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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# Working with Database Metadata

With engines and SQL execution down, we are ready to begin some Alchemy. The central element of both SQLAlchemy Core and ORM is the SQL Expression Language which allows for fluent, composable construction of SQL queries. The foundation for these queries are Python objects that represent database concepts like tables and columns. These objects are known collectively as database metadata.

The most common foundational objects for database metadata in SQLAlchemy are known as `MetaData`, `Table`, and `Column`. The sections below will illustrate how these objects are used in both a Core-oriented style as well as an ORM-oriented style.

ORM readers, stay with us!

As with other sections, Core users can skip the ORM sections, but ORM users would best be familiar with these objects from both perspectives.

## Setting up MetaData with Table objects

When we work with a relational database, the basic structure that we create and query from is known as a table. In SQLAlchemy, the “table” is represented by a Python object similarly named `Table`.

To start using the SQLAlchemy Expression Language, we will want to have `Table` objects constructed that represent all of the database tables we are interested in working with. Each `Table` may be declared, meaning we explicitly spell out in source code what the table looks like, or may be reflected, which means we generate the object based on what’s already present in a particular database. The two approaches can also be blended in many ways.

Whether we will declare or reflect our tables, we start out with a collection that will be where we place our tables known as the `MetaData` object. This object is essentially a facade around a Python dictionary that stores a series of `Table` objects keyed to their string name. Constructing this object looks like:

    >>> from sqlalchemy import MetaData
    >>> metadata_obj = MetaData()

Having a single `MetaData` object for an entire application is the most common case, represented as a module-level variable in a single place in an application, often in a “models” or “dbschema” type of package. There can be multiple `MetaData` collections as well, however it’s typically most helpful if a series of `Table` objects that are related to each other belong to a single `MetaData` collection.

Once we have a `MetaData` object, we can declare some `Table` objects. This tutorial will start with the classic SQLAlchemy tutorial model, that of the table `user`, which would for example represent the users of a website, and the table `address`, representing a list of email addresses associated with rows in the `user` table. We normally assign each `Table` object to a variable that will be how we will refer to the table in application code:

    >>> from sqlalchemy import Table, Column, Integer, String
    >>> user_table = Table(
    ...     "user_account",
    ...     metadata_obj,
    ...     Column('id', Integer, primary_key=True),
    ...     Column('name', String(30)),
    ...     Column('fullname', String)
    ... )

We can observe that the above `Table` construct looks a lot like a SQL CREATE TABLE statement; starting with the table name, then listing out each column, where each column has a name and a datatype. The objects we use above are:

- `Table` - represents a database table and assigns itself to a `MetaData` collection.

- `Column` - represents a column in a database table, and assigns itself to a `Table` object. The `Column` usually includes a string name and a type object. The collection of `Column` objects in terms of the parent `Table` are typically accessed via an associative array located at `Table.c`:

      >>> user_table.c.name
      Column('name', String(length=30), table=<user_account>)

      >>> user_table.c.keys()
      ['id', 'name', 'fullname']

- `Integer`, `String` - these classes represent SQL datatypes and can be passed to a `Column` with or without necessarily being instantiated. Above, we want to give a length of “30” to the “name” column, so we instantiated `String(30)`. But for “id” and “fullname” we did not specify these, so we can send the class itself.

See also

The reference and API documentation for `MetaData`, `Table` and `Column` is at Describing Databases with MetaData. The reference documentation for datatypes is at Column and Data Types.

In an upcoming section, we will illustrate one of the fundamental functions of `Table` which is to generate DDL on a particular database connection. But first we will declare a second `Table`.

## Declaring Simple Constraints

The first `Column` in the above `user_table` includes the `Column.primary_key` parameter which is a shorthand technique of indicating that this `Column` should be part of the primary key for this table. The primary key itself is normally declared implicitly and is represented by the `PrimaryKeyConstraint` construct, which we can see on the `Table.primary_key` attribute on the `Table` object:

    >>> user_table.primary_key
    PrimaryKeyConstraint(Column('id', Integer(), table=<user_account>, primary_key=True, nullable=False))

The constraint that is most typically declared explicitly is the `ForeignKeyConstraint` object that corresponds to a database foreign key constraint. When we declare tables that are related to each other, SQLAlchemy uses the presence of these foreign key constraint declarations not only so that they are emitted within CREATE statements to the database, but also to assist in constructing SQL expressions.

