Release: 1.4.46 | Release Date: January 3, 2023

# SQLAlchemy 1.4 Documentation

# PostgreSQL

Support for the PostgreSQL database.

The following table summarizes current support levels for database release versions.

Supported PostgreSQL versions
Support type | Versions
--- | ---
Fully tested in CI | 9.6, 10, 11, 12, 13, 14
Normal support | 9.6+
Best effort | 8+

## DBAPI Support

The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.

- psycopg2

- pg8000

- asyncpg

- psycopg2cffi

- py-postgresql

- pygresql

## Sequences/SERIAL/IDENTITY

PostgreSQL supports sequences, and SQLAlchemy uses these as the default means of creating new primary key values for integer-based primary key columns. When creating tables, SQLAlchemy will issue the `SERIAL` datatype for integer-based primary key columns, which generates a sequence and server side default corresponding to the column.

To specify a specific named sequence to be used for primary key generation, use the `Sequence()` construct:

    Table('sometable', metadata,
            Column('id', Integer, Sequence('some_id_seq'), primary_key=True)
        )

When SQLAlchemy issues a single INSERT statement, to fulfill the contract of having the “last insert identifier” available, a RETURNING clause is added to the INSERT statement which specifies the primary key columns should be returned after the statement completes. The RETURNING functionality only takes place if PostgreSQL 8.2 or later is in use. As a fallback approach, the sequence, whether specified explicitly or implicitly via `SERIAL`, is executed independently beforehand, the returned value to be used in the subsequent insert. Note that when an `insert()` construct is executed using “executemany” semantics, the “last inserted identifier” functionality does not apply; no RETURNING clause is emitted nor is the sequence pre-executed in this case.

To force the usage of RETURNING by default off, specify the flag `implicit_returning=False` to `create_engine()`.

### PostgreSQL 10 and above IDENTITY columns

PostgreSQL 10 and above have a new IDENTITY feature that supersedes the use of SERIAL. The `Identity` construct in a `Column` can be used to control its behavior:

    from sqlalchemy import Table, Column, MetaData, Integer, Computed

    metadata = MetaData()

    data = Table(
        "data",
        metadata,
        Column(
            'id', Integer, Identity(start=42, cycle=True), primary_key=True
        ),
        Column('data', String)
    )

The CREATE TABLE for the above `Table` object would be:

    CREATE TABLE data (
        id INTEGER GENERATED BY DEFAULT AS IDENTITY (START WITH 42 CYCLE),
        data VARCHAR,
        PRIMARY KEY (id)
    )

Changed in version 1.4: Added `Identity` construct in a `Column` to specify the option of an autoincrementing column.

Note

Previous versions of SQLAlchemy did not have built-in support for rendering of IDENTITY, and could use the following compilation hook to replace occurrences of SERIAL with IDENTITY:

    from sqlalchemy.schema import CreateColumn
    from sqlalchemy.ext.compiler import compiles

    @compiles(CreateColumn, 'postgresql')
    def use_identity(element, compiler, **kw):
        text = compiler.visit_create_column(element, **kw)
        text = text.replace(
            "SERIAL", "INT GENERATED BY DEFAULT AS IDENTITY"
         )
        return text

Using the above, a table such as:

    t = Table(
        't', m,
        Column('id', Integer, primary_key=True),
        Column('data', String)
    )

Will generate on the backing database as:

    CREATE TABLE t (
        id INT GENERATED BY DEFAULT AS IDENTITY,
        data VARCHAR,
        PRIMARY KEY (id)
    )

## Server Side Cursors

Server-side cursor support is available for the psycopg2, asyncpg dialects and may also be available in others.

Server side cursors are enabled on a per-statement basis by using the `Connection.execution_options.stream_results` connection execution option:

    with engine.connect() as conn:
        result = conn.execution_options(stream_results=True).execute(text("select * from table"))

Note that some kinds of SQL statements may not be supported with server side cursors; generally, only SQL statements that return rows should be used with this option.

Deprecated since version 1.4: The dialect-level server_side_cursors flag is deprecated and will be removed in a future release. Please use the `Connection.stream_results` execution option for unbuffered cursor support.

See also

Using Server Side Cursors (a.k.a. stream results)

## Transaction Isolation Level

Most SQLAlchemy dialects support setting of transaction isolation level using the `create_engine.isolation_level` parameter at the `create_engine()` level, and at the `Connection` level via the `Connection.execution_options.isolation_level` parameter.

For PostgreSQL dialects, this feature works either by making use of the DBAPI-specific features, such as psycopg2’s isolation level flags which will embed the isolation level setting inline with the `"BEGIN"` statement, or for DBAPIs with no direct support by emitting `SET SESSION CHARACTERISTICS AS TRANSACTION ISOLATION LEVEL <level>` ahead of the `"BEGIN"` statement emitted by the DBAPI. For the special AUTOCOMMIT isolation level, DBAPI-specific techniques are used which is typically an `.autocommit` flag on the DBAPI connection object.

To set isolation level using `create_engine()`:

    engine = create_engine(
        "postgresql+pg8000://scott:tiger@localhost/test",
        isolation_level = "REPEATABLE READ"
    )

To set using per-connection execution options:

    with engine.connect() as conn:
        conn = conn.execution_options(
            isolation_level="REPEATABLE READ"
        )
        with conn.begin():
            # ... work with transaction

There are also more options for isolation level configurations, such as “sub-engine” objects linked to a main `Engine` which each apply different isolation level settings. See the discussion at Setting Transaction Isolation Levels including DBAPI Autocommit for background.

Valid values for `isolation_level` on most PostgreSQL dialects include:

- `READ COMMITTED`

- `READ UNCOMMITTED`

- `REPEATABLE READ`

- `SERIALIZABLE`

- `AUTOCOMMIT`

See also

Setting Transaction Isolation Levels including DBAPI Autocommit

Setting READ ONLY / DEFERRABLE

Psycopg2 Transaction Isolation Level

pg8000 Transaction Isolation Level

## Setting READ ONLY / DEFERRABLE

Most PostgreSQL dialects support setting the “READ ONLY” and “DEFERRABLE” characteristics of the transaction, which is in addition to the isolation level setting. These two attributes can be established either in conjunction with or independently of the isolation level by passing the `postgresql_readonly` and `postgresql_deferrable` flags with `Connection.execution_options()`. The example below illustrates passing the `"SERIALIZABLE"` isolation level at the same time as setting “READ ONLY” and “DEFERRABLE”:

    with engine.connect() as conn:
        conn = conn.execution_options(
            isolation_level="SERIALIZABLE",
            postgresql_readonly=True,
            postgresql_deferrable=True
        )
        with conn.begin():
            #  ... work with transaction

Note that some DBAPIs such as asyncpg only support “readonly” with SERIALIZABLE isolation.

New in version 1.4: added support for the `postgresql_readonly` and `postgresql_deferrable` execution options.

## Temporary Table / Resource Reset for Connection Pooling

The `QueuePool` connection pool implementation used by the SQLAlchemy `Engine` object includes reset on return behavior that will invoke the DBAPI `.rollback()` method when connections are returned to the pool. While this rollback will clear out the immediate state used by the previous transaction, it does not cover a wider range of session-level state, including temporary tables as well as other server state such as prepared statement handles and statement caches. The PostgreSQL database includes a variety of commands which may be used to reset this state, including `DISCARD`, `RESET`, `DEALLOCATE`, and `UNLISTEN`.

To install one or more of these commands as the means of performing reset-on-return, the `PoolEvents.reset()` event hook may be used, as demonstrated in the example below (requires SQLAlchemy 1.4.43 or greater). The implementation will end transactions in progress as well as discard temporary tables using the `CLOSE`, `RESET` and `DISCARD` commands; see the PostgreSQL documentation for background on what each of these statements do.

The `create_engine.pool_reset_on_return` parameter is set to `None` so that the custom scheme can replace the default behavior completely. The custom hook implementation calls `.rollback()` in any case, as it’s usually important that the DBAPI’s own tracking of commit/rollback will remain consistent with the state of the transaction:

    from sqlalchemy import create_engine
    from sqlalchemy import event

    postgresql_engine = create_engine(
        "postgresql+pyscopg2://scott:tiger@hostname/dbname",

        # disable default reset-on-return scheme
        pool_reset_on_return=None,
    )

    @event.listens_for(postgresql_engine, "reset")
    def _reset_postgresql(dbapi_connection, connection_record, reset_state):
        dbapi_connection.execute("CLOSE ALL")
        dbapi_connection.execute("RESET ALL")
        dbapi_connection.execute("DISCARD TEMP")

        # so that the DBAPI itself knows that the connection has been
        # reset
        dbapi_connection.rollback()

Changed in version 1.4.43: Ensured the `PoolEvents.reset()` event is invoked for all “reset” occurrences, so that it’s appropriate as a place for custom “reset” handlers. Previous schemes which use the `PoolEvents.checkin()` handler remain usable as well.

See also

Reset On Return - in the Connection Pooling documentation

## Setting Alternate Search Paths on Connect

The PostgreSQL `search_path` variable refers to the list of schema names that will be implicitly referred towards when a particular table or other object is referenced in a SQL statement. As detailed in the next section Remote-Schema Table Introspection and PostgreSQL search_path, SQLAlchemy is generally organized around the concept of keeping this variable at its default value of `public`, however, in order to have it set to any arbitrary name or names when connections are used automatically, the “SET SESSION search_path” command may be invoked for all connections in a pool using the following event handler, as discussed at Setting a Default Schema for New Connections:

    from sqlalchemy import event
    from sqlalchemy import create_engine

    engine = create_engine("postgresql+psycopg2://scott:tiger@host/dbname")

    @event.listens_for(engine, "connect", insert=True)
    def set_search_path(dbapi_connection, connection_record):
        existing_autocommit = dbapi_connection.autocommit
        dbapi_connection.autocommit = True
        cursor = dbapi_connection.cursor()
        cursor.execute("SET SESSION search_path='%s'" % schema_name)
        cursor.close()
        dbapi_connection.autocommit = existing_autocommit

The reason the recipe is complicated by use of the `.autocommit` DBAPI attribute is so that when the `SET SESSION search_path` directive is invoked, it is invoked outside of the scope of any transaction and therefore will not be reverted when the DBAPI connection has a rollback.

See also

Setting a Default Schema for New Connections - in the Describing Databases with MetaData documentation

## Remote-Schema Table Introspection and PostgreSQL search_path

Section Best Practices Summarized

keep the `search_path` variable set to its default of `public`, without any other schema names. For other schema names, name these explicitly within `Table` definitions. Alternatively, the `postgresql_ignore_search_path` option will cause all reflected `Table` objects to have a `Table.schema` attribute set up.

The PostgreSQL dialect can reflect tables from any schema, as outlined in Reflecting Tables from Other Schemas.

With regards to tables which these `Table` objects refer to via foreign key constraint, a decision must be made as to how the `.schema` is represented in those remote tables, in the case where that remote schema name is also a member of the current PostgreSQL search path.

By default, the PostgreSQL dialect mimics the behavior encouraged by PostgreSQL’s own `pg_get_constraintdef()` builtin procedure. This function returns a sample definition for a particular foreign key constraint, omitting the referenced schema name from that definition when the name is also in the PostgreSQL schema search path. The interaction below illustrates this behavior:

    test=> CREATE TABLE test_schema.referred(id INTEGER PRIMARY KEY);
    CREATE TABLE
    test=> CREATE TABLE referring(
    test(>         id INTEGER PRIMARY KEY,
    test(>         referred_id INTEGER REFERENCES test_schema.referred(id));
    CREATE TABLE
    test=> SET search_path TO public, test_schema;
    test=> SELECT pg_catalog.pg_get_constraintdef(r.oid, true) FROM
    test-> pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n
    test-> ON n.oid = c.relnamespace
    test-> JOIN pg_catalog.pg_constraint r  ON c.oid = r.conrelid
    test-> WHERE c.relname='referring' AND r.contype = 'f'
    test-> ;
                   pg_get_constraintdef
    ---------------------------------------------------
     FOREIGN KEY (referred_id) REFERENCES referred(id)
    (1 row)

Above, we created a table `referred` as a member of the remote schema `test_schema`, however when we added `test_schema` to the PG `search_path` and then asked `pg_get_constraintdef()` for the `FOREIGN KEY` syntax, `test_schema` was not included in the output of the function.

On the other hand, if we set the search path back to the typical default of `public`:

    test=> SET search_path TO public;
    SET

The same query against `pg_get_constraintdef()` now returns the fully schema-qualified name for us:

    test=> SELECT pg_catalog.pg_get_constraintdef(r.oid, true) FROM
    test-> pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n
    test-> ON n.oid = c.relnamespace
    test-> JOIN pg_catalog.pg_constraint r  ON c.oid = r.conrelid
    test-> WHERE c.relname='referring' AND r.contype = 'f';
                         pg_get_constraintdef
    ---------------------------------------------------------------
     FOREIGN KEY (referred_id) REFERENCES test_schema.referred(id)
    (1 row)

SQLAlchemy will by default use the return value of `pg_get_constraintdef()` in order to determine the remote schema name. That is, if our `search_path` were set to include `test_schema`, and we invoked a table reflection process as follows:

    >>> from sqlalchemy import Table, MetaData, create_engine, text
    >>> engine = create_engine("postgresql://scott:tiger@localhost/test")
    >>> with engine.connect() as conn:
    ...     conn.execute(text("SET search_path TO test_schema, public"))
    ...     metadata_obj = MetaData()
    ...     referring = Table('referring', metadata_obj,
    ...                       autoload_with=conn)
    ...
    <sqlalchemy.engine.result.CursorResult object at 0x101612ed0>

The above process would deliver to the `MetaData.tables` collection `referred` table named without the schema:

    >>> metadata_obj.tables['referred'].schema is None
    True

To alter the behavior of reflection such that the referred schema is maintained regardless of the `search_path` setting, use the `postgresql_ignore_search_path` option, which can be specified as a dialect-specific argument to both `Table` as well as `MetaData.reflect()`:

    >>> with engine.connect() as conn:
    ...     conn.execute(text("SET search_path TO test_schema, public"))
    ...     metadata_obj = MetaData()
    ...     referring = Table('referring', metadata_obj,
    ...                       autoload_with=conn,
    ...                       postgresql_ignore_search_path=True)
    ...
    <sqlalchemy.engine.result.CursorResult object at 0x1016126d0>

We will now have `test_schema.referred` stored as schema-qualified:

    >>> metadata_obj.tables['test_schema.referred'].schema
    'test_schema'

Note that in all cases, the “default” schema is always reflected as `None`. The “default” schema on PostgreSQL is that which is returned by the PostgreSQL `current_schema()` function. On a typical PostgreSQL installation, this is the name `public`. So a table that refers to another which is in the `public` (i.e. default) schema will always have the `.schema` attribute set to `None`.

See also

Interaction of Schema-qualified Reflection with the Default Schema - discussion of the issue from a backend-agnostic perspective

The Schema Search Path - on the PostgreSQL website.

