SQL and Generic Functions — SQLAlchemy 2.0 Documentation

Release: 2.0.49 | Release Date: April 3, 2026

# SQLAlchemy 2.0 Documentation

# SQL and Generic Functions

SQL functions are invoked by using the `func` namespace. See the tutorial at Working with SQL Functions for background on how to use the `func` object to render SQL functions in statements.

See also

Working with SQL Functions - in the SQLAlchemy Unified Tutorial

## Function API

The base API for SQL functions, which provides for the `func` namespace as well as classes that may be used for extensibility.

Object Name | Description
--- | ---
AnsiFunction | Define a function in “ansi” format, which doesn’t render parenthesis.
Function | Describe a named SQL function.
FunctionElement | Base for SQL function-oriented constructs.
GenericFunction | Define a ‘generic’ function.
register_function(identifier, fn[, package]) | Associate a callable with a particular func. name.

class sqlalchemy.sql.functions.AnsiFunction

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Define a function in “ansi” format, which doesn’t render parenthesis.

class sqlalchemy.sql.functions.Function

  inherits from `sqlalchemy.sql.functions.FunctionElement`

  Describe a named SQL function.

  The `Function` object is typically generated from the `func` generation object.

  Parameters:

    - *clauses – list of column expressions that form the arguments of the SQL function call.

    - type_ – optional `TypeEngine` datatype object that will be used as the return value of the column expression generated by this function call.

    - packagenames –

      a string which indicates package prefix names to be prepended to the function name when the SQL is generated. The `func` generator creates these when it is called using dotted format, e.g.:

          func.mypackage.some_function(col1, col2)

  See also

  Working with SQL Functions - in the SQLAlchemy Unified Tutorial

  `func` - namespace which produces registered or ad-hoc `Function` instances.

  `GenericFunction` - allows creation of registered function types.

  Member Name | Description
  --- | ---
  __init__() | Construct a `Function`.

  method `sqlalchemy.sql.functions.Function.` __init__(name: str, *clauses: _ColumnExpressionOrLiteralArgument[Any], type_: _TypeEngineArgument[_T] | None = None, packagenames: Tuple[str, ...] | None = None) → None

    Construct a `Function`.

    The `func` construct is normally used to construct new `Function` instances.

class sqlalchemy.sql.functions.FunctionElement

  inherits from `sqlalchemy.sql.expression.Executable`, `sqlalchemy.sql.expression.ColumnElement`, `sqlalchemy.sql.expression.FromClause`, `sqlalchemy.sql.expression.Generative`

  Base for SQL function-oriented constructs.

  This is a generic type, meaning that type checkers and IDEs can be instructed on the types to expect in a `Result` for this function. See `GenericFunction` for an example of how this is done.

  See also

  Working with SQL Functions - in the SQLAlchemy Unified Tutorial

  `Function` - named SQL function.

  `func` - namespace which produces registered or ad-hoc `Function` instances.

  `GenericFunction` - allows creation of registered function types.

  Member Name | Description
  --- | ---
  __init__() | Construct a `FunctionElement`.
  alias() | Produce a `Alias` construct against this `FunctionElement`.
  as_comparison() | Interpret this expression as a boolean comparison between two values.
  c | synonym for `FunctionElement.columns`.
  clauses | Return the underlying `ClauseList` which contains the arguments for this `FunctionElement`.
  column_valued() | Return this `FunctionElement` as a column expression that selects from itself as a FROM clause.
  columns | The set of columns exported by this `FunctionElement`.
  filter() | Produce a FILTER clause against this function.
  over() | Produce an OVER clause against this function.
  scalar_table_valued() | Return a column expression that’s against this `FunctionElement` as a scalar table-valued expression.
  select() | Produce a `select()` construct against this `FunctionElement`.
  self_group() | Apply a ‘grouping’ to this `ClauseElement`.
  table_valued() | Return a `TableValuedAlias` representation of this `FunctionElement` with table-valued expressions added.
  within_group() | Produce a WITHIN GROUP (ORDER BY expr) clause against this function.
  within_group_type() | For types that define their return type as based on the criteria within a WITHIN GROUP (ORDER BY) expression, called by the `WithinGroup` construct.

  method `sqlalchemy.sql.functions.FunctionElement.` __init__(*clauses: _ColumnExpressionOrLiteralArgument[Any]) → None

    Construct a `FunctionElement`.

