Release: 2.0.26 | Release Date: February 11, 2024

# SQLAlchemy 2.0 Documentation

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy Unified Tutorial.

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# Using UPDATE and DELETE Statements

So far we’ve covered `Insert`, so that we can get some data into our database, and then spent a lot of time on `Select` which handles the broad range of usage patterns used for retrieving data from the database. In this section we will cover the `Update` and `Delete` constructs, which are used to modify existing rows as well as delete existing rows. This section will cover these constructs from a Core-centric perspective.

ORM Readers - As was the case mentioned at Using INSERT Statements, the `Update` and `Delete` operations when used with the ORM are usually invoked internally from the `Session` object as part of the unit of work process.

However, unlike `Insert`, the `Update` and `Delete` constructs can also be used directly with the ORM, using a pattern known as “ORM-enabled update and delete”; for this reason, familiarity with these constructs is useful for ORM use. Both styles of use are discussed in the sections Updating ORM Objects using the Unit of Work pattern and Deleting ORM Objects using the Unit of Work pattern.

## The update() SQL Expression Construct

The `update()` function generates a new instance of `Update` which represents an UPDATE statement in SQL, that will update existing data in a table.

Like the `insert()` construct, there is a “traditional” form of `update()`, which emits UPDATE against a single table at a time and does not return any rows. However some backends support an UPDATE statement that may modify multiple tables at once, and the UPDATE statement also supports RETURNING such that columns contained in matched rows may be returned in the result set.

A basic UPDATE looks like:

    >>> from sqlalchemy import update
    >>> stmt = (
    ...     update(user_table)
    ...     .where(user_table.c.name == "patrick")
    ...     .values(fullname="Patrick the Star")
    ... )
    >>> print(stmt)
    UPDATE user_account SET fullname=:fullname WHERE user_account.name = :name_1

The `Update.values()` method controls the contents of the SET elements of the UPDATE statement. This is the same method shared by the `Insert` construct. Parameters can normally be passed using the column names as keyword arguments.

UPDATE supports all the major SQL forms of UPDATE, including updates against expressions, where we can make use of `Column` expressions:

    >>> stmt = update(user_table).values(fullname="Username: " + user_table.c.name)
    >>> print(stmt)
    UPDATE user_account SET fullname=(:name_1 || user_account.name)

To support UPDATE in an “executemany” context, where many parameter sets will be invoked against the same statement, the `bindparam()` construct may be used to set up bound parameters; these replace the places that literal values would normally go:

    >>> from sqlalchemy import bindparam
    >>> stmt = (
    ...     update(user_table)
    ...     .where(user_table.c.name == bindparam("oldname"))
    ...     .values(name=bindparam("newname"))
    ... )
    >>> with engine.begin() as conn:
    ...     conn.execute(
    ...         stmt,
    ...         [
    ...             {"oldname": "jack", "newname": "ed"},
    ...             {"oldname": "wendy", "newname": "mary"},
    ...             {"oldname": "jim", "newname": "jake"},
    ...         ],
    ...     )
    BEGIN (implicit)
    UPDATE user_account SET name=? WHERE user_account.name = ?
    [...] [('ed', 'jack'), ('mary', 'wendy'), ('jake', 'jim')]
    <sqlalchemy.engine.cursor.CursorResult object at 0x...>
    COMMIT

Other techniques which may be applied to UPDATE include:

### Correlated Updates

An UPDATE statement can make use of rows in other tables by using a correlated subquery. A subquery may be used anywhere a column expression might be placed:

    >>> scalar_subq = (
    ...     select(address_table.c.email_address)
    ...     .where(address_table.c.user_id == user_table.c.id)
    ...     .order_by(address_table.c.id)
    ...     .limit(1)
    ...     .scalar_subquery()
    ... )
    >>> update_stmt = update(user_table).values(fullname=scalar_subq)
    >>> print(update_stmt)
    UPDATE user_account SET fullname=(SELECT address.email_address
    FROM address
    WHERE address.user_id = user_account.id ORDER BY address.id
    LIMIT :param_1)

### UPDATE..FROM

Some databases such as PostgreSQL and MySQL support a syntax “UPDATE FROM” where additional tables may be stated directly in a special FROM clause. This syntax will be generated implicitly when additional tables are located in the WHERE clause of the statement:

    >>> update_stmt = (
    ...     update(user_table)
    ...     .where(user_table.c.id == address_table.c.user_id)
    ...     .where(address_table.c.email_address == "patrick@aol.com")
    ...     .values(fullname="Pat")
    ... )
    >>> print(update_stmt)
    UPDATE user_account SET fullname=:fullname FROM address
    WHERE user_account.id = address.user_id AND address.email_address = :email_address_1

There is also a MySQL specific syntax that can UPDATE multiple tables. This requires we refer to `Table` objects in the VALUES clause in order to refer to additional tables:

    >>> update_stmt = (
    ...     update(user_table)
    ...     .where(user_table.c.id == address_table.c.user_id)
    ...     .where(address_table.c.email_address == "patrick@aol.com")
    ...     .values(
    ...         {
    ...             user_table.c.fullname: "Pat",
    ...             address_table.c.email_address: "pat@aol.com",
    ...         }
    ...     )
    ... )
    >>> from sqlalchemy.dialects import mysql
    >>> print(update_stmt.compile(dialect=mysql.dialect()))
    UPDATE user_account, address
    SET address.email_address=%s, user_account.fullname=%s
    WHERE user_account.id = address.user_id AND address.email_address = %s