## INSERT/UPDATE…RETURNING

The dialect supports PG 8.2’s `INSERT..RETURNING`, `UPDATE..RETURNING` and `DELETE..RETURNING` syntaxes. `INSERT..RETURNING` is used by default for single-row INSERT statements in order to fetch newly generated primary key identifiers. To specify an explicit `RETURNING` clause, use the `_UpdateBase.returning()` method on a per-statement basis:

    # INSERT..RETURNING
    result = table.insert().returning(table.c.col1, table.c.col2).\
        values(name='foo')
    print(result.fetchall())

    # UPDATE..RETURNING
    result = table.update().returning(table.c.col1, table.c.col2).\
        where(table.c.name=='foo').values(name='bar')
    print(result.fetchall())

    # DELETE..RETURNING
    result = table.delete().returning(table.c.col1, table.c.col2).\
        where(table.c.name=='foo')
    print(result.fetchall())

## INSERT…ON CONFLICT (Upsert)

Starting with version 9.5, PostgreSQL allows “upserts” (update or insert) of rows into a table via the `ON CONFLICT` clause of the `INSERT` statement. A candidate row will only be inserted if that row does not violate any unique constraints. In the case of a unique constraint violation, a secondary action can occur which can be either “DO UPDATE”, indicating that the data in the target row should be updated, or “DO NOTHING”, which indicates to silently skip this row.

Conflicts are determined using existing unique constraints and indexes. These constraints may be identified either using their name as stated in DDL, or they may be inferred by stating the columns and conditions that comprise the indexes.

SQLAlchemy provides `ON CONFLICT` support via the PostgreSQL-specific `insert()` function, which provides the generative methods `Insert.on_conflict_do_update()` and `Insert.on_conflict_do_nothing()`:

    >>> from sqlalchemy.dialects.postgresql import insert
    >>> insert_stmt = insert(my_table).values(
    ...     id='some_existing_id',
    ...     data='inserted value')
    >>> do_nothing_stmt = insert_stmt.on_conflict_do_nothing(
    ...     index_elements=['id']
    ... )
    >>> print(do_nothing_stmt)
    INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
    ON CONFLICT (id) DO NOTHING
    >>> do_update_stmt = insert_stmt.on_conflict_do_update(
    ...     constraint='pk_my_table',
    ...     set_=dict(data='updated value')
    ... )
    >>> print(do_update_stmt)
    INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
    ON CONFLICT ON CONSTRAINT pk_my_table DO UPDATE SET data = %(param_1)s

New in version 1.1.

See also

INSERT .. ON CONFLICT - in the PostgreSQL documentation.

### Specifying the Target

Both methods supply the “target” of the conflict using either the named constraint or by column inference:

- The `Insert.on_conflict_do_update.index_elements` argument specifies a sequence containing string column names, `Column` objects, and/or SQL expression elements, which would identify a unique index:

      >>> do_update_stmt = insert_stmt.on_conflict_do_update(
      ...     index_elements=['id'],
      ...     set_=dict(data='updated value')
      ... )
      >>> print(do_update_stmt)
      INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
      ON CONFLICT (id) DO UPDATE SET data = %(param_1)s
      >>> do_update_stmt = insert_stmt.on_conflict_do_update(
      ...     index_elements=[my_table.c.id],
      ...     set_=dict(data='updated value')
      ... )
      >>> print(do_update_stmt)
      INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
      ON CONFLICT (id) DO UPDATE SET data = %(param_1)s

- When using `Insert.on_conflict_do_update.index_elements` to infer an index, a partial index can be inferred by also specifying the use the `Insert.on_conflict_do_update.index_where` parameter:

      >>> stmt = insert(my_table).values(user_email='a@b.com', data='inserted data')
      >>> stmt = stmt.on_conflict_do_update(
      ...     index_elements=[my_table.c.user_email],
      ...     index_where=my_table.c.user_email.like('%@gmail.com'),
      ...     set_=dict(data=stmt.excluded.data)
      ... )
      >>> print(stmt)
      INSERT INTO my_table (data, user_email)
      VALUES (%(data)s, %(user_email)s) ON CONFLICT (user_email)
      WHERE user_email LIKE %(user_email_1)s DO UPDATE SET data = excluded.data

- The `Insert.on_conflict_do_update.constraint` argument is used to specify an index directly rather than inferring it. This can be the name of a UNIQUE constraint, a PRIMARY KEY constraint, or an INDEX:

      >>> do_update_stmt = insert_stmt.on_conflict_do_update(
      ...     constraint='my_table_idx_1',
      ...     set_=dict(data='updated value')
      ... )
      >>> print(do_update_stmt)
      INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
      ON CONFLICT ON CONSTRAINT my_table_idx_1 DO UPDATE SET data = %(param_1)s
      >>> do_update_stmt = insert_stmt.on_conflict_do_update(
      ...     constraint='my_table_pk',
      ...     set_=dict(data='updated value')
      ... )
      >>> print(do_update_stmt)
      INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
      ON CONFLICT ON CONSTRAINT my_table_pk DO UPDATE SET data = %(param_1)s

- The `Insert.on_conflict_do_update.constraint` argument may also refer to a SQLAlchemy construct representing a constraint, e.g. `UniqueConstraint`, `PrimaryKeyConstraint`, `Index`, or `ExcludeConstraint`. In this use, if the constraint has a name, it is used directly. Otherwise, if the constraint is unnamed, then inference will be used, where the expressions and optional WHERE clause of the constraint will be spelled out in the construct. This use is especially convenient to refer to the named or unnamed primary key of a `Table` using the `Table.primary_key` attribute:

      >>> do_update_stmt = insert_stmt.on_conflict_do_update(
      ...     constraint=my_table.primary_key,
      ...     set_=dict(data='updated value')
      ... )
      >>> print(do_update_stmt)
      INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
      ON CONFLICT (id) DO UPDATE SET data = %(param_1)s

### The SET Clause

`ON CONFLICT...DO UPDATE` is used to perform an update of the already existing row, using any combination of new values as well as values from the proposed insertion. These values are specified using the `Insert.on_conflict_do_update.set_` parameter. This parameter accepts a dictionary which consists of direct values for UPDATE:

    >>> stmt = insert(my_table).values(id='some_id', data='inserted value')
    >>> do_update_stmt = stmt.on_conflict_do_update(
    ...     index_elements=['id'],
    ...     set_=dict(data='updated value')
    ... )
    >>> print(do_update_stmt)
    INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
    ON CONFLICT (id) DO UPDATE SET data = %(param_1)s

Warning

The `Insert.on_conflict_do_update()` method does not take into account Python-side default UPDATE values or generation functions, e.g. those specified using `Column.onupdate`. These values will not be exercised for an ON CONFLICT style of UPDATE, unless they are manually specified in the `Insert.on_conflict_do_update.set_` dictionary.

### Updating using the Excluded INSERT Values

In order to refer to the proposed insertion row, the special alias `Insert.excluded` is available as an attribute on the `Insert` object; this object is a `ColumnCollection` which alias contains all columns of the target table:

    >>> stmt = insert(my_table).values(
    ...     id='some_id',
    ...     data='inserted value',
    ...     author='jlh'
    ... )
    >>> do_update_stmt = stmt.on_conflict_do_update(
    ...     index_elements=['id'],
    ...     set_=dict(data='updated value', author=stmt.excluded.author)
    ... )
    >>> print(do_update_stmt)
    INSERT INTO my_table (id, data, author)
    VALUES (%(id)s, %(data)s, %(author)s)
    ON CONFLICT (id) DO UPDATE SET data = %(param_1)s, author = excluded.author

### Additional WHERE Criteria

The `Insert.on_conflict_do_update()` method also accepts a WHERE clause using the `Insert.on_conflict_do_update.where` parameter, which will limit those rows which receive an UPDATE:

    >>> stmt = insert(my_table).values(
    ...     id='some_id',
    ...     data='inserted value',
    ...     author='jlh'
    ... )
    >>> on_update_stmt = stmt.on_conflict_do_update(
    ...     index_elements=['id'],
    ...     set_=dict(data='updated value', author=stmt.excluded.author),
    ...     where=(my_table.c.status == 2)
    ... )
    >>> print(on_update_stmt)
    INSERT INTO my_table (id, data, author)
    VALUES (%(id)s, %(data)s, %(author)s)
    ON CONFLICT (id) DO UPDATE SET data = %(param_1)s, author = excluded.author
    WHERE my_table.status = %(status_1)s

### Skipping Rows with DO NOTHING

`ON CONFLICT` may be used to skip inserting a row entirely if any conflict with a unique or exclusion constraint occurs; below this is illustrated using the `Insert.on_conflict_do_nothing()` method:

    >>> stmt = insert(my_table).values(id='some_id', data='inserted value')
    >>> stmt = stmt.on_conflict_do_nothing(index_elements=['id'])
    >>> print(stmt)
    INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
    ON CONFLICT (id) DO NOTHING

If `DO NOTHING` is used without specifying any columns or constraint, it has the effect of skipping the INSERT for any unique or exclusion constraint violation which occurs:

    >>> stmt = insert(my_table).values(id='some_id', data='inserted value')
    >>> stmt = stmt.on_conflict_do_nothing()
    >>> print(stmt)
    INSERT INTO my_table (id, data) VALUES (%(id)s, %(data)s)
    ON CONFLICT DO NOTHING

## Full Text Search

SQLAlchemy makes available the PostgreSQL `@@` operator via the `ColumnElement.match()` method on any textual column expression.

On the PostgreSQL dialect, an expression like the following:

    select(sometable.c.text.match("search string"))

will emit to the database:

    SELECT text @@ to_tsquery('search string') FROM table

Various other PostgreSQL text search functions such as `to_tsquery()`, `to_tsvector()`, and `plainto_tsquery()` are available by explicitly using the standard SQLAlchemy `func` construct.

For example:

    select(func.to_tsvector('fat cats ate rats').match('cat & rat'))

Emits the equivalent of:

    SELECT to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')

The `TSVECTOR` type can provide for explicit CAST:

    from sqlalchemy.dialects.postgresql import TSVECTOR
    from sqlalchemy import select, cast
    select(cast("some text", TSVECTOR))

produces a statement equivalent to:

    SELECT CAST('some text' AS TSVECTOR) AS anon_1

Tip

It’s important to remember that text searching in PostgreSQL is powerful but complicated, and SQLAlchemy users are advised to reference the PostgreSQL documentation regarding Full Text Search.

There are important differences between `to_tsquery` and `plainto_tsquery`, the most significant of which is that `to_tsquery` expects specially formatted “querytext” that is written to PostgreSQL’s own specification, while `plainto_tsquery` expects unformatted text that is transformed into `to_tsquery` compatible querytext. This means the input to `.match()` under PostgreSQL may be incompatible with the input to `.match()` under another database backend. SQLAlchemy users who support multiple backends are advised to carefully implement their usage of `.match()` to work around these constraints.

Full Text Searches in PostgreSQL are influenced by a combination of: the PostgreSQL setting of `default_text_search_config`, the `regconfig` used to build the GIN/GiST indexes, and the `regconfig` optionally passed in during a query.

When performing a Full Text Search against a column that has a GIN or GiST index that is already pre-computed (which is common on full text searches) one may need to explicitly pass in a particular PostgreSQL `regconfig` value to ensure the query-planner utilizes the index and does not re-compute the column on demand.

In order to provide for this explicit query planning, or to use different search strategies, the `match` method accepts a `postgresql_regconfig` keyword argument:

    select(mytable.c.id).where(
        mytable.c.title.match('somestring', postgresql_regconfig='english')
    )

Emits the equivalent of:

    SELECT mytable.id FROM mytable
    WHERE mytable.title @@ to_tsquery('english', 'somestring')

One can also specifically pass in a ‘regconfig’ value to the `to_tsvector()` command as the initial argument:

    select(mytable.c.id).where(
            func.to_tsvector('english', mytable.c.title )\
            .match('somestring', postgresql_regconfig='english')
        )

produces a statement equivalent to:

    SELECT mytable.id FROM mytable
    WHERE to_tsvector('english', mytable.title) @@
        to_tsquery('english', 'somestring')

It is recommended that you use the `EXPLAIN ANALYZE...` tool from PostgreSQL to ensure that you are generating queries with SQLAlchemy that take full advantage of any indexes you may have created for full text search.

See also

Full Text Search - in the PostgreSQL documentation

## FROM ONLY …

The dialect supports PostgreSQL’s ONLY keyword for targeting only a particular table in an inheritance hierarchy. This can be used to produce the `SELECT ... FROM ONLY`, `UPDATE ONLY ...`, and `DELETE FROM ONLY ...` syntaxes. It uses SQLAlchemy’s hints mechanism:

    # SELECT ... FROM ONLY ...
    result = table.select().with_hint(table, 'ONLY', 'postgresql')
    print(result.fetchall())

    # UPDATE ONLY ...
    table.update(values=dict(foo='bar')).with_hint('ONLY',
                                                   dialect_name='postgresql')

    # DELETE FROM ONLY ...
    table.delete().with_hint('ONLY', dialect_name='postgresql')

## PostgreSQL-Specific Index Options

Several extensions to the `Index` construct are available, specific to the PostgreSQL dialect.

### Covering Indexes

The `postgresql_include` option renders INCLUDE(colname) for the given string names:

    Index("my_index", table.c.x, postgresql_include=['y'])

would render the index as `CREATE INDEX my_index ON table (x) INCLUDE (y)`

Note that this feature requires PostgreSQL 11 or later.

New in version 1.4.

### Partial Indexes

Partial indexes add criterion to the index definition so that the index is applied to a subset of rows. These can be specified on `Index` using the `postgresql_where` keyword argument:

    Index('my_index', my_table.c.id, postgresql_where=my_table.c.value > 10)

### Operator Classes

PostgreSQL allows the specification of an operator class for each column of an index (see https://www.postgresql.org/docs/current/interactive/indexes-opclass.html). The `Index` construct allows these to be specified via the `postgresql_ops` keyword argument:

    Index(
        'my_index', my_table.c.id, my_table.c.data,
        postgresql_ops={
            'data': 'text_pattern_ops',
            'id': 'int4_ops'
        })

Note that the keys in the `postgresql_ops` dictionaries are the “key” name of the `Column`, i.e. the name used to access it from the `.c` collection of `Table`, which can be configured to be different than the actual name of the column as expressed in the database.

If `postgresql_ops` is to be used against a complex SQL expression such as a function call, then to apply to the column it must be given a label that is identified in the dictionary by name, e.g.:

    Index(
        'my_index', my_table.c.id,
        func.lower(my_table.c.data).label('data_lower'),
        postgresql_ops={
            'data_lower': 'text_pattern_ops',
            'id': 'int4_ops'
        })

Operator classes are also supported by the `ExcludeConstraint` construct using the `ExcludeConstraint.ops` parameter. See that parameter for details.

New in version 1.3.21: added support for operator classes with `ExcludeConstraint`.

### Index Types

PostgreSQL provides several index types: B-Tree, Hash, GiST, and GIN, as well as the ability for users to create their own (see https://www.postgresql.org/docs/current/static/indexes-types.html). These can be specified on `Index` using the `postgresql_using` keyword argument:

    Index('my_index', my_table.c.data, postgresql_using='gin')

The value passed to the keyword argument will be simply passed through to the underlying CREATE INDEX command, so it must be a valid index type for your version of PostgreSQL.