    Parameters:

      - *clauses – list of column expressions that form the arguments of the SQL function call.

      - **kwargs – additional kwargs are typically consumed by subclasses.

    See also

    `func`

    `Function`

  method `sqlalchemy.sql.functions.FunctionElement.` alias(name: str | None = None, joins_implicitly: bool = False) → TableValuedAlias

    Produce a `Alias` construct against this `FunctionElement`.

    Tip

    The `FunctionElement.alias()` method is part of the mechanism by which “table valued” SQL functions are created. However, most use cases are covered by higher level methods on `FunctionElement` including `FunctionElement.table_valued()`, and `FunctionElement.column_valued()`.

    This construct wraps the function in a named alias which is suitable for the FROM clause, in the style accepted for example by PostgreSQL. A column expression is also provided using the special `.column` attribute, which may be used to refer to the output of the function as a scalar value in the columns or where clause, for a backend such as PostgreSQL.

    For a full table-valued expression, use the `FunctionElement.table_valued()` method first to establish named columns.

    e.g.:

        >>> from sqlalchemy import func, select, column
        >>> data_view = func.unnest([1, 2, 3]).alias("data_view")
        >>> print(select(data_view.column))
        SELECT data_view
        FROM unnest(:unnest_1) AS data_view

    The `FunctionElement.column_valued()` method provides a shortcut for the above pattern:

        >>> data_view = func.unnest([1, 2, 3]).column_valued("data_view")
        >>> print(select(data_view))
        SELECT data_view
        FROM unnest(:unnest_1) AS data_view

    Added in version 1.4.0b2: Added the `.column` accessor

    Parameters:

      - name – alias name, will be rendered as `AS <name>` in the FROM clause

      - joins_implicitly –

        when True, the table valued function may be used in the FROM clause without any explicit JOIN to other tables in the SQL query, and no “cartesian product” warning will be generated. May be useful for SQL functions such as `func.json_each()`.

        Added in version 1.4.33.

    See also

    Table-Valued Functions - in the SQLAlchemy Unified Tutorial

    `FunctionElement.table_valued()`

    `FunctionElement.scalar_table_valued()`

    `FunctionElement.column_valued()`

  method `sqlalchemy.sql.functions.FunctionElement.` as_comparison(left_index: int, right_index: int) → FunctionAsBinary

    Interpret this expression as a boolean comparison between two values.

    This method is used for an ORM use case described at Custom operators based on SQL functions.

    A hypothetical SQL function “is_equal()” which compares to values for equality would be written in the Core expression language as:

        expr = func.is_equal("a", "b")

    If “is_equal()” above is comparing “a” and “b” for equality, the `FunctionElement.as_comparison()` method would be invoked as:

        expr = func.is_equal("a", "b").as_comparison(1, 2)

    Where above, the integer value “1” refers to the first argument of the “is_equal()” function and the integer value “2” refers to the second.

    This would create a `BinaryExpression` that is equivalent to:

        BinaryExpression("a", "b", operator=op.eq)

    However, at the SQL level it would still render as “is_equal(‘a’, ‘b’)”.

    The ORM, when it loads a related object or collection, needs to be able to manipulate the “left” and “right” sides of the ON clause of a JOIN expression. The purpose of this method is to provide a SQL function construct that can also supply this information to the ORM, when used with the `relationship.primaryjoin` parameter. The return value is a containment object called `FunctionAsBinary`.

    An ORM example is as follows:

        class Venue(Base):
            __tablename__ = "venue"
            id = Column(Integer, primary_key=True)
            name = Column(String)

            descendants = relationship(
                "Venue",
                primaryjoin=func.instr(
                    remote(foreign(name)), name + "/"
                ).as_comparison(1, 2)
                == 1,
                viewonly=True,
                order_by=name,
            )

    Above, the “Venue” class can load descendant “Venue” objects by determining if the name of the parent Venue is contained within the start of the hypothetical descendant value’s name, e.g. “parent1” would match up to “parent1/child1”, but not to “parent2/child1”.

    Possible use cases include the “materialized path” example given above, as well as making use of special SQL functions such as geometric functions to create join conditions.

    Parameters:

      - left_index – the integer 1-based index of the function argument that serves as the “left” side of the expression.

      - right_index – the integer 1-based index of the function argument that serves as the “right” side of the expression.

    Added in version 1.3.