A `ForeignKeyConstraint` that involves only a single column on the target table is typically declared using a column-level shorthand notation via the `ForeignKey` object. Below we declare a second table `address` that will have a foreign key constraint referring to the `user` table:

    >>> from sqlalchemy import ForeignKey
    >>> address_table = Table(
    ...     "address",
    ...     metadata_obj,
    ...     Column('id', Integer, primary_key=True),
    ...     Column('user_id', ForeignKey('user_account.id'), nullable=False),
    ...     Column('email_address', String, nullable=False)
    ... )

The table above also features a third kind of constraint, which in SQL is the “NOT NULL” constraint, indicated above using the `Column.nullable` parameter.

Tip

When using the `ForeignKey` object within a `Column` definition, we can omit the datatype for that `Column`; it is automatically inferred from that of the related column, in the above example the `Integer` datatype of the `user_account.id` column.

In the next section we will emit the completed DDL for the `user` and `address` table to see the completed result.

## Emitting DDL to the Database

We’ve constructed a fairly elaborate object hierarchy to represent two database tables, starting at the root `MetaData` object, then into two `Table` objects, each of which hold onto a collection of `Column` and `Constraint` objects. This object structure will be at the center of most operations we perform with both Core and ORM going forward.

The first useful thing we can do with this structure will be to emit CREATE TABLE statements, or DDL, to our SQLite database so that we can insert and query data from them. We have already all the tools needed to do so, by invoking the `MetaData.create_all()` method on our `MetaData`, sending it the `Engine` that refers to the target database:

    >>> metadata_obj.create_all(engine)
    BEGIN (implicit)
    PRAGMA main.table_...info("user_account")
    ...
    PRAGMA main.table_...info("address")
    ...
    CREATE TABLE user_account (
        id INTEGER NOT NULL,
        name VARCHAR(30),
        fullname VARCHAR,
        PRIMARY KEY (id)
    )
    ...
    CREATE TABLE address (
        id INTEGER NOT NULL,
        user_id INTEGER NOT NULL,
        email_address VARCHAR NOT NULL,
        PRIMARY KEY (id),
        FOREIGN KEY(user_id) REFERENCES user_account (id)
    )
    ...
    COMMIT

The DDL create process by default includes some SQLite-specific PRAGMA statements that test for the existence of each table before emitting a CREATE. The full series of steps are also included within a BEGIN/COMMIT pair to accommodate for transactional DDL (SQLite does actually support transactional DDL, however the `sqlite3` database driver historically runs DDL in “autocommit” mode).

The create process also takes care of emitting CREATE statements in the correct order; above, the FOREIGN KEY constraint is dependent on the `user` table existing, so the `address` table is created second. In more complicated dependency scenarios the FOREIGN KEY constraints may also be applied to tables after the fact using ALTER.

The `MetaData` object also features a `MetaData.drop_all()` method that will emit DROP statements in the reverse order as it would emit CREATE in order to drop schema elements.

Migration tools are usually appropriate

Overall, the CREATE / DROP feature of `MetaData` is useful for test suites, small and/or new applications, and applications that use short-lived databases. For management of an application database schema over the long term however, a schema management tool such as Alembic, which builds upon SQLAlchemy, is likely a better choice, as it can manage and orchestrate the process of incrementally altering a fixed database schema over time as the design of the application changes.

## Defining Table Metadata with the ORM

This ORM-only section will provide an example declaring the same database structure illustrated in the previous section, using a more ORM-centric configuration paradigm. When using the ORM, the process by which we declare `Table` metadata is usually combined with the process of declaring mapped classes. The mapped class is any Python class we’d like to create, which will then have attributes on it that will be linked to the columns in a database table. While there are a few varieties of how this is achieved, the most common style is known as declarative, and allows us to declare our user-defined classes and `Table` metadata at once.