### Index Storage Parameters

PostgreSQL allows storage parameters to be set on indexes. The storage parameters available depend on the index method used by the index. Storage parameters can be specified on `Index` using the `postgresql_with` keyword argument:

    Index('my_index', my_table.c.data, postgresql_with={"fillfactor": 50})

New in version 1.0.6.

PostgreSQL allows to define the tablespace in which to create the index. The tablespace can be specified on `Index` using the `postgresql_tablespace` keyword argument:

    Index('my_index', my_table.c.data, postgresql_tablespace='my_tablespace')

New in version 1.1.

Note that the same option is available on `Table` as well.

### Indexes with CONCURRENTLY

The PostgreSQL index option CONCURRENTLY is supported by passing the flag `postgresql_concurrently` to the `Index` construct:

    tbl = Table('testtbl', m, Column('data', Integer))

    idx1 = Index('test_idx1', tbl.c.data, postgresql_concurrently=True)

The above index construct will render DDL for CREATE INDEX, assuming PostgreSQL 8.2 or higher is detected or for a connection-less dialect, as:

    CREATE INDEX CONCURRENTLY test_idx1 ON testtbl (data)

For DROP INDEX, assuming PostgreSQL 9.2 or higher is detected or for a connection-less dialect, it will emit:

    DROP INDEX CONCURRENTLY test_idx1

New in version 1.1: support for CONCURRENTLY on DROP INDEX. The CONCURRENTLY keyword is now only emitted if a high enough version of PostgreSQL is detected on the connection (or for a connection-less dialect).

When using CONCURRENTLY, the PostgreSQL database requires that the statement be invoked outside of a transaction block. The Python DBAPI enforces that even for a single statement, a transaction is present, so to use this construct, the DBAPI’s “autocommit” mode must be used:

    metadata = MetaData()
    table = Table(
        "foo", metadata,
        Column("id", String))
    index = Index(
        "foo_idx", table.c.id, postgresql_concurrently=True)

    with engine.connect() as conn:
        with conn.execution_options(isolation_level='AUTOCOMMIT'):
            table.create(conn)

See also

Transaction Isolation Level

## PostgreSQL Index Reflection

The PostgreSQL database creates a UNIQUE INDEX implicitly whenever the UNIQUE CONSTRAINT construct is used. When inspecting a table using `Inspector`, the `Inspector.get_indexes()` and the `Inspector.get_unique_constraints()` will report on these two constructs distinctly; in the case of the index, the key `duplicates_constraint` will be present in the index entry if it is detected as mirroring a constraint. When performing reflection using `Table(..., autoload_with=engine)`, the UNIQUE INDEX is not returned in `Table.indexes` when it is detected as mirroring a `UniqueConstraint` in the `Table.constraints` collection .

Changed in version 1.0.0: - `Table` reflection now includes `UniqueConstraint` objects present in the `Table.constraints` collection; the PostgreSQL backend will no longer include a “mirrored” `Index` construct in `Table.indexes` if it is detected as corresponding to a unique constraint.

## Special Reflection Options

The `Inspector` used for the PostgreSQL backend is an instance of `PGInspector`, which offers additional methods:

    from sqlalchemy import create_engine, inspect

    engine = create_engine("postgresql+psycopg2://localhost/test")
    insp = inspect(engine)  # will be a PGInspector

    print(insp.get_enums())

Object Name | Description
--- | ---
PGInspector |

class sqlalchemy.dialects.postgresql.base.PGInspector(bind)

  Members

  get_enums(), get_foreign_table_names(), get_table_oid(), get_view_names()

  Class signature

  class `sqlalchemy.dialects.postgresql.base.PGInspector` (`sqlalchemy.engine.reflection.Inspector`)

  method `sqlalchemy.dialects.postgresql.base.PGInspector.` get_enums(schema=None)

    Return a list of ENUM objects.

    Each member is a dictionary containing these fields:

    > - name - name of the enum

    > - schema - the schema name for the enum.

    > - visible - boolean, whether or not this enum is visible in the default search path.

    > - labels - a list of string labels that apply to the enum.

    Parameters:

      schema – schema name. If None, the default schema (typically ‘public’) is used. May also be set to ‘*’ to indicate load enums for all schemas.

    New in version 1.0.0.

  method `sqlalchemy.dialects.postgresql.base.PGInspector.` get_foreign_table_names(schema=None)

    Return a list of FOREIGN TABLE names.

    Behavior is similar to that of `Inspector.get_table_names()`, except that the list is limited to those tables that report a `relkind` value of `f`.

    New in version 1.0.0.

  method `sqlalchemy.dialects.postgresql.base.PGInspector.` get_table_oid(table_name, schema=None)

    Return the OID for the given table name.

  method `sqlalchemy.dialects.postgresql.base.PGInspector.` get_view_names(schema=None, include=('plain', 'materialized'))

    Return all view names in schema.

    Parameters:

      - schema – Optional, retrieve names from a non-default schema. For special quoting, use `quoted_name`.

      - include –

        specify which types of views to return. Passed as a string value (for a single type) or a tuple (for any number of types). Defaults to `('plain', 'materialized')`.

        New in version 1.1.

## PostgreSQL Table Options

Several options for CREATE TABLE are supported directly by the PostgreSQL dialect in conjunction with the `Table` construct:

- `TABLESPACE`:

      Table("some_table", metadata, ..., postgresql_tablespace='some_tablespace')

  The above option is also available on the `Index` construct.

- `ON COMMIT`:

      Table("some_table", metadata, ..., postgresql_on_commit='PRESERVE ROWS')

- `WITH OIDS`:

      Table("some_table", metadata, ..., postgresql_with_oids=True)

- `WITHOUT OIDS`:

      Table("some_table", metadata, ..., postgresql_with_oids=False)

- `INHERITS`:

      Table("some_table", metadata, ..., postgresql_inherits="some_supertable")

      Table("some_table", metadata, ..., postgresql_inherits=("t1", "t2", ...))

      .. versionadded:: 1.0.0

- `PARTITION BY`:

      Table("some_table", metadata, ...,
            postgresql_partition_by='LIST (part_column)')

      .. versionadded:: 1.2.6

See also

PostgreSQL CREATE TABLE options - in the PostgreSQL documentation.

## PostgreSQL Constraint Options

The following option(s) are supported by the PostgreSQL dialect in conjunction with selected constraint constructs:

- `NOT VALID`: This option applies towards CHECK and FOREIGN KEY constraints when the constraint is being added to an existing table via ALTER TABLE, and has the effect that existing rows are not scanned during the ALTER operation against the constraint being added.

  When using a SQL migration tool such as Alembic that renders ALTER TABLE constructs, the `postgresql_not_valid` argument may be specified as an additional keyword argument within the operation that creates the constraint, as in the following Alembic example:

      def update():
          op.create_foreign_key(
              "fk_user_address",
              "address",
              "user",
              ["user_id"],
              ["id"],
              postgresql_not_valid=True
          )

  The keyword is ultimately accepted directly by the `CheckConstraint`, `ForeignKeyConstraint` and `ForeignKey` constructs; when using a tool like Alembic, dialect-specific keyword arguments are passed through to these constructs from the migration operation directives:

      CheckConstraint("some_field IS NOT NULL", postgresql_not_valid=True)

      ForeignKeyConstraint(["some_id"], ["some_table.some_id"], postgresql_not_valid=True)

  New in version 1.4.32.

  See also

  PostgreSQL ALTER TABLE options - in the PostgreSQL documentation.

## Table values, Table and Column valued functions, Row and Tuple objects

PostgreSQL makes great use of modern SQL forms such as table-valued functions, tables and rows as values. These constructs are commonly used as part of PostgreSQL’s support for complex datatypes such as JSON, ARRAY, and other datatypes. SQLAlchemy’s SQL expression language has native support for most table-valued and row-valued forms.

### Table-Valued Functions

Many PostgreSQL built-in functions are intended to be used in the FROM clause of a SELECT statement, and are capable of returning table rows or sets of table rows. A large portion of PostgreSQL’s JSON functions for example such as `json_array_elements()`, `json_object_keys()`, `json_each_text()`, `json_each()`, `json_to_record()`, `json_populate_recordset()` use such forms. These classes of SQL function calling forms in SQLAlchemy are available using the `FunctionElement.table_valued()` method in conjunction with `Function` objects generated from the `func` namespace.

Examples from PostgreSQL’s reference documentation follow below:

- `json_each()`:

      >>> from sqlalchemy import select, func
      >>> stmt = select(func.json_each('{"a":"foo", "b":"bar"}').table_valued("key", "value"))
      >>> print(stmt)
      SELECT anon_1.key, anon_1.value
      FROM json_each(:json_each_1) AS anon_1

- `json_populate_record()`:

      >>> from sqlalchemy import select, func, literal_column
      >>> stmt = select(
      ...     func.json_populate_record(
      ...         literal_column("null::myrowtype"),
      ...         '{"a":1,"b":2}'
      ...     ).table_valued("a", "b", name="x")
      ... )
      >>> print(stmt)
      SELECT x.a, x.b
      FROM json_populate_record(null::myrowtype, :json_populate_record_1) AS x

- `json_to_record()` - this form uses a PostgreSQL specific form of derived columns in the alias, where we may make use of `column()` elements with types to produce them. The `FunctionElement.table_valued()` method produces a `TableValuedAlias` construct, and the method `TableValuedAlias.render_derived()` method sets up the derived columns specification:

      >>> from sqlalchemy import select, func, column, Integer, Text
      >>> stmt = select(
      ...     func.json_to_record('{"a":1,"b":[1,2,3],"c":"bar"}').table_valued(
      ...         column("a", Integer), column("b", Text), column("d", Text),
      ...     ).render_derived(name="x", with_types=True)
      ... )
      >>> print(stmt)
      SELECT x.a, x.b, x.d
      FROM json_to_record(:json_to_record_1) AS x(a INTEGER, b TEXT, d TEXT)

- `WITH ORDINALITY` - part of the SQL standard, `WITH ORDINALITY` adds an ordinal counter to the output of a function and is accepted by a limited set of PostgreSQL functions including `unnest()` and `generate_series()`. The `FunctionElement.table_valued()` method accepts a keyword parameter `with_ordinality` for this purpose, which accepts the string name that will be applied to the “ordinality” column:

      >>> from sqlalchemy import select, func
      >>> stmt = select(
      ...     func.generate_series(4, 1, -1).
      ...     table_valued("value", with_ordinality="ordinality").
      ...     render_derived()
      ... )
      >>> print(stmt)
      SELECT anon_1.value, anon_1.ordinality
      FROM generate_series(:generate_series_1, :generate_series_2, :generate_series_3)
      WITH ORDINALITY AS anon_1(value, ordinality)

New in version 1.4.0b2.

See also

Table-Valued Functions - in the SQLAlchemy 1.4 / 2.0 Tutorial

### Column Valued Functions

Similar to the table valued function, a column valued function is present in the FROM clause, but delivers itself to the columns clause as a single scalar value. PostgreSQL functions such as `json_array_elements()`, `unnest()` and `generate_series()` may use this form. Column valued functions are available using the `FunctionElement.column_valued()` method of `FunctionElement`:

- `json_array_elements()`:

      >>> from sqlalchemy import select, func
      >>> stmt = select(func.json_array_elements('["one", "two"]').column_valued("x"))
      >>> print(stmt)
      SELECT x
      FROM json_array_elements(:json_array_elements_1) AS x

- `unnest()` - in order to generate a PostgreSQL ARRAY literal, the `array()` construct may be used:

      >>> from sqlalchemy.dialects.postgresql import array
      >>> from sqlalchemy import select, func
      >>> stmt = select(func.unnest(array([1, 2])).column_valued())
      >>> print(stmt)
      SELECT anon_1
      FROM unnest(ARRAY[%(param_1)s, %(param_2)s]) AS anon_1

  The function can of course be used against an existing table-bound column that’s of type `ARRAY`:

      >>> from sqlalchemy import table, column, ARRAY, Integer
      >>> from sqlalchemy import select, func
      >>> t = table("t", column('value', ARRAY(Integer)))
      >>> stmt = select(func.unnest(t.c.value).column_valued("unnested_value"))
      >>> print(stmt)
      SELECT unnested_value
      FROM unnest(t.value) AS unnested_value

See also

Column Valued Functions - Table Valued Function as a Scalar Column - in the SQLAlchemy 1.4 / 2.0 Tutorial

### Row Types

Built-in support for rendering a `ROW` may be approximated using `func.ROW` with the `sqlalchemy.func` namespace, or by using the `tuple_()` construct:

    >>> from sqlalchemy import table, column, func, tuple_
    >>> t = table("t", column("id"), column("fk"))
    >>> stmt = t.select().where(
    ...     tuple_(t.c.id, t.c.fk) > (1,2)
    ... ).where(
    ...     func.ROW(t.c.id, t.c.fk) < func.ROW(3, 7)
    ... )
    >>> print(stmt)
    SELECT t.id, t.fk
    FROM t
    WHERE (t.id, t.fk) > (:param_1, :param_2) AND ROW(t.id, t.fk) < ROW(:ROW_1, :ROW_2)

See also

PostgreSQL Row Constructors

PostgreSQL Row Constructor Comparison

### Table Types passed to Functions

PostgreSQL supports passing a table as an argument to a function, which it refers towards as a “record” type. SQLAlchemy `FromClause` objects such as `Table` support this special form using the `FromClause.table_valued()` method, which is comparable to the `FunctionElement.table_valued()` method except that the collection of columns is already established by that of the `FromClause` itself:

    >>> from sqlalchemy import table, column, func, select
    >>> a = table( "a", column("id"), column("x"), column("y"))
    >>> stmt = select(func.row_to_json(a.table_valued()))
    >>> print(stmt)
    SELECT row_to_json(a) AS row_to_json_1
    FROM a

New in version 1.4.0b2.

## ARRAY Types

The PostgreSQL dialect supports arrays, both as multidimensional column types as well as array literals:

- `ARRAY` - ARRAY datatype

- `array` - array literal

- `array_agg()` - ARRAY_AGG SQL function

- `aggregate_order_by` - helper for PG’s ORDER BY aggregate function syntax.

## JSON Types

The PostgreSQL dialect supports both JSON and JSONB datatypes, including psycopg2’s native support and support for all of PostgreSQL’s special operators:

- `JSON`

- `JSONB`

## HSTORE Type

The PostgreSQL HSTORE type as well as hstore literals are supported:

- `HSTORE` - HSTORE datatype

- `hstore` - hstore literal

## ENUM Types

PostgreSQL has an independently creatable TYPE structure which is used to implement an enumerated type. This approach introduces significant complexity on the SQLAlchemy side in terms of when this type should be CREATED and DROPPED. The type object is also an independently reflectable entity. The following sections should be consulted:

- `ENUM` - DDL and typing support for ENUM.

- `PGInspector.get_enums()` - retrieve a listing of current ENUM types

- `ENUM.create()` , `ENUM.drop()` - individual CREATE and DROP commands for ENUM.

