Release: 1.4.39 | Release Date: June 24, 2022

# SQLAlchemy 1.4 Documentation

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy 1.4 / 2.0 Tutorial.

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# Inserting Rows with Core

When using Core, a SQL INSERT statement is generated using the `insert()` function - this function generates a new instance of `Insert` which represents an INSERT statement in SQL, that adds new data into a table.

ORM Readers - The way that rows are INSERTed into the database from an ORM perspective makes use of object-centric APIs on the `Session` object known as the unit of work process, and is fairly different from the Core-only approach described here. The more ORM-focused sections later starting at Inserting Rows with the ORM subsequent to the Expression Language sections introduce this.

## The insert() SQL Expression Construct

A simple example of `Insert` illustrating the target table and the VALUES clause at once:

    >>> from sqlalchemy import insert
    >>> stmt = insert(user_table).values(name='spongebob', fullname="Spongebob Squarepants")

The above `stmt` variable is an instance of `Insert`. Most SQL expressions can be stringified in place as a means to see the general form of what’s being produced:

    >>> print(stmt)
    INSERT INTO user_account (name, fullname) VALUES (:name, :fullname)

The stringified form is created by producing a `Compiled` form of the object which includes a database-specific string SQL representation of the statement; we can acquire this object directly using the `ClauseElement.compile()` method:

    >>> compiled = stmt.compile()

Our `Insert` construct is an example of a “parameterized” construct, illustrated previously at Sending Parameters; to view the `name` and `fullname` bound parameters, these are available from the `Compiled` construct as well:

    >>> compiled.params
    {'name': 'spongebob', 'fullname': 'Spongebob Squarepants'}

## Executing the Statement

Invoking the statement we can INSERT a row into `user_table`. The INSERT SQL as well as the bundled parameters can be seen in the SQL logging:

    >>> with engine.connect() as conn:
    ...     result = conn.execute(stmt)
    ...     conn.commit()
    BEGIN (implicit)
    INSERT INTO user_account (name, fullname) VALUES (?, ?)
    [...] ('spongebob', 'Spongebob Squarepants')
    COMMIT

In its simple form above, the INSERT statement does not return any rows, and if only a single row is inserted, it will usually include the ability to return information about column-level default values that were generated during the INSERT of that row, most commonly an integer primary key value. In the above case the first row in a SQLite database will normally return `1` for the first integer primary key value, which we can acquire using the `CursorResult.inserted_primary_key` accessor:

    >>> result.inserted_primary_key
    (1,)

Tip

`CursorResult.inserted_primary_key` returns a tuple because a primary key may contain multiple columns. This is known as a composite primary key. The `CursorResult.inserted_primary_key` is intended to always contain the complete primary key of the record just inserted, not just a “cursor.lastrowid” kind of value, and is also intended to be populated regardless of whether or not “autoincrement” were used, hence to express a complete primary key it’s a tuple.

Changed in version 1.4.8: the tuple returned by `CursorResult.inserted_primary_key` is now a named tuple fulfilled by returning it as a `Row` object.

## INSERT usually generates the “values” clause automatically

The example above made use of the `Insert.values()` method to explicitly create the VALUES clause of the SQL INSERT statement. This method in fact has some variants that allow for special forms such as multiple rows in one statement and insertion of SQL expressions. However the usual way that `Insert` is used is such that the VALUES clause is generated automatically from the parameters passed to the `Connection.execute()` method; below we INSERT two more rows to illustrate this:

    >>> with engine.connect() as conn:
    ...     result = conn.execute(
    ...         insert(user_table),
    ...         [
    ...             {"name": "sandy", "fullname": "Sandy Cheeks"},
    ...             {"name": "patrick", "fullname": "Patrick Star"}
    ...         ]
    ...     )
    ...     conn.commit()
    BEGIN (implicit)
    INSERT INTO user_account (name, fullname) VALUES (?, ?)
    [...] (('sandy', 'Sandy Cheeks'), ('patrick', 'Patrick Star'))
    COMMIT

The execution above features “executemany” form first illustrated at Sending Multiple Parameters, however unlike when using the `text()` construct, we didn’t have to spell out any SQL. By passing a dictionary or list of dictionaries to the `Connection.execute()` method in conjunction with the `Insert` construct, the `Connection` ensures that the column names which are passed will be expressed in the VALUES clause of the `Insert` construct automatically.