    See also

    Custom operators based on SQL functions - example use within the ORM

  attribute `sqlalchemy.sql.functions.FunctionElement.` c

    synonym for `FunctionElement.columns`.

  attribute `sqlalchemy.sql.functions.FunctionElement.` clauses

    Return the underlying `ClauseList` which contains the arguments for this `FunctionElement`.

  method `sqlalchemy.sql.functions.FunctionElement.` column_valued(name: str | None = None, joins_implicitly: bool = False) → TableValuedColumn[_T]

    Return this `FunctionElement` as a column expression that selects from itself as a FROM clause.

    E.g.:

        >>> from sqlalchemy import select, func
        >>> gs = func.generate_series(1, 5, -1).column_valued()
        >>> print(select(gs))
        SELECT anon_1
        FROM generate_series(:generate_series_1, :generate_series_2, :generate_series_3) AS anon_1

    This is shorthand for:

        gs = func.generate_series(1, 5, -1).alias().column

    Parameters:

      - name – optional name to assign to the alias name that’s generated. If omitted, a unique anonymizing name is used.

      - joins_implicitly –

        when True, the “table” portion of the column valued function may be a member of the FROM clause without any explicit JOIN to other tables in the SQL query, and no “cartesian product” warning will be generated. May be useful for SQL functions such as `func.json_array_elements()`.

        Added in version 1.4.46.

    See also

    Column Valued Functions - Table Valued Function as a Scalar Column - in the SQLAlchemy Unified Tutorial

    Column Valued Functions - in the PostgreSQL documentation

    `FunctionElement.table_valued()`

  attribute `sqlalchemy.sql.functions.FunctionElement.` columns

    The set of columns exported by this `FunctionElement`.

    This is a placeholder collection that allows the function to be placed in the FROM clause of a statement:

        >>> from sqlalchemy import column, select, func
        >>> stmt = select(column("x"), column("y")).select_from(func.myfunction())
        >>> print(stmt)
        SELECT x, y FROM myfunction()

    The above form is a legacy feature that is now superseded by the fully capable `FunctionElement.table_valued()` method; see that method for details.

    See also

    `FunctionElement.table_valued()` - generates table-valued SQL function expressions.

  property entity_namespace: _EntityNamespace

    overrides FromClause.entity_namespace as functions are generally column expressions and not FromClauses.

  property exported_columns: ColumnCollection[str, KeyedColumnElement[Any]]

    A `ColumnCollection` that represents the “exported” columns of this `Selectable`.

    The “exported” columns for a `FromClause` object are synonymous with the `FromClause.columns` collection.

    Added in version 1.4.

    See also

    `Selectable.exported_columns`

    `SelectBase.exported_columns`

  method `sqlalchemy.sql.functions.FunctionElement.` filter(*criterion: _ColumnExpressionArgument[bool]) → Self | FunctionFilter[_T]

    Produce a FILTER clause against this function.

    Used against aggregate and window functions, for database backends that support the “FILTER” clause.

    The expression:

        func.count(1).filter(True)

    is shorthand for:

        from sqlalchemy import funcfilter

        funcfilter(func.count(1), True)

    See also

    Special Modifiers WITHIN GROUP, FILTER - in the SQLAlchemy Unified Tutorial

    `FunctionFilter`

    `funcfilter()`

  method `sqlalchemy.sql.functions.FunctionElement.` over(*, partition_by: _ByArgument | None = None, order_by: _ByArgument | None = None, rows: Tuple[int | None, int | None] | None = None, range_: Tuple[int | None, int | None] | None = None, groups: Tuple[int | None, int | None] | None = None) → Over[_T]

    Produce an OVER clause against this function.

    Used against aggregate or so-called “window” functions, for database backends that support window functions.

    The expression:

        func.row_number().over(order_by="x")

    is shorthand for:

        from sqlalchemy import over

        over(func.row_number(), order_by="x")

    See `over()` for a full description.

    See also

    `over()`

    Using Window Functions - in the SQLAlchemy Unified Tutorial

  method `sqlalchemy.sql.functions.FunctionElement.` scalar_table_valued(name: str, type_: _TypeEngineArgument[_T] | None = None) → ScalarFunctionColumn[_T]

    Return a column expression that’s against this `FunctionElement` as a scalar table-valued expression.

