Release: 0.9.10 legacy version | Release Date: July 22, 2015

SQLAlchemy 0.9 Documentation

Column Insert/Update Defaults

SQLAlchemy provides a very rich featureset regarding column level events which take place during INSERT and UPDATE statements. Options include:

  • Scalar values used as defaults during INSERT and UPDATE operations
  • Python functions which execute upon INSERT and UPDATE operations
  • SQL expressions which are embedded in INSERT statements (or in some cases execute beforehand)
  • SQL expressions which are embedded in UPDATE statements
  • Server side default values used during INSERT
  • Markers for server-side triggers used during UPDATE

The general rule for all insert/update defaults is that they only take effect if no value for a particular column is passed as an execute() parameter; otherwise, the given value is used.

Scalar Defaults

The simplest kind of default is a scalar value used as the default value of a column:

Table("mytable", meta,
    Column("somecolumn", Integer, default=12)

Above, the value “12” will be bound as the column value during an INSERT if no other value is supplied.

A scalar value may also be associated with an UPDATE statement, though this is not very common (as UPDATE statements are usually looking for dynamic defaults):

Table("mytable", meta,
    Column("somecolumn", Integer, onupdate=25)

Python-Executed Functions

The default and onupdate keyword arguments also accept Python functions. These functions are invoked at the time of insert or update if no other value for that column is supplied, and the value returned is used for the column’s value. Below illustrates a crude “sequence” that assigns an incrementing counter to a primary key column:

# a function which counts upwards
i = 0
def mydefault():
    global i
    i += 1
    return i

t = Table("mytable", meta,
    Column('id', Integer, primary_key=True, default=mydefault),

It should be noted that for real “incrementing sequence” behavior, the built-in capabilities of the database should normally be used, which may include sequence objects or other autoincrementing capabilities. For primary key columns, SQLAlchemy will in most cases use these capabilities automatically. See the API documentation for Column including the autoincrement flag, as well as the section on Sequence later in this chapter for background on standard primary key generation techniques.

To illustrate onupdate, we assign the Python datetime function now to the onupdate attribute:

import datetime

t = Table("mytable", meta,
    Column('id', Integer, primary_key=True),

    # define 'last_updated' to be populated with
    Column('last_updated', DateTime,,

When an update statement executes and no value is passed for last_updated, the Python function is executed and its return value used as the value for last_updated. Notice that we provide now as the function itself without calling it (i.e. there are no parenthesis following) - SQLAlchemy will execute the function at the time the statement executes.

Context-Sensitive Default Functions

The Python functions used by default and onupdate may also make use of the current statement’s context in order to determine a value. The context of a statement is an internal SQLAlchemy object which contains all information about the statement being executed, including its source expression, the parameters associated with it and the cursor. The typical use case for this context with regards to default generation is to have access to the other values being inserted or updated on the row. To access the context, provide a function that accepts a single context argument:

def mydefault(context):
    return context.current_parameters['counter'] + 12

t = Table('mytable', meta,
    Column('counter', Integer),
    Column('counter_plus_twelve', Integer, default=mydefault, onupdate=mydefault)

Above we illustrate a default function which will execute for all INSERT and UPDATE statements where a value for counter_plus_twelve was otherwise not provided, and the value will be that of whatever value is present in the execution for the counter column, plus the number 12.

While the context object passed to the default function has many attributes, the current_parameters member is a special member provided only during the execution of a default function for the purposes of deriving defaults from its existing values. For a single statement that is executing many sets of bind parameters, the user-defined function is called for each set of parameters, and current_parameters will be provided with each individual parameter set for each execution.

