.. _connections_toplevel:

====================================
Working with Engines and Connections
====================================

.. module:: sqlalchemy.engine

This section details direct usage of the :class:`_engine.Engine`,
:class:`_engine.Connection`, and related objects. Its important to note that when
using the SQLAlchemy ORM, these objects are not generally accessed; instead,
the :class:`.Session` object is used as the interface to the database.
However, for applications that are built around direct usage of textual SQL
statements and/or SQL expression constructs without involvement by the ORM's
higher level management services, the :class:`_engine.Engine` and
:class:`_engine.Connection` are king (and queen?) - read on.

Basic Usage
===========

Recall from :doc:`/core/engines` that an :class:`_engine.Engine` is created via
the :func:`_sa.create_engine` call::

    engine = create_engine("mysql://scott:tiger@localhost/test")

The typical usage of :func:`_sa.create_engine` is once per particular database
URL, held globally for the lifetime of a single application process. A single
:class:`_engine.Engine` manages many individual :term:`DBAPI` connections on behalf of
the process and is intended to be called upon in a concurrent fashion. The
:class:`_engine.Engine` is **not** synonymous to the DBAPI ``connect`` function, which
represents just one connection resource - the :class:`_engine.Engine` is most
efficient when created just once at the module level of an application, not
per-object or per-function call.

.. sidebar:: tip

    When using an :class:`_engine.Engine` with multiple Python processes, such as when
    using ``os.fork`` or Python ``multiprocessing``, it's important that the
    engine is initialized per process.  See :ref:`pooling_multiprocessing` for
    details.

The most basic function of the :class:`_engine.Engine` is to provide access to a
:class:`_engine.Connection`, which can then invoke SQL statements.   To emit
a textual statement to the database looks like::

    from sqlalchemy import text

    with engine.connect() as connection:
        result = connection.execute(text("select username from users"))
        for row in result:
            print("username:", row["username"])

Above, the :meth:`_engine.Engine.connect` method returns a :class:`_engine.Connection`
object, and by using it in a Python context manager (e.g. the ``with:``
statement) the :meth:`_engine.Connection.close` method is automatically invoked at the
end of the block.  The :class:`_engine.Connection`, is a **proxy** object for an
actual DBAPI connection. The DBAPI connection is retrieved from the connection
pool at the point at which :class:`_engine.Connection` is created.

The object returned is known as :class:`_engine.CursorResult`, which
references a DBAPI cursor and provides methods for fetching rows
similar to that of the DBAPI cursor.   The DBAPI cursor will be closed
by the :class:`_engine.CursorResult` when all of its result rows (if any) are
exhausted.  A :class:`_engine.CursorResult` that returns no rows, such as that of
an UPDATE statement (without any returned rows),
releases cursor resources immediately upon construction.

When the :class:`_engine.Connection` is closed at the end of the ``with:`` block, the
referenced DBAPI connection is :term:`released` to the connection pool.   From
the perspective of the database itself, the connection pool will not actually
"close" the connection assuming the pool has room to store this connection  for
the next use.  When the connection is returned to the pool for re-use, the
pooling mechanism issues a ``rollback()`` call on the DBAPI connection so that
any transactional state or locks are removed, and the connection is ready for
its next use.

Our example above illustrated the execution of a textual SQL string, which
should be invoked by using the :func:`_expression.text` construct to indicate that
we'd like to use textual SQL.  The :meth:`_engine.Connection.execute` method can of
course accommodate more than that, including the variety of SQL expression
constructs described in :ref:`sqlexpression_toplevel`.


Using Transactions
==================

.. note::

  This section describes how to use transactions when working directly
  with :class:`_engine.Engine` and :class:`_engine.Connection` objects. When using the
  SQLAlchemy ORM, the public API for transaction control is via the
  :class:`.Session` object, which makes usage of the :class:`.Transaction`
  object internally. See :ref:`unitofwork_transaction` for further
  information.

The :class:`~sqlalchemy.engine.Connection` object provides a :meth:`_engine.Connection.begin`
method which returns a :class:`.Transaction` object.  Like the :class:`_engine.Connection`
itself, this object is usually used within a Python ``with:`` block so
that its scope is managed::

    with engine.connect() as connection:
        with connection.begin():
            r1 = connection.execute(table1.select())
            connection.execute(table1.insert(), {"col1": 7, "col2": "this is some data"})

The above block can be stated more simply by using the :meth:`_engine.Engine.begin`
method of :class:`_engine.Engine`::

    # runs a transaction
    with engine.begin() as connection:
        r1 = connection.execute(table1.select())
        connection.execute(table1.insert(), {"col1": 7, "col2": "this is some data"})

The block managed by each ``.begin()`` method has the behavior such that
the transaction is committed when the block completes.   If an exception is
raised, the transaction is instead rolled back, and the exception propagated
outwards.

The underlying object used to represent the transaction is the
:class:`.Transaction` object.  This object is returned by the
:meth:`_engine.Connection.begin` method and includes the methods
:meth:`.Transaction.commit` and :meth:`.Transaction.rollback`.   The context
manager calling form, which invokes these methods automatically, is recommended
as a best practice.

.. _connections_nested_transactions:

Nesting of Transaction Blocks
-----------------------------

.. deprecated:: 1.4 The "transaction nesting" feature of SQLAlchemy is a legacy feature
   that is deprecated in the 1.4 release and will be removed in SQLAlchemy 2.0.
   The pattern has proven to be a little too awkward and complicated, unless an
   application makes more of a first-class framework around the behavior.  See
   the following subsection :ref:`connections_avoid_nesting`.

The :class:`.Transaction` object also handles "nested" behavior by keeping
track of the outermost begin/commit pair. In this example, two functions both
issue a transaction on a :class:`_engine.Connection`, but only the outermost
:class:`.Transaction` object actually takes effect when it is committed.

.. sourcecode:: python+sql

    # method_a starts a transaction and calls method_b
    def method_a(connection):
        with connection.begin():  # open a transaction
            method_b(connection)


    # method_b also starts a transaction
    def method_b(connection):
        with connection.begin():  # open a transaction - this runs in the
            # context of method_a's transaction
            connection.execute(text("insert into mytable values ('bat', 'lala')"))
            connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})


    # open a Connection and call method_a
    with engine.connect() as conn:
        method_a(conn)

Above, ``method_a`` is called first, which calls ``connection.begin()``. Then
it calls ``method_b``. When ``method_b`` calls ``connection.begin()``, it just
increments a counter that is decremented when it calls ``commit()``. If either
``method_a`` or ``method_b`` calls ``rollback()``, the whole transaction is
rolled back. The transaction is not committed until ``method_a`` calls the
``commit()`` method. This "nesting" behavior allows the creation of functions
which "guarantee" that a transaction will be used if one was not already
available, but will automatically participate in an enclosing transaction if
one exists.

.. _connections_avoid_nesting:

Arbitrary Transaction Nesting as an Antipattern
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

With many years of experience, the above "nesting" pattern has not proven to
be very popular, and where it has been observed in large projects such
as Openstack, it tends to be complicated.

The most ideal way to organize an application would have a single, or at
least very few, points at which the "beginning" and "commit" of all
database transactions is demarcated.   This is also the general
idea discussed in terms of the ORM at :ref:`session_faq_whentocreate`.  To
adapt the example from the previous section to this practice looks like::


    # method_a calls method_b
    def method_a(connection):
        method_b(connection)


    # method_b uses the connection and assumes the transaction
    # is external
    def method_b(connection):
        connection.execute(text("insert into mytable values ('bat', 'lala')"))
        connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})


    # open a Connection inside of a transaction and call method_a
    with engine.begin() as conn:
        method_a(conn)

That is, ``method_a()`` and ``method_b()`` do not deal with the details
of the transaction at all; the transactional scope of the connection is
defined **externally** to the functions that have a SQL dialogue with the
connection.

It may be observed that the above code has fewer lines, and less indentation
which tends to correlate with lower :term:`cyclomatic complexity`.   The
above code is organized such that ``method_a()`` and ``method_b()`` are always
invoked from a point at which a transaction is begun.  The previous
version of the example features a ``method_a()`` and a ``method_b()`` that are
trying to be agnostic of this fact, which suggests they are prepared for
at least twice as many potential codepaths through them.

.. _connections_subtransactions:

Migrating from the "nesting" pattern
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

As SQLAlchemy's intrinsic-nested pattern is considered legacy, an application
that for either legacy or novel reasons still seeks to have a context that
automatically frames transactions should seek to maintain this functionality
through the use of a custom Python context manager.  A similar example is also
provided in terms of the ORM in the "seealso" section below.

To provide backwards compatibility for applications that make use of this
pattern, the following context manager or a similar implementation based on
a decorator may be used::

    import contextlib


    @contextlib.contextmanager
    def transaction(connection):
        if not connection.in_transaction():
            with connection.begin():
                yield connection
        else:
            yield connection

The above contextmanager would be used as::

    # method_a starts a transaction and calls method_b
    def method_a(connection):
        with transaction(connection):  # open a transaction
            method_b(connection)


    # method_b either starts a transaction, or uses the one already
    # present
    def method_b(connection):
        with transaction(connection):  # open a transaction
            connection.execute(text("insert into mytable values ('bat', 'lala')"))
            connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})


    # open a Connection and call method_a
    with engine.connect() as conn:
        method_a(conn)

A similar approach may be taken such that connectivity is established
on demand as well; the below approach features a single-use context manager
that accesses an enclosing state in order to test if connectivity is already
present::

    import contextlib


    def connectivity(engine):
        connection = None

        @contextlib.contextmanager
        def connect():
            nonlocal connection

            if connection is None:
                connection = engine.connect()
                with connection:
                    with connection.begin():
                        yield connection
            else:
                yield connection

        return connect

Using the above would look like::

    # method_a passes along connectivity context, at the same time
    # it chooses to establish a connection by calling "with"
    def method_a(connectivity):
        with connectivity():
            method_b(connectivity)


    # method_b also wants to use a connection from the context, so it
    # also calls "with:", but also it actually uses the connection.
    def method_b(connectivity):
        with connectivity() as connection:
            connection.execute(text("insert into mytable values ('bat', 'lala')"))
            connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})


    # create a new connection/transaction context object and call
    # method_a
    method_a(connectivity(engine))

The above context manager acts not only as a "transaction" context but also
as a context that manages having an open connection against a particular
:class:`_engine.Engine`.   When using the ORM :class:`_orm.Session`, this
connectivty management is provided by the :class:`_orm.Session` itself.
An overview of ORM connectivity patterns is at :ref:`unitofwork_transaction`.