    The returned expression is similar to that returned by a single column accessed off of a `FunctionElement.table_valued()` construct, except no FROM clause is generated; the function is rendered in the similar way as a scalar subquery.

    E.g.:

        >>> from sqlalchemy import func, select
        >>> fn = func.jsonb_each("{'k', 'v'}").scalar_table_valued("key")
        >>> print(select(fn))
        SELECT (jsonb_each(:jsonb_each_1)).key

    Added in version 1.4.0b2.

    See also

    `FunctionElement.table_valued()`

    `FunctionElement.alias()`

    `FunctionElement.column_valued()`

  method `sqlalchemy.sql.functions.FunctionElement.` select() → Select

    Produce a `select()` construct against this `FunctionElement`.

    This is shorthand for:

        s = select(function_element)

  method `sqlalchemy.sql.functions.FunctionElement.` self_group(against: OperatorType | None = None) → ClauseElement

    Apply a ‘grouping’ to this `ClauseElement`.

    This method is overridden by subclasses to return a “grouping” construct, i.e. parenthesis. In particular it’s used by “binary” expressions to provide a grouping around themselves when placed into a larger expression, as well as by `select()` constructs when placed into the FROM clause of another `select()`. (Note that subqueries should be normally created using the `Select.alias()` method, as many platforms require nested SELECT statements to be named).

    As expressions are composed together, the application of `self_group()` is automatic - end-user code should never need to use this method directly. Note that SQLAlchemy’s clause constructs take operator precedence into account - so parenthesis might not be needed, for example, in an expression like `x OR (y AND z)` - AND takes precedence over OR.

    The base `self_group()` method of `ClauseElement` just returns self.

  method `sqlalchemy.sql.functions.FunctionElement.` table_valued(*expr: _ColumnExpressionOrStrLabelArgument[Any], **kw: Any) → TableValuedAlias

    Return a `TableValuedAlias` representation of this `FunctionElement` with table-valued expressions added.

    e.g. to use the SQLite form of `generate_series()` (including hidden columns “start”, “stop”, “step”):

        >>> fn = func.generate_series(1, 5).table_valued(
        ...     "value", "start", "stop", "step"
        ... )

        >>> print(select(fn))
        SELECT anon_1.value, anon_1.start, anon_1.stop, anon_1.step
        FROM generate_series(:generate_series_1, :generate_series_2) AS anon_1
        >>> print(select(fn.c.value, fn.c.stop).where(fn.c.value > 2))
        SELECT anon_1.value, anon_1.stop
        FROM generate_series(:generate_series_1, :generate_series_2) AS anon_1
        WHERE anon_1.value > :value_1

    Backends like PostgreSQL need the accessed columns to be explicitly named in “AS” clause. To achieve this, use `TableValuedAlias.render_derived()`; be sure to consult the PostgreSQL-specific documentation for table valued functions for additional examples:

        >>> fn = func.generate_series(1, 5).table_valued("value").render_derived()

        >>> print(select(fn))
        SELECT anon_1.value FROM
        generate_series(:generate_series_1, :generate_series_2) AS anon_1(value)

    A WITH ORDINALITY expression may be generated by passing the keyword argument `FunctionElement.table_valued.with_ordinality`, illustrated below using PostgreSQL’s syntax:

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

    Parameters:

      - *expr – A series of string column names that will be added to the `.c` collection of the resulting `TableValuedAlias` construct as columns. `column()` objects with or without datatypes may also be used.

      - name – optional name to assign to the alias name that’s generated. If omitted, a unique anonymizing name is used.

      - with_ordinality – string name that when present results in the `WITH ORDINALITY` clause being added to the alias, and the given string name will be added as a column to the .c collection of the resulting `TableValuedAlias`.

      - joins_implicitly –

        when True, the table valued function may be used in the FROM clause without any explicit JOIN to other tables in the SQL query, and no “cartesian product” warning will be generated. May be useful for SQL functions such as `func.json_each()`.

        Added in version 1.4.33.

    Added in version 1.4.0b2.

    See also

    Table-Valued Functions - in the SQLAlchemy Unified Tutorial

    Table-Valued Functions on PostgreSQL - in the PostgreSQL documentation

    `FunctionElement.scalar_table_valued()` - variant of `FunctionElement.table_valued()` which delivers the complete table valued expression as a scalar column expression

    `FunctionElement.column_valued()`

    `TableValuedAlias.render_derived()` - renders the alias using a derived column clause, e.g. `AS name(col1, col2, ...)`

  method `sqlalchemy.sql.functions.FunctionElement.` within_group(*order_by: _ColumnExpressionArgument[Any]) → WithinGroup[_T]

    Produce a WITHIN GROUP (ORDER BY expr) clause against this function.