SQL Expressions

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,,

    # define 'key' to pull its default from the 'keyvalues' table
    Column('key', String(20),'type1', limit=1)),

    # define 'last_modified' to use the current_timestamp SQL function on update
    Column('last_modified', DateTime, onupdate=func.utc_timestamp())

Above, the create_date column will be populated with the result of the now() SQL function (which, depending on backend, compiles into NOW() or CURRENT_TIMESTAMP in most cases) during an INSERT statement, and the key column with the result of a SELECT subquery from another table. The last_modified column will be populated with the value of UTC_TIMESTAMP(), a function specific to MySQL, when an UPDATE statement is emitted for this table.

Note that when using func functions, unlike when using Python datetime functions we do call the function, i.e. with parenthesis “()” - this is because what we want in this case is the return value of the function, which is the SQL expression construct that will be rendered into the INSERT or UPDATE statement.

The above SQL functions are usually executed “inline” with the INSERT or UPDATE statement being executed, meaning, a single statement is executed which embeds the given expressions or subqueries within the VALUES or SET clause of the statement. Although in some cases, the function is “pre-executed” in a SELECT statement of its own beforehand. This happens when all of the following is true:

  • the column is a primary key column
  • the database dialect does not support a usable cursor.lastrowid accessor (or equivalent); this currently includes PostgreSQL, Oracle, and Firebird, as well as some MySQL dialects.
  • the dialect does not support the “RETURNING” clause or similar, or the implicit_returning flag is set to False for the dialect. Dialects which support RETURNING currently include Postgresql, Oracle, Firebird, and MS-SQL.
  • 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, and the statement has not defined an explicit returning() clause.

Whether or not the default generation clause “pre-executes” is not something that normally needs to be considered, unless it is being addressed for performance reasons.

When the statement is executed with a single set of parameters (that is, it is not an “executemany” style 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 inserted_primary_key collection contains a list of primary key values for the row inserted (a list so that single-column and composite-column primary keys are represented in the same format).

Server Side Defaults

A variant on the SQL expression default is the server_default, which gets placed in the CREATE TABLE statement during a create() operation:

t = Table('test', meta,
    Column('abc', String(20), server_default='abc'),
    Column('created_at', DateTime, server_default=text("sysdate"))

A create call for the above table will produce:

    abc varchar(20) default 'abc',
    created_at datetime default sysdate

The behavior of server_default 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.

Triggered Columns

Columns with values set by a database trigger or other external process may be called out using FetchedValue as a marker:

t = Table('test', meta,
    Column('abc', String(20), server_default=FetchedValue()),
    Column('def', String(20), server_onupdate=FetchedValue())

Changed in version 0.8.0b2,0.7.10: The for_update argument on FetchedValue is set automatically when specified as the server_onupdate argument. If using an older version, specify the onupdate above as server_onupdate=FetchedValue(for_update=True).

These markers do not emit a “default” clause when the table is created, however they do set the same internal flags as a static server_default clause, providing hints to higher-level tools that a “post-fetch” of these rows should be performed after an insert or update.


It’s generally not appropriate to use FetchedValue in conjunction with a primary key column, particularly when using the ORM or any other scenario where the ResultProxy.inserted_primary_key attribute is required. This is becaue the “post-fetch” operation requires that the primary key value already be available, so that the row can be selected on its primary key.

For a server-generated primary key value, all databases provide special accessors or other techniques in order to acquire the “last inserted primary key” column of a table. These mechanisms aren’t affected by the presence of FetchedValue. For special situations where triggers are used to generate primary key values, and the database in use does not support the RETURNING clause, it may be necessary to forego the usage of the trigger and instead apply the SQL expression or function as a “pre execute” expression:

t = Table('test', meta,
        Column('abc', MyType, default=func.generate_new_value(), primary_key=True)

Where above, when Table.insert() is used, the func.generate_new_value() expression will be pre-executed in the context of a scalar SELECT statement, and the new value will be applied to the subsequent INSERT, while at the same time being made available to the ResultProxy.inserted_primary_key attribute.

Defining Sequences

SQLAlchemy represents database sequences using the Sequence object, which is considered to be a special case of “column default”. It only has an effect on databases which have explicit support for sequences, which currently includes Postgresql, Oracle, and Firebird. The Sequence object is otherwise ignored.