.. seealso::

  :ref:`session_subtransactions` - ORM version

.. _autocommit:

Library Level (e.g. emulated) Autocommit
==========================================

.. deprecated:: 1.4  The "autocommit" feature of SQLAlchemy Core is deprecated
   and will not be present in version 2.0 of SQLAlchemy.   DBAPI-level
   AUTOCOMMIT is now widely available which offers superior performance
   and occurs transparently.  See :ref:`migration_20_autocommit` for background.

.. note:: This section discusses the feature within SQLAlchemy that automatically
   invokes the ``.commit()`` method on a DBAPI connection, however this is against
   a DBAPI connection that **is itself transactional**.  For true AUTOCOMMIT,
   see the next section :ref:`dbapi_autocommit`.

The previous transaction example illustrates how to use :class:`.Transaction`
so that several executions can take part in the same transaction. What happens
when we issue an INSERT, UPDATE or DELETE call without using
:class:`.Transaction`?  While some DBAPI
implementations provide various special "non-transactional" modes, the core
behavior of DBAPI per PEP-0249 is that a *transaction is always in progress*,
providing only ``rollback()`` and ``commit()`` methods but no ``begin()``.
SQLAlchemy assumes this is the case for any given DBAPI.

Given this requirement, SQLAlchemy implements its own "autocommit" feature which
works completely consistently across all backends. This is achieved by
detecting statements which represent data-changing operations, i.e. INSERT,
UPDATE, DELETE, as well as data definition language (DDL) statements such as
CREATE TABLE, ALTER TABLE, and then issuing a COMMIT automatically if no
transaction is in progress. The detection is based on the presence of the
``autocommit=True`` execution option on the statement.   If the statement
is a text-only statement and the flag is not set, a regular expression is used
to detect INSERT, UPDATE, DELETE, as well as a variety of other commands
for a particular backend::

    conn = engine.connect()
    conn.execute(text("INSERT INTO users VALUES (1, 'john')"))  # autocommits

The "autocommit" feature is only in effect when no :class:`.Transaction` has
otherwise been declared.   This means the feature is not generally used with
the ORM, as the :class:`.Session` object by default always maintains an
ongoing :class:`.Transaction`.

Full control of the "autocommit" behavior is available using the generative
:meth:`_engine.Connection.execution_options` method provided on :class:`_engine.Connection`
and :class:`_engine.Engine`, using the "autocommit" flag which will
turn on or off the autocommit for the selected scope. For example, a
:func:`_expression.text` construct representing a stored procedure that commits might use
it so that a SELECT statement will issue a COMMIT::

    with engine.connect().execution_options(autocommit=True) as conn:
        conn.execute(text("SELECT my_mutating_procedure()"))

.. _dbapi_autocommit:

Setting Transaction Isolation Levels including DBAPI Autocommit
=================================================================

Most DBAPIs support the concept of configurable transaction :term:`isolation` levels.
These are traditionally the four levels "READ UNCOMMITTED", "READ COMMITTED",
"REPEATABLE READ" and "SERIALIZABLE".  These are usually applied to a
DBAPI connection before it begins a new transaction, noting that most
DBAPIs will begin this transaction implicitly when SQL statements are first
emitted.

DBAPIs that support isolation levels also usually support the concept of true
"autocommit", which means that the DBAPI connection itself will be placed into
a non-transactional autocommit mode. This usually means that the typical DBAPI
behavior of emitting "BEGIN" to the database automatically no longer occurs,
but it may also include other directives. SQLAlchemy treats the concept of
"autocommit" like any other isolation level; in that it is an isolation level
that loses not only "read committed" but also loses atomicity.

.. tip::

  It is important to note, as will be discussed further in the section below at
  :ref:`dbapi_autocommit_understanding`, that "autocommit" isolation level like
  any other isolation level does **not** affect the "transactional" behavior of
  the :class:`_engine.Connection` object, which continues to call upon DBAPI
  ``.commit()`` and ``.rollback()`` methods (they just have no effect under
  autocommit), and for which the ``.begin()`` method assumes the DBAPI will
  start a transaction implicitly (which means that SQLAlchemy's "begin" **does
  not change autocommit mode**).

SQLAlchemy dialects should support these isolation levels as well as autocommit
to as great a degree as possible.   The levels are set via family of
"execution_options" parameters and methods that are throughout the Core, such
as the :meth:`_engine.Connection.execution_options` method.   The parameter is
known as :paramref:`_engine.Connection.execution_options.isolation_level` and
the values are strings which are typically a subset of the following names::

    # possible values for Connection.execution_options(isolation_level="<value>")

    "AUTOCOMMIT"
    "READ COMMITTED"
    "READ UNCOMMITTED"
    "REPEATABLE READ"
    "SERIALIZABLE"

Not every DBAPI supports every value; if an unsupported value is used for a
certain backend, an error is raised.

For example, to force REPEATABLE READ on a specific connection, then
begin a transaction::

  with engine.connect().execution_options(isolation_level="REPEATABLE READ") as connection:
      with connection.begin():
          connection.execute(<statement>)

.. note::  The return value of
   the :meth:`_engine.Connection.execution_options` method is a so-called
   "branched" connection under the SQLAlchemy 1.x series when not using
   :paramref:`_sa.create_engine.future` mode, which is a shallow
   copy of the original :class:`_engine.Connection` object.  Despite this,
   the ``isolation_level`` execution option applies to the
   original :class:`_engine.Connection` object and all "branches" overall.

   When using :paramref:`_sa.create_engine.future` mode (i.e. :term:`2.0 style`
   usage), the concept of these so-called "branched" connections is removed,
   and :meth:`_engine.Connection.execution_options` returns the **same**
   :class:`_engine.Connection` object without creating any copies.

The :paramref:`_engine.Connection.execution_options.isolation_level` option may
also be set engine wide, as is often preferable.  This is achieved by
passing it within the :paramref:`_sa.create_engine.execution_options`
parameter to :func:`_sa.create_engine`::


    from sqlalchemy import create_engine

    eng = create_engine(
        "postgresql://scott:tiger@localhost/test",
        execution_options={"isolation_level": "REPEATABLE READ"},
    )

With the above setting, the DBAPI connection will be set to use a
``"REPEATABLE READ"`` isolation level setting for each new transaction
begun.

An application that frequently chooses to run operations within different
isolation levels may wish to create multiple "sub-engines" of a lead
:class:`_engine.Engine`, each of which will be configured to a different
isolation level.  One such use case is an application that has operations
that break into "transactional" and "read-only" operations, a separate
:class:`_engine.Engine` that makes use of ``"AUTOCOMMIT"`` may be
separated off from the main engine::

    from sqlalchemy import create_engine

    eng = create_engine("postgresql://scott:tiger@localhost/test")

    autocommit_engine = eng.execution_options(isolation_level="AUTOCOMMIT")

Above, the :meth:`_engine.Engine.execution_options` method creates a shallow
copy of the original :class:`_engine.Engine`.  Both ``eng`` and
``autocommit_engine`` share the same dialect and connection pool.  However, the
"AUTOCOMMIT" mode will be set upon connections when they are acquired from the
``autocommit_engine``.

The isolation level setting, regardless of which one it is, is unconditionally
reverted when a connection is returned to the connection pool.


.. note:: The :paramref:`_engine.Connection.execution_options.isolation_level`
   parameter necessarily does not apply to statement level options, such as
   that of :meth:`_sql.Executable.execution_options`.  This because the option
   must be set on a DBAPI connection on a per-transaction basis.

.. seealso::

      :ref:`SQLite Transaction Isolation <sqlite_isolation_level>`

      :ref:`PostgreSQL Transaction Isolation <postgresql_isolation_level>`

      :ref:`MySQL Transaction Isolation <mysql_isolation_level>`

      :ref:`SQL Server Transaction Isolation <mssql_isolation_level>`

      :ref:`session_transaction_isolation` - for the ORM

      :ref:`faq_execute_retry_autocommit` - a recipe that uses DBAPI autocommit
      to transparently reconnect to the database for read-only operations

.. _dbapi_autocommit_understanding:

Understanding the DBAPI-Level Autocommit Isolation Level
---------------------------------------------------------

In the parent section, we introduced the concept of the :paramref:`_engine.Connection.execution_options.isolation_level`
parameter and how it can be used to set database isolation levels, including
DBAPI-level "autocommit" which is treated by SQLAlchemy as another transaction
isolation level.   In this section we will attempt to clarify the implications
of this approach.

If we wanted to check out a :class:`_engine.Connection` object and use it
"autocommit" mode, we would proceed as follows::

  with engine.connect() as connection:
      connection.execution_options(isolation_level="AUTOCOMMIT")
      connection.execute(<statement>)
      connection.execute(<statement>)

Above illustrates normal usage of "DBAPI autocommit" mode.   There is no
need to make use of methods such as :meth:`_engine.Connection.begin`
or :meth:`_future.Connection.commit` (noting the latter applies to :term:`2.0 style` usage).

What's important to note however is that the above autocommit mode is
**persistent on that particular Connection until we change it directly using
isolation_level again**.  The isolation level is also reset on the DBAPI
connection when we :term:`release` the connection
back to the connection pool.  However, calling upon :meth:`_engine.Connection.begin`
**will not** change the isolation level, meaning we stay in autocommit.  The
example below illustrates this::

  with engine.connect() as connection:
      connection = connection.execution_options(isolation_level="AUTOCOMMIT")

      # this begin() does nothing, isolation stays at AUTOCOMMIT
      with connection.begin() as trans:
          connection.execute(<statement>)
          connection.execute(<statement>)

When we run a block like the above with logging turned on, the logging
will attempt to indicate that while a DBAPI level ``.commit()`` is called,
it probably will have no effect due to autocommit mode::

    INFO sqlalchemy.engine.Engine BEGIN (implicit)
    ...
    INFO sqlalchemy.engine.Engine COMMIT using DBAPI connection.commit(), DBAPI should ignore due to autocommit mode

Similarly, when using :term:`2.0 style` :paramref:`_sa.create_engine.future`
mode, the :class:`_engine.Connection` will use :ref:`autobegin <migration_20_autocommit>`
behavior, meaning that the pattern below will raise an error::

  engine = create_engine(..., future=True)

  with engine.connect() as connection:
      connection = connection.execution_options(isolation_level="AUTOCOMMIT")

      # "transaction" is autobegin (but has no effect due to autocommit)
      connection.execute(<statement>)

      # this will raise; "transaction" is already begun
      with connection.begin() as trans:
          connection.execute(<statement>)

This is all to demonstrate that the autocommit isolation level setting is
**completely independent from the begin/commit behavior of the SQLAlchemy
Connection object**. The "autocommit" mode will not interact with :meth:`_engine.Connection.begin`
in any way and the :class:`_engine.Connection` does not consult this status
when performing its own state changes with regards to the transaction (with
the exception of suggesting within engine logging that these blocks are not
actually committing).  The rationale for this design is to maintain a
completely consistent usage pattern with the :class:`_engine.Connection` where
DBAPI-autocommit mode can be changed independently without indicating any code
changes elsewhere.