### Using ENUM with ARRAY

The combination of ENUM and ARRAY is not directly supported by backend DBAPIs at this time. Prior to SQLAlchemy 1.3.17, a special workaround was needed in order to allow this combination to work, described below.

Changed in version 1.3.17: The combination of ENUM and ARRAY is now directly handled by SQLAlchemy’s implementation without any workarounds needed.

    from sqlalchemy import TypeDecorator
    from sqlalchemy.dialects.postgresql import ARRAY

    class ArrayOfEnum(TypeDecorator):
        impl = ARRAY

        def bind_expression(self, bindvalue):
            return sa.cast(bindvalue, self)

        def result_processor(self, dialect, coltype):
            super_rp = super(ArrayOfEnum, self).result_processor(
                dialect, coltype)

            def handle_raw_string(value):
                inner = re.match(r"^{(.*)}$", value).group(1)
                return inner.split(",") if inner else []

            def process(value):
                if value is None:
                    return None
                return super_rp(handle_raw_string(value))
            return process

E.g.:

    Table(
        'mydata', metadata,
        Column('id', Integer, primary_key=True),
        Column('data', ArrayOfEnum(ENUM('a', 'b, 'c', name='myenum')))

    )

This type is not included as a built-in type as it would be incompatible with a DBAPI that suddenly decides to support ARRAY of ENUM directly in a new version.

### Using JSON/JSONB with ARRAY

Similar to using ENUM, prior to SQLAlchemy 1.3.17, for an ARRAY of JSON/JSONB we need to render the appropriate CAST. Current psycopg2 drivers accommodate the result set correctly without any special steps.

Changed in version 1.3.17: The combination of JSON/JSONB and ARRAY is now directly handled by SQLAlchemy’s implementation without any workarounds needed.

    class CastingArray(ARRAY):
        def bind_expression(self, bindvalue):
            return sa.cast(bindvalue, self)

E.g.:

    Table(
        'mydata', metadata,
        Column('id', Integer, primary_key=True),
        Column('data', CastingArray(JSONB))
    )

## PostgreSQL Data Types and Custom SQL Constructs

As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with PostgreSQL are importable from the top level dialect, whether they originate from `sqlalchemy.types` or from the local dialect:

    from sqlalchemy.dialects.postgresql import (
        ARRAY,
        BIGINT,
        BIT,
        BOOLEAN,
        BYTEA,
        CHAR,
        CIDR,
        DATE,
        DOUBLE_PRECISION,
        ENUM,
        FLOAT,
        HSTORE,
        INET,
        INTEGER,
        INTERVAL,
        JSON,
        JSONB,
        MACADDR,
        MACADDR8,
        MONEY,
        NUMERIC,
        OID,
        REAL,
        SMALLINT,
        TEXT,
        TIME,
        TIMESTAMP,
        UUID,
        VARCHAR,
        INT4RANGE,
        INT8RANGE,
        NUMRANGE,
        DATERANGE,
        TSRANGE,
        TSTZRANGE,
        TSVECTOR,
    )

Types which are specific to PostgreSQL, or have PostgreSQL-specific construction arguments, are as follows:

Object Name | Description
--- | ---
aggregate_order_by | Represent a PostgreSQL aggregate order by expression.
All(other, arrexpr[, operator]) | A synonym for the ARRAY-level `Comparator.all()` method. See that method for details.
Any(other, arrexpr[, operator]) | A synonym for the ARRAY-level `Comparator.any()` method. See that method for details.
array | A PostgreSQL ARRAY literal.
ARRAY | PostgreSQL ARRAY type.
array_agg(*arg, **kw) | PostgreSQL-specific form of `array_agg`, ensures return type is `ARRAY` and not the plain `ARRAY`, unless an explicit `type_` is passed.
BIT |
BYTEA |
CIDR |
ENUM | PostgreSQL ENUM type.
HSTORE | Represent the PostgreSQL HSTORE type.
hstore | Construct an hstore value within a SQL expression using the PostgreSQL `hstore()` function.
INET |
INTERVAL | PostgreSQL INTERVAL type.
JSON | Represent the PostgreSQL JSON type.
JSONB | Represent the PostgreSQL JSONB type.
MACADDR |
MACADDR8 |
MONEY | Provide the PostgreSQL MONEY type.
OID | Provide the PostgreSQL OID type.
REGCLASS | Provide the PostgreSQL REGCLASS type.
TIME | PostgreSQL TIME type.
TIMESTAMP | Provide the PostgreSQL TIMESTAMP type.
TSVECTOR | The `TSVECTOR` type implements the PostgreSQL text search type TSVECTOR.
UUID | PostgreSQL UUID type.

class sqlalchemy.dialects.postgresql.aggregate_order_by(target, *order_by)

  Represent a PostgreSQL aggregate order by expression.

  E.g.:

      from sqlalchemy.dialects.postgresql import aggregate_order_by
      expr = func.array_agg(aggregate_order_by(table.c.a, table.c.b.desc()))
      stmt = select(expr)

  would represent the expression:

      SELECT array_agg(a ORDER BY b DESC) FROM table;

  Similarly:

      expr = func.string_agg(
          table.c.a,
          aggregate_order_by(literal_column("','"), table.c.a)
      )
      stmt = select(expr)

  Would represent:

      SELECT string_agg(a, ',' ORDER BY a) FROM table;

  New in version 1.1.

  Changed in version 1.2.13: - the ORDER BY argument may be multiple terms

  See also

  `array_agg`

  Class signature

  class `sqlalchemy.dialects.postgresql.aggregate_order_by` (`sqlalchemy.sql.expression.ColumnElement`)

class sqlalchemy.dialects.postgresql.array(clauses, **kw)

  A PostgreSQL ARRAY literal.

  This is used to produce ARRAY literals in SQL expressions, e.g.:

      from sqlalchemy.dialects.postgresql import array
      from sqlalchemy.dialects import postgresql
      from sqlalchemy import select, func

      stmt = select(array([1,2]) + array([3,4,5]))

      print(stmt.compile(dialect=postgresql.dialect()))

  Produces the SQL:

      SELECT ARRAY[%(param_1)s, %(param_2)s] ||
          ARRAY[%(param_3)s, %(param_4)s, %(param_5)s]) AS anon_1

  An instance of `array` will always have the datatype `ARRAY`. The “inner” type of the array is inferred from the values present, unless the `type_` keyword argument is passed:

      array(['foo', 'bar'], type_=CHAR)

  Multidimensional arrays are produced by nesting `array` constructs. The dimensionality of the final `ARRAY` type is calculated by recursively adding the dimensions of the inner `ARRAY` type:

      stmt = select(
          array([
              array([1, 2]), array([3, 4]), array([column('q'), column('x')])
          ])
      )
      print(stmt.compile(dialect=postgresql.dialect()))

  Produces:

      SELECT ARRAY[ARRAY[%(param_1)s, %(param_2)s],
      ARRAY[%(param_3)s, %(param_4)s], ARRAY[q, x]] AS anon_1

  New in version 1.3.6: added support for multidimensional array literals

  See also

  `ARRAY`

  Class signature

  class `sqlalchemy.dialects.postgresql.array` (`sqlalchemy.sql.expression.ClauseList`, `sqlalchemy.sql.expression.ColumnElement`)

class sqlalchemy.dialects.postgresql.ARRAY(item_type, as_tuple=False, dimensions=None, zero_indexes=False)

  PostgreSQL ARRAY type.

  Changed in version 1.1: The `ARRAY` type is now a subclass of the core `ARRAY` type.

  The `ARRAY` type is constructed in the same way as the core `ARRAY` type; a member type is required, and a number of dimensions is recommended if the type is to be used for more than one dimension:

      from sqlalchemy.dialects import postgresql

      mytable = Table("mytable", metadata,
              Column("data", postgresql.ARRAY(Integer, dimensions=2))
          )

  The `ARRAY` type provides all operations defined on the core `ARRAY` type, including support for “dimensions”, indexed access, and simple matching such as `Comparator.any()` and `Comparator.all()`. `ARRAY` class also provides PostgreSQL-specific methods for containment operations, including `Comparator.contains()` `Comparator.contained_by()`, and `Comparator.overlap()`, e.g.:

      mytable.c.data.contains([1, 2])

  The `ARRAY` type may not be supported on all PostgreSQL DBAPIs; it is currently known to work on psycopg2 only.

  Additionally, the `ARRAY` type does not work directly in conjunction with the `ENUM` type. For a workaround, see the special type at Using ENUM with ARRAY.

  Detecting Changes in ARRAY columns when using the ORM

  The `ARRAY` type, when used with the SQLAlchemy ORM, does not detect in-place mutations to the array. In order to detect these, the `sqlalchemy.ext.mutable` extension must be used, using the `MutableList` class:

      from sqlalchemy.dialects.postgresql import ARRAY
      from sqlalchemy.ext.mutable import MutableList

      class SomeOrmClass(Base):
          # ...

          data = Column(MutableList.as_mutable(ARRAY(Integer)))

  This extension will allow “in-place” changes such to the array such as `.append()` to produce events which will be detected by the unit of work. Note that changes to elements inside the array, including subarrays that are mutated in place, are not detected.

  Alternatively, assigning a new array value to an ORM element that replaces the old one will always trigger a change event.

  See also

  `ARRAY` - base array type

  `array` - produces a literal array value.

  Members

  contained_by(), contains(), overlap(), __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.ARRAY` (`sqlalchemy.types.ARRAY`)

  class Comparator(expr)

    Define comparison operations for `ARRAY`.

    Note that these operations are in addition to those provided by the base `Comparator` class, including `Comparator.any()` and `Comparator.all()`.

    Class signature

    class `sqlalchemy.dialects.postgresql.ARRAY.Comparator` (`sqlalchemy.types.Comparator`)

    method `sqlalchemy.dialects.postgresql.ARRAY.Comparator.` contained_by(other)

      Boolean expression. Test if elements are a proper subset of the elements of the argument array expression.

    method `sqlalchemy.dialects.postgresql.ARRAY.Comparator.` contains(other, **kwargs)

      Boolean expression. Test if elements are a superset of the elements of the argument array expression.

      kwargs may be ignored by this operator but are required for API conformance.

    method `sqlalchemy.dialects.postgresql.ARRAY.Comparator.` overlap(other)

      Boolean expression. Test if array has elements in common with an argument array expression.

  method `sqlalchemy.dialects.postgresql.ARRAY.` __init__(item_type, as_tuple=False, dimensions=None, zero_indexes=False)

    Construct an ARRAY.

    E.g.:

        Column('myarray', ARRAY(Integer))

    Arguments are:

    Parameters:

      - item_type – The data type of items of this array. Note that dimensionality is irrelevant here, so multi-dimensional arrays like `INTEGER[][]`, are constructed as `ARRAY(Integer)`, not as `ARRAY(ARRAY(Integer))` or such.

      - as_tuple=False – Specify whether return results should be converted to tuples from lists. DBAPIs such as psycopg2 return lists by default. When tuples are returned, the results are hashable.

      - dimensions – if non-None, the ARRAY will assume a fixed number of dimensions. This will cause the DDL emitted for this ARRAY to include the exact number of bracket clauses `[]`, and will also optimize the performance of the type overall. Note that PG arrays are always implicitly “non-dimensioned”, meaning they can store any number of dimensions no matter how they were declared.

      - zero_indexes=False –

        when True, index values will be converted between Python zero-based and PostgreSQL one-based indexes, e.g. a value of one will be added to all index values before passing to the database.

        New in version 0.9.5.

function sqlalchemy.dialects.postgresql.array_agg(*arg, **kw)

  PostgreSQL-specific form of `array_agg`, ensures return type is `ARRAY` and not the plain `ARRAY`, unless an explicit `type_` is passed.

  New in version 1.1.

function sqlalchemy.dialects.postgresql.Any(other, arrexpr, operator=<built-in function eq>)

  A synonym for the ARRAY-level `Comparator.any()` method. See that method for details.

function sqlalchemy.dialects.postgresql.All(other, arrexpr, operator=<built-in function eq>)

  A synonym for the ARRAY-level `Comparator.all()` method. See that method for details.

class sqlalchemy.dialects.postgresql.BIT(length=None, varying=False)

  Class signature

  class `sqlalchemy.dialects.postgresql.BIT` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.BYTEA(length=None)

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.BYTEA` (`sqlalchemy.types.LargeBinary`)

  method `sqlalchemy.dialects.postgresql.BYTEA.` __init__(length=None)

    inherited from the `sqlalchemy.types.LargeBinary.__init__` method of `LargeBinary`

    Construct a LargeBinary type.

    Parameters:

      length – optional, a length for the column for use in DDL statements, for those binary types that accept a length, such as the MySQL BLOB type.

class sqlalchemy.dialects.postgresql.CIDR

  Class signature

  class `sqlalchemy.dialects.postgresql.CIDR` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.DOUBLE_PRECISION(precision=None, asdecimal=False, decimal_return_scale=None)

  Class signature

  class `sqlalchemy.dialects.postgresql.DOUBLE_PRECISION` (`sqlalchemy.types.Float`)

  method `sqlalchemy.dialects.postgresql.DOUBLE_PRECISION.` __init__(precision=None, asdecimal=False, decimal_return_scale=None)

    inherited from the `sqlalchemy.types.Float.__init__` method of `Float`

    Construct a Float.

    Parameters:

      - precision – the numeric precision for use in DDL `CREATE TABLE`.

      - asdecimal – the same flag as that of `Numeric`, but defaults to `False`. Note that setting this flag to `True` results in floating point conversion.

      - decimal_return_scale –

        Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.

        New in version 0.9.0.

class sqlalchemy.dialects.postgresql.ENUM(*enums, **kw)

  PostgreSQL ENUM type.

  This is a subclass of `Enum` which includes support for PG’s `CREATE TYPE` and `DROP TYPE`.

  When the builtin type `Enum` is used and the `Enum.native_enum` flag is left at its default of True, the PostgreSQL backend will use a `ENUM` type as the implementation, so the special create/drop rules will be used.

  The create/drop behavior of ENUM is necessarily intricate, due to the awkward relationship the ENUM type has in relationship to the parent table, in that it may be “owned” by just a single table, or may be shared among many tables.