The Sequence may be placed on any column as a “default” generator to be used during INSERT operations, and can also be configured to fire off during UPDATE operations if desired. It is most commonly used in conjunction with a single integer primary key column:

table = Table("cartitems", meta,
    Column("cart_id", Integer, Sequence('cart_id_seq'), primary_key=True),
    Column("description", String(40)),
    Column("createdate", DateTime())

Where above, the table “cartitems” is associated with a sequence named “cart_id_seq”. When INSERT statements take place for “cartitems”, and no value is passed for the “cart_id” column, the “cart_id_seq” sequence will be used to generate a value.

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 Sequence object also implements special functionality to accommodate Postgresql’s SERIAL datatype. The SERIAL type in PG automatically generates a sequence that is used implicitly during inserts. This means that if a Table object defines a Sequence on its primary key column so that it works with Oracle and Firebird, the Sequence would get in the way of the “implicit” sequence that PG would normally use. For this use case, add the flag optional=True to the Sequence object - this indicates that the Sequence should only be used if the database provides no other option for generating primary key identifiers.

The Sequence object also has the ability to be executed standalone like a SQL expression, which has the effect of calling its “next value” function:

seq = Sequence('some_sequence')
nextid = connection.execute(seq)

Associating a Sequence as the Server Side Default

When we associate a Sequence with a Column as above, this association is an in-Python only association. The CREATE TABLE that would be generated for our Table would not refer to this sequence. If we want the sequence to be used as a server-side default, meaning it takes place even if we emit INSERT commands to the table from the SQL commandline, we can use the Column.server_default parameter in conjunction with the value-generation function of the sequence, available from the Sequence.next_value() method:

cart_id_seq = Sequence('cart_id_seq')
table = Table("cartitems", meta,
        "cart_id", Integer, cart_id_seq,
        server_default=cart_id_seq.next_value(), primary_key=True),
    Column("description", String(40)),
    Column("createdate", DateTime())

The above metadata will generate a CREATE TABLE statement on Postgresql as:

CREATE TABLE cartitems (
    cart_id INTEGER DEFAULT nextval('cart_id_seq') NOT NULL,
    description VARCHAR(40),
    PRIMARY KEY (cart_id)

We place the Sequence also as a Python-side default above, that is, it is mentioned twice in the Column definition. Depending on the backend in use, this may not be strictly necessary, for example on the Postgresql backend the Core will use RETURNING to access the newly generated primary key value in any case. However, for the best compatibility, Sequence was originally intended to be a Python-side directive first and foremost so it’s probably a good idea to specify it in this way as well.

Default Objects API

class sqlalchemy.schema.ColumnDefault(arg, **kwargs)

Bases: sqlalchemy.schema.DefaultGenerator

A plain default value on a column.

This could correspond to a constant, a callable function, or a SQL clause.

ColumnDefault is generated automatically whenever the default, onupdate arguments of Column are used. A ColumnDefault can be passed positionally as well.

For example, the following:

Column('foo', Integer, default=50)

Is equivalent to:

Column('foo', Integer, ColumnDefault(50))
class sqlalchemy.schema.DefaultClause(arg, for_update=False, _reflected=False)

Bases: sqlalchemy.schema.FetchedValue

A DDL-specified DEFAULT column value.

DefaultClause is a FetchedValue that also generates a “DEFAULT” clause when “CREATE TABLE” is emitted.

DefaultClause is generated automatically whenever the server_default, server_onupdate arguments of Column are used. A DefaultClause can be passed positionally as well.