Isolation level settings, including autocommit mode, are reset automatically
when the connection is released back to the connection pool. Therefore it is
preferable to avoid trying to switch isolation levels on a single
:class:`_engine.Connection` object as this leads to excess verbosity.

To illustrate how to use "autocommit" in an ad-hoc mode within the scope of a
single :class:`_engine.Connection` checkout, the
:paramref:`_engine.Connection.execution_options.isolation_level` parameter
must be re-applied with the previous isolation level.
We can write our above block "correctly" as (noting 2.0 style usage below)::

    # if we wanted to flip autocommit on and off on a single connection/
    # which... we usually don't.

    engine = create_engine(..., future=True)

    with engine.connect() as connection:

        connection.execution_options(isolation_level="AUTOCOMMIT")

        # run statement(s) in autocommit mode
        connection.execute(<statement>)

        # "commit" the autobegun "transaction" (2.0/future mode only)
        connection.commit()

        # switch to default isolation level
        connection.execution_options(isolation_level=connection.default_isolation_level)

        # use a begin block
        with connection.begin() as trans:
            connection.execute(<statement>)

Above, to manually revert the isolation level we made use of
:attr:`_engine.Connection.default_isolation_level` to restore the default
isolation level (assuming that's what we want here). However, it's
probably a better idea to work with the architecture of of the
:class:`_engine.Connection` which already handles resetting of isolation level
automatically upon checkin. The **preferred** way to write the above is to
use two blocks ::

    engine = create_engine(..., future=True)

    # use an autocommit block
    with engine.connect().execution_options(isolation_level="AUTOCOMMIT") as connection:

        # run statement in autocommit mode
        connection.execute(<statement>)

    # use a regular block
    with engine.begin() as connection:
        connection.execute(<statement>)

To sum up:

1. "DBAPI level autocommit" isolation level is entirely independent of the
   :class:`_engine.Connection` object's notion of "begin" and "commit"
2. use individual :class:`_engine.Connection` checkouts per isolation level.
   Avoid trying to change back and forth between "autocommit" on a single
   connection checkout; let the engine do the work of restoring default
   isolation levels

.. _engine_stream_results:

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

Some backends feature explicit support for the concept of "server
side cursors" versus "client side cursors".  A client side cursor here
means that the database driver fully fetches all rows from a result set
into memory before returning from a statement execution.  Drivers such as
those of PostgreSQL and MySQL/MariaDB generally use client side cursors
by default.   A server side cursor, by contrast, indicates that result rows
remain pending within the database server's state as result rows are consumed
by the client.  The drivers for Oracle generally use a "server side" model,
for example, and the SQLite dialect, while not using a real "client / server"
architecture, still uses an unbuffered result fetching approach that will
leave result rows outside of process memory before they are consumed.

.. topic:: What we really mean is "buffered" vs. "unbuffered" results

  Server side cursors also imply a wider set of features with relational
  databases, such as the ability to "scroll" a cursor forwards and backwards.
  SQLAlchemy does not include any explicit support for these behaviors; within
  SQLAlchemy itself, the general term "server side cursors" should be considered
  to mean "unbuffered results" and "client side cursors" means "result rows
  are buffered into memory before the first row is returned".   To work with
  a richer "server side cursor" featureset specific to a certain DBAPI driver,
  see the section :ref:`dbapi_connections_cursor`.

From this basic architecture it follows that a "server side cursor" is more
memory efficient when fetching very large result sets, while at the same time
may introduce more complexity in the client/server communication process
and be less efficient for small result sets (typically less than 10000 rows).

For those dialects that have conditional support for buffered or unbuffered
results, there are usually caveats to the use of the "unbuffered", or server
side cursor mode.   When using the psycopg2 dialect for example, an error is
raised if a server side cursor is used with any kind of DML or DDL statement.
When using MySQL drivers with a server side cursor, the DBAPI connection is in
a more fragile state and does not recover as gracefully from error conditions
nor will it allow a rollback to proceed until the cursor is fully closed.

For this reason, SQLAlchemy's dialects will always default to the less error
prone version of a cursor, which means for PostgreSQL and MySQL dialects
it defaults to a buffered, "client side" cursor where the full set of results
is pulled into memory before any fetch methods are called from the cursor.
This mode of operation is appropriate in the **vast majority** of cases;
unbuffered cursors are not generally useful except in the uncommon case
of an application fetching a very large number of rows in chunks, where
the processing of these rows can be complete before more rows are fetched.

For database drivers that provide client and server side cursor options,
the :paramref:`_engine.Connection.execution_options.stream_results`
and :paramref:`_engine.Connection.execution_options.yield_per` execution
options provide access to "server side cursors" on a per-:class:`_engine.Connection`
or per-statement basis.    Similar options exist when using an ORM
:class:`_orm.Session` as well.


Streaming with a fixed buffer via yield_per
--------------------------------------------

As individual row-fetch operations with fully unbuffered server side cursors
are typically more expensive than fetching batches of rows at once, The
:paramref:`_engine.Connection.execution_options.yield_per` execution option
configures a :class:`_engine.Connection` or statement to make use of
server-side cursors as are available, while at the same time configuring a
fixed-size buffer of rows that will retrieve rows from the server in batches as
they are consumed. This parameter may be to a positive integer value using the
:meth:`_engine.Connection.execution_options` method on
:class:`_engine.Connection` or on a statement using the
:meth:`.Executable.execution_options` method.

.. versionadded:: 1.4.40 :paramref:`_engine.Connection.execution_options.yield_per` as a
   Core-only option is new as of SQLAlchemy 1.4.40; for prior 1.4 versions,
   use :paramref:`_engine.Connection.execution_options.stream_results`
   directly in combination with :meth:`_engine.Result.yield_per`.

Using this option is equivalent to manually setting the
:paramref:`_engine.Connection.execution_options.stream_results` option,
described in the next section, and then invoking the
:meth:`_engine.Result.yield_per` method on the :class:`_engine.Result`
object with the given integer value.   In both cases, the effect this
combination has includes:

* server side cursors mode is selected for the given backend, if available
  and not already the default behavior for that backend
* as result rows are fetched, they will be buffered in batches, where the
  size of each batch up until the last batch will be equal to the integer
  argument passed to the
  :paramref:`_engine.Connection.execution_options.yield_per` option or the
  :meth:`_engine.Result.yield_per` method; the last batch is then sized against
  the remaining rows fewer than this size
* The default partition size used by the :meth:`_engine.Result.partitions`
  method, if used, will be made equal to this integer size as well.

These three behaviors are illustrated in the example below::

    with engine.connect() as conn:
        result = conn.execution_options(yield_per=100).execute(text("select * from table"))

        for partition in result.partitions():
            # partition is an iterable that will be at most 100 items
            for row in partition:
                print(f"{row}")

The above example illustrates the combination of ``yield_per=100`` along
with using the :meth:`_engine.Result.partitions` method to run processing
on rows in batches that match the size fetched from the server.   The
use of :meth:`_engine.Result.partitions` is optional, and if the
:class:`_engine.Result` is iterated directly, a new batch of rows will be
buffered for each 100 rows fetched.    Calling a method such as
:meth:`_engine.Result.all` should **not** be used, as this will fully
fetch all remaining rows at once and defeat the purpose of using ``yield_per``.

The :paramref:`_engine.Connection.execution_options.yield_per` option
is portable to the ORM as well, used by a :class:`_orm.Session` to fetch
ORM objects, where it also limits the amount of ORM objects generated at once.
See the section :ref:`orm_queryguide_yield_per` - in the :ref:`queryguide_toplevel`
for further background on using
:paramref:`_engine.Connection.execution_options.yield_per` with the ORM.

.. versionadded:: 1.4.40 Added
   :paramref:`_engine.Connection.execution_options.yield_per`
   as a Core level execution option to conveniently set streaming results,
   buffer size, and partition size all at once in a manner that is transferrable
   to that of the ORM's similar use case.

.. _engine_stream_results_sr:

Streaming with a dynamically growing buffer using stream_results
-----------------------------------------------------------------

To enable server side cursors without a specific partition size, the
:paramref:`_engine.Connection.execution_options.stream_results` option may be
used, which like :paramref:`_engine.Connection.execution_options.yield_per` may
be called on the :class:`_engine.Connection` object or the statement object.

When a :class:`_engine.Result` object delivered using the
:paramref:`_engine.Connection.execution_options.stream_results` option
is iterated directly, rows are fetched internally
using a default buffering scheme that buffers first a small set of rows,
then a larger and larger buffer on each fetch up to a pre-configured limit
of 1000 rows.   The maximum size of this buffer can be affected using the
:paramref:`_engine.Connection.execution_options.max_row_buffer` execution option::

    with engine.connect() as conn:
        conn = conn.execution_options(stream_results=True, max_row_buffer=100)
        result = conn.execute(text("select * from table"))

        for row in result:
            print(f"{row}")

While the :paramref:`_engine.Connection.execution_options.stream_results`
option may be combined with use of the :meth:`_engine.Result.partitions`
method, a specific partition size should be passed to
:meth:`_engine.Result.partitions` so that the entire result is not fetched.
It is usually more straightforward to use the
:paramref:`_engine.Connection.execution_options.yield_per` option when setting
up to use the :meth:`_engine.Result.partitions` method.

.. seealso::

    :ref:`orm_queryguide_yield_per` - in the :ref:`queryguide_toplevel`

    :meth:`_engine.Result.partitions`

    :meth:`_engine.Result.yield_per`

.. _dbengine_implicit:


Connectionless Execution, Implicit Execution
============================================

.. deprecated:: 2.0 The features of "connectionless" and "implicit" execution
   in SQLAlchemy are deprecated and will be removed in version 2.0.  See
   :ref:`migration_20_implicit_execution` for background.

Recall from the first section we mentioned executing with and without explicit
usage of :class:`_engine.Connection`. "Connectionless" execution
refers to the usage of the ``execute()`` method on an object
which is not a :class:`_engine.Connection`.  This was illustrated using the
:meth:`_engine.Engine.execute` method of :class:`_engine.Engine`::

    result = engine.execute(text("select username from users"))
    for row in result:
        print("username:", row["username"])

In addition to "connectionless" execution, it is also possible
to use the :meth:`~.Executable.execute` method of
any :class:`.Executable` construct, which is a marker for SQL expression objects
that support execution.   The SQL expression object itself references an
:class:`_engine.Engine` or :class:`_engine.Connection` known as the **bind**, which it uses
in order to provide so-called "implicit" execution services.