  When using `Enum` or `ENUM` in an “inline” fashion, the `CREATE TYPE` and `DROP TYPE` is emitted corresponding to when the `Table.create()` and `Table.drop()` methods are called:

      table = Table('sometable', metadata,
          Column('some_enum', ENUM('a', 'b', 'c', name='myenum'))
      )

      table.create(engine)  # will emit CREATE ENUM and CREATE TABLE
      table.drop(engine)  # will emit DROP TABLE and DROP ENUM

  To use a common enumerated type between multiple tables, the best practice is to declare the `Enum` or `ENUM` independently, and associate it with the `MetaData` object itself:

      my_enum = ENUM('a', 'b', 'c', name='myenum', metadata=metadata)

      t1 = Table('sometable_one', metadata,
          Column('some_enum', myenum)
      )

      t2 = Table('sometable_two', metadata,
          Column('some_enum', myenum)
      )

  When this pattern is used, care must still be taken at the level of individual table creates. Emitting CREATE TABLE without also specifying `checkfirst=True` will still cause issues:

      t1.create(engine) # will fail: no such type 'myenum'

  If we specify `checkfirst=True`, the individual table-level create operation will check for the `ENUM` and create if not exists:

      # will check if enum exists, and emit CREATE TYPE if not
      t1.create(engine, checkfirst=True)

  When using a metadata-level ENUM type, the type will always be created and dropped if either the metadata-wide create/drop is called:

      metadata.create_all(engine)  # will emit CREATE TYPE
      metadata.drop_all(engine)  # will emit DROP TYPE

  The type can also be created and dropped directly:

      my_enum.create(engine)
      my_enum.drop(engine)

  Changed in version 1.0.0: The PostgreSQL `ENUM` type now behaves more strictly with regards to CREATE/DROP. A metadata-level ENUM type will only be created and dropped at the metadata level, not the table level, with the exception of `table.create(checkfirst=True)`. The `table.drop()` call will now emit a DROP TYPE for a table-level enumerated type.

  Members

  __init__(), create(), drop()

  Class signature

  class `sqlalchemy.dialects.postgresql.ENUM` (`sqlalchemy.types.NativeForEmulated`, `sqlalchemy.types.Enum`)

  method `sqlalchemy.dialects.postgresql.ENUM.` __init__(*enums, **kw)

    Construct an `ENUM`.

    Arguments are the same as that of `Enum`, but also including the following parameters.

    Parameters:

      create_type – Defaults to True. Indicates that `CREATE TYPE` should be emitted, after optionally checking for the presence of the type, when the parent table is being created; and additionally that `DROP TYPE` is called when the table is dropped. When `False`, no check will be performed and no `CREATE TYPE` or `DROP TYPE` is emitted, unless `ENUM.create()` or `ENUM.drop()` are called directly. Setting to `False` is helpful when invoking a creation scheme to a SQL file without access to the actual database - the `ENUM.create()` and `ENUM.drop()` methods can be used to emit SQL to a target bind.

  method `sqlalchemy.dialects.postgresql.ENUM.` create(bind=None, checkfirst=True)

    Emit `CREATE TYPE` for this `ENUM`.

    If the underlying dialect does not support PostgreSQL CREATE TYPE, no action is taken.

    Parameters:

      - bind – a connectable `Engine`, `Connection`, or similar object to emit SQL.

      - checkfirst – if `True`, a query against the PG catalog will be first performed to see if the type does not exist already before creating.

  method `sqlalchemy.dialects.postgresql.ENUM.` drop(bind=None, checkfirst=True)

    Emit `DROP TYPE` for this `ENUM`.

    If the underlying dialect does not support PostgreSQL DROP TYPE, no action is taken.

    Parameters:

      - bind – a connectable `Engine`, `Connection`, or similar object to emit SQL.

      - checkfirst – if `True`, a query against the PG catalog will be first performed to see if the type actually exists before dropping.

class sqlalchemy.dialects.postgresql.HSTORE(text_type=None)

  Represent the PostgreSQL HSTORE type.

  The `HSTORE` type stores dictionaries containing strings, e.g.:

      data_table = Table('data_table', metadata,
          Column('id', Integer, primary_key=True),
          Column('data', HSTORE)
      )

      with engine.connect() as conn:
          conn.execute(
              data_table.insert(),
              data = {"key1": "value1", "key2": "value2"}
          )

  `HSTORE` provides for a wide range of operations, including:

  - Index operations:

        data_table.c.data['some key'] == 'some value'

  - Containment operations:

        data_table.c.data.has_key('some key')

        data_table.c.data.has_all(['one', 'two', 'three'])

  - Concatenation:

        data_table.c.data + {"k1": "v1"}

  For a full list of special methods see `comparator_factory`.

  Detecting Changes in HSTORE columns when using the ORM

  For usage with the SQLAlchemy ORM, it may be desirable to combine the usage of `HSTORE` with `MutableDict` dictionary now part of the `sqlalchemy.ext.mutable` extension. This extension will allow “in-place” changes to the dictionary, e.g. addition of new keys or replacement/removal of existing keys to/from the current dictionary, to produce events which will be detected by the unit of work:

      from sqlalchemy.ext.mutable import MutableDict

      class MyClass(Base):
          __tablename__ = 'data_table'

          id = Column(Integer, primary_key=True)
          data = Column(MutableDict.as_mutable(HSTORE))

      my_object = session.query(MyClass).one()

      # in-place mutation, requires Mutable extension
      # in order for the ORM to detect
      my_object.data['some_key'] = 'some value'

      session.commit()

  When the `sqlalchemy.ext.mutable` extension is not used, the ORM will not be alerted to any changes to the contents of an existing dictionary, unless that dictionary value is re-assigned to the HSTORE-attribute itself, thus generating a change event.

  See also

  `hstore` - render the PostgreSQL `hstore()` function.

  Members

  array(), contained_by(), contains(), defined(), delete(), has_all(), has_any(), has_key(), keys(), matrix(), slice(), vals(), __init__(), bind_processor(), comparator_factory, hashable, result_processor()

  Class signature

  class `sqlalchemy.dialects.postgresql.HSTORE` (`sqlalchemy.types.Indexable`, `sqlalchemy.types.Concatenable`, `sqlalchemy.types.TypeEngine`)

  class Comparator(expr)

    Define comparison operations for `HSTORE`.

    Class signature

    class `sqlalchemy.dialects.postgresql.HSTORE.Comparator` (`sqlalchemy.types.Comparator`, `sqlalchemy.types.Comparator`)

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` array()

      Text array expression. Returns array of alternating keys and values.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` contained_by(other)

      Boolean expression. Test if keys are a proper subset of the keys of the argument jsonb expression.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` contains(other, **kwargs)

      Boolean expression. Test if keys (or array) are a superset of/contained the keys of the argument jsonb expression.

      kwargs may be ignored by this operator but are required for API conformance.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` defined(key)

      Boolean expression. Test for presence of a non-NULL value for the key. Note that the key may be a SQLA expression.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` delete(key)

      HStore expression. Returns the contents of this hstore with the given key deleted. Note that the key may be a SQLA expression.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` has_all(other)

      Boolean expression. Test for presence of all keys in jsonb

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` has_any(other)

      Boolean expression. Test for presence of any key in jsonb

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` has_key(other)

      Boolean expression. Test for presence of a key. Note that the key may be a SQLA expression.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` keys()

      Text array expression. Returns array of keys.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` matrix()

      Text array expression. Returns array of [key, value] pairs.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` slice(array)

      HStore expression. Returns a subset of an hstore defined by array of keys.

    method `sqlalchemy.dialects.postgresql.HSTORE.Comparator.` vals()

      Text array expression. Returns array of values.

  method `sqlalchemy.dialects.postgresql.HSTORE.` __init__(text_type=None)

    Construct a new `HSTORE`.

    Parameters:

      text_type –

      the type that should be used for indexed values. Defaults to `Text`.

      New in version 1.1.0.

  method `sqlalchemy.dialects.postgresql.HSTORE.` bind_processor(dialect)

    Return a conversion function for processing bind values.

    Returns a callable which will receive a bind parameter value as the sole positional argument and will return a value to send to the DB-API.

    If processing is not necessary, the method should return `None`.

    Note

    This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a `TypeEngine` class in order to provide an alternate `TypeEngine.bind_processor()` method, unless subclassing the `UserDefinedType` class explicitly.

    To provide alternate behavior for `TypeEngine.bind_processor()`, implement a `TypeDecorator` class and provide an implementation of `TypeDecorator.process_bind_param()`.

    See also

    Augmenting Existing Types

    Parameters:

      dialect – Dialect instance in use.

  attribute `sqlalchemy.dialects.postgresql.HSTORE.` comparator_factory

    alias of `Comparator`

  attribute `sqlalchemy.dialects.postgresql.HSTORE.` hashable = False

    Flag, if False, means values from this type aren’t hashable.

    Used by the ORM when uniquing result lists.

  method `sqlalchemy.dialects.postgresql.HSTORE.` result_processor(dialect, coltype)

    Return a conversion function for processing result row values.

    Returns a callable which will receive a result row column value as the sole positional argument and will return a value to return to the user.

    If processing is not necessary, the method should return `None`.

    Note

    This method is only called relative to a dialect specific type object, which is often private to a dialect in use and is not the same type object as the public facing one, which means it’s not feasible to subclass a `TypeEngine` class in order to provide an alternate `TypeEngine.result_processor()` method, unless subclassing the `UserDefinedType` class explicitly.

    To provide alternate behavior for `TypeEngine.result_processor()`, implement a `TypeDecorator` class and provide an implementation of `TypeDecorator.process_result_value()`.

    See also

    Augmenting Existing Types

    Parameters:

      - dialect – Dialect instance in use.

      - coltype – DBAPI coltype argument received in cursor.description.

class sqlalchemy.dialects.postgresql.hstore(*args, **kwargs)

  Construct an hstore value within a SQL expression using the PostgreSQL `hstore()` function.

  The `hstore` function accepts one or two arguments as described in the PostgreSQL documentation.

  E.g.:

      from sqlalchemy.dialects.postgresql import array, hstore

      select(hstore('key1', 'value1'))

      select(
          hstore(
              array(['key1', 'key2', 'key3']),
              array(['value1', 'value2', 'value3'])
          )
      )

  See also

  `HSTORE` - the PostgreSQL `HSTORE` datatype.

  Members

  inherit_cache, type

  Class signature

  class `sqlalchemy.dialects.postgresql.hstore` (`sqlalchemy.sql.functions.GenericFunction`)

  attribute `sqlalchemy.dialects.postgresql.hstore.` inherit_cache = True

    Indicate if this `HasCacheKey` instance should make use of the cache key generation scheme used by its immediate superclass.

    The attribute defaults to `None`, which indicates that a construct has not yet taken into account whether or not its appropriate for it to participate in caching; this is functionally equivalent to setting the value to `False`, except that a warning is also emitted.

    This flag can be set to `True` on a particular class, if the SQL that corresponds to the object does not change based on attributes which are local to this class, and not its superclass.

    See also

    Enabling Caching Support for Custom Constructs - General guideslines for setting the `HasCacheKey.inherit_cache` attribute for third-party or user defined SQL constructs.

  attribute `sqlalchemy.dialects.postgresql.hstore.` type

    alias of `HSTORE`

class sqlalchemy.dialects.postgresql.INET

  Class signature

  class `sqlalchemy.dialects.postgresql.INET` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.INTERVAL(precision=None, fields=None)

  PostgreSQL INTERVAL type.

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.INTERVAL` (`sqlalchemy.types.NativeForEmulated`, `sqlalchemy.types._AbstractInterval`)

  method `sqlalchemy.dialects.postgresql.INTERVAL.` __init__(precision=None, fields=None)

    Construct an INTERVAL.

    Parameters:

      - precision – optional integer precision value

      - fields –

        string fields specifier. allows storage of fields to be limited, such as `"YEAR"`, `"MONTH"`, `"DAY TO HOUR"`, etc.

        New in version 1.2.

class sqlalchemy.dialects.postgresql.JSON(none_as_null=False, astext_type=None)

  Represent the PostgreSQL JSON type.

  `JSON` is used automatically whenever the base `JSON` datatype is used against a PostgreSQL backend, however base `JSON` datatype does not provide Python accessors for PostgreSQL-specific comparison methods such as `Comparator.astext()`; additionally, to use PostgreSQL `JSONB`, the `JSONB` datatype should be used explicitly.

  See also

  `JSON` - main documentation for the generic cross-platform JSON datatype.

  The operators provided by the PostgreSQL version of `JSON` include:

  - Index operations (the `->` operator):

        data_table.c.data['some key']

        data_table.c.data[5]

  - Index operations returning text (the `->>` operator):

        data_table.c.data['some key'].astext == 'some value'

    Note that equivalent functionality is available via the `Comparator.as_string` accessor.

  - Index operations with CAST (equivalent to `CAST(col ->> ['some key'] AS <type>)`):

        data_table.c.data['some key'].astext.cast(Integer) == 5

    Note that equivalent functionality is available via the `Comparator.as_integer` and similar accessors.

  - Path index operations (the `#>` operator):

        data_table.c.data[('key_1', 'key_2', 5, ..., 'key_n')]

  - Path index operations returning text (the `#>>` operator):

        data_table.c.data[('key_1', 'key_2', 5, ..., 'key_n')].astext == 'some value'

  Changed in version 1.1: The `ColumnElement.cast()` operator on JSON objects now requires that the `Comparator.astext` modifier be called explicitly, if the cast works only from a textual string.

  Index operations return an expression object whose type defaults to `JSON` by default, so that further JSON-oriented instructions may be called upon the result type.

  Custom serializers and deserializers are specified at the dialect level, that is using `create_engine()`. The reason for this is that when using psycopg2, the DBAPI only allows serializers at the per-cursor or per-connection level. E.g.:

      engine = create_engine("postgresql://scott:tiger@localhost/test",
                              json_serializer=my_serialize_fn,
                              json_deserializer=my_deserialize_fn
                      )

  When using the psycopg2 dialect, the json_deserializer is registered against the database using `psycopg2.extras.register_default_json`.

  See also

  `JSON` - Core level JSON type

  `JSONB`

  Changed in version 1.1: `JSON` is now a PostgreSQL- specific specialization of the new `JSON` type.

  Members

  astext, __init__(), comparator_factory

  Class signature

  class `sqlalchemy.dialects.postgresql.JSON` (`sqlalchemy.types.JSON`)

  class Comparator(expr)

    Define comparison operations for `JSON`.

    Class signature

    class `sqlalchemy.dialects.postgresql.JSON.Comparator` (`sqlalchemy.types.Comparator`)

    attribute `sqlalchemy.dialects.postgresql.JSON.Comparator.` astext

      On an indexed expression, use the “astext” (e.g. “->>”) conversion when rendered in SQL.

      E.g.:

          select(data_table.c.data['some key'].astext)

      See also

      `ColumnElement.cast()`

  method `sqlalchemy.dialects.postgresql.JSON.` __init__(none_as_null=False, astext_type=None)

    Construct a `JSON` type.

    Parameters:

      - none_as_null –

        if True, persist the value `None` as a SQL NULL value, not the JSON encoding of `null`. Note that when this flag is False, the `null()` construct can still be used to persist a NULL value:

            from sqlalchemy import null
            conn.execute(table.insert(), data=null())

        Changed in version 0.9.8: - Added `none_as_null`, and `null()` is now supported in order to persist a NULL value.

        See also

        `JSON.NULL`

      - astext_type –

        the type to use for the `Comparator.astext` accessor on indexed attributes. Defaults to `Text`.

        New in version 1.1.

  attribute `sqlalchemy.dialects.postgresql.JSON.` comparator_factory

    alias of `Comparator`

class sqlalchemy.dialects.postgresql.JSONB(none_as_null=False, astext_type=None)

  Represent the PostgreSQL JSONB type.