For example, the following:

Column('foo', Integer, server_default="50")

Is equivalent to:

Column('foo', Integer, DefaultClause("50"))
class sqlalchemy.schema.DefaultGenerator(for_update=False)

Bases: sqlalchemy.schema._NotAColumnExpr, sqlalchemy.schema.SchemaItem

Base class for column default values.

class sqlalchemy.schema.FetchedValue(for_update=False)

Bases: sqlalchemy.schema._NotAColumnExpr, sqlalchemy.sql.expression.SchemaEventTarget

A marker for a transparent database-side default.

Use FetchedValue when the database is configured to provide some automatic default for a column.


Column('foo', Integer, FetchedValue())

Would indicate that some trigger or default generator will create a new value for the foo column during an INSERT.

class sqlalchemy.schema.PassiveDefault(*arg, **kw)

Bases: sqlalchemy.schema.DefaultClause

A DDL-specified DEFAULT column value.

Deprecated since version 0.6: PassiveDefault is deprecated. Use DefaultClause.

class sqlalchemy.schema.Sequence(name, start=None, increment=None, schema=None, optional=False, quote=None, metadata=None, quote_schema=None, for_update=False)

Bases: sqlalchemy.schema.DefaultGenerator

Represents a named database sequence.

The Sequence object represents the name and configurational parameters of a database sequence. It also represents a construct that can be “executed” by a SQLAlchemy Engine or Connection, rendering the appropriate “next value” function for the target database and returning a result.

The Sequence is typically associated with a primary key column:

some_table = Table(
    'some_table', metadata,
    Column('id', Integer, Sequence('some_table_seq'),

When CREATE TABLE is emitted for the above Table, if the target platform supports sequences, a CREATE SEQUENCE statement will be emitted as well. For platforms that don’t support sequences, the Sequence construct is ignored.

__init__(name, start=None, increment=None, schema=None, optional=False, quote=None, metadata=None, quote_schema=None, for_update=False)

Construct a Sequence object.

  • name – The name of the sequence.
  • start – the starting index of the sequence. This value is used when the CREATE SEQUENCE command is emitted to the database as the value of the “START WITH” clause. If None, the clause is omitted, which on most platforms indicates a starting value of 1.
  • increment – the increment value of the sequence. This value is used when the CREATE SEQUENCE command is emitted to the database as the value of the “INCREMENT BY” clause. If None, the clause is omitted, which on most platforms indicates an increment of 1.
  • schema – Optional schema name for the sequence, if located in a schema other than the default.
  • optional – boolean value, when True, indicates that this Sequence object only needs to be explicitly generated on backends that don’t provide another way to generate primary key identifiers. Currently, it essentially means, “don’t create this sequence on the Postgresql backend, where the SERIAL keyword creates a sequence for us automatically”.
  • quote – boolean value, when True or False, explicitly forces quoting of the schema name on or off. When left at its default of None, normal quoting rules based on casing and reserved words take place.
  • quote_schema – set the quoting preferences for the schema name.
  • metadata

    optional MetaData object which will be associated with this Sequence. A Sequence that is associated with a MetaData gains access to the bind of that MetaData, meaning the Sequence.create() and Sequence.drop() methods will make usage of that engine automatically.

    Changed in version 0.7: Additionally, the appropriate CREATE SEQUENCE/ DROP SEQUENCE DDL commands will be emitted corresponding to this Sequence when MetaData.create_all() and MetaData.drop_all() are invoked.

    Note that when a Sequence is applied to a Column, the Sequence is automatically associated with the MetaData object of that column’s parent Table, when that association is made. The Sequence will then be subject to automatic CREATE SEQUENCE/DROP SEQUENCE corresponding to when the Table object itself is created or dropped, rather than that of the MetaData object overall.

  • for_update – Indicates this Sequence, when associated with a Column, should be invoked for UPDATE statements on that column’s table, rather than for INSERT statements, when no value is otherwise present for that column in the statement.
create(bind=None, checkfirst=True)

Creates this sequence in the database.

drop(bind=None, checkfirst=True)

Drops this sequence from the database.


Return a next_value function element which will render the appropriate increment function for this Sequence within any SQL expression.

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