Given a table as below::

    from sqlalchemy import MetaData, Table, Column, Integer

    metadata_obj = MetaData()
    users_table = Table(
        "users",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("name", String(50)),
    )

Explicit execution delivers the SQL text or constructed SQL expression to the
:meth:`_engine.Connection.execute` method of :class:`~sqlalchemy.engine.Connection`:

.. sourcecode:: python+sql

    engine = create_engine('sqlite:///file.db')
    with engine.connect() as connection:
        result = connection.execute(users_table.select())
        for row in result:
            # ....

Explicit, connectionless execution delivers the expression to the
:meth:`_engine.Engine.execute` method of :class:`~sqlalchemy.engine.Engine`:

.. sourcecode:: python+sql

    engine = create_engine('sqlite:///file.db')
    result = engine.execute(users_table.select())
    for row in result:
        # ....
    result.close()

Implicit execution is also connectionless, and makes usage of the :meth:`~.Executable.execute` method
on the expression itself.   This method is provided as part of the
:class:`.Executable` class, which refers to a SQL statement that is sufficient
for being invoked against the database.    The method makes usage of
the assumption that either an
:class:`~sqlalchemy.engine.Engine` or
:class:`~sqlalchemy.engine.Connection` has been **bound** to the expression
object.   By "bound" we mean that the special attribute :attr:`_schema.MetaData.bind`
has been used to associate a series of
:class:`_schema.Table` objects and all SQL constructs derived from them with a specific
engine::

    engine = create_engine('sqlite:///file.db')
    metadata_obj.bind = engine
    result = users_table.select().execute()
    for row in result:
        # ....
    result.close()

Above, we associate an :class:`_engine.Engine` with a :class:`_schema.MetaData` object using
the special attribute :attr:`_schema.MetaData.bind`.  The :func:`_expression.select` construct produced
from the :class:`_schema.Table` object has a method :meth:`~.Executable.execute`, which will
search for an :class:`_engine.Engine` that's "bound" to the :class:`_schema.Table`.

Overall, the usage of "bound metadata" has three general effects:

* SQL statement objects gain an :meth:`.Executable.execute` method which automatically
  locates a "bind" with which to execute themselves.
* The ORM :class:`.Session` object supports using "bound metadata" in order
  to establish which :class:`_engine.Engine` should be used to invoke SQL statements
  on behalf of a particular mapped class, though the :class:`.Session`
  also features its own explicit system of establishing complex :class:`_engine.Engine`/
  mapped class configurations.
* The :meth:`_schema.MetaData.create_all`, :meth:`_schema.MetaData.drop_all`, :meth:`_schema.Table.create`,
  :meth:`_schema.Table.drop`, and "autoload" features all make usage of the bound
  :class:`_engine.Engine` automatically without the need to pass it explicitly.

.. note::

    The concepts of "bound metadata" and "implicit execution" are not emphasized in modern SQLAlchemy.
    While they offer some convenience, they are no longer required by any API and
    are never necessary.

    In applications where multiple :class:`_engine.Engine` objects are present, each one logically associated
    with a certain set of tables (i.e. *vertical sharding*), the "bound metadata" technique can be used
    so that individual :class:`_schema.Table` can refer to the appropriate :class:`_engine.Engine` automatically;
    in particular this is supported within the ORM via the :class:`.Session` object
    as a means to associate :class:`_schema.Table` objects with an appropriate :class:`_engine.Engine`,
    as an alternative to using the bind arguments accepted directly by the :class:`.Session`.

    However, the "implicit execution" technique is not at all appropriate for use with the
    ORM, as it bypasses the transactional context maintained by the :class:`.Session`.

    Overall, in the *vast majority* of cases, "bound metadata" and "implicit execution"
    are **not useful**.   While "bound metadata" has a marginal level of usefulness with regards to
    ORM configuration, "implicit execution" is a very old usage pattern that in most
    cases is more confusing than it is helpful, and its usage is discouraged.
    Both patterns seem to encourage the overuse of expedient "short cuts" in application design
    which lead to problems later on.

    Modern SQLAlchemy usage, especially the ORM, places a heavy stress on working within the context
    of a transaction at all times; the "implicit execution" concept makes the job of
    associating statement execution with a particular transaction much more difficult.
    The :meth:`.Executable.execute` method on a particular SQL statement
    usually implies that the execution is not part of any particular transaction, which is
    usually not the desired effect.

In both "connectionless" examples, the
:class:`~sqlalchemy.engine.Connection` is created behind the scenes; the
:class:`~sqlalchemy.engine.CursorResult` returned by the ``execute()``
call references the :class:`~sqlalchemy.engine.Connection` used to issue
the SQL statement. When the :class:`_engine.CursorResult` is closed, the underlying
:class:`_engine.Connection` is closed for us, resulting in the
DBAPI connection being returned to the pool with transactional resources removed.

.. _schema_translating:

Translation of Schema Names
===========================

To support multi-tenancy applications that distribute common sets of tables
into multiple schemas, the
:paramref:`.Connection.execution_options.schema_translate_map`
execution option may be used to repurpose a set of :class:`_schema.Table` objects
to render under different schema names without any changes.

Given a table::

    user_table = Table(
        "user",
        metadata_obj,
        Column("id", Integer, primary_key=True),
        Column("name", String(50)),
    )

The "schema" of this :class:`_schema.Table` as defined by the
:paramref:`_schema.Table.schema` attribute is ``None``.  The
:paramref:`.Connection.execution_options.schema_translate_map` can specify
that all :class:`_schema.Table` objects with a schema of ``None`` would instead
render the schema as ``user_schema_one``::

    connection = engine.connect().execution_options(
        schema_translate_map={None: "user_schema_one"}
    )

    result = connection.execute(user_table.select())

The above code will invoke SQL on the database of the form::

    SELECT user_schema_one.user.id, user_schema_one.user.name FROM
    user_schema_one.user

That is, the schema name is substituted with our translated name.  The
map can specify any number of target->destination schemas::

    connection = engine.connect().execution_options(
        schema_translate_map={
            None: "user_schema_one",  # no schema name -> "user_schema_one"
            "special": "special_schema",  # schema="special" becomes "special_schema"
            "public": None,  # Table objects with schema="public" will render with no schema
        }
    )

The :paramref:`.Connection.execution_options.schema_translate_map` parameter
affects all DDL and SQL constructs generated from the SQL expression language,
as derived from the :class:`_schema.Table` or :class:`.Sequence` objects.
It does **not** impact literal string SQL used via the :func:`_expression.text`
construct nor via plain strings passed to :meth:`_engine.Connection.execute`.

The feature takes effect **only** in those cases where the name of the
schema is derived directly from that of a :class:`_schema.Table` or :class:`.Sequence`;
it does not impact methods where a string schema name is passed directly.
By this pattern, it takes effect within the "can create" / "can drop" checks
performed by methods such as :meth:`_schema.MetaData.create_all` or
:meth:`_schema.MetaData.drop_all` are called, and it takes effect when
using table reflection given a :class:`_schema.Table` object.  However it does
**not** affect the operations present on the :class:`_reflection.Inspector` object,
as the schema name is passed to these methods explicitly.

.. tip::

  To use the schema translation feature with the ORM :class:`_orm.Session`,
  set this option at the level of the :class:`_engine.Engine`, then pass that engine
  to the :class:`_orm.Session`.  The :class:`_orm.Session` uses a new
  :class:`_engine.Connection` for each transaction::

      schema_engine = engine.execution_options(schema_translate_map={...})

      session = Session(schema_engine)

      ...

  .. warning::

    When using the ORM :class:`_orm.Session` without extensions, the schema
    translate feature is only supported as
    **a single schema translate map per Session**.   It will **not work** if
    different schema translate maps are given on a per-statement basis, as
    the ORM :class:`_orm.Session` does not take current schema translate
    values into account for individual objects.

    To use a single :class:`_orm.Session` with multiple ``schema_translate_map``
    configurations, the :ref:`horizontal_sharding_toplevel` extension may
    be used.  See the example at :ref:`examples_sharding`.


.. versionadded:: 1.1

.. _sql_caching:


SQL Compilation Caching
=======================

.. versionadded:: 1.4  SQLAlchemy now has a transparent query caching system
   that substantially lowers the Python computational overhead involved in
   converting SQL statement constructs into SQL strings across both
   Core and ORM.   See the introduction at :ref:`change_4639`.

SQLAlchemy includes a comprehensive caching system for the SQL compiler as well
as its ORM variants.   This caching system is transparent within the
:class:`.Engine` and provides that the SQL compilation process for a given Core
or ORM SQL statement, as well as related computations which assemble
result-fetching mechanics for that statement, will only occur once for that
statement object and all others with the identical
structure, for the duration that the particular structure remains within the
engine's "compiled cache". By "statement objects that have the identical
structure", this generally corresponds to a SQL statement that is
constructed within a function and is built each time that function runs::

    def run_my_statement(connection, parameter):
        stmt = select(table)
        stmt = stmt.where(table.c.col == parameter)
        stmt = stmt.order_by(table.c.id)
        return connection.execute(stmt)

The above statement will generate SQL resembling
``SELECT id, col FROM table WHERE col = :col ORDER BY id``, noting that
while the value of ``parameter`` is a plain Python object such as a string
or an integer, the string SQL form of the statement does not include this
value as it uses bound parameters.  Subsequent invocations of the above
``run_my_statement()`` function will use a cached compilation construct
within the scope of the ``connection.execute()`` call for enhanced performance.

.. note:: it is important to note that the SQL compilation cache is caching
   the **SQL string that is passed to the database only**, and **not the data**
   returned by a query.   It is in no way a data cache and does not
   impact the results returned for a particular SQL statement nor does it
   imply any memory use linked to fetching of result rows.

While SQLAlchemy has had a rudimentary statement cache since the early 1.x
series, and additionally has featured the "Baked Query" extension for the ORM,
both of these systems required a high degree of special API use in order for
the cache to be effective.  The new cache as of 1.4 is instead completely
automatic and requires no change in programming style to be effective.

The cache is automatically used without any configurational changes and no
special steps are needed in order to enable it. The following sections
detail the configuration and advanced usage patterns for the cache.


Configuration
-------------

The cache itself is a dictionary-like object called an ``LRUCache``, which is
an internal SQLAlchemy dictionary subclass that tracks the usage of particular
keys and features a periodic "pruning" step which removes the least recently
used items when the size of the cache reaches a certain threshold.  The size
of this cache defaults to 500 and may be configured using the
:paramref:`_sa.create_engine.query_cache_size` parameter::

    engine = create_engine("postgresql://scott:tiger@localhost/test", query_cache_size=1200)

The size of the cache can grow to be a factor of 150% of the size given, before
it's pruned back down to the target size.  A cache of size 1200 above can therefore
grow to be 1800 elements in size at which point it will be pruned to 1200.

The sizing of the cache is based on a single entry per unique SQL statement rendered,
per engine.   SQL statements generated from both the Core and the ORM are
treated equally.  DDL statements will usually not be cached.  In order to determine
what the cache is doing, engine logging will include details about the
cache's behavior, described in the next section.