  The `JSONB` type stores arbitrary JSONB format data, e.g.:

      data_table = Table('data_table', metadata,
          Column('id', Integer, primary_key=True),
          Column('data', JSONB)
      )

      with engine.connect() as conn:
          conn.execute(
              data_table.insert(),
              data = {"key1": "value1", "key2": "value2"}
          )

  The `JSONB` type includes all operations provided by `JSON`, including the same behaviors for indexing operations. It also adds additional operators specific to JSONB, including `Comparator.has_key()`, `Comparator.has_all()`, `Comparator.has_any()`, `Comparator.contains()`, and `Comparator.contained_by()`.

  Like the `JSON` type, the `JSONB` type does not detect in-place changes when used with the ORM, unless the `sqlalchemy.ext.mutable` extension is used.

  Custom serializers and deserializers are shared with the `JSON` class, using the `json_serializer` and `json_deserializer` keyword arguments. These must be specified at the dialect level using `create_engine()`. When using psycopg2, the serializers are associated with the jsonb type using `psycopg2.extras.register_default_jsonb` on a per-connection basis, in the same way that `psycopg2.extras.register_default_json` is used to register these handlers with the json type.

  New in version 0.9.7.

  See also

  `JSON`

  Members

  contained_by(), contains(), has_all(), has_any(), has_key(), comparator_factory

  Class signature

  class `sqlalchemy.dialects.postgresql.JSONB` (`sqlalchemy.dialects.postgresql.json.JSON`)

  class Comparator(expr)

    Define comparison operations for `JSON`.

    Class signature

    class `sqlalchemy.dialects.postgresql.JSONB.Comparator` (`sqlalchemy.dialects.postgresql.json.Comparator`)

    method `sqlalchemy.dialects.postgresql.JSONB.Comparator.` contained_by(other)

      Boolean expression. Test if keys are a proper subset of the keys of the argument jsonb expression.

    method `sqlalchemy.dialects.postgresql.JSONB.Comparator.` contains(other, **kwargs)

      Boolean expression. Test if keys (or array) are a superset of/contained the keys of the argument jsonb expression.

      kwargs may be ignored by this operator but are required for API conformance.

    method `sqlalchemy.dialects.postgresql.JSONB.Comparator.` has_all(other)

      Boolean expression. Test for presence of all keys in jsonb

    method `sqlalchemy.dialects.postgresql.JSONB.Comparator.` has_any(other)

      Boolean expression. Test for presence of any key in jsonb

    method `sqlalchemy.dialects.postgresql.JSONB.Comparator.` has_key(other)

      Boolean expression. Test for presence of a key. Note that the key may be a SQLA expression.

  attribute `sqlalchemy.dialects.postgresql.JSONB.` comparator_factory

    alias of `Comparator`

class sqlalchemy.dialects.postgresql.MACADDR

  Class signature

  class `sqlalchemy.dialects.postgresql.MACADDR` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.MACADDR8

  Class signature

  class `sqlalchemy.dialects.postgresql.MACADDR8` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.MONEY

  Provide the PostgreSQL MONEY type.

  Depending on driver, result rows using this type may return a string value which includes currency symbols.

  For this reason, it may be preferable to provide conversion to a numerically-based currency datatype using `TypeDecorator`:

      import re
      import decimal
      from sqlalchemy import TypeDecorator

      class NumericMoney(TypeDecorator):
          impl = MONEY

          def process_result_value(self, value: Any, dialect: Any) -> None:
              if value is not None:
                  # adjust this for the currency and numeric
                  m = re.match(r"\$([\d.]+)", value)
                  if m:
                      value = decimal.Decimal(m.group(1))
              return value

  Alternatively, the conversion may be applied as a CAST using the `TypeDecorator.column_expression()` method as follows:

      import decimal
      from sqlalchemy import cast
      from sqlalchemy import TypeDecorator

      class NumericMoney(TypeDecorator):
          impl = MONEY

          def column_expression(self, column: Any):
              return cast(column, Numeric())

  New in version 1.2.

  Class signature

  class `sqlalchemy.dialects.postgresql.MONEY` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.OID

  Provide the PostgreSQL OID type.

  New in version 0.9.5.

  Class signature

  class `sqlalchemy.dialects.postgresql.OID` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.REAL(precision=None, asdecimal=False, decimal_return_scale=None)

  The SQL REAL type.

  Class signature

  class `sqlalchemy.dialects.postgresql.REAL` (`sqlalchemy.types.Float`)

  method `sqlalchemy.dialects.postgresql.REAL.` __init__(precision=None, asdecimal=False, decimal_return_scale=None)

    inherited from the `sqlalchemy.types.Float.__init__` method of `Float`

    Construct a Float.

    Parameters:

      - precision – the numeric precision for use in DDL `CREATE TABLE`.

      - asdecimal – the same flag as that of `Numeric`, but defaults to `False`. Note that setting this flag to `True` results in floating point conversion.

      - decimal_return_scale –

        Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don’t have a notion of “scale”, so by default the float type looks for the first ten decimal places when converting. Specifying this value will override that length. Note that the MySQL float types, which do include “scale”, will use “scale” as the default for decimal_return_scale, if not otherwise specified.

        New in version 0.9.0.

class sqlalchemy.dialects.postgresql.REGCLASS

  Provide the PostgreSQL REGCLASS type.

  New in version 1.2.7.

  Class signature

  class `sqlalchemy.dialects.postgresql.REGCLASS` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.TIMESTAMP(timezone=False, precision=None)

  Provide the PostgreSQL TIMESTAMP type.

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.TIMESTAMP` (`sqlalchemy.types.TIMESTAMP`)

  method `sqlalchemy.dialects.postgresql.TIMESTAMP.` __init__(timezone=False, precision=None)

    Construct a TIMESTAMP.

    Parameters:

      - timezone – boolean value if timezone present, default False

      - precision –

        optional integer precision value

        New in version 1.4.

class sqlalchemy.dialects.postgresql.TIME(timezone=False, precision=None)

  PostgreSQL TIME type.

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.TIME` (`sqlalchemy.types.TIME`)

  method `sqlalchemy.dialects.postgresql.TIME.` __init__(timezone=False, precision=None)

    Construct a TIME.

    Parameters:

      - timezone – boolean value if timezone present, default False

      - precision –

        optional integer precision value

        New in version 1.4.

class sqlalchemy.dialects.postgresql.TSVECTOR

  The `TSVECTOR` type implements the PostgreSQL text search type TSVECTOR.

  It can be used to do full text queries on natural language documents.

  New in version 0.9.0.

  See also

  Full Text Search

  Class signature

  class `sqlalchemy.dialects.postgresql.TSVECTOR` (`sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.UUID(as_uuid=False)

  PostgreSQL UUID type.

  Represents the UUID column type, interpreting data either as natively returned by the DBAPI or as Python uuid objects.

  The UUID type is currently known to work within the prominent DBAPI drivers supported by SQLAlchemy including psycopg2, pg8000 and asyncpg. Support for other DBAPI drivers may be incomplete or non-present.

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.UUID` (`sqlalchemy.types.TypeEngine`)

  method `sqlalchemy.dialects.postgresql.UUID.` __init__(as_uuid=False)

    Construct a UUID type.

    Parameters:

      as_uuid=False – if True, values will be interpreted as Python uuid objects, converting to/from string via the DBAPI.

### Range Types

The new range column types found in PostgreSQL 9.2 onwards are catered for by the following types:

Object Name | Description
--- | ---
DATERANGE | Represent the PostgreSQL DATERANGE type.
INT4RANGE | Represent the PostgreSQL INT4RANGE type.
INT8RANGE | Represent the PostgreSQL INT8RANGE type.
NUMRANGE | Represent the PostgreSQL NUMRANGE type.
RangeOperators | This mixin provides functionality for the Range Operators listed in the Range Operators table of the PostgreSQL documentation for Range Functions and Operators. It is used by all the range types provided in the `postgres` dialect and can likely be used for any range types you create yourself.
TSRANGE | Represent the PostgreSQL TSRANGE type.
TSTZRANGE | Represent the PostgreSQL TSTZRANGE type.

class sqlalchemy.dialects.postgresql.INT4RANGE

  Represent the PostgreSQL INT4RANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.INT4RANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.INT8RANGE

  Represent the PostgreSQL INT8RANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.INT8RANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.NUMRANGE

  Represent the PostgreSQL NUMRANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.NUMRANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.DATERANGE

  Represent the PostgreSQL DATERANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.DATERANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.TSRANGE

  Represent the PostgreSQL TSRANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.TSRANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

class sqlalchemy.dialects.postgresql.TSTZRANGE

  Represent the PostgreSQL TSTZRANGE type.

  Class signature

  class `sqlalchemy.dialects.postgresql.TSTZRANGE` (`sqlalchemy.dialects.postgresql.ranges.RangeOperators`, `sqlalchemy.types.TypeEngine`)

The types above get most of their functionality from the following mixin:

class sqlalchemy.dialects.postgresql.ranges.RangeOperators

  This mixin provides functionality for the Range Operators listed in the Range Operators table of the PostgreSQL documentation for Range Functions and Operators. It is used by all the range types provided in the `postgres` dialect and can likely be used for any range types you create yourself.

  Members

  __ne__(), adjacent_to(), contained_by(), contains(), not_extend_left_of(), not_extend_right_of(), overlaps(), strictly_left_of(), strictly_right_of()

  No extra support is provided for the Range Functions listed in the Range Functions table of the PostgreSQL documentation. For these, the normal `func()` object should be used.

  class comparator_factory(expr)

    Define comparison operations for range types.

    Class signature

    class `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory` (`sqlalchemy.types.Comparator`)

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` __ne__(other)

      Boolean expression. Returns true if two ranges are not equal

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` adjacent_to(other)

      Boolean expression. Returns true if the range in the column is adjacent to the range in the operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` contained_by(other)

      Boolean expression. Returns true if the column is contained within the right hand operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` contains(other, **kw)

      Boolean expression. Returns true if the right hand operand, which can be an element or a range, is contained within the column.

      kwargs may be ignored by this operator but are required for API conformance.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` not_extend_left_of(other)

      Boolean expression. Returns true if the range in the column does not extend left of the range in the operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` not_extend_right_of(other)

      Boolean expression. Returns true if the range in the column does not extend right of the range in the operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` overlaps(other)

      Boolean expression. Returns true if the column overlaps (has points in common with) the right hand operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` strictly_left_of(other)

      Boolean expression. Returns true if the column is strictly left of the right hand operand.

    method `sqlalchemy.dialects.postgresql.ranges.RangeOperators.comparator_factory.` strictly_right_of(other)

      Boolean expression. Returns true if the column is strictly right of the right hand operand.

Warning

The range type DDL support should work with any PostgreSQL DBAPI driver, however the data types returned may vary. If you are using `psycopg2`, it’s recommended to upgrade to version 2.5 or later before using these column types.

When instantiating models that use these column types, you should pass whatever data type is expected by the DBAPI driver you’re using for the column type. For `psycopg2` these are `psycopg2.extras.NumericRange`, `psycopg2.extras.DateRange`, `psycopg2.extras.DateTimeRange` and `psycopg2.extras.DateTimeTZRange` or the class you’ve registered with `psycopg2.extras.register_range`.

For example:

    from psycopg2.extras import DateTimeRange
    from sqlalchemy.dialects.postgresql import TSRANGE

    class RoomBooking(Base):

        __tablename__ = 'room_booking'

        room = Column(Integer(), primary_key=True)
        during = Column(TSRANGE())

    booking = RoomBooking(
        room=101,
        during=DateTimeRange(datetime(2013, 3, 23), None)
    )

## PostgreSQL Constraint Types

SQLAlchemy supports PostgreSQL EXCLUDE constraints via the `ExcludeConstraint` class:

Object Name | Description
--- | ---
ExcludeConstraint | A table-level EXCLUDE constraint.

class sqlalchemy.dialects.postgresql.ExcludeConstraint(*elements, **kw)

  A table-level EXCLUDE constraint.

  Defines an EXCLUDE constraint as described in the PostgreSQL documentation.

  Members

  __init__()

  Class signature

  class `sqlalchemy.dialects.postgresql.ExcludeConstraint` (`sqlalchemy.schema.ColumnCollectionConstraint`)

  method `sqlalchemy.dialects.postgresql.ExcludeConstraint.` __init__(*elements, **kw)

    Create an `ExcludeConstraint` object.

    E.g.:

        const = ExcludeConstraint(
            (Column('period'), '&&'),
            (Column('group'), '='),
            where=(Column('group') != 'some group'),
            ops={'group': 'my_operator_class'}
        )

    The constraint is normally embedded into the `Table` construct directly, or added later using `append_constraint()`:

        some_table = Table(
            'some_table', metadata,
            Column('id', Integer, primary_key=True),
            Column('period', TSRANGE()),
            Column('group', String)
        )

        some_table.append_constraint(
            ExcludeConstraint(
                (some_table.c.period, '&&'),
                (some_table.c.group, '='),
                where=some_table.c.group != 'some group',
                name='some_table_excl_const',
                ops={'group': 'my_operator_class'}
            )
        )

    Parameters:

      - *elements – A sequence of two tuples of the form `(column, operator)` where “column” is a SQL expression element or a raw SQL string, most typically a `Column` object, and “operator” is a string containing the operator to use. In order to specify a column name when a `Column` object is not available, while ensuring that any necessary quoting rules take effect, an ad-hoc `Column` or `column()` object should be used.

      - name – Optional, the in-database name of this constraint.

      - deferrable – Optional bool. If set, emit DEFERRABLE or NOT DEFERRABLE when issuing DDL for this constraint.

      - initially – Optional string. If set, emit INITIALLY <value> when issuing DDL for this constraint.

      - using – Optional string. If set, emit USING <index_method> when issuing DDL for this constraint. Defaults to ‘gist’.

      - where –

        Optional SQL expression construct or literal SQL string. If set, emit WHERE <predicate> when issuing DDL for this constraint.

        Warning

        The `ExcludeConstraint.where` argument to `ExcludeConstraint` can be passed as a Python string argument, which will be treated as trusted SQL text and rendered as given. DO NOT PASS UNTRUSTED INPUT TO THIS PARAMETER.

      - ops –

        Optional dictionary. Used to define operator classes for the elements; works the same way as that of the postgresql_ops parameter specified to the `Index` construct.

        New in version 1.3.21.

        See also

        Operator Classes - general description of how PostgreSQL operator classes are specified.

For example:

    from sqlalchemy.dialects.postgresql import ExcludeConstraint, TSRANGE

    class RoomBooking(Base):

        __tablename__ = "room_booking"

        room = Column(Integer(), primary_key=True)
        during = Column(TSRANGE())

        __table_args__ = (ExcludeConstraint(("room", "="), ("during", "&&")),)

## PostgreSQL DML Constructs

Object Name | Description
--- | ---
insert(table[, values, inline, bind, ...], **dialect_kw) | Construct an `Insert` object.
Insert | PostgreSQL-specific implementation of INSERT.

function sqlalchemy.dialects.postgresql.insert(table, values=None, inline=False, bind=None, prefixes=None, returning=None, return_defaults=False, **dialect_kw)

  Construct an `Insert` object.