### Setting up the Registry

When using the ORM, the `MetaData` collection remains present, however it itself is contained within an ORM-only object known as the `registry`. We create a `registry` by constructing it:

    >>> from sqlalchemy.orm import registry
    >>> mapper_registry = registry()

The above `registry`, when constructed, automatically includes a `MetaData` object that will store a collection of `Table` objects:

    >>> mapper_registry.metadata
    MetaData()

Instead of declaring `Table` objects directly, we will now declare them indirectly through directives applied to our mapped classes. In the most common approach, each mapped class descends from a common base class known as the declarative base. We get a new declarative base from the `registry` using the `registry.generate_base()` method:

    >>> Base = mapper_registry.generate_base()

Tip

The steps of creating the `registry` and “declarative base” classes can be combined into one step using the historically familiar `declarative_base()` function:

    from sqlalchemy.orm import declarative_base
    Base = declarative_base()

### Declaring Mapped Classes

The `Base` object above is a Python class which will serve as the base class for the ORM mapped classes we declare. We can now define ORM mapped classes for the `user` and `address` table in terms of new classes `User` and `Address`:

    >>> from sqlalchemy.orm import relationship
    >>> class User(Base):
    ...     __tablename__ = 'user_account'
    ...
    ...     id = Column(Integer, primary_key=True)
    ...     name = Column(String(30))
    ...     fullname = Column(String)
    ...
    ...     addresses = relationship("Address", back_populates="user")
    ...
    ...     def __repr__(self):
    ...        return f"User(id={self.id!r}, name={self.name!r}, fullname={self.fullname!r})"

    >>> class Address(Base):
    ...     __tablename__ = 'address'
    ...
    ...     id = Column(Integer, primary_key=True)
    ...     email_address = Column(String, nullable=False)
    ...     user_id = Column(Integer, ForeignKey('user_account.id'))
    ...
    ...     user = relationship("User", back_populates="addresses")
    ...
    ...     def __repr__(self):
    ...         return f"Address(id={self.id!r}, email_address={self.email_address!r})"

The above two classes are now our mapped classes, and are available for use in ORM persistence and query operations, which will be described later. But they also include `Table` objects that were generated as part of the declarative mapping process, and are equivalent to the ones that we declared directly in the previous Core section. We can see these `Table` objects from a declarative mapped class using the `.__table__` attribute:

    >>> User.__table__
    Table('user_account', MetaData(),
        Column('id', Integer(), table=<user_account>, primary_key=True, nullable=False),
        Column('name', String(length=30), table=<user_account>),
        Column('fullname', String(), table=<user_account>), schema=None)

This `Table` object was generated from the declarative process based on the `.__tablename__` attribute defined on each of our classes, as well as through the use of `Column` objects assigned to class-level attributes within the classes. These `Column` objects can usually be declared without an explicit “name” field inside the constructor, as the Declarative process will name them automatically based on the attribute name that was used.

See also

Declarative Mapping - overview of Declarative class mapping

### Other Mapped Class Details

For a few quick explanations for the classes above, note the following attributes:

- the classes have an automatically generated __init__() method - both classes by default receive an `__init__()` method that allows for parameterized construction of the objects. We are free to provide our own `__init__()` method as well. The `__init__()` allows us to create instances of `User` and `Address` passing attribute names, most of which above are linked directly to `Column` objects, as parameter names:

      >>> sandy = User(name="sandy", fullname="Sandy Cheeks")

  More detail on this method is at Default Constructor.

- we provided a __repr__() method - this is fully optional, and is strictly so that our custom classes have a descriptive string representation and is not otherwise required:

      >>> sandy
      User(id=None, name='sandy', fullname='Sandy Cheeks')

  An interesting thing to note above is that the `id` attribute automatically returns `None` when accessed, rather than raising `AttributeError` as would be the usual Python behavior for missing attributes.

- we also included a bidirectional relationship - this is another fully optional construct, where we made use of an ORM construct called `relationship()` on both classes, which indicates to the ORM that these `User` and `Address` classes refer to each other in a one to many / many to one relationship. The use of `relationship()` above is so that we may demonstrate its behavior later in this tutorial; it is not required in order to define the `Table` structure.