.. _sql_caching_logging:

Estimating Cache Performance Using Logging
------------------------------------------

The above cache size of 1200 is actually fairly large.   For small applications,
a size of 100 is likely sufficient.  To estimate the optimal size of the cache,
assuming enough memory is present on the target host, the size of the cache
should be based on the number of unique SQL strings that may be rendered for the
target engine in use.    The most expedient way to see this is to use
SQL echoing, which is most directly enabled by using the
:paramref:`_sa.create_engine.echo` flag, or by using Python logging; see the
section :ref:`dbengine_logging` for background on logging configuration.

As an example, we will examine the logging produced by the following program::

  from sqlalchemy import Column
  from sqlalchemy import create_engine
  from sqlalchemy import ForeignKey
  from sqlalchemy import Integer
  from sqlalchemy import String
  from sqlalchemy.ext.declarative import declarative_base
  from sqlalchemy.orm import relationship
  from sqlalchemy.orm import Session

  Base = declarative_base()


  class A(Base):
      __tablename__ = "a"

      id = Column(Integer, primary_key=True)
      data = Column(String)
      bs = relationship("B")


  class B(Base):
      __tablename__ = "b"
      id = Column(Integer, primary_key=True)
      a_id = Column(ForeignKey("a.id"))
      data = Column(String)


  e = create_engine("sqlite://", echo=True)
  Base.metadata.create_all(e)

  s = Session(e)

  s.add_all([A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()])])
  s.commit()

  for a_rec in s.query(A):
      print(a_rec.bs)

When run, each SQL statement that's logged will include a bracketed
cache statistics badge to the left of the parameters passed.   The four
types of message we may see are summarized as follows:

* ``[raw sql]`` - the driver or the end-user emitted raw SQL using
  :meth:`.Connection.exec_driver_sql` - caching does not apply

* ``[no key]`` - the statement object is a DDL statement that is not cached, or
  the statement object contains uncacheable elements such as user-defined
  constructs or arbitrarily large VALUES clauses.

* ``[generated in Xs]`` - the statement was a **cache miss** and had to be
  compiled, then stored in the cache.  it took X seconds to produce the
  compiled construct.  The number X will be in the small fractional seconds.

* ``[cached since Xs ago]`` - the statement was a **cache hit** and did not
  have to be recompiled.  The statement has been stored in the cache since
  X seconds ago.  The number X will be proportional to how long the application
  has been running and how long the statement has been cached, so for example
  would be 86400 for a 24 hour period.

Each badge is described in more detail below.

The first statements we see for the above program will be the SQLite dialect
checking for the existence of the "a" and "b" tables::

  INFO sqlalchemy.engine.Engine PRAGMA temp.table_info("a")
  INFO sqlalchemy.engine.Engine [raw sql] ()
  INFO sqlalchemy.engine.Engine PRAGMA main.table_info("b")
  INFO sqlalchemy.engine.Engine [raw sql] ()

For the above two SQLite PRAGMA statements, the badge reads ``[raw sql]``,
which indicates the driver is sending a Python string directly to the
database using :meth:`.Connection.exec_driver_sql`.  Caching does not apply
to such statements because they already exist in string form, and there
is nothing known about what kinds of result rows will be returned since
SQLAlchemy does not parse SQL strings ahead of time.

The next statements we see are the CREATE TABLE statements::

  INFO sqlalchemy.engine.Engine
  CREATE TABLE a (
    id INTEGER NOT NULL,
    data VARCHAR,
    PRIMARY KEY (id)
  )

  INFO sqlalchemy.engine.Engine [no key 0.00007s] ()
  INFO sqlalchemy.engine.Engine
  CREATE TABLE b (
    id INTEGER NOT NULL,
    a_id INTEGER,
    data VARCHAR,
    PRIMARY KEY (id),
    FOREIGN KEY(a_id) REFERENCES a (id)
  )

  INFO sqlalchemy.engine.Engine [no key 0.00006s] ()

For each of these statements, the badge reads ``[no key 0.00006s]``.  This
indicates that these two particular statements, caching did not occur because
the DDL-oriented :class:`_schema.CreateTable` construct did not produce a
cache key.  DDL constructs generally do not participate in caching because
they are not typically subject to being repeated a second time and DDL
is also a database configurational step where performance is not as critical.

The ``[no key]`` badge is important for one other reason, as it can be produced
for SQL statements that are cacheable except for some particular sub-construct
that is not currently cacheable.   Examples of this include custom user-defined
SQL elements that don't define caching parameters, as well as some constructs
that generate arbitrarily long and non-reproducible SQL strings, the main
examples being the :class:`.Values` construct as well as when using "multivalued
inserts" with the :meth:`.Insert.values` method.

So far our cache is still empty.  The next statements will be cached however,
a segment looks like::


  INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?)
  INFO sqlalchemy.engine.Engine [generated in 0.00011s] (None,)
  INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?)
  INFO sqlalchemy.engine.Engine [cached since 0.0003533s ago] (None,)
  INFO sqlalchemy.engine.Engine INSERT INTO a (data) VALUES (?)
  INFO sqlalchemy.engine.Engine [cached since 0.0005326s ago] (None,)
  INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?)
  INFO sqlalchemy.engine.Engine [generated in 0.00010s] (1, None)
  INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?)
  INFO sqlalchemy.engine.Engine [cached since 0.0003232s ago] (1, None)
  INFO sqlalchemy.engine.Engine INSERT INTO b (a_id, data) VALUES (?, ?)
  INFO sqlalchemy.engine.Engine [cached since 0.0004887s ago] (1, None)

Above, we see essentially two unique SQL strings; ``"INSERT INTO a (data) VALUES (?)"``
and ``"INSERT INTO b (a_id, data) VALUES (?, ?)"``.  Since SQLAlchemy uses
bound parameters for all literal values, even though these statements are
repeated many times for different objects, because the parameters are separate,
the actual SQL string stays the same.

.. note:: the above two statements are generated by the ORM unit of work
   process, and in fact will be caching these in a separate cache that is
   local to each mapper.  However the mechanics and terminology are the same.
   The section :ref:`engine_compiled_cache` below will describe how user-facing
   code can also use an alternate caching container on a per-statement basis.

The caching badge we see for the first occurrence of each of these two
statements is ``[generated in 0.00011s]``. This indicates that the statement
was **not in the cache, was compiled into a String in .00011s and was then
cached**.   When we see the ``[generated]`` badge, we know that this means
there was a **cache miss**.  This is to be expected for the first occurrence of
a particular statement.  However, if lots of new ``[generated]`` badges are
observed for a long-running application that is generally using the same series
of SQL statements over and over, this may be a sign that the
:paramref:`_sa.create_engine.query_cache_size` parameter is too small.  When a
statement that was cached is then evicted from the cache due to the LRU
cache pruning lesser used items, it will display the ``[generated]`` badge
when it is next used.

The caching badge that we then see for the subsequent occurrences of each of
these two statements looks like ``[cached since 0.0003533s ago]``.  This
indicates that the statement **was found in the cache, and was originally
placed into the cache .0003533 seconds ago**.   It is important to note that
while the ``[generated]`` and ``[cached since]`` badges refer to a number of
seconds, they mean different things; in the case of ``[generated]``, the number
is a rough timing of how long it took to compile the statement, and will be an
extremely small amount of time.   In the case of ``[cached since]``, this is
the total time that a statement has been present in the cache.  For an
application that's been running for six hours, this number may read ``[cached
since 21600 seconds ago]``, and that's a good thing.    Seeing high numbers for
"cached since" is an indication that these statements have not been subject to
cache misses for a long time.  Statements that frequently have a low number of
"cached since" even if the application has been running a long time may
indicate these statements are too frequently subject to cache misses, and that
the
:paramref:`_sa.create_engine.query_cache_size` may need to be increased.

Our example program then performs some SELECTs where we can see the same
pattern of "generated" then "cached", for the SELECT of the "a" table as well
as for subsequent lazy loads of the "b" table::

  INFO sqlalchemy.engine.Engine SELECT a.id AS a_id, a.data AS a_data
  FROM a
  INFO sqlalchemy.engine.Engine [generated in 0.00009s] ()
  INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  FROM b
  WHERE ? = b.a_id
  INFO sqlalchemy.engine.Engine [generated in 0.00010s] (1,)
  INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  FROM b
  WHERE ? = b.a_id
  INFO sqlalchemy.engine.Engine [cached since 0.0005922s ago] (2,)
  INFO sqlalchemy.engine.Engine SELECT b.id AS b_id, b.a_id AS b_a_id, b.data AS b_data
  FROM b
  WHERE ? = b.a_id

From our above program, a full run shows a total of four distinct SQL strings
being cached.   Which indicates a cache size of **four** would be sufficient.   This is
obviously an extremely small size, and the default size of 500 is fine to be left
at its default.

How much memory does the cache use?
-----------------------------------

The previous section detailed some techniques to check if the
:paramref:`_sa.create_engine.query_cache_size` needs to be bigger.   How do we know
if the cache is not too large?   The reason we may want to set
:paramref:`_sa.create_engine.query_cache_size` to not be higher than a certain
number would be because we have an application that may make use of a very large
number of different statements, such as an application that is building queries
on the fly from a search UX, and we don't want our host to run out of memory
if for example, a hundred thousand different queries were run in the past 24 hours
and they were all cached.

It is extremely difficult to measure how much memory is occupied by Python
data structures, however using a process to measure growth in memory via ``top`` as a
successive series of 250 new statements are added to the cache suggest a
moderate Core statement takes up about 12K while a small ORM statement takes about
20K, including result-fetching structures which for the ORM will be much greater.


.. _engine_compiled_cache:

Disabling or using an alternate dictionary to cache some (or all) statements
-----------------------------------------------------------------------------

The internal cache used is known as ``LRUCache``, but this is mostly just
a dictionary.  Any dictionary may be used as a cache for any series of
statements by using the :paramref:`.Connection.execution_options.compiled_cache`
option as an execution option.  Execution options may be set on a statement,
on an :class:`_engine.Engine` or :class:`_engine.Connection`, as well as
when using the ORM :meth:`_orm.Session.execute` method for SQLAlchemy-2.0
style invocations.   For example, to run a series of SQL statements and have
them cached in a particular dictionary::

  my_cache = {}
  with engine.connect().execution_options(compiled_cache=my_cache) as conn:
      conn.execute(table.select())

The SQLAlchemy ORM uses the above technique to hold onto per-mapper caches
within the unit of work "flush" process that are separate from the default
cache configured on the :class:`_engine.Engine`, as well as for some
relationship loader queries.