    Used against so-called “ordered set aggregate” and “hypothetical set aggregate” functions, including `percentile_cont`, `rank`, `dense_rank`, etc.

    See `within_group()` for a full description.

    See also

    Special Modifiers WITHIN GROUP, FILTER - in the SQLAlchemy Unified Tutorial

  method `sqlalchemy.sql.functions.FunctionElement.` within_group_type(within_group: WithinGroup[_S]) → TypeEngine | None

    For types that define their return type as based on the criteria within a WITHIN GROUP (ORDER BY) expression, called by the `WithinGroup` construct.

    Returns None by default, in which case the function’s normal `.type` is used.

class sqlalchemy.sql.functions.GenericFunction

  inherits from `sqlalchemy.sql.functions.Function`

  Define a ‘generic’ function.

  A generic function is a pre-established `Function` class that is instantiated automatically when called by name from the `func` attribute. Note that calling any name from `func` has the effect that a new `Function` instance is created automatically, given that name. The primary use case for defining a `GenericFunction` class is so that a function of a particular name may be given a fixed return type. It can also include custom argument parsing schemes as well as additional methods.

  Subclasses of `GenericFunction` are automatically registered under the name of the class. For example, a user-defined function `as_utc()` would be available immediately:

      from sqlalchemy.sql.functions import GenericFunction
      from sqlalchemy.types import DateTime

      class as_utc(GenericFunction):
          type = DateTime()
          inherit_cache = True

      print(select(func.as_utc()))

  User-defined generic functions can be organized into packages by specifying the “package” attribute when defining `GenericFunction`. Third party libraries containing many functions may want to use this in order to avoid name conflicts with other systems. For example, if our `as_utc()` function were part of a package “time”:

      class as_utc(GenericFunction):
          type = DateTime()
          package = "time"
          inherit_cache = True

  The above function would be available from `func` using the package name `time`:

      print(select(func.time.as_utc()))

  A final option is to allow the function to be accessed from one name in `func` but to render as a different name. The `identifier` attribute will override the name used to access the function as loaded from `func`, but will retain the usage of `name` as the rendered name:

      class GeoBuffer(GenericFunction):
          type = Geometry()
          package = "geo"
          name = "ST_Buffer"
          identifier = "buffer"
          inherit_cache = True

  The above function will render as follows:

      >>> print(func.geo.buffer())
      ST_Buffer()

  The name will be rendered as is, however without quoting unless the name contains special characters that require quoting. To force quoting on or off for the name, use the `quoted_name` construct:

      from sqlalchemy.sql import quoted_name

      class GeoBuffer(GenericFunction):
          type = Geometry()
          package = "geo"
          name = quoted_name("ST_Buffer", True)
          identifier = "buffer"
          inherit_cache = True

  The above function will render as:

      >>> print(func.geo.buffer())
      "ST_Buffer"()

  Type parameters for this class as a generic type can be passed and should match the type seen in a `Result`. For example:

      class as_utc(GenericFunction[datetime.datetime]):
          type = DateTime()
          inherit_cache = True

  The above indicates that the following expression returns a `datetime` object:

      connection.scalar(select(func.as_utc()))

  Added in version 1.3.13: The `quoted_name` construct is now recognized for quoting when used with the “name” attribute of the object, so that quoting can be forced on or off for the function name.

function sqlalchemy.sql.functions.register_function(identifier: str, fn: Type[Function[Any]], package: str = '_default') → None

  Associate a callable with a particular func. name.

  This is normally called by GenericFunction, but is also available by itself so that a non-Function construct can be associated with the `func` accessor (i.e. CAST, EXTRACT).

## Selected “Known” Functions

These are `GenericFunction` implementations for a selected set of common SQL functions that set up the expected return type for each function automatically. The are invoked in the same way as any other member of the `func` namespace:

    select(func.count("*")).select_from(some_table)

Note that any name not known to `func` generates the function name as is - there is no restriction on what SQL functions can be called, known or unknown to SQLAlchemy, built-in or user defined. The section here only describes those functions where SQLAlchemy already knows what argument and return types are in use.

