Release: 2.1.0b1 | Release Date: not released yet

# SQLAlchemy 2.1 Documentation

# Operator Reference

This section details usage of the operators that are available to construct SQL expressions.

These methods are presented in terms of the `Operators` and `ColumnOperators` base classes. The methods are then available on descendants of these classes, including:

- `Column` objects

- `ColumnElement` objects more generally, which are the root of all Core SQL Expression language column-level expressions

- `InstrumentedAttribute` objects, which are ORM level mapped attributes.

The operators are first introduced in the tutorial sections, including:

- SQLAlchemy Unified Tutorial - unified tutorial in 2.0 style

- Object Relational Tutorial - ORM tutorial in 1.x style

- SQL Expression Language Tutorial - Core tutorial in 1.x style

## Comparison Operators

Basic comparisons which apply to many datatypes, including numerics, strings, dates, and many others:

- `ColumnOperators.__eq__()` (Python “`==`” operator):

      >>> print(column("x") == 5)
      x = :x_1

- `ColumnOperators.__ne__()` (Python “`!=`” operator):

      >>> print(column("x") != 5)
      x != :x_1

- `ColumnOperators.__gt__()` (Python “`>`” operator):

      >>> print(column("x") > 5)
      x > :x_1

- `ColumnOperators.__lt__()` (Python “`<`” operator):

      >>> print(column("x") < 5)
      x < :x_1

- `ColumnOperators.__ge__()` (Python “`>=`” operator):

      >>> print(column("x") >= 5)
      x >= :x_1

- `ColumnOperators.__le__()` (Python “`<=`” operator):

      >>> print(column("x") <= 5)
      x <= :x_1

- `ColumnOperators.between()`:

      >>> print(column("x").between(5, 10))
      x BETWEEN :x_1 AND :x_2

## IN Comparisons

The SQL IN operator is a subject all its own in SQLAlchemy. As the IN operator is usually used against a list of fixed values, SQLAlchemy’s feature of bound parameter coercion makes use of a special form of SQL compilation that renders an interim SQL string for compilation that’s formed into the final list of bound parameters in a second step. In other words, “it just works”.

### IN against a list of values

IN is available most typically by passing a list of values to the `ColumnOperators.in_()` method:

    >>> print(column("x").in_([1, 2, 3]))
    x IN (__[POSTCOMPILE_x_1])

The special bound form `__[POSTCOMPILE` is rendered into individual parameters at execution time, illustrated below:

    >>> stmt = select(User.id).where(User.id.in_([1, 2, 3]))
    >>> result = conn.execute(stmt)
    SELECT user_account.id
    FROM user_account
    WHERE user_account.id IN (?, ?, ?)
    [...] (1, 2, 3)

### Empty IN Expressions

SQLAlchemy produces a mathematically valid result for an empty IN expression by rendering a backend-specific subquery that returns no rows. Again in other words, “it just works”:

    >>> stmt = select(User.id).where(User.id.in_([]))
    >>> result = conn.execute(stmt)
    SELECT user_account.id
    FROM user_account
    WHERE user_account.id IN (SELECT 1 FROM (SELECT 1) WHERE 1!=1)
    [...] ()

The “empty set” subquery above generalizes correctly and is also rendered in terms of the IN operator which remains in place.

### NOT IN

“NOT IN” is available via the `ColumnOperators.not_in()` operator:

    >>> print(column("x").not_in([1, 2, 3]))
    (x NOT IN (__[POSTCOMPILE_x_1]))

This is typically more easily available by negating with the `~` operator:

    >>> print(~column("x").in_([1, 2, 3]))
    (x NOT IN (__[POSTCOMPILE_x_1]))

### Tuple IN Expressions

Comparison of tuples to tuples is common with IN, as among other use cases accommodates for the case when matching rows to a set of potential composite primary key values. The `tuple_()` construct provides the basic building block for tuple comparisons. The `Tuple.in_()` operator then receives a list of tuples:

    >>> from sqlalchemy import tuple_
    >>> tup = tuple_(column("x", Integer), column("y", Integer))
    >>> expr = tup.in_([(1, 2), (3, 4)])
    >>> print(expr)
    (x, y) IN (__[POSTCOMPILE_param_1])

To illustrate the parameters rendered:

    >>> tup = tuple_(User.id, Address.id)
    >>> stmt = select(User.name).join(Address).where(tup.in_([(1, 1), (2, 2)]))
    >>> conn.execute(stmt).all()
    SELECT user_account.name
    FROM user_account JOIN address ON user_account.id = address.user_id
    WHERE (user_account.id, address.id) IN (VALUES (?, ?), (?, ?))
    [...] (1, 1, 2, 2)
    [('spongebob',), ('sandy',)]