The cache can also be disabled with this argument by sending a value of
``None``::

  # disable caching for this connection
  with engine.connect().execution_options(compiled_cache=None) as conn:
      conn.execute(table.select())

.. _engine_thirdparty_caching:

Caching for Third Party Dialects
---------------------------------

The caching feature requires that the dialect's compiler produces SQL
strings that are safe to reuse for many statement invocations, given
a particular cache key that is keyed to that SQL string.  This means
that any literal values in a statement, such as the LIMIT/OFFSET values for
a SELECT, can not be hardcoded in the dialect's compilation scheme, as
the compiled string will not be re-usable.   SQLAlchemy supports rendered
bound parameters using the :meth:`_sql.BindParameter.render_literal_execute`
method which can be applied to the existing ``Select._limit_clause`` and
``Select._offset_clause`` attributes by a custom compiler, which
are illustrated later in this section.

As there are many third party dialects, many of which may be generating literal
values from SQL statements without the benefit of the newer "literal execute"
feature, SQLAlchemy as of version 1.4.5 has added an attribute to dialects
known as :attr:`_engine.Dialect.supports_statement_cache`. This attribute is
checked at runtime for its presence directly on a particular dialect's class,
even if it's already present on a superclass, so that even a third party
dialect that subclasses an existing cacheable SQLAlchemy dialect such as
``sqlalchemy.dialects.postgresql.PGDialect`` must still explicitly include this
attribute for caching to be enabled. The attribute should **only** be enabled
once the dialect has been altered as needed and tested for reusability of
compiled SQL statements with differing parameters.

For all third party dialects that don't support this attribute, the logging for
such a dialect will indicate ``dialect does not support caching``.

When a dialect has been tested against caching, and in particular the SQL
compiler has been updated to not render any literal LIMIT / OFFSET within
a SQL string directly, dialect authors can apply the attribute as follows::

    from sqlalchemy.engine.default import DefaultDialect


    class MyDialect(DefaultDialect):
        supports_statement_cache = True

The flag needs to be applied to all subclasses of the dialect as well::

    class MyDBAPIForMyDialect(MyDialect):
        supports_statement_cache = True

.. versionadded:: 1.4.5

    Added the :attr:`.Dialect.supports_statement_cache` attribute.

The typical case for dialect modification follows.

Example: Rendering LIMIT / OFFSET with post compile parameters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

As an example, suppose a dialect overrides the :meth:`.SQLCompiler.limit_clause`
method, which produces the "LIMIT / OFFSET" clause for a SQL statement,
like this::

    # pre 1.4 style code
    def limit_clause(self, select, **kw):
        text = ""
        if select._limit is not None:
            text += " \n LIMIT %d" % (select._limit,)
        if select._offset is not None:
            text += " \n OFFSET %d" % (select._offset,)
        return text

The above routine renders the :attr:`.Select._limit` and
:attr:`.Select._offset` integer values as literal integers embedded in the SQL
statement. This is a common requirement for databases that do not support using
a bound parameter within the LIMIT/OFFSET clauses of a SELECT statement.
However, rendering the integer value within the initial compilation stage is
directly **incompatible** with caching as the limit and offset integer values
of a :class:`.Select` object are not part of the cache key, so that many
:class:`.Select` statements with different limit/offset values would not render
with the correct value.

The correction for the above code is to move the literal integer into
SQLAlchemy's :ref:`post-compile <change_4808>` facility, which will render the
literal integer outside of the initial compilation stage, but instead at
execution time before the statement is sent to the DBAPI.  This is accessed
within the compilation stage using the :meth:`_sql.BindParameter.render_literal_execute`
method, in conjunction with using the :attr:`.Select._limit_clause` and
:attr:`.Select._offset_clause` attributes, which represent the LIMIT/OFFSET
as a complete SQL expression, as follows::

    # 1.4 cache-compatible code
    def limit_clause(self, select, **kw):
        text = ""

        limit_clause = select._limit_clause
        offset_clause = select._offset_clause

        if select._simple_int_clause(limit_clause):
            text += " \n LIMIT %s" % (
                self.process(limit_clause.render_literal_execute(), **kw)
            )
        elif limit_clause is not None:
            # assuming the DB doesn't support SQL expressions for LIMIT.
            # Otherwise render here normally
            raise exc.CompileError(
                "dialect 'mydialect' can only render simple integers for LIMIT"
            )
        if select._simple_int_clause(offset_clause):
            text += " \n OFFSET %s" % (
                self.process(offset_clause.render_literal_execute(), **kw)
            )
        elif offset_clause is not None:
            # assuming the DB doesn't support SQL expressions for OFFSET.
            # Otherwise render here normally
            raise exc.CompileError(
                "dialect 'mydialect' can only render simple integers for OFFSET"
            )

        return text

The approach above will generate a compiled SELECT statement that looks like::

    SELECT x FROM y
    LIMIT __[POSTCOMPILE_param_1]
    OFFSET __[POSTCOMPILE_param_2]

Where above, the ``__[POSTCOMPILE_param_1]`` and ``__[POSTCOMPILE_param_2]``
indicators will be populated with their corresponding integer values at
statement execution time, after the SQL string has been retrieved from the
cache.

After changes like the above have been made as appropriate, the
:attr:`.Dialect.supports_statement_cache` flag should be set to ``True``.
It is strongly recommended that third party dialects make use of the
`dialect third party test suite <https://github.com/sqlalchemy/sqlalchemy/blob/main/README.dialects.rst>`_
which will assert that operations like
SELECTs with LIMIT/OFFSET are correctly rendered and cached.

.. seealso::

    :ref:`faq_new_caching` - in the :ref:`faq_toplevel` section

.. _engine_lambda_caching:

Using Lambdas to add significant speed gains to statement production
--------------------------------------------------------------------

.. deepalchemy:: This technique is generally non-essential except in very performance
   intensive scenarios, and intended for experienced Python programmers.
   While fairly straightforward, it involves metaprogramming concepts that are
   not appropriate for novice Python developers.  The lambda approach can be
   applied to at a later time to existing code with a minimal amount of effort.

Python functions, typically expressed as lambdas, may be used to generate
SQL expressions which are cacheable based on the Python code location of
the lambda function itself as well as the closure variables within the
lambda.   The rationale is to allow caching of not only the SQL string-compiled
form of a SQL expression construct as is SQLAlchemy's normal behavior when
the lambda system isn't used, but also the in-Python composition
of the SQL expression construct itself, which also has some degree of
Python overhead.

The lambda SQL expression feature is available as a performance enhancing
feature, and is also optionally used in the :func:`_orm.with_loader_criteria`
ORM option in order to provide a generic SQL fragment.

Synopsis
^^^^^^^^

Lambda statements are constructed using the :func:`_sql.lambda_stmt` function,
which returns an instance of :class:`_sql.StatementLambdaElement`, which is
itself an executable statement construct.    Additional modifiers and criteria
are added to the object using the Python addition operator ``+``, or
alternatively the :meth:`_sql.StatementLambdaElement.add_criteria` method which
allows for more options.

It is assumed that the :func:`_sql.lambda_stmt` construct is being invoked
within an enclosing function or method that expects to be used many times
within an application, so that subsequent executions beyond the first one
can take advantage of the compiled SQL being cached.  When the lambda is
constructed inside of an enclosing function in Python it is then subject
to also having closure variables, which are significant to the whole
approach::

    from sqlalchemy import lambda_stmt


    def run_my_statement(connection, parameter):
        stmt = lambda_stmt(lambda: select(table))
        stmt += lambda s: s.where(table.c.col == parameter)
        stmt += lambda s: s.order_by(table.c.id)

        return connection.execute(stmt)


    with engine.connect() as conn:
        result = run_my_statement(some_connection, "some parameter")

Above, the three ``lambda`` callables that are used to define the structure
of a SELECT statement are invoked exactly once, and the resulting SQL
string cached in the compilation cache of the engine.   From that point
forward, the ``run_my_statement()`` function may be invoked any number
of times and the ``lambda`` callables within it will not be called, only
used as cache keys to retrieve the already-compiled SQL.

.. note::  It is important to note that there is already SQL caching in place
   when the lambda system is not used.   The lambda system only adds an
   additional layer of work reduction per SQL statement invoked by caching
   the building up of the SQL construct itself and also using a simpler
   cache key.


Quick Guidelines for Lambdas
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Above all, the emphasis within the lambda SQL system is ensuring that there
is never a mismatch between the cache key generated for a lambda and the
SQL string it will produce.   The :class:`_sql.LambdaElement` and related
objects will run and analyze the given lambda in order to calculate how
it should be cached on each run, trying to detect any potential problems.
Basic guidelines include:

* **Any kind of statement is supported** - while it's expected that
  :func:`_sql.select` constructs are the prime use case for :func:`_sql.lambda_stmt`,
  DML statements such as :func:`_sql.insert` and :func:`_sql.update` are
  equally usable::

    def upd(id_, newname):
        stmt = lambda_stmt(lambda: users.update())
        stmt += lambda s: s.values(name=newname)
        stmt += lambda s: s.where(users.c.id == id_)
        return stmt


    with engine.begin() as conn:
        conn.execute(upd(7, "foo"))

  ..

* **ORM use cases directly supported as well** - the :func:`_sql.lambda_stmt`
  can accommodate ORM functionality completely and used directly with
  :meth:`_orm.Session.execute`::

    def select_user(session, name):
        stmt = lambda_stmt(lambda: select(User))
        stmt += lambda s: s.where(User.name == name)

        row = session.execute(stmt).first()
        return row

  ..

* **Bound parameters are automatically accommodated** - in contrast to SQLAlchemy's
  previous "baked query" system, the lambda SQL system accommodates for
  Python literal values which become SQL bound parameters automatically.
  This means that even though a given lambda runs only once, the values that
  become bound parameters are extracted from the **closure** of the lambda
  on every run:

  .. sourcecode:: pycon+sql

        >>> def my_stmt(x, y):
        ...     stmt = lambda_stmt(lambda: select(func.max(x, y)))
        ...     return stmt
        >>> engine = create_engine("sqlite://", echo=True)
        >>> with engine.connect() as conn:
        ...     print(conn.scalar(my_stmt(5, 10)))
        ...     print(conn.scalar(my_stmt(12, 8)))
        {opensql}SELECT max(?, ?) AS max_1
        [generated in 0.00057s] (5, 10){stop}
        10
        {opensql}SELECT max(?, ?) AS max_1
        [cached since 0.002059s ago] (12, 8){stop}
        12

  Above, :class:`_sql.StatementLambdaElement` extracted the values of ``x``
  and ``y`` from the **closure** of the lambda that is generated each time
  ``my_stmt()`` is invoked; these were substituted into the cached SQL
  construct as the values of the parameters.