Object Name | Description
--- | ---
aggregate_strings | Implement a generic string aggregation function.
array_agg | Support for the ARRAY_AGG function.
char_length | The CHAR_LENGTH() SQL function.
coalesce |
concat | The SQL CONCAT() function, which concatenates strings.
count | The ANSI COUNT aggregate function. With no arguments, emits COUNT *.
cube | Implement the `CUBE` grouping operation.
cume_dist | Implement the `cume_dist` hypothetical-set aggregate function.
current_date | The CURRENT_DATE() SQL function.
current_time | The CURRENT_TIME() SQL function.
current_timestamp | The CURRENT_TIMESTAMP() SQL function.
current_user | The CURRENT_USER() SQL function.
dense_rank | Implement the `dense_rank` hypothetical-set aggregate function.
grouping_sets | Implement the `GROUPING SETS` grouping operation.
localtime | The localtime() SQL function.
localtimestamp | The localtimestamp() SQL function.
max | The SQL MAX() aggregate function.
min | The SQL MIN() aggregate function.
mode | Implement the `mode` ordered-set aggregate function.
next_value | Represent the ‘next value’, given a `Sequence` as its single argument.
now | The SQL now() datetime function.
percent_rank | Implement the `percent_rank` hypothetical-set aggregate function.
percentile_cont | Implement the `percentile_cont` ordered-set aggregate function.
percentile_disc | Implement the `percentile_disc` ordered-set aggregate function.
random | The RANDOM() SQL function.
rank | Implement the `rank` hypothetical-set aggregate function.
rollup | Implement the `ROLLUP` grouping operation.
session_user | The SESSION_USER() SQL function.
sum | The SQL SUM() aggregate function.
sysdate | The SYSDATE() SQL function.
user | The USER() SQL function.

class sqlalchemy.sql.functions.aggregate_strings

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement a generic string aggregation function.

  This function will concatenate non-null values into a string and separate the values by a delimiter.

  This function is compiled on a per-backend basis, into functions such as `group_concat()`, `string_agg()`, or `LISTAGG()`.

  e.g. Example usage with delimiter ‘.’:

      stmt = select(func.aggregate_strings(table.c.str_col, "."))

  The return type of this function is `String`.

class sqlalchemy.sql.functions.array_agg

  inherits from `sqlalchemy.sql.functions.ReturnTypeFromArgs`

  Support for the ARRAY_AGG function.

  The `func.array_agg(expr)` construct returns an expression of type `ARRAY`.

  e.g.:

      stmt = select(func.array_agg(table.c.values)[2:5])

  See also

  `array_agg()` - PostgreSQL-specific version that returns `ARRAY`, which has PG-specific operators added.

class sqlalchemy.sql.functions.char_length

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  The CHAR_LENGTH() SQL function.

class sqlalchemy.sql.functions.coalesce

  inherits from `sqlalchemy.sql.functions.ReturnTypeFromOptionalArgs`

class sqlalchemy.sql.functions.concat

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  The SQL CONCAT() function, which concatenates strings.

  E.g.:

      >>> print(select(func.concat("a", "b")))
      SELECT concat(:concat_2, :concat_3) AS concat_1

  String concatenation in SQLAlchemy is more commonly available using the Python `+` operator with string datatypes, which will render a backend-specific concatenation operator, such as :

      >>> print(select(literal("a") + "b"))
      SELECT :param_1 || :param_2 AS anon_1

class sqlalchemy.sql.functions.count

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  The ANSI COUNT aggregate function. With no arguments, emits COUNT *.

  E.g.:

      from sqlalchemy import func
      from sqlalchemy import select
      from sqlalchemy import table, column

      my_table = table("some_table", column("id"))

      stmt = select(func.count()).select_from(my_table)

  Executing `stmt` would emit:

      SELECT count(*) AS count_1
      FROM some_table

class sqlalchemy.sql.functions.cube

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `CUBE` grouping operation.