### Subquery IN

Finally, the `ColumnOperators.in_()` and `ColumnOperators.not_in()` operators work with subqueries. The form provides that a `Select` construct is passed in directly, without any explicit conversion to a named subquery:

    >>> print(column("x").in_(select(user_table.c.id)))
    x IN (SELECT user_account.id
    FROM user_account)

Tuples work as expected:

    >>> print(
    ...     tuple_(column("x"), column("y")).in_(
    ...         select(user_table.c.id, address_table.c.id).join(address_table)
    ...     )
    ... )
    (x, y) IN (SELECT user_account.id, address.id
    FROM user_account JOIN address ON user_account.id = address.user_id)

## Identity Comparisons

These operators involve testing for special SQL values such as `NULL`, boolean constants such as `true` or `false` which some databases support:

- `ColumnOperators.is_()`:

  This operator will provide exactly the SQL for “x IS y”, most often seen as “<expr> IS NULL”. The `NULL` constant is most easily acquired using regular Python `None`:

      >>> print(column("x").is_(None))
      x IS NULL

  SQL NULL is also explicitly available, if needed, using the `null()` construct:

      >>> from sqlalchemy import null
      >>> print(column("x").is_(null()))
      x IS NULL

  The `ColumnOperators.is_()` operator is automatically invoked when using the `ColumnOperators.__eq__()` overloaded operator, i.e. `==`, in conjunction with the `None` or `null()` value. In this way, there’s typically not a need to use `ColumnOperators.is_()` explicitly, particularly when used with a dynamic value:

      >>> a = None
      >>> print(column("x") == a)
      x IS NULL

  Note that the Python `is` operator is not overloaded. Even though Python provides hooks to overload operators such as `==` and `!=`, it does not provide any way to redefine `is`.

- `ColumnOperators.is_not()`:

  Similar to `ColumnOperators.is_()`, produces “IS NOT”:

      >>> print(column("x").is_not(None))
      x IS NOT NULL

  Is similarly equivalent to `!= None`:

      >>> print(column("x") != None)
      x IS NOT NULL

- `ColumnOperators.is_distinct_from()`:

  Produces SQL IS DISTINCT FROM:

      >>> print(column("x").is_distinct_from("some value"))
      x IS DISTINCT FROM :x_1

- `ColumnOperators.isnot_distinct_from()`:

  Produces SQL IS NOT DISTINCT FROM:

      >>> print(column("x").isnot_distinct_from("some value"))
      x IS NOT DISTINCT FROM :x_1

## String Comparisons

- `ColumnOperators.like()`:

      >>> print(column("x").like("word"))
      x LIKE :x_1

- `ColumnOperators.ilike()`:

  Case insensitive LIKE makes use of the SQL `lower()` function on a generic backend. On the PostgreSQL backend it will use `ILIKE`:

      >>> print(column("x").ilike("word"))
      lower(x) LIKE lower(:x_1)

- `ColumnOperators.notlike()`:

      >>> print(column("x").notlike("word"))
      x NOT LIKE :x_1

- `ColumnOperators.notilike()`:

      >>> print(column("x").notilike("word"))
      lower(x) NOT LIKE lower(:x_1)

## String Containment

String containment operators are basically built as a combination of LIKE and the string concatenation operator, which is `||` on most backends or sometimes a function like `concat()`:

- `ColumnOperators.startswith()`:

      >>> print(column("x").startswith("word"))
      x LIKE :x_1 || '%'

- `ColumnOperators.endswith()`:

      >>> print(column("x").endswith("word"))
      x LIKE '%' || :x_1

- `ColumnOperators.contains()`:

      >>> print(column("x").contains("word"))
      x LIKE '%' || :x_1 || '%'

## String matching

Matching operators are always backend-specific and may provide different behaviors and results on different databases:

- `ColumnOperators.match()`:

  This is a dialect-specific operator that makes use of the MATCH feature of the underlying database, if available:

      >>> print(column("x").match("word"))
      x MATCH :x_1

- `ColumnOperators.regexp_match()`:

  This operator is dialect specific. We can illustrate it in terms of for example the PostgreSQL dialect:

      >>> from sqlalchemy.dialects import postgresql
      >>> print(column("x").regexp_match("word").compile(dialect=postgresql.dialect()))
      x ~ %(x_1)s::VARCHAR

  Or MySQL:

      >>> from sqlalchemy.dialects import mysql
      >>> print(column("x").regexp_match("word").compile(dialect=mysql.dialect()))
      x REGEXP %s

## String Alteration

- `ColumnOperators.concat()`:

  String concatenation:

      >>> print(column("x").concat("some string"))
      x || :x_1

  This operator is available via `ColumnOperators.__add__()`, that is, the Python `+` operator, when working with a column expression that derives from `String`:

      >>> print(column("x", String) + "some string")
      x || :x_1

  The operator will produce the appropriate database-specific construct, such as on MySQL it’s historically been the `concat()` SQL function:

      >>> print((column("x", String) + "some string").compile(dialect=mysql.dialect()))
      concat(x, %s)

- `ColumnOperators.regexp_replace()`:

  Complementary to `ColumnOperators.regexp()` this produces REGEXP REPLACE equivalent for the backends which support it:

      >>> print(column("x").regexp_replace("foo", "bar").compile(dialect=postgresql.dialect()))
      REGEXP_REPLACE(x, %(x_1)s::VARCHAR, %(x_2)s::VARCHAR)

- `ColumnOperators.collate()`:

  Produces the COLLATE SQL operator which provides for specific collations at expression time:

      >>> print(
      ...     (column("x").collate("latin1_german2_ci") == "Müller").compile(
      ...         dialect=mysql.dialect()
      ...     )
      ... )
      (x COLLATE latin1_german2_ci) = %s

  To use COLLATE against a literal value, use the `literal()` construct:

      >>> from sqlalchemy import literal
      >>> print(
      ...     (literal("Müller").collate("latin1_german2_ci") == column("x")).compile(
      ...         dialect=mysql.dialect()
      ...     )
      ... )
      (%s COLLATE latin1_german2_ci) = x

## Arithmetic Operators

- `ColumnOperators.__add__()`, `ColumnOperators.__radd__()` (Python “`+`” operator):

      >>> print(column("x") + 5)
      x + :x_1
      >>> print(5 + column("x"))
      :x_1 + x

  Note that when the datatype of the expression is `String` or similar, the `ColumnOperators.__add__()` operator instead produces string concatenation.

- `ColumnOperators.__sub__()`, `ColumnOperators.__rsub__()` (Python “`-`” operator):

      >>> print(column("x") - 5)
      x - :x_1
      >>> print(5 - column("x"))
      :x_1 - x

- `ColumnOperators.__mul__()`, `ColumnOperators.__rmul__()` (Python “`*`” operator):

      >>> print(column("x") * 5)
      x * :x_1
      >>> print(5 * column("x"))
      :x_1 * x

- `ColumnOperators.__truediv__()`, `ColumnOperators.__rtruediv__()` (Python “`/`” operator). This is the Python `truediv` operator, which will ensure integer true division occurs:

      >>> print(column("x") / 5)
      x / CAST(:x_1 AS NUMERIC)
      >>> print(5 / column("x"))
      :x_1 / CAST(x AS NUMERIC)

  Changed in version 2.0: The Python `/` operator now ensures integer true division takes place

- `ColumnOperators.__floordiv__()`, `ColumnOperators.__rfloordiv__()` (Python “`//`” operator). This is the Python `floordiv` operator, which will ensure floor division occurs. For the default backend as well as backends such as PostgreSQL, the SQL `/` operator normally behaves this way for integer values:

      >>> print(column("x") // 5)
      x / :x_1
      >>> print(5 // column("x", Integer))
      :x_1 / x

  For backends that don’t use floor division by default, or when used with numeric values, the FLOOR() function is used to ensure floor division:

      >>> print(column("x") // 5.5)
      FLOOR(x / :x_1)
      >>> print(5 // column("x", Numeric))
      FLOOR(:x_1 / x)

  Added in version 2.0: Support for FLOOR division

- `ColumnOperators.__mod__()`, `ColumnOperators.__rmod__()` (Python “`%`” operator):

      >>> print(column("x") % 5)
      x % :x_1
      >>> print(5 % column("x"))
      :x_1 % x

## Bitwise Operators

Bitwise operator functions provide uniform access to bitwise operators across different backends, which are expected to operate on compatible values such as integers and bit-strings (e.g. PostgreSQL `BIT` and similar). Note that these are not general boolean operators.

Added in version 2.0.2: Added dedicated operators for bitwise operations.

- `ColumnOperators.bitwise_not()`, `bitwise_not()`. Available as a column-level method, producing a bitwise NOT clause against a parent object:

      >>> print(column("x").bitwise_not())
      ~x

  This operator is also available as a column-expression-level method, applying bitwise NOT to an individual column expression:

      >>> from sqlalchemy import bitwise_not
      >>> print(bitwise_not(column("x")))
      ~x

- `ColumnOperators.bitwise_and()` produces bitwise AND:

      >>> print(column("x").bitwise_and(5))
      x & :x_1

- `ColumnOperators.bitwise_or()` produces bitwise OR:

      >>> print(column("x").bitwise_or(5))
      x | :x_1

- `ColumnOperators.bitwise_xor()` produces bitwise XOR:

      >>> print(column("x").bitwise_xor(5))
      x ^ :x_1

  For PostgreSQL dialects, “#” is used to represent bitwise XOR; this emits automatically when using one of these backends:

      >>> from sqlalchemy.dialects import postgresql
      >>> print(column("x").bitwise_xor(5).compile(dialect=postgresql.dialect()))
      x # %(x_1)s::INTEGER

- `ColumnOperators.bitwise_rshift()`, `ColumnOperators.bitwise_lshift()` produce bitwise shift operators:

      >>> print(column("x").bitwise_rshift(5))
      x >> :x_1
      >>> print(column("x").bitwise_lshift(5))
      x << :x_1

## Using Conjunctions and Negations

The most common conjunction, “AND”, is automatically applied if we make repeated use of the `Select.where()` method, as well as similar methods such as `Update.where()` and `Delete.where()`:

    >>> print(
    ...     select(address_table.c.email_address)
    ...     .where(user_table.c.name == "squidward")
    ...     .where(address_table.c.user_id == user_table.c.id)
    ... )
    SELECT address.email_address
    FROM address, user_account
    WHERE user_account.name = :name_1 AND address.user_id = user_account.id

`Select.where()`, `Update.where()` and `Delete.where()` also accept multiple expressions with the same effect:

    >>> print(
    ...     select(address_table.c.email_address).where(
    ...         user_table.c.name == "squidward",
    ...         address_table.c.user_id == user_table.c.id,
    ...     )
    ... )
    SELECT address.email_address
    FROM address, user_account
    WHERE user_account.name = :name_1 AND address.user_id = user_account.id

The “AND” conjunction, as well as its partner “OR”, are both available directly using the `and_()` and `or_()` functions:

    >>> from sqlalchemy import and_, or_
    >>> print(
    ...     select(address_table.c.email_address).where(
    ...         and_(
    ...             or_(user_table.c.name == "squidward", user_table.c.name == "sandy"),
    ...             address_table.c.user_id == user_table.c.id,
    ...         )
    ...     )
    ... )
    SELECT address.email_address
    FROM address, user_account
    WHERE (user_account.name = :name_1 OR user_account.name = :name_2)
    AND address.user_id = user_account.id

A negation is available using the `not_()` function. This will typically invert the operator in a boolean expression:

    >>> from sqlalchemy import not_
    >>> print(not_(column("x") == 5))
    x != :x_1

It also may apply a keyword such as `NOT` when appropriate:

    >>> from sqlalchemy import Boolean
    >>> print(not_(column("x", Boolean)))
    NOT x

## Conjunction Operators

The above conjunction functions `and_()`, `or_()`, `not_()` are also available as overloaded Python operators:

Note

The Python `&`, `|` and `~` operators take high precedence in the language; as a result, parenthesis must usually be applied for operands that themselves contain expressions, as indicated in the examples below.

- `Operators.__and__()` (Python “`&`” operator):

  The Python binary `&` operator is overloaded to behave the same as `and_()` (note parenthesis around the two operands):

      >>> print((column("x") == 5) & (column("y") == 10))
      x = :x_1 AND y = :y_1

- `Operators.__or__()` (Python “`|`” operator):

  The Python binary `|` operator is overloaded to behave the same as `or_()` (note parenthesis around the two operands):

      >>> print((column("x") == 5) | (column("y") == 10))
      x = :x_1 OR y = :y_1

- `Operators.__invert__()` (Python “`~`” operator):

  The Python binary `~` operator is overloaded to behave the same as `not_()`, either inverting the existing operator, or applying the `NOT` keyword to the expression as a whole:

      >>> print(~(column("x") == 5))
      x != :x_1
      >>> from sqlalchemy import Boolean
      >>> print(~column("x", Boolean))
      NOT x

## Parentheses and Grouping

Parenthesization of expressions is rendered based on operator precedence, not the placement of parentheses in Python code, since there is no means of detecting parentheses from interpreted Python expressions. So an expression like:

    >>> expr = or_(
    ...     User.name == "squidward", and_(Address.user_id == User.id, User.name == "sandy")
    ... )

won’t include parentheses, because the AND operator takes natural precedence over OR:

    >>> print(expr)
    user_account.name = :name_1 OR address.user_id = user_account.id AND user_account.name = :name_2

Whereas this one, where OR would otherwise not be evaluated before the AND, does:

    >>> expr = and_(
    ...     Address.user_id == User.id, or_(User.name == "squidward", User.name == "sandy")
    ... )
    >>> print(expr)
    address.user_id = user_account.id AND (user_account.name = :name_1 OR user_account.name = :name_2)

The same behavior takes effect for math operators. In the parenthesized Python expression below, the multiplication operator naturally takes precedence over the addition operator, therefore the SQL will not include parentheses:

    >>> print(column("q") + (column("x") * column("y")))
    q + x * y

Whereas this one, where the addition operator would not otherwise occur before the multiplication operator, does get parentheses:

    >>> print(column("q") * (column("x") + column("y")))
    q * (x + y)

More background on this is in the FAQ at Why are the parentheses rules like this?.