### Parameter Ordered Updates

Another MySQL-only behavior is that the order of parameters in the SET clause of an UPDATE actually impacts the evaluation of each expression. For this use case, the `Update.ordered_values()` method accepts a sequence of tuples so that this order may be controlled [2]:

    >>> update_stmt = update(some_table).ordered_values(
    ...     (some_table.c.y, 20), (some_table.c.x, some_table.c.y + 10)
    ... )
    >>> print(update_stmt)
    UPDATE some_table SET y=:y, x=(some_table.y + :y_1)

[ 2 ]

While Python dictionaries are guaranteed to be insert ordered as of Python 3.7, the `Update.ordered_values()` method still provides an additional measure of clarity of intent when it is essential that the SET clause of a MySQL UPDATE statement proceed in a specific way.

## The delete() SQL Expression Construct

The `delete()` function generates a new instance of `Delete` which represents a DELETE statement in SQL, that will delete rows from a table.

The `delete()` statement from an API perspective is very similar to that of the `update()` construct, traditionally returning no rows but allowing for a RETURNING variant on some database backends.

    >>> from sqlalchemy import delete
    >>> stmt = delete(user_table).where(user_table.c.name == "patrick")
    >>> print(stmt)
    DELETE FROM user_account WHERE user_account.name = :name_1

### Multiple Table Deletes

Like `Update`, `Delete` supports the use of correlated subqueries in the WHERE clause as well as backend-specific multiple table syntaxes, such as `DELETE FROM..USING` on MySQL:

    >>> delete_stmt = (
    ...     delete(user_table)
    ...     .where(user_table.c.id == address_table.c.user_id)
    ...     .where(address_table.c.email_address == "patrick@aol.com")
    ... )
    >>> from sqlalchemy.dialects import mysql
    >>> print(delete_stmt.compile(dialect=mysql.dialect()))
    DELETE FROM user_account USING user_account, address
    WHERE user_account.id = address.user_id AND address.email_address = %s

## Getting Affected Row Count from UPDATE, DELETE

Both `Update` and `Delete` support the ability to return the number of rows matched after the statement proceeds, for statements that are invoked using Core `Connection`, i.e. `Connection.execute()`. Per the caveats mentioned below, this value is available from the `CursorResult.rowcount` attribute:

    >>> with engine.begin() as conn:
    ...     result = conn.execute(
    ...         update(user_table)
    ...         .values(fullname="Patrick McStar")
    ...         .where(user_table.c.name == "patrick")
    ...     )
    ...     print(result.rowcount)
    BEGIN (implicit)
    UPDATE user_account SET fullname=? WHERE user_account.name = ?
    [...] ('Patrick McStar', 'patrick')
    1
    COMMIT

Tip

The `CursorResult` class is a subclass of `Result` which contains additional attributes that are specific to the DBAPI `cursor` object. An instance of this subclass is returned when a statement is invoked via the `Connection.execute()` method. When using the ORM, the `Session.execute()` method returns an object of this type for all INSERT, UPDATE, and DELETE statements.

Facts about `CursorResult.rowcount`:

- The value returned is the number of rows matched by the WHERE clause of the statement. It does not matter if the row were actually modified or not.

- `CursorResult.rowcount` is not necessarily available for an UPDATE or DELETE statement that uses RETURNING.

- For an executemany execution, `CursorResult.rowcount` may not be available either, which depends highly on the DBAPI module in use as well as configured options. The attribute `CursorResult.supports_sane_multi_rowcount` indicates if this value will be available for the current backend in use.

- Some drivers, particularly third party dialects for non-relational databases, may not support `CursorResult.rowcount` at all. The `CursorResult.supports_sane_rowcount` will indicate this.

- “rowcount” is used by the ORM unit of work process to validate that an UPDATE or DELETE statement matched the expected number of rows, and is also essential for the ORM versioning feature documented at Configuring a Version Counter.

## Using RETURNING with UPDATE, DELETE

Like the `Insert` construct, `Update` and `Delete` also support the RETURNING clause which is added by using the `Update.returning()` and `Delete.returning()` methods. When these methods are used on a backend that supports RETURNING, selected columns from all rows that match the WHERE criteria of the statement will be returned in the `Result` object as rows that can be iterated:

    >>> update_stmt = (
    ...     update(user_table)
    ...     .where(user_table.c.name == "patrick")
    ...     .values(fullname="Patrick the Star")
    ...     .returning(user_table.c.id, user_table.c.name)
    ... )
    >>> print(update_stmt)
    UPDATE user_account SET fullname=:fullname
    WHERE user_account.name = :name_1
    RETURNING user_account.id, user_account.name
    >>> delete_stmt = (
    ...     delete(user_table)
    ...     .where(user_table.c.name == "patrick")
    ...     .returning(user_table.c.id, user_table.c.name)
    ... )
    >>> print(delete_stmt)
    DELETE FROM user_account
    WHERE user_account.name = :name_1
    RETURNING user_account.id, user_account.name

## Further Reading for UPDATE, DELETE

See also

API documentation for UPDATE / DELETE:

- `Update`

- `Delete`

ORM-enabled UPDATE and DELETE:

ORM-Enabled INSERT, UPDATE, and DELETE statements - in the ORM Querying Guide

SQLAlchemy 1.4 / 2.0 Tutorial

Next Tutorial Section: Data Manipulation with the ORM