Release: 2.0.40 | Release Date: March 27, 2025

# SQLAlchemy 2.0 Documentation

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy Unified Tutorial.

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# Using INSERT Statements

When using Core as well as when using the ORM for bulk operations, a SQL INSERT statement is generated directly 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 -

This section details the Core means of generating an individual SQL INSERT statement in order to add new rows to a table. When using the ORM, we normally use another tool that rides on top of this called the unit of work, which will automate the production of many INSERT statements at once. However, understanding how the Core handles data creation and manipulation is very useful even when the ORM is running it for us. Additionally, the ORM supports direct use of INSERT using a feature called Bulk / Multi Row INSERT, upsert, UPDATE and DELETE.

To skip directly to how to INSERT rows with the ORM using normal unit of work patterns, see Inserting Rows using the ORM Unit of Work pattern.

## 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. If we don’t actually use `Insert.values()` and just print out an “empty” statement, we get an INSERT for every column in the table:

    >>> print(insert(user_table))
    INSERT INTO user_account (id, name, fullname) VALUES (:id, :name, :fullname)

If we take an `Insert` construct that has not had `Insert.values()` called upon it and execute it rather than print it, the statement will be compiled to a string based on the parameters that we passed to the `Connection.execute()` method, and only include columns relevant to the parameters that were passed. This is actually the usual way that `Insert` is used to insert rows without having to type out an explicit VALUES clause. The example below illustrates a two-column INSERT statement being executed with a list of parameters at once:

    >>> 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

With that, we have some more interesting data in our tables that we will make use of in the upcoming sections.

Tip

A true “empty” INSERT that inserts only the “defaults” for a table without including any explicit values at all is generated if we indicate `Insert.values()` with no arguments; not every database backend supports this, but here’s what SQLite produces:

    >>> print(insert(user_table).values().compile(engine))
    INSERT INTO user_account DEFAULT VALUES

## 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.

For INSERT statements, the RETURNING feature may be used both for single-row statements as well as for statements that INSERT multiple rows at once. Support for multiple-row INSERT with RETURNING is dialect specific, however is supported for all the dialects that are included in SQLAlchemy which support RETURNING. See the section “Insert Many Values” Behavior for INSERT statements for background on this feature.

See also

Bulk INSERT with or without RETURNING is also supported by the ORM. See ORM Bulk INSERT Statements for reference documentation.

## INSERT…FROM SELECT

A less used feature of `Insert`, but here for completeness, the `Insert` construct can compose an INSERT that gets rows directly from a SELECT using the `Insert.from_select()` method. This method accepts a `select()` construct, which is discussed in the next section, along with a list of column names to be targeted in the actual INSERT. In the example below, rows are added to the `address` table which are derived from rows in the `user_account` table, giving each user a free email address at `aol.com`:

    >>> 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

This construct is used when one wants to copy data from some other part of the database directly into a new set of rows, without actually fetching and re-sending the data from the client.

See also

`Insert` - in the SQL Expression API documentation

SQLAlchemy 1.4 / 2.0 Tutorial

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