  This documentation is inherited from `sqlalchemy.sql.expression.insert()`; this constructor, `sqlalchemy.dialects.postgresql.insert()`, creates a `sqlalchemy.dialects.postgresql.Insert` object. See that class for additional details describing this subclass.

  E.g.:

      from sqlalchemy import insert

      stmt = (
          insert(user_table).
          values(name='username', fullname='Full Username')
      )

  Similar functionality is available via the `TableClause.insert()` method on `Table`.

  See also

  Inserting Rows with Core - in the SQLAlchemy 1.4 / 2.0 Tutorial

  Parameters:

    - table – `TableClause` which is the subject of the insert.

    - values –

      collection of values to be inserted; see `Insert.values()` for a description of allowed formats here. Can be omitted entirely; a `Insert` construct will also dynamically render the VALUES clause at execution time based on the parameters passed to `Connection.execute()`.

      Deprecated since version 1.4: The `insert.values` parameter will be removed in SQLAlchemy 2.0. Please refer to the `Insert.values()` method.

    - inline –

      if True, no attempt will be made to retrieve the SQL-generated default values to be provided within the statement; in particular, this allows SQL expressions to be rendered ‘inline’ within the statement without the need to pre-execute them beforehand; for backends that support “returning”, this turns off the “implicit returning” feature for the statement.

      Deprecated since version 1.4: The `insert.inline` parameter will be removed in SQLAlchemy 2.0. Please use the `Insert.inline()` method.

  If both `Insert.values` and compile-time bind parameters are present, the compile-time bind parameters override the information specified within `Insert.values` on a per-key basis.

  The keys within `Insert.values` can be either `Column` objects or their string identifiers. Each key may reference one of:

  - a literal data value (i.e. string, number, etc.);

  - a Column object;

  - a SELECT statement.

  If a `SELECT` statement is specified which references this `INSERT` statement’s table, the statement will be correlated against the `INSERT` statement.

  See also

  Inserting Rows with Core - in the SQLAlchemy 1.4 / 2.0 Tutorial

class sqlalchemy.dialects.postgresql.Insert(table, values=None, inline=False, bind=None, prefixes=None, returning=None, return_defaults=False, **dialect_kw)

  PostgreSQL-specific implementation of INSERT.

  Adds methods for PG-specific syntaxes such as ON CONFLICT.

  The `Insert` object is created using the `sqlalchemy.dialects.postgresql.insert()` function.

  New in version 1.1.

  Members

  excluded, inherit_cache, on_conflict_do_nothing(), on_conflict_do_update()

  Class signature

  class `sqlalchemy.dialects.postgresql.Insert` (`sqlalchemy.sql.expression.Insert`)

  attribute `sqlalchemy.dialects.postgresql.Insert.` excluded

    Provide the `excluded` namespace for an ON CONFLICT statement

    PG’s ON CONFLICT clause allows reference to the row that would be inserted, known as `excluded`. This attribute provides all columns in this row to be referenceable.

    Tip

    The `Insert.excluded` attribute is an instance of `ColumnCollection`, which provides an interface the same as that of the `Table.c` collection described at Accessing Tables and Columns. With this collection, ordinary names are accessible like attributes (e.g. `stmt.excluded.some_column`), but special names and dictionary method names should be accessed using indexed access, such as `stmt.excluded["column name"]` or `stmt.excluded["values"]`. See the docstring for `ColumnCollection` for further examples.

    See also

    INSERT…ON CONFLICT (Upsert) - example of how to use `Insert.excluded`

  attribute `sqlalchemy.dialects.postgresql.Insert.` inherit_cache = False

    Indicate if this `HasCacheKey` instance should make use of the cache key generation scheme used by its immediate superclass.

    The attribute defaults to `None`, which indicates that a construct has not yet taken into account whether or not its appropriate for it to participate in caching; this is functionally equivalent to setting the value to `False`, except that a warning is also emitted.

    This flag can be set to `True` on a particular class, if the SQL that corresponds to the object does not change based on attributes which are local to this class, and not its superclass.

    See also

    Enabling Caching Support for Custom Constructs - General guideslines for setting the `HasCacheKey.inherit_cache` attribute for third-party or user defined SQL constructs.

  method `sqlalchemy.dialects.postgresql.Insert.` on_conflict_do_nothing(constraint=None, index_elements=None, index_where=None)

    Specifies a DO NOTHING action for ON CONFLICT clause.

    The `constraint` and `index_elements` arguments are optional, but only one of these can be specified.

    Parameters:

      - constraint – The name of a unique or exclusion constraint on the table, or the constraint object itself if it has a .name attribute.

      - index_elements – A sequence consisting of string column names, `Column` objects, or other column expression objects that will be used to infer a target index.

      - index_where –

        Additional WHERE criterion that can be used to infer a conditional target index.

        New in version 1.1.

    See also

    INSERT…ON CONFLICT (Upsert)

  method `sqlalchemy.dialects.postgresql.Insert.` on_conflict_do_update(constraint=None, index_elements=None, index_where=None, set_=None, where=None)

    Specifies a DO UPDATE SET action for ON CONFLICT clause.

    Either the `constraint` or `index_elements` argument is required, but only one of these can be specified.

    Parameters:

      - constraint – The name of a unique or exclusion constraint on the table, or the constraint object itself if it has a .name attribute.

      - index_elements – A sequence consisting of string column names, `Column` objects, or other column expression objects that will be used to infer a target index.

      - index_where – Additional WHERE criterion that can be used to infer a conditional target index.

      - set_ –

        A dictionary or other mapping object where the keys are either names of columns in the target table, or `Column` objects or other ORM-mapped columns matching that of the target table, and expressions or literals as values, specifying the `SET` actions to take.

        New in version 1.4: The `Insert.on_conflict_do_update.set_` parameter supports `Column` objects from the target `Table` as keys.

        Warning

        This dictionary does not take into account Python-specified default UPDATE values or generation functions, e.g. those specified using `Column.onupdate`. These values will not be exercised for an ON CONFLICT style of UPDATE, unless they are manually specified in the `Insert.on_conflict_do_update.set_` dictionary.

      - where –

        Optional argument. If present, can be a literal SQL string or an acceptable expression for a `WHERE` clause that restricts the rows affected by `DO UPDATE SET`. Rows not meeting the `WHERE` condition will not be updated (effectively a `DO NOTHING` for those rows).

        New in version 1.1.

    See also

    INSERT…ON CONFLICT (Upsert)

## psycopg2

Support for the PostgreSQL database via the psycopg2 driver.

### DBAPI

Documentation and download information (if applicable) for psycopg2 is available at: https://pypi.org/project/psycopg2/

### Connecting

Connect String:

    postgresql+psycopg2://user:password@host:port/dbname[?key=value&key=value...]

### psycopg2 Connect Arguments

Keyword arguments that are specific to the SQLAlchemy psycopg2 dialect may be passed to `create_engine()`, and include the following:

- `isolation_level`: This option, available for all PostgreSQL dialects, includes the `AUTOCOMMIT` isolation level when using the psycopg2 dialect. This option sets the default isolation level for the connection that is set immediately upon connection to the database before the connection is pooled. This option is generally superseded by the more modern `Connection.execution_options.isolation_level` execution option, detailed at Setting Transaction Isolation Levels including DBAPI Autocommit.

  See also

  Psycopg2 Transaction Isolation Level

  Setting Transaction Isolation Levels including DBAPI Autocommit

- `client_encoding`: sets the client encoding in a libpq-agnostic way, using psycopg2’s `set_client_encoding()` method.

  See also

  Unicode with Psycopg2

- `use_native_unicode`: Under Python 2 only, this can be set to False to disable the use of psycopg2’s native Unicode support.

  See also

  Disabling Native Unicode

- `executemany_mode`, `executemany_batch_page_size`, `executemany_values_page_size`: Allows use of psycopg2 extensions for optimizing “executemany”-style queries. See the referenced section below for details.

  See also

  Psycopg2 Fast Execution Helpers

Tip

The above keyword arguments are dialect keyword arguments, meaning that they are passed as explicit keyword arguments to `create_engine()`:

    engine = create_engine(
        "postgresql+psycopg2://scott:tiger@localhost/test",
        isolation_level="SERIALIZABLE",
    )

These should not be confused with DBAPI connect arguments, which are passed as part of the `create_engine.connect_args` dictionary and/or are passed in the URL query string, as detailed in the section Custom DBAPI connect() arguments / on-connect routines.

### SSL Connections

The psycopg2 module has a connection argument named `sslmode` for controlling its behavior regarding secure (SSL) connections. The default is `sslmode=prefer`; it will attempt an SSL connection and if that fails it will fall back to an unencrypted connection. `sslmode=require` may be used to ensure that only secure connections are established. Consult the psycopg2 / libpq documentation for further options that are available.

Note that `sslmode` is specific to psycopg2 so it is included in the connection URI:

    engine = sa.create_engine(
        "postgresql+psycopg2://scott:tiger@192.168.0.199:5432/test?sslmode=require"
    )

### Unix Domain Connections

psycopg2 supports connecting via Unix domain connections. When the `host` portion of the URL is omitted, SQLAlchemy passes `None` to psycopg2, which specifies Unix-domain communication rather than TCP/IP communication:

    create_engine("postgresql+psycopg2://user:password@/dbname")

By default, the socket file used is to connect to a Unix-domain socket in `/tmp`, or whatever socket directory was specified when PostgreSQL was built. This value can be overridden by passing a pathname to psycopg2, using `host` as an additional keyword argument:

    create_engine("postgresql+psycopg2://user:password@/dbname?host=/var/lib/postgresql")

See also

PQconnectdbParams

### Specifying multiple fallback hosts

psycopg2 supports multiple connection points in the connection string. When the `host` parameter is used multiple times in the query section of the URL, SQLAlchemy will create a single string of the host and port information provided to make the connections. Tokens may consist of `host::port` or just `host`; in the latter case, the default port is selected by libpq. In the example below, three host connections are specified, for `HostA::PortA`, `HostB` connecting to the default port, and `HostC::PortC`:

    create_engine(
        "postgresql+psycopg2://user:password@/dbname?host=HostA:PortA&host=HostB&host=HostC:PortC"
    )

As an alternative, libpq query string format also may be used; this specifies `host` and `port` as single query string arguments with comma-separated lists - the default port can be chosen by indicating an empty value in the comma separated list:

    create_engine(
        "postgresql+psycopg2://user:password@/dbname?host=HostA,HostB,HostC&port=PortA,,PortC"
    )

With either URL style, connections to each host is attempted based on a configurable strategy, which may be configured using the libpq `target_session_attrs` parameter. Per libpq this defaults to `any` which indicates a connection to each host is then attempted until a connection is successful. Other strategies include `primary`, `prefer-standby`, etc. The complete list is documented by PostgreSQL at libpq connection strings.

For example, to indicate two hosts using the `primary` strategy:

    create_engine(
        "postgresql+psycopg2://user:password@/dbname?host=HostA:PortA&host=HostB&host=HostC:PortC&target_session_attrs=primary"
    )

Changed in version 1.4.40: Port specification in psycopg2 multiple host format is repaired, previously ports were not correctly interpreted in this context. libpq comma-separated format is also now supported.

New in version 1.3.20: Support for multiple hosts in PostgreSQL connection string.

See also

libpq connection strings - please refer to this section in the libpq documentation for complete background on multiple host support.

### Empty DSN Connections / Environment Variable Connections

The psycopg2 DBAPI can connect to PostgreSQL by passing an empty DSN to the libpq client library, which by default indicates to connect to a localhost PostgreSQL database that is open for “trust” connections. This behavior can be further tailored using a particular set of environment variables which are prefixed with `PG_...`, which are consumed by `libpq` to take the place of any or all elements of the connection string.

For this form, the URL can be passed without any elements other than the initial scheme:

    engine = create_engine('postgresql+psycopg2://')

In the above form, a blank “dsn” string is passed to the `psycopg2.connect()` function which in turn represents an empty DSN passed to libpq.

New in version 1.3.2: support for parameter-less connections with psycopg2.

See also

Environment Variables - PostgreSQL documentation on how to use `PG_...` environment variables for connections.

### Per-Statement/Connection Execution Options

The following DBAPI-specific options are respected when used with `Connection.execution_options()`, `Executable.execution_options()`, `Query.execution_options()`, in addition to those not specific to DBAPIs:

- `isolation_level` - Set the transaction isolation level for the lifespan of a `Connection` (can only be set on a connection, not a statement or query). See Psycopg2 Transaction Isolation Level.

- `stream_results` - Enable or disable usage of psycopg2 server side cursors - this feature makes use of “named” cursors in combination with special result handling methods so that result rows are not fully buffered. Defaults to False, meaning cursors are buffered by default.

- `max_row_buffer` - when using `stream_results`, an integer value that specifies the maximum number of rows to buffer at a time. This is interpreted by the `BufferedRowCursorResult`, and if omitted the buffer will grow to ultimately store 1000 rows at a time.

  Changed in version 1.4: The `max_row_buffer` size can now be greater than 1000, and the buffer will grow to that size.

### Psycopg2 Fast Execution Helpers

Modern versions of psycopg2 include a feature known as Fast Execution Helpers , which have been shown in benchmarking to improve psycopg2’s executemany() performance, primarily with INSERT statements, by multiple orders of magnitude. SQLAlchemy internally makes use of these extensions for `executemany()` style calls, which correspond to lists of parameters being passed to `Connection.execute()` as detailed in multiple parameter sets. The ORM also uses this mode internally whenever possible.

The two available extensions on the psycopg2 side are the `execute_values()` and `execute_batch()` functions. The psycopg2 dialect defaults to using the `execute_values()` extension for all qualifying INSERT statements.

Changed in version 1.4: The psycopg2 dialect now defaults to a new mode `"values_only"` for `executemany_mode`, which allows an order of magnitude performance improvement for INSERT statements, but does not include “batch” mode for UPDATE and DELETE statements which removes the ability of `cursor.rowcount` to function correctly.

The use of these extensions is controlled by the `executemany_mode` flag which may be passed to `create_engine()`:

    engine = create_engine(
        "postgresql+psycopg2://scott:tiger@host/dbname",
        executemany_mode='values_plus_batch')

Possible options for `executemany_mode` include:

- `values_only` - this is the default value. the psycopg2 execute_values() extension is used for qualifying INSERT statements, which rewrites the INSERT to include multiple VALUES clauses so that many parameter sets can be inserted with one statement.

  New in version 1.4: Added `"values_only"` setting for `executemany_mode` which is also now the default.

- `None` - No psycopg2 extensions are not used, and the usual `cursor.executemany()` method is used when invoking statements with multiple parameter sets.

- `'batch'` - Uses `psycopg2.extras.execute_batch` for all qualifying INSERT, UPDATE and DELETE statements, so that multiple copies of a SQL query, each one corresponding to a parameter set passed to `executemany()`, are joined into a single SQL string separated by a semicolon. When using this mode, the `CursorResult.rowcount` attribute will not contain a value for executemany-style executions.

- `'values_plus_batch'`- `execute_values` is used for qualifying INSERT statements, `execute_batch` is used for UPDATE and DELETE. When using this mode, the `CursorResult.rowcount` attribute will not contain a value for executemany-style executions against UPDATE and DELETE statements.

