATE TABLE statement during a create()
operation:
t = Table('test', meta, Column('mycolumn', DateTime, PassiveDefault("sysdate")) )
A create call for the above table will produce:
CREATE TABLE test ( mycolumn datetime default sysdate )
The behavior of PassiveDefault
is similar to that of a regular SQL default; if it's placed on a primary key column for a database which doesn't have a way to "postfetch" the ID, and the statement is not "inlined", the SQL expression is pre-executed; otherwise, SQLAlchemy lets the default fire off on the database side normally.
A table with a sequence looks like:
table = Table("cartitems", meta, Column("cart_id", Integer, Sequence('cart_id_seq'), primary_key=True), Column("description", String(40)), Column("createdate", DateTime()) )
The Sequence
object works a lot like the default
keyword on Column
, except that it only takes effect on a database which supports sequences. When used with a database that does not support sequences, the Sequence
object has no effect; therefore it's safe to place on a table which is used against multiple database backends. The same rules for pre- and inline execution apply.
When the Sequence
is associated with a table, CREATE and DROP statements issued for that table will also issue CREATE/DROP for the sequence object as well, thus "bundling" the sequence object with its parent table.
The flag optional=True
on Sequence
will produce a sequence that is only used on databases which have no "autoincrementing" capability. For example, Postgres supports primary key generation using the SERIAL keyword, whereas Oracle has no such capability. Therefore, a Sequence
placed on a primary key column with optional=True
will only be used with an Oracle backend but not Postgres.
A sequence can also be executed standalone, using an Engine
or Connection
, returning its next value in a database-independent fashion:
seq = Sequence('some_sequence') nextid = connection.execute(seq)
Unique constraints can be created anonymously on a single column using the unique
keyword on Column
. Explicitly named unique constraints and/or those with multiple columns are created via the UniqueConstraint
table-level construct.
meta = MetaData() mytable = Table('mytable', meta, # per-column anonymous unique constraint Column('col1', Integer, unique=True), Column('col2', Integer), Column('col3', Integer), # explicit/composite unique constraint. 'name' is optional. UniqueConstraint('col2', 'col3', name='uix_1') )
Check constraints can be named or unnamed and can be created at the Column or Table level, using the CheckConstraint
construct. The text of the check constraint is passed directly through to the database, so there is limited "database independent" behavior. Column level check constraints generally should only refer to the column to which they are placed, while table level constraints can refer to any columns in the table.
Note that some databases do not actively support check constraints such as MySQL and sqlite.
meta = MetaData() mytable = Table('mytable', meta, # per-column CHECK constraint Column('col1', Integer, CheckConstraint('col1>5')), Column('col2', Integer), Column('col3', Integer), # table level CHECK constraint. 'name' is optional. CheckConstraint('col2 > col3 + 5', name='check1') )
Indexes can be created anonymously (using an auto-generated name "ix_index
keyword on Column
, which also modifies the usage of unique
to apply the uniqueness to the index itself, instead of adding a separate UNIQUE constraint. For indexes with specific names or which encompass more than one column, use the Index
construct, which requires a name.
Note that the Index
construct is created externally to the table which it corresponds, using Column
objects and not strings.
meta = MetaData() mytable = Table('mytable', meta, # an indexed column, with index "ix_mytable_col1" Column('col1', Integer, index=True), # a uniquely indexed column with index "ix_mytable_col2" Column('col2', Integer, index=True, unique=True), Column('col3', Integer), Column('col4', Integer), Column('col5', Integer), Column('col6', Integer), ) # place an index on col3, col4 Index('idx_col34', mytable.c.col3, mytable.c.col4) # place a unique index on col5, col6 Index('myindex', mytable.c.col5, mytable.c.col6, unique=True)
The Index
objects will be created along with the CREATE statements for the table itself. An index can also be created on its own independently of the table:
# create a table sometable.create() # define an index i = Index('someindex', sometable.c.col5) # create the index, will use the table's bound connectable if the `bind` keyword argument not specified i.create()
A Table
object created against a specific MetaData
object can be re-created against a new MetaData using the tometadata
method:
# create two metadata meta1 = MetaData('sqlite:///querytest.db') meta2 = MetaData() # load 'users' from the sqlite engine users_table = Table('users', meta1, autoload=True) # create the same Table object for the plain metadata users_table_2 = users_table.tometadata(meta2)