Release: 2.0.35 | Release Date: September 16, 2024

# SQLAlchemy 2.0 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

  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, %(x_2)s)

- `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)

  New 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.

New 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

- `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