### Emitting DDL to the database

This section is named the same as the section Emitting DDL to the Database discussed in terms of Core. This is because emitting DDL with our ORM mapped classes is not any different. If we wanted to emit DDL for the `Table` objects we’ve created as part of our declaratively mapped classes, we still can use `MetaData.create_all()` as before.

In our case, we have already generated the `user` and `address` tables in our SQLite database. If we had not done so already, we would be free to make use of the `MetaData` associated with our `registry` and ORM declarative base class in order to do so, using `MetaData.create_all()`:

    # emit CREATE statements given ORM registry
    mapper_registry.metadata.create_all(engine)

    # the identical MetaData object is also present on the
    # declarative base
    Base.metadata.create_all(engine)

### Combining Core Table Declarations with ORM Declarative

As an alternative approach to the mapping process shown previously at Declaring Mapped Classes, we may also make use of the `Table` objects we created directly in the section Setting up MetaData with Table objects in conjunction with declarative mapped classes from a `declarative_base()` generated base class.

This form is called hybrid table, and it consists of assigning to the `.__table__` attribute directly, rather than having the declarative process generate it:

    mapper_registry = registry()
    Base = mapper_registry.generate_base()

    class User(Base):
        __table__ = user_table

        addresses = relationship("Address", back_populates="user")

        def __repr__(self):
            return f"User({self.name!r}, {self.fullname!r})"

    class Address(Base):
        __table__ = address_table

        user = relationship("User", back_populates="addresses")

        def __repr__(self):
            return f"Address({self.email_address!r})"

Note

The above example is an alternative form to the mapping that’s first illustrated previously at Declaring Mapped Classes. This example is for illustrative purposes only, and is not part of this tutorial’s “doctest” steps, and as such does not need to be run for readers who are executing code examples. The mapping here and the one at Declaring Mapped Classes produce equivalent mappings, but in general one would use only one of these two forms for particular mapped class.

The above two classes are equivalent to those which we declared in the previous mapping example.

The traditional “declarative base” approach using `__tablename__` to automatically generate `Table` objects remains the most popular method to declare table metadata. However, disregarding the ORM mapping functionality it achieves, as far as table declaration it’s merely a syntactical convenience on top of the `Table` constructor.

We will next refer to our ORM mapped classes above when we talk about data manipulation in terms of the ORM, in the section Inserting Rows with the ORM.

## Table Reflection

To round out the section on working with table metadata, we will illustrate another operation that was mentioned at the beginning of the section, that of table reflection. Table reflection refers to the process of generating `Table` and related objects by reading the current state of a database. Whereas in the previous sections we’ve been declaring `Table` objects in Python and then emitting DDL to the database, the reflection process does it in reverse.

As an example of reflection, we will create a new `Table` object which represents the `some_table` object we created manually in the earlier sections of this document. There are again some varieties of how this is performed, however the most basic is to construct a `Table` object, given the name of the table and a `MetaData` collection to which it will belong, then instead of indicating individual `Column` and `Constraint` objects, pass it the target `Engine` using the `Table.autoload_with` parameter:

    >>> some_table = Table("some_table", metadata_obj, autoload_with=engine)
    BEGIN (implicit)
    PRAGMA main.table_...info("some_table")
    [raw sql] ()
    SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = ? AND type = 'table'
    [raw sql] ('some_table',)
    PRAGMA main.foreign_key_list("some_table")
    ...
    PRAGMA main.index_list("some_table")
    ...
    ROLLBACK

At the end of the process, the `some_table` object now contains the information about the `Column` objects present in the table, and the object is usable in exactly the same way as a `Table` that we declared explicitly:

    >>> some_table
    Table('some_table', MetaData(),
        Column('x', INTEGER(), table=<some_table>),
        Column('y', INTEGER(), table=<some_table>),
        schema=None)

See also

Read more about table and schema reflection at Reflecting Database Objects.

For ORM-related variants of table reflection, the section Mapping Declaratively with Reflected Tables includes an overview of the available options.

SQLAlchemy 1.4 / 2.0 Tutorial

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