* **The lambda should ideally produce an identical SQL structure in all cases** -
  Avoid using conditionals or custom callables inside of lambdas that might make
  it produce different SQL based on inputs; if a function might conditionally
  use two different SQL fragments, use two separate lambdas::

        # **Don't** do this:

        def my_stmt(parameter, thing=False):
            stmt = lambda_stmt(lambda: select(table))
            stmt += (
                lambda s: s.where(table.c.x > parameter) if thing
                else s.where(table.c.y == parameter)
            return stmt

        # **Do** do this:

        def my_stmt(parameter, thing=False):
            stmt = lambda_stmt(lambda: select(table))
            if thing:
                stmt += lambda s: s.where(table.c.x > parameter)
            else:
                stmt += lambda s: s.where(table.c.y == parameter)
            return stmt

  There are a variety of failures which can occur if the lambda does not
  produce a consistent SQL construct and some are not trivially detectable
  right now.

* **Don't use functions inside the lambda to produce bound values** - the
  bound value tracking approach requires that the actual value to be used in
  the SQL statement be locally present in the closure of the lambda.  This is
  not possible if values are generated from other functions, and the
  :class:`_sql.LambdaElement` should normally raise an error if this is
  attempted::

    >>> def my_stmt(x, y):
    ...     def get_x():
    ...         return x
    ...
    ...     def get_y():
    ...         return y
    ...
    ...     stmt = lambda_stmt(lambda: select(func.max(get_x(), get_y())))
    ...     return stmt
    >>> with engine.connect() as conn:
    ...     print(conn.scalar(my_stmt(5, 10)))
    Traceback (most recent call last):
      # ...
    sqlalchemy.exc.InvalidRequestError: Can't invoke Python callable get_x()
    inside of lambda expression argument at
    <code object <lambda> at 0x7fed15f350e0, file "<stdin>", line 6>;
    lambda SQL constructs should not invoke functions from closure variables
    to produce literal values since the lambda SQL system normally extracts
    bound values without actually invoking the lambda or any functions within it.

  Above, the use of ``get_x()`` and ``get_y()``, if they are necessary, should
  occur **outside** of the lambda and assigned to a local closure variable::

    >>> def my_stmt(x, y):
    ...     def get_x():
    ...         return x
    ...
    ...     def get_y():
    ...         return y
    ...
    ...     x_param, y_param = get_x(), get_y()
    ...     stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))
    ...     return stmt

  ..

* **Avoid referring to non-SQL constructs inside of lambdas as they are not
  cacheable by default** - this issue refers to how the :class:`_sql.LambdaElement`
  creates a cache key from other closure variables within the statement.  In order
  to provide the best guarantee of an accurate cache key, all objects located
  in the closure of the lambda are considered to be significant, and none
  will be assumed to be appropriate for a cache key by default.
  So the following example will also raise a rather detailed error message::

    >>> class Foo:
    ...     def __init__(self, x, y):
    ...         self.x = x
    ...         self.y = y
    >>> def my_stmt(foo):
    ...     stmt = lambda_stmt(lambda: select(func.max(foo.x, foo.y)))
    ...     return stmt
    >>> with engine.connect() as conn:
    ...     print(conn.scalar(my_stmt(Foo(5, 10))))
    Traceback (most recent call last):
      # ...
    sqlalchemy.exc.InvalidRequestError: Closure variable named 'foo' inside of
    lambda callable <code object <lambda> at 0x7fed15f35450, file
    "<stdin>", line 2> does not refer to a cacheable SQL element, and also
    does not appear to be serving as a SQL literal bound value based on the
    default SQL expression returned by the function.  This variable needs to
    remain outside the scope of a SQL-generating lambda so that a proper cache
    key may be generated from the lambda's state.  Evaluate this variable
    outside of the lambda, set track_on=[<elements>] to explicitly select
    closure elements to track, or set track_closure_variables=False to exclude
    closure variables from being part of the cache key.

  The above error indicates that :class:`_sql.LambdaElement` will not assume
  that the ``Foo`` object passed in will continue to behave the same in all
  cases.    It also won't assume it can use ``Foo`` as part of the cache key
  by default; if it were to use the ``Foo`` object as part of the cache key,
  if there were many different ``Foo`` objects this would fill up the cache
  with duplicate information, and would also hold long-lasting references to
  all of these objects.

  The best way to resolve the above situation is to not refer to ``foo``
  inside of the lambda, and refer to it **outside** instead::

    >>> def my_stmt(foo):
    ...     x_param, y_param = foo.x, foo.y
    ...     stmt = lambda_stmt(lambda: select(func.max(x_param, y_param)))
    ...     return stmt

  In some situations, if the SQL structure of the lambda is guaranteed to
  never change based on input, to pass ``track_closure_variables=False``
  which will disable any tracking of closure variables other than those
  used for bound parameters::

    >>> def my_stmt(foo):
    ...     stmt = lambda_stmt(
    ...         lambda: select(func.max(foo.x, foo.y)), track_closure_variables=False
    ...     )
    ...     return stmt

  There is also the option to add objects to the element to explicitly form
  part of the cache key, using the ``track_on`` parameter; using this parameter
  allows specific values to serve as the cache key and will also prevent other
  closure variables from being considered.  This is useful for cases where part
  of the SQL being constructed originates from a contextual object of some sort
  that may have many different values.  In the example below, the first
  segment of the SELECT statement will disable tracking of the ``foo`` variable,
  whereas the second segment will explicitly track ``self`` as part of the
  cache key::

    >>> def my_stmt(self, foo):
    ...     stmt = lambda_stmt(
    ...         lambda: select(*self.column_expressions), track_closure_variables=False
    ...     )
    ...     stmt = stmt.add_criteria(lambda: self.where_criteria, track_on=[self])
    ...     return stmt

  Using ``track_on`` means the given objects will be stored long term in the
  lambda's internal cache and will have strong references for as long as the
  cache doesn't clear out those objects (an LRU scheme of 1000 entries is used
  by default).

  ..


Cache Key Generation
^^^^^^^^^^^^^^^^^^^^

In order to understand some of the options and behaviors which occur
with lambda SQL constructs, an understanding of the caching system
is helpful.

SQLAlchemy's caching system normally generates a cache key from a given
SQL expression construct by producing a structure that represents all the
state within the construct::

    >>> from sqlalchemy import select, column
    >>> stmt = select(column("q"))
    >>> cache_key = stmt._generate_cache_key()
    >>> print(cache_key)  # somewhat paraphrased
    CacheKey(key=(
      '0',
      <class 'sqlalchemy.sql.selectable.Select'>,
      '_raw_columns',
      (
        (
          '1',
          <class 'sqlalchemy.sql.elements.ColumnClause'>,
          'name',
          'q',
          'type',
          (
            <class 'sqlalchemy.sql.sqltypes.NullType'>,
          ),
        ),
      ),
      # a few more elements are here, and many more for a more
      # complicated SELECT statement
    ),)


The above key is stored in the cache which is essentially a dictionary, and the
value is a construct that among other things stores the string form of the SQL
statement, in this case the phrase "SELECT q".  We can observe that even for an
extremely short query the cache key is pretty verbose as it has to represent
everything that may vary about what's being rendered and potentially executed.

The lambda construction system by contrast creates a different kind of cache
key::

    >>> from sqlalchemy import lambda_stmt
    >>> stmt = lambda_stmt(lambda: select(column("q")))
    >>> cache_key = stmt._generate_cache_key()
    >>> print(cache_key)
    CacheKey(key=(
      <code object <lambda> at 0x7fed1617c710, file "<stdin>", line 1>,
      <class 'sqlalchemy.sql.lambdas.StatementLambdaElement'>,
    ),)

Above, we see a cache key that is vastly shorter than that of the non-lambda
statement, and additionally that production of the ``select(column("q"))``
construct itself was not even necessary; the Python lambda itself contains
an attribute called ``__code__`` which refers to a Python code object that
within the runtime of the application is immutable and permanent.

When the lambda also includes closure variables, in the normal case that these
variables refer to SQL constructs such as column objects, they become
part of the cache key, or if they refer to literal values that will be bound
parameters, they are placed in a separate element of the cache key::

    >>> def my_stmt(parameter):
    ...     col = column("q")
    ...     stmt = lambda_stmt(lambda: select(col))
    ...     stmt += lambda s: s.where(col == parameter)
    ...     return stmt

The above :class:`_sql.StatementLambdaElement` includes two lambdas, both
of which refer to the ``col`` closure variable, so the cache key will
represent both of these segments as well as the ``column()`` object::

    >>> stmt = my_stmt(5)
    >>> key = stmt._generate_cache_key()
    >>> print(key)
    CacheKey(key=(
      <code object <lambda> at 0x7f07323c50e0, file "<stdin>", line 3>,
      (
        '0',
        <class 'sqlalchemy.sql.elements.ColumnClause'>,
        'name',
        'q',
        'type',
        (
          <class 'sqlalchemy.sql.sqltypes.NullType'>,
        ),
      ),
      <code object <lambda> at 0x7f07323c5190, file "<stdin>", line 4>,
      <class 'sqlalchemy.sql.lambdas.LinkedLambdaElement'>,
      (
        '0',
        <class 'sqlalchemy.sql.elements.ColumnClause'>,
        'name',
        'q',
        'type',
        (
          <class 'sqlalchemy.sql.sqltypes.NullType'>,
        ),
      ),
      (
        '0',
        <class 'sqlalchemy.sql.elements.ColumnClause'>,
        'name',
        'q',
        'type',
        (
          <class 'sqlalchemy.sql.sqltypes.NullType'>,
        ),
      ),
    ),)


The second part of the cache key has retrieved the bound parameters that will
be used when the statement is invoked::

    >>> key.bindparams
    [BindParameter('%(139668884281280 parameter)s', 5, type_=Integer())]


For a series of examples of "lambda" caching with performance comparisons,
see the "short_selects" test suite within the :ref:`examples_performance`
performance example.

.. _engine_disposal:

Engine Disposal
===============

The :class:`_engine.Engine` refers to a connection pool, which means under normal
circumstances, there are open database connections present while the
:class:`_engine.Engine` object is still resident in memory.   When an :class:`_engine.Engine`
is garbage collected, its connection pool is no longer referred to by
that :class:`_engine.Engine`, and assuming none of its connections are still checked
out, the pool and its connections will also be garbage collected, which has the
effect of closing out the actual database connections as well.   But otherwise,
the :class:`_engine.Engine` will hold onto open database connections assuming
it uses the normally default pool implementation of :class:`.QueuePool`.

The :class:`_engine.Engine` is intended to normally be a permanent
fixture established up-front and maintained throughout the lifespan of an
application.  It is **not** intended to be created and disposed on a
per-connection basis; it is instead a registry that maintains both a pool
of connections as well as configurational information about the database
and DBAPI in use, as well as some degree of internal caching of per-database
resources.