  This function is used as part of the GROUP BY of a statement, e.g. `Select.group_by()`:

      stmt = select(
          func.sum(table.c.value), table.c.col_1, table.c.col_2
      ).group_by(func.cube(table.c.col_1, table.c.col_2))

  Added in version 1.2.

class sqlalchemy.sql.functions.cume_dist

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `cume_dist` hypothetical-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is `Numeric`.

class sqlalchemy.sql.functions.current_date

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The CURRENT_DATE() SQL function.

class sqlalchemy.sql.functions.current_time

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The CURRENT_TIME() SQL function.

class sqlalchemy.sql.functions.current_timestamp

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The CURRENT_TIMESTAMP() SQL function.

class sqlalchemy.sql.functions.current_user

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The CURRENT_USER() SQL function.

class sqlalchemy.sql.functions.dense_rank

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `dense_rank` hypothetical-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is `Integer`.

class sqlalchemy.sql.functions.grouping_sets

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `GROUPING SETS` grouping operation.

  This function is used as part of the GROUP BY of a statement, e.g. `Select.group_by()`:

      stmt = select(
          func.sum(table.c.value), table.c.col_1, table.c.col_2
      ).group_by(func.grouping_sets(table.c.col_1, table.c.col_2))

  In order to group by multiple sets, use the `tuple_()` construct:

      from sqlalchemy import tuple_

      stmt = select(
          func.sum(table.c.value), table.c.col_1, table.c.col_2, table.c.col_3
      ).group_by(
          func.grouping_sets(
              tuple_(table.c.col_1, table.c.col_2),
              tuple_(table.c.value, table.c.col_3),
          )
      )

  Added in version 1.2.

class sqlalchemy.sql.functions.localtime

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The localtime() SQL function.

class sqlalchemy.sql.functions.localtimestamp

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The localtimestamp() SQL function.

class sqlalchemy.sql.functions.max

  inherits from `sqlalchemy.sql.functions.ReturnTypeFromArgs`

  The SQL MAX() aggregate function.

class sqlalchemy.sql.functions.min

  inherits from `sqlalchemy.sql.functions.ReturnTypeFromArgs`

  The SQL MIN() aggregate function.

class sqlalchemy.sql.functions.mode

  inherits from `sqlalchemy.sql.functions.OrderedSetAgg`

  Implement the `mode` ordered-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is the same as the sort expression.

class sqlalchemy.sql.functions.next_value

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Represent the ‘next value’, given a `Sequence` as its single argument.

  Compiles into the appropriate function on each backend, or will raise NotImplementedError if used on a backend that does not provide support for sequences.

class sqlalchemy.sql.functions.now

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  The SQL now() datetime function.

  SQLAlchemy dialects will usually render this particular function in a backend-specific way, such as rendering it as `CURRENT_TIMESTAMP`.

class sqlalchemy.sql.functions.percent_rank

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `percent_rank` hypothetical-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is `Numeric`.

class sqlalchemy.sql.functions.percentile_cont

  inherits from `sqlalchemy.sql.functions.OrderedSetAgg`

  Implement the `percentile_cont` ordered-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is the same as the sort expression, or if the arguments are an array, an `ARRAY` of the sort expression’s type.

class sqlalchemy.sql.functions.percentile_disc

  inherits from `sqlalchemy.sql.functions.OrderedSetAgg`

  Implement the `percentile_disc` ordered-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is the same as the sort expression, or if the arguments are an array, an `ARRAY` of the sort expression’s type.

class sqlalchemy.sql.functions.random

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  The RANDOM() SQL function.

class sqlalchemy.sql.functions.rank

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `rank` hypothetical-set aggregate function.

  This function must be used with the `FunctionElement.within_group()` modifier to supply a sort expression to operate upon.

  The return type of this function is `Integer`.

class sqlalchemy.sql.functions.rollup

  inherits from `sqlalchemy.sql.functions.GenericFunction`

  Implement the `ROLLUP` grouping operation.

  This function is used as part of the GROUP BY of a statement, e.g. `Select.group_by()`:

      stmt = select(
          func.sum(table.c.value), table.c.col_1, table.c.col_2
      ).group_by(func.rollup(table.c.col_1, table.c.col_2))

  Added in version 1.2.

class sqlalchemy.sql.functions.session_user

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The SESSION_USER() SQL function.

class sqlalchemy.sql.functions.sum

  inherits from `sqlalchemy.sql.functions.ReturnTypeFromArgs`

  The SQL SUM() aggregate function.

class sqlalchemy.sql.functions.sysdate

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The SYSDATE() SQL function.

class sqlalchemy.sql.functions.user

  inherits from `sqlalchemy.sql.functions.AnsiFunction`

  The USER() SQL function.