By “qualifying statements”, we mean that the statement being executed must be a Core `insert()`, `update()` or `delete()` construct, and not a plain textual SQL string or one constructed using `text()`. When using the ORM, all insert/update/delete statements used by the ORM flush process are qualifying.

The “page size” for the “values” and “batch” strategies can be affected by using the `executemany_batch_page_size` and `executemany_values_page_size` engine parameters. These control how many parameter sets should be represented in each execution. The “values” page size defaults to 1000, which is different that psycopg2’s default. The “batch” page size defaults to 100. These can be affected by passing new values to `create_engine()`:

    engine = create_engine(
        "postgresql+psycopg2://scott:tiger@host/dbname",
        executemany_mode='values',
        executemany_values_page_size=10000, executemany_batch_page_size=500)

Changed in version 1.4: The default for `executemany_values_page_size` is now 1000, up from 100.

See also

Sending Multiple Parameters - General information on using the `Connection` object to execute statements in such a way as to make use of the DBAPI `.executemany()` method.

### Unicode with Psycopg2

The psycopg2 DBAPI driver supports Unicode data transparently. Under Python 2 only, the SQLAlchemy psycopg2 dialect will enable the `psycopg2.extensions.UNICODE` extension by default to ensure Unicode is handled properly; under Python 3, this is psycopg2’s default behavior.

The client character encoding can be controlled for the psycopg2 dialect in the following ways:

- For PostgreSQL 9.1 and above, the `client_encoding` parameter may be passed in the database URL; this parameter is consumed by the underlying `libpq` PostgreSQL client library:

      engine = create_engine("postgresql+psycopg2://user:pass@host/dbname?client_encoding=utf8")

  Alternatively, the above `client_encoding` value may be passed using `create_engine.connect_args` for programmatic establishment with `libpq`:

      engine = create_engine(
          "postgresql+psycopg2://user:pass@host/dbname",
          connect_args={'client_encoding': 'utf8'}
      )

- For all PostgreSQL versions, psycopg2 supports a client-side encoding value that will be passed to database connections when they are first established. The SQLAlchemy psycopg2 dialect supports this using the `client_encoding` parameter passed to `create_engine()`:

      engine = create_engine(
          "postgresql+psycopg2://user:pass@host/dbname",
          client_encoding="utf8"
      )

  Tip

  The above `client_encoding` parameter admittedly is very similar in appearance to usage of the parameter within the `create_engine.connect_args` dictionary; the difference above is that the parameter is consumed by psycopg2 and is passed to the database connection using `SET client_encoding TO 'utf8'`; in the previously mentioned style, the parameter is instead passed through psycopg2 and consumed by the `libpq` library.

- A common way to set up client encoding with PostgreSQL databases is to ensure it is configured within the server-side postgresql.conf file; this is the recommended way to set encoding for a server that is consistently of one encoding in all databases:

      # postgresql.conf file

      # client_encoding = sql_ascii # actually, defaults to database
                                   # encoding
      client_encoding = utf8

#### Disabling Native Unicode

Under Python 2 only, SQLAlchemy can also be instructed to skip the usage of the psycopg2 `UNICODE` extension and to instead utilize its own unicode encode/decode services, which are normally reserved only for those DBAPIs that don’t fully support unicode directly. Passing `use_native_unicode=False` to `create_engine()` will disable usage of `psycopg2.extensions. UNICODE`. SQLAlchemy will instead encode data itself into Python bytestrings on the way in and coerce from bytes on the way back, using the value of the `create_engine()` `encoding` parameter, which defaults to `utf-8`. SQLAlchemy’s own unicode encode/decode functionality is steadily becoming obsolete as most DBAPIs now support unicode fully.

### Transactions

The psycopg2 dialect fully supports SAVEPOINT and two-phase commit operations.

### Psycopg2 Transaction Isolation Level

As discussed in Transaction Isolation Level, all PostgreSQL dialects support setting of transaction isolation level both via the `isolation_level` parameter passed to `create_engine()` , as well as the `isolation_level` argument used by `Connection.execution_options()`. When using the psycopg2 dialect , these options make use of psycopg2’s `set_isolation_level()` connection method, rather than emitting a PostgreSQL directive; this is because psycopg2’s API-level setting is always emitted at the start of each transaction in any case.

The psycopg2 dialect supports these constants for isolation level:

- `READ COMMITTED`

- `READ UNCOMMITTED`

- `REPEATABLE READ`

- `SERIALIZABLE`

- `AUTOCOMMIT`

See also

Transaction Isolation Level

pg8000 Transaction Isolation Level

### NOTICE logging

The psycopg2 dialect will log PostgreSQL NOTICE messages via the `sqlalchemy.dialects.postgresql` logger. When this logger is set to the `logging.INFO` level, notice messages will be logged:

    import logging

    logging.getLogger('sqlalchemy.dialects.postgresql').setLevel(logging.INFO)

Above, it is assumed that logging is configured externally. If this is not the case, configuration such as `logging.basicConfig()` must be utilized:

    import logging

    logging.basicConfig()   # log messages to stdout
    logging.getLogger('sqlalchemy.dialects.postgresql').setLevel(logging.INFO)

See also

Logging HOWTO - on the python.org website

### HSTORE type

The `psycopg2` DBAPI includes an extension to natively handle marshalling of the HSTORE type. The SQLAlchemy psycopg2 dialect will enable this extension by default when psycopg2 version 2.4 or greater is used, and it is detected that the target database has the HSTORE type set up for use. In other words, when the dialect makes the first connection, a sequence like the following is performed:

1. Request the available HSTORE oids using `psycopg2.extras.HstoreAdapter.get_oids()`. If this function returns a list of HSTORE identifiers, we then determine that the `HSTORE` extension is present. This function is skipped if the version of psycopg2 installed is less than version 2.4.

2. If the `use_native_hstore` flag is at its default of `True`, and we’ve detected that `HSTORE` oids are available, the `psycopg2.extensions.register_hstore()` extension is invoked for all connections.

The `register_hstore()` extension has the effect of all Python dictionaries being accepted as parameters regardless of the type of target column in SQL. The dictionaries are converted by this extension into a textual HSTORE expression. If this behavior is not desired, disable the use of the hstore extension by setting `use_native_hstore` to `False` as follows:

    engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test",
                use_native_hstore=False)

The `HSTORE` type is still supported when the `psycopg2.extensions.register_hstore()` extension is not used. It merely means that the coercion between Python dictionaries and the HSTORE string format, on both the parameter side and the result side, will take place within SQLAlchemy’s own marshalling logic, and not that of `psycopg2` which may be more performant.

## pg8000

Support for the PostgreSQL database via the pg8000 driver.

### DBAPI

Documentation and download information (if applicable) for pg8000 is available at: https://pypi.org/project/pg8000/

### Connecting

Connect String:

    postgresql+pg8000://user:password@host:port/dbname[?key=value&key=value...]

Changed in version 1.4: The pg8000 dialect has been updated for version 1.16.6 and higher, and is again part of SQLAlchemy’s continuous integration with full feature support.

### Unicode

pg8000 will encode / decode string values between it and the server using the PostgreSQL `client_encoding` parameter; by default this is the value in the `postgresql.conf` file, which often defaults to `SQL_ASCII`. Typically, this can be changed to `utf-8`, as a more useful default:

    #client_encoding = sql_ascii # actually, defaults to database
                                 # encoding
    client_encoding = utf8

The `client_encoding` can be overridden for a session by executing the SQL:

SET CLIENT_ENCODING TO ‘utf8’;

SQLAlchemy will execute this SQL on all new connections based on the value passed to `create_engine()` using the `client_encoding` parameter:

    engine = create_engine(
        "postgresql+pg8000://user:pass@host/dbname", client_encoding='utf8')

### SSL Connections

pg8000 accepts a Python `SSLContext` object which may be specified using the `create_engine.connect_args` dictionary:

    import ssl
    ssl_context = ssl.create_default_context()
    engine = sa.create_engine(
        "postgresql+pg8000://scott:tiger@192.168.0.199/test",
        connect_args={"ssl_context": ssl_context},
    )

If the server uses an automatically-generated certificate that is self-signed or does not match the host name (as seen from the client), it may also be necessary to disable hostname checking:

    import ssl
    ssl_context = ssl.create_default_context()
    ssl_context.check_hostname = False
    ssl_context.verify_mode = ssl.CERT_NONE
    engine = sa.create_engine(
        "postgresql+pg8000://scott:tiger@192.168.0.199/test",
        connect_args={"ssl_context": ssl_context},
    )

### pg8000 Transaction Isolation Level

The pg8000 dialect offers the same isolation level settings as that of the psycopg2 dialect:

- `READ COMMITTED`

- `READ UNCOMMITTED`

- `REPEATABLE READ`

- `SERIALIZABLE`

- `AUTOCOMMIT`

See also

Transaction Isolation Level

Psycopg2 Transaction Isolation Level

## asyncpg

Support for the PostgreSQL database via the asyncpg driver.

### DBAPI

Documentation and download information (if applicable) for asyncpg is available at: https://magicstack.github.io/asyncpg/

### Connecting

Connect String:

    postgresql+asyncpg://user:password@host:port/dbname[?key=value&key=value...]

The asyncpg dialect is SQLAlchemy’s first Python asyncio dialect.

Using a special asyncio mediation layer, the asyncpg dialect is usable as the backend for the SQLAlchemy asyncio extension package.

This dialect should normally be used only with the `create_async_engine()` engine creation function:

    from sqlalchemy.ext.asyncio import create_async_engine
    engine = create_async_engine("postgresql+asyncpg://user:pass@hostname/dbname")

The dialect can also be run as a “synchronous” dialect within the `create_engine()` function, which will pass “await” calls into an ad-hoc event loop. This mode of operation is of limited use and is for special testing scenarios only. The mode can be enabled by adding the SQLAlchemy-specific flag `async_fallback` to the URL in conjunction with `create_engine()`:

    # for testing purposes only; do not use in production!
    engine = create_engine("postgresql+asyncpg://user:pass@hostname/dbname?async_fallback=true")

New in version 1.4.

Note

By default asyncpg does not decode the `json` and `jsonb` types and returns them as strings. SQLAlchemy sets default type decoder for `json` and `jsonb` types using the python builtin `json.loads` function. The json implementation used can be changed by setting the attribute `json_deserializer` when creating the engine with `create_engine()` or `create_async_engine()`.

### Prepared Statement Cache

The asyncpg SQLAlchemy dialect makes use of `asyncpg.connection.prepare()` for all statements. The prepared statement objects are cached after construction which appears to grant a 10% or more performance improvement for statement invocation. The cache is on a per-DBAPI connection basis, which means that the primary storage for prepared statements is within DBAPI connections pooled within the connection pool. The size of this cache defaults to 100 statements per DBAPI connection and may be adjusted using the `prepared_statement_cache_size` DBAPI argument (note that while this argument is implemented by SQLAlchemy, it is part of the DBAPI emulation portion of the asyncpg dialect, therefore is handled as a DBAPI argument, not a dialect argument):

    engine = create_async_engine("postgresql+asyncpg://user:pass@hostname/dbname?prepared_statement_cache_size=500")

To disable the prepared statement cache, use a value of zero:

    engine = create_async_engine("postgresql+asyncpg://user:pass@hostname/dbname?prepared_statement_cache_size=0")

New in version 1.4.0b2: Added `prepared_statement_cache_size` for asyncpg.

Warning

The `asyncpg` database driver necessarily uses caches for PostgreSQL type OIDs, which become stale when custom PostgreSQL datatypes such as `ENUM` objects are changed via DDL operations. Additionally, prepared statements themselves which are optionally cached by SQLAlchemy’s driver as described above may also become “stale” when DDL has been emitted to the PostgreSQL database which modifies the tables or other objects involved in a particular prepared statement.

The SQLAlchemy asyncpg dialect will invalidate these caches within its local process when statements that represent DDL are emitted on a local connection, but this is only controllable within a single Python process / database engine. If DDL changes are made from other database engines and/or processes, a running application may encounter asyncpg exceptions `InvalidCachedStatementError` and/or `InternalServerError("cache lookup failed for type <oid>")` if it refers to pooled database connections which operated upon the previous structures. The SQLAlchemy asyncpg dialect will recover from these error cases when the driver raises these exceptions by clearing its internal caches as well as those of the asyncpg driver in response to them, but cannot prevent them from being raised in the first place if the cached prepared statement or asyncpg type caches have gone stale, nor can it retry the statement as the PostgreSQL transaction is invalidated when these errors occur.

### Disabling the PostgreSQL JIT to improve ENUM datatype handling

Asyncpg has an issue when using PostgreSQL ENUM datatypes, where upon the creation of new database connections, an expensive query may be emitted in order to retrieve metadata regarding custom types which has been shown to negatively affect performance. To mitigate this issue, the PostgreSQL “jit” setting may be disabled from the client using this setting passed to `create_async_engine()`:

    engine = create_async_engine(
        "postgresql+asyncpg://user:password@localhost/tmp",
        connect_args={"server_settings": {"jit": "off"}},
    )

See also

https://github.com/MagicStack/asyncpg/issues/727

## psycopg2cffi

Support for the PostgreSQL database via the psycopg2cffi driver.

### DBAPI

Documentation and download information (if applicable) for psycopg2cffi is available at: https://pypi.org/project/psycopg2cffi/

### Connecting

Connect String:

    postgresql+psycopg2cffi://user:password@host:port/dbname[?key=value&key=value...]

`psycopg2cffi` is an adaptation of `psycopg2`, using CFFI for the C layer. This makes it suitable for use in e.g. PyPy. Documentation is as per `psycopg2`.

New in version 1.0.0.

See also

`sqlalchemy.dialects.postgresql.psycopg2`

## py-postgresql

Support for the PostgreSQL database via the py-postgresql driver.

### DBAPI

Documentation and download information (if applicable) for py-postgresql is available at: https://python.projects.pgfoundry.org/

### Connecting

Connect String:

    postgresql+pypostgresql://user:password@host:port/dbname[?key=value&key=value...]

Note

The pypostgresql dialect is not tested as part of SQLAlchemy’s continuous integration and may have unresolved issues. The recommended PostgreSQL driver is psycopg2.

Deprecated since version 1.4: The py-postgresql DBAPI is deprecated and will be removed in a future version. This DBAPI is superseded by the external version available at external-dialect. Please use the external version or one of the supported DBAPIs to connect to PostgreSQL.

## pygresql

Support for the PostgreSQL database via the pygresql driver.

### DBAPI

Documentation and download information (if applicable) for pygresql is available at: https://www.pygresql.org/

### Connecting

Connect String:

    postgresql+pygresql://user:password@host:port/dbname[?key=value&key=value...]

Note

The pygresql dialect is not tested as part of SQLAlchemy’s continuous integration and may have unresolved issues. The recommended PostgreSQL dialect is psycopg2.

Deprecated since version 1.4: The pygresql DBAPI is deprecated and will be removed in a future version. Please use one of the supported DBAPIs to connect to PostgreSQL.