Deep Alchemy

Hi, welcome to the first edition of Deep Alchemy. The person on the left is known as The Alchemist, and you’ll note they are not a wizard, as the pointy hat is not sticking upwards. The Alchemist comes around to describe things that are generally more advanced and/or tricky and additionally not usually needed, but for whatever reason they feel you should know about this thing that SQLAlchemy can do.

In this edition, towards the goal of having some interesting data in the `address_table` as well, below is a more advanced example illustrating how the `Insert.values()` method may be used explicitly while at the same time including for additional VALUES generated from the parameters. A scalar subquery is constructed, making use of the `select()` construct introduced in the next section, and the parameters used in the subquery are set up using an explicit bound parameter name, established using the `bindparam()` construct.

This is some slightly deeper alchemy just so that we can add related rows without fetching the primary key identifiers from the `user_table` operation into the application. Most Alchemists will simply use the ORM which takes care of things like this for us.

    >>> from sqlalchemy import select, bindparam
    >>> scalar_subq = (
    ...     select(user_table.c.id).
    ...     where(user_table.c.name==bindparam('username')).
    ...     scalar_subquery()
    ... )

    >>> with engine.connect() as conn:
    ...     result = conn.execute(
    ...         insert(address_table).values(user_id=scalar_subq),
    ...         [
    ...             {"username": 'spongebob', "email_address": "spongebob@sqlalchemy.org"},
    ...             {"username": 'sandy', "email_address": "sandy@sqlalchemy.org"},
    ...             {"username": 'sandy', "email_address": "sandy@squirrelpower.org"},
    ...         ]
    ...     )
    ...     conn.commit()
    BEGIN (implicit)
    INSERT INTO address (user_id, email_address) VALUES ((SELECT user_account.id
    FROM user_account
    WHERE user_account.name = ?), ?)
    [...] (('spongebob', 'spongebob@sqlalchemy.org'), ('sandy', 'sandy@sqlalchemy.org'),
    ('sandy', 'sandy@squirrelpower.org'))
    COMMIT

## INSERT…FROM SELECT

The `Insert` construct can compose an INSERT that gets rows directly from a SELECT using the `Insert.from_select()` method:

    >>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
    >>> insert_stmt = insert(address_table).from_select(
    ...     ["user_id", "email_address"], select_stmt
    ... )
    >>> print(insert_stmt)
    INSERT INTO address (user_id, email_address)
    SELECT user_account.id, user_account.name || :name_1 AS anon_1
    FROM user_account

## INSERT…RETURNING

The RETURNING clause for supported backends is used automatically in order to retrieve the last inserted primary key value as well as the values for server defaults. However the RETURNING clause may also be specified explicitly using the `Insert.returning()` method; in this case, the `Result` object that’s returned when the statement is executed has rows which can be fetched:

    >>> insert_stmt = insert(address_table).returning(address_table.c.id, address_table.c.email_address)
    >>> print(insert_stmt)
    INSERT INTO address (id, user_id, email_address)
    VALUES (:id, :user_id, :email_address)
    RETURNING address.id, address.email_address

It can also be combined with `Insert.from_select()`, as in the example below that builds upon the example stated in INSERT…FROM SELECT:

    >>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
    >>> insert_stmt = insert(address_table).from_select(
    ...     ["user_id", "email_address"], select_stmt
    ... )
    >>> print(insert_stmt.returning(address_table.c.id, address_table.c.email_address))
    INSERT INTO address (user_id, email_address)
    SELECT user_account.id, user_account.name || :name_1 AS anon_1
    FROM user_account RETURNING address.id, address.email_address

Tip

The RETURNING feature is also supported by UPDATE and DELETE statements, which will be introduced later in this tutorial. The RETURNING feature is generally 1 only supported for statement executions that use a single set of bound parameters; that is, it wont work with the “executemany” form introduced at Sending Multiple Parameters. Additionally, some dialects such as the Oracle dialect only allow RETURNING to return a single row overall, meaning it won’t work with “INSERT..FROM SELECT” nor will it work with multiple row `Update` or `Delete` forms.

1

  There is internal support for the `psycopg2` dialect to INSERT many rows at once and also support RETURNING, which is leveraged by the SQLAlchemy ORM. However this feature has not been generalized to all dialects and is not yet part of SQLAlchemy’s regular API.

See also

`Insert` - in the SQL Expression API documentation

SQLAlchemy 1.4 / 2.0 Tutorial

Next Tutorial Section: Selecting Rows with Core or ORM