However, there are many cases where it is desirable that all connection resources
referred to by the :class:`_engine.Engine` be completely closed out.  It's
generally not a good idea to rely on Python garbage collection for this
to occur for these cases; instead, the :class:`_engine.Engine` can be explicitly disposed using
the :meth:`_engine.Engine.dispose` method.   This disposes of the engine's
underlying connection pool and replaces it with a new one that's empty.
Provided that the :class:`_engine.Engine`
is discarded at this point and no longer used, all **checked-in** connections
which it refers to will also be fully closed.

Valid use cases for calling :meth:`_engine.Engine.dispose` include:

* When a program wants to release any remaining checked-in connections
  held by the connection pool and expects to no longer be connected
  to that database at all for any future operations.

* When a program uses multiprocessing or ``fork()``, and an
  :class:`_engine.Engine` object is copied to the child process,
  :meth:`_engine.Engine.dispose` should be called so that the engine creates
  brand new database connections local to that fork.   Database connections
  generally do **not** travel across process boundaries.  Use the
  :paramref:`.Engine.dispose.close` parameter set to False in this case.
  See the section :ref:`pooling_multiprocessing` for more background on this
  use case.

* Within test suites or multitenancy scenarios where many
  ad-hoc, short-lived :class:`_engine.Engine` objects may be created and disposed.


Connections that are **checked out** are **not** discarded when the
engine is disposed or garbage collected, as these connections are still
strongly referenced elsewhere by the application.
However, after :meth:`_engine.Engine.dispose` is called, those
connections are no longer associated with that :class:`_engine.Engine`; when they
are closed, they will be returned to their now-orphaned connection pool
which will ultimately be garbage collected, once all connections which refer
to it are also no longer referenced anywhere.
Since this process is not easy to control, it is strongly recommended that
:meth:`_engine.Engine.dispose` is called only after all checked out connections
are checked in or otherwise de-associated from their pool.

An alternative for applications that are negatively impacted by the
:class:`_engine.Engine` object's use of connection pooling is to disable pooling
entirely.  This typically incurs only a modest performance impact upon the
use of new connections, and means that when a connection is checked in,
it is entirely closed out and is not held in memory.  See :ref:`pool_switching`
for guidelines on how to disable pooling.

.. seealso::

    :ref:`pooling_toplevel`

    :ref:`pooling_multiprocessing`

.. _dbapi_connections:

Working with Driver SQL and Raw DBAPI Connections
=================================================

The introduction on using :meth:`_engine.Connection.execute` made use of the
:func:`_expression.text` construct in order to illustrate how textual SQL statements
may be invoked.  When working with SQLAlchemy, textual SQL is actually more
of the exception rather than the norm, as the Core expression language
and the ORM both abstract away the textual representation of SQL.  However, the
:func:`_expression.text` construct itself also provides some abstraction of textual
SQL in that it normalizes how bound parameters are passed, as well as that
it supports datatyping behavior for parameters and result set rows.

Invoking SQL strings directly to the driver
--------------------------------------------

For the use case where one wants to invoke textual SQL directly passed to the
underlying driver (known as the :term:`DBAPI`) without any intervention
from the :func:`_expression.text` construct, the :meth:`_engine.Connection.exec_driver_sql`
method may be used::

    with engine.connect() as conn:
        conn.exec_driver_sql("SET param='bar'")

.. versionadded:: 1.4  Added the :meth:`_engine.Connection.exec_driver_sql` method.

.. _dbapi_connections_cursor:

Working with the DBAPI cursor directly
--------------------------------------

There are some cases where SQLAlchemy does not provide a genericized way
at accessing some :term:`DBAPI` functions, such as calling stored procedures as well
as dealing with multiple result sets.  In these cases, it's just as expedient
to deal with the raw DBAPI connection directly.

The most common way to access the raw DBAPI connection is to get it
from an already present :class:`_engine.Connection` object directly.  It is
present using the :attr:`_engine.Connection.connection` attribute::

    connection = engine.connect()
    dbapi_conn = connection.connection

The DBAPI connection here is actually a "proxied" in terms of the
originating connection pool, however this is an implementation detail
that in most cases can be ignored.    As this DBAPI connection is still
contained within the scope of an owning :class:`_engine.Connection` object, it is
best to make use of the :class:`_engine.Connection` object for most features such
as transaction control as well as calling the :meth:`_engine.Connection.close`
method; if these operations are performed on the DBAPI connection directly,
the owning :class:`_engine.Connection` will not be aware of these changes in state.

To overcome the limitations imposed by the DBAPI connection that is
maintained by an owning :class:`_engine.Connection`, a DBAPI connection is also
available without the need to procure a
:class:`_engine.Connection` first, using the :meth:`_engine.Engine.raw_connection` method
of :class:`_engine.Engine`::

    dbapi_conn = engine.raw_connection()

This DBAPI connection is again a "proxied" form as was the case before.
The purpose of this proxying is now apparent, as when we call the ``.close()``
method of this connection, the DBAPI connection is typically not actually
closed, but instead :term:`released` back to the
engine's connection pool::

    dbapi_conn.close()

While SQLAlchemy may in the future add built-in patterns for more DBAPI
use cases, there are diminishing returns as these cases tend to be rarely
needed and they also vary highly dependent on the type of DBAPI in use,
so in any case the direct DBAPI calling pattern is always there for those
cases where it is needed.

.. seealso::

    :ref:`faq_dbapi_connection` - includes additional details about how
    the DBAPI connection is accessed as well as the "driver" connection
    when using asyncio drivers.

Some recipes for DBAPI connection use follow.

.. _stored_procedures:

Calling Stored Procedures and User Defined Functions
------------------------------------------------------

SQLAlchemy supports calling stored procedures and user defined functions
several ways. Please note that all DBAPIs have different practices, so you must
consult your underlying DBAPI's documentation for specifics in relation to your
particular usage. The following examples are hypothetical and may not work with
your underlying DBAPI.

For stored procedures or functions with special syntactical or parameter concerns,
DBAPI-level `callproc <https://legacy.python.org/dev/peps/pep-0249/#callproc>`_
may potentially be used with your DBAPI. An example of this pattern is::

    connection = engine.raw_connection()
    try:
        cursor_obj = connection.cursor()
        cursor_obj.callproc("my_procedure", ["x", "y", "z"])
        results = list(cursor_obj.fetchall())
        cursor_obj.close()
        connection.commit()
    finally:
        connection.close()

.. note::

  Not all DBAPIs use `callproc` and overall usage details will vary. The above
  example is only an illustration of how it might look to use a particular DBAPI
  function.

Your DBAPI may not have a ``callproc`` requirement *or* may require a stored
procedure or user defined function to be invoked with another pattern, such as
normal SQLAlchemy connection usage. One example of this usage pattern is,
*at the time of this documentation's writing*, executing a stored procedure in
the PostgreSQL database with the psycopg2 DBAPI, which should be invoked
with normal connection usage::

    connection.execute("CALL my_procedure();")

This above example is hypothetical. The underlying database is not guaranteed to
support "CALL" or "SELECT" in these situations, and the keyword may vary
dependent on the function being a stored procedure or a user defined function.
You should consult your underlying DBAPI and database documentation in these
situations to determine the correct syntax and patterns to use.


Multiple Result Sets
--------------------

Multiple result set support is available from a raw DBAPI cursor using the
`nextset <https://legacy.python.org/dev/peps/pep-0249/#nextset>`_ method::

    connection = engine.raw_connection()
    try:
        cursor_obj = connection.cursor()
        cursor_obj.execute("select * from table1; select * from table2")
        results_one = cursor_obj.fetchall()
        cursor_obj.nextset()
        results_two = cursor_obj.fetchall()
        cursor_obj.close()
    finally:
        connection.close()

Registering New Dialects
========================

The :func:`_sa.create_engine` function call locates the given dialect
using setuptools entrypoints.   These entry points can be established
for third party dialects within the setup.py script.  For example,
to create a new dialect "foodialect://", the steps are as follows:

1. Create a package called ``foodialect``.
2. The package should have a module containing the dialect class,
   which is typically a subclass of :class:`sqlalchemy.engine.default.DefaultDialect`.
   In this example let's say it's called ``FooDialect`` and its module is accessed
   via ``foodialect.dialect``.
3. The entry point can be established in setup.py as follows::

      entry_points = """
      [sqlalchemy.dialects]
      foodialect = foodialect.dialect:FooDialect
      """

If the dialect is providing support for a particular DBAPI on top of
an existing SQLAlchemy-supported database, the name can be given
including a database-qualification.  For example, if ``FooDialect``
were in fact a MySQL dialect, the entry point could be established like this::

      entry_points = """
      [sqlalchemy.dialects]
      mysql.foodialect = foodialect.dialect:FooDialect
      """

The above entrypoint would then be accessed as ``create_engine("mysql+foodialect://")``.

Registering Dialects In-Process
-------------------------------

SQLAlchemy also allows a dialect to be registered within the current process, bypassing
the need for separate installation.   Use the ``register()`` function as follows::

    from sqlalchemy.dialects import registry

    registry.register("mysql.foodialect", "myapp.dialect", "MyMySQLDialect")

The above will respond to ``create_engine("mysql+foodialect://")`` and load the
``MyMySQLDialect`` class from the ``myapp.dialect`` module.


Connection / Engine API
=======================

.. autoclass:: Connection
   :members:

.. autoclass:: CreateEnginePlugin
   :members:

.. autoclass:: Engine
   :members:

.. autoclass:: ExceptionContext
   :members:

.. autoclass:: NestedTransaction
    :members:
    :inherited-members:

.. autoclass:: RootTransaction
    :members:
    :inherited-members:

.. autoclass:: Transaction
    :members:

.. autoclass:: TwoPhaseTransaction
    :members:
    :inherited-members:


Result Set API
=================

.. autoclass:: BaseCursorResult
    :members:

.. autoclass:: ChunkedIteratorResult
    :members:

.. autoclass:: FilterResult
    :members:

.. autoclass:: FrozenResult
    :members:

.. autoclass:: IteratorResult
    :members:

.. autoclass:: LegacyRow
    :members:

.. autoclass:: MergedResult
    :members:

.. autoclass:: Result
    :members:
    :inherited-members:
    :exclude-members: memoized_attribute, memoized_instancemethod

.. autoclass:: ScalarResult
    :members:
    :inherited-members:
    :exclude-members: memoized_attribute, memoized_instancemethod

.. autoclass:: MappingResult
    :members:
    :inherited-members:
    :exclude-members: memoized_attribute, memoized_instancemethod

.. autoclass:: CursorResult
    :members:
    :inherited-members:
    :exclude-members: memoized_attribute, memoized_instancemethod

.. autoclass:: LegacyCursorResult
    :members:

.. autoclass:: Row
    :members:
    :private-members: _asdict, _fields, _mapping

.. autoclass:: RowMapping
    :members:
