Working with Engines and Connections — SQLAlchemy 2.0 Documentation

Release: 2.0.39 | Release Date: March 11, 2025

# SQLAlchemy 2.0 Documentation

# Working with Engines and Connections

This section details direct usage of the `Engine`, `Connection`, and related objects. Its important to note that when using the SQLAlchemy ORM, these objects are not generally accessed; instead, the `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 `Engine` and `Connection` are king (and queen?) - read on.

## Basic Usage

Recall from Engine Configuration that an `Engine` is created via the `create_engine()` call:

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

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

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

The object returned is known as `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 `CursorResult` when all of its result rows (if any) are exhausted. A `CursorResult` that returns no rows, such as that of an UPDATE statement (without any returned rows), releases cursor resources immediately upon construction.

When the `Connection` is closed at the end of the `with:` block, the referenced DBAPI connection is 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 (this is known as Reset On Return), 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 `text()` construct to indicate that we’d like to use textual SQL. The `Connection.execute()` method can of course accommodate more than that; see Working with Data in the SQLAlchemy Unified Tutorial for a tutorial.

## Using Transactions

Note

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

### Commit As You Go

The `Connection` object always emits SQL statements within the context of a transaction block. The first time the `Connection.execute()` method is called to execute a SQL statement, this transaction is begun automatically, using a behavior known as autobegin. The transaction remains in place for the scope of the `Connection` object until the `Connection.commit()` or `Connection.rollback()` methods are called. Subsequent to the transaction ending, the `Connection` waits for the `Connection.execute()` method to be called again, at which point it autobegins again.

This calling style is known as commit as you go, and is illustrated in the example below:

    with engine.connect() as connection:
        connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
        connection.execute(
            some_other_table.insert(), {"q": 8, "p": "this is some more data"}
        )

        connection.commit()  # commit the transaction

the Python DBAPI is where autobegin actually happens

The design of “commit as you go” is intended to be complementary to the design of the DBAPI, which is the underlying database interface that SQLAlchemy interacts with. In the DBAPI, the `connection` object does not assume changes to the database will be automatically committed, instead requiring in the default case that the `connection.commit()` method is called in order to commit changes to the database. It should be noted that the DBAPI itself does not have a begin() method at all. All Python DBAPIs implement “autobegin” as the primary means of managing transactions, and handle the job of emitting a statement like BEGIN on the connection when SQL statements are first emitted. SQLAlchemy’s API is basically re-stating this behavior in terms of higher level Python objects.

In “commit as you go” style, we can call upon `Connection.commit()` and `Connection.rollback()` methods freely within an ongoing sequence of other statements emitted using `Connection.execute()`; each time the transaction is ended, and a new statement is emitted, a new transaction begins implicitly:

    with engine.connect() as connection:
        connection.execute(text("<some statement>"))
        connection.commit()  # commits "some statement"

        # new transaction starts
        connection.execute(text("<some other statement>"))
        connection.rollback()  # rolls back "some other statement"

        # new transaction starts
        connection.execute(text("<a third statement>"))
        connection.commit()  # commits "a third statement"

New in version 2.0: “commit as you go” style is a new feature of SQLAlchemy 2.0. It is also available in SQLAlchemy 1.4’s “transitional” mode when using a “future” style engine.

### Begin Once

The `Connection` object provides a more explicit transaction management style known as begin once. In contrast to “commit as you go”, “begin once” allows the start point of the transaction to be stated explicitly, and allows that the transaction itself may be framed out as a context manager block so that the end of the transaction is instead implicit. To use “begin once”, the `Connection.begin()` method is used, which returns a `Transaction` object which represents the DBAPI transaction. This object also supports explicit management via its own `Transaction.commit()` and `Transaction.rollback()` methods, but as a preferred practice also supports the context manager interface, where it will commit itself when the block ends normally and emit a rollback if an exception is raised, before propagating the exception outwards. Below illustrates the form of a “begin once” block:

    with engine.connect() as connection:
        with connection.begin():
            connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
            connection.execute(
                some_other_table.insert(), {"q": 8, "p": "this is some more data"}
            )

        # transaction is committed

### Connect and Begin Once from the Engine

A convenient shorthand form for the above “begin once” block is to use the `Engine.begin()` method at the level of the originating `Engine` object, rather than performing the two separate steps of `Engine.connect()` and `Connection.begin()`; the `Engine.begin()` method returns a special context manager that internally maintains both the context manager for the `Connection` as well as the context manager for the `Transaction` normally returned by the `Connection.begin()` method:

    with engine.begin() as connection:
        connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})
        connection.execute(
            some_other_table.insert(), {"q": 8, "p": "this is some more data"}
        )

    # transaction is committed, and Connection is released to the connection
    # pool

Tip

Within the `Engine.begin()` block, we can call upon the `Connection.commit()` or `Connection.rollback()` methods, which will end the transaction normally demarcated by the block ahead of time. However, if we do so, no further SQL operations may be emitted on the `Connection` until the block ends:

    >>> from sqlalchemy import create_engine
    >>> e = create_engine("sqlite://", echo=True)
    >>> with e.begin() as conn:
    ...     conn.commit()
    ...     conn.begin()
    2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine BEGIN (implicit)
    2021-11-08 09:49:07,517 INFO sqlalchemy.engine.Engine COMMIT
    Traceback (most recent call last):
    ...
    sqlalchemy.exc.InvalidRequestError: Can't operate on closed transaction inside
    context manager.  Please complete the context manager before emitting
    further commands.

### Mixing Styles

The “commit as you go” and “begin once” styles can be freely mixed within a single `Engine.connect()` block, provided that the call to `Connection.begin()` does not conflict with the “autobegin” behavior. To accomplish this, `Connection.begin()` should only be called either before any SQL statements have been emitted, or directly after a previous call to `Connection.commit()` or `Connection.rollback()`:

    with engine.connect() as connection:
        with connection.begin():
            # run statements in a "begin once" block
            connection.execute(some_table.insert(), {"x": 7, "y": "this is some data"})

        # transaction is committed

        # run a new statement outside of a block. The connection
        # autobegins
        connection.execute(
            some_other_table.insert(), {"q": 8, "p": "this is some more data"}
        )

        # commit explicitly
        connection.commit()

        # can use a "begin once" block here
        with connection.begin():
            # run more statements
            connection.execute(...)

When developing code that uses “begin once”, the library will raise `InvalidRequestError` if a transaction was already “autobegun”.

## Setting Transaction Isolation Levels including DBAPI Autocommit

Most DBAPIs support the concept of configurable transaction 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 Understanding the DBAPI-Level Autocommit Isolation Level, that “autocommit” isolation level like any other isolation level does not affect the “transactional” behavior of the `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.

### Setting Isolation Level or DBAPI Autocommit for a Connection

For an individual `Connection` object that’s acquired from `Engine.connect()`, the isolation level can be set for the duration of that `Connection` object using the `Connection.execution_options()` method. The parameter is known as `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(text("<statement>"))

Tip

The return value of the `Connection.execution_options()` method is the same `Connection` object upon which the method was called, meaning, it modifies the state of the `Connection` object in place. This is a new behavior as of SQLAlchemy 2.0. This behavior does not apply to the `Engine.execution_options()` method; that method still returns a copy of the `Engine` and as described below may be used to construct multiple `Engine` objects with different execution options, which nonetheless share the same dialect and connection pool.

Note

The `Connection.execution_options.isolation_level` parameter necessarily does not apply to statement level options, such as that of `Executable.execution_options()`, and will be rejected if set at this level. This because the option must be set on a DBAPI connection on a per-transaction basis.

### Setting Isolation Level or DBAPI Autocommit for an Engine

The `Connection.execution_options.isolation_level` option may also be set engine wide, as is often preferable. This may be achieved by passing the `create_engine.isolation_level` parameter to `create_engine()`:

    from sqlalchemy import create_engine

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

With the above setting, each new DBAPI connection the moment it’s created will be set to use a `"REPEATABLE READ"` isolation level setting for all subsequent operations.

### Maintaining Multiple Isolation Levels for a Single Engine

The isolation level may also be set per engine, with a potentially greater level of flexibility, using either the `create_engine.execution_options` parameter to `create_engine()` or the `Engine.execution_options()` method, the latter of which will create a copy of the `Engine` that shares the dialect and connection pool of the original engine, but has its own per-connection isolation level setting:

    from sqlalchemy import create_engine

    eng = create_engine(
        "postgresql+psycopg2://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; but the connection as pooled will be reset to the original isolation level that was present when the connection first occurred. At the level of `create_engine()`, the end effect is not any different from using the `create_engine.isolation_level` parameter.

However, an application that frequently chooses to run operations within different isolation levels may wish to create multiple “sub-engines” of a lead `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 `Engine` that makes use of `"AUTOCOMMIT"` may be separated off from the main engine:

    from sqlalchemy import create_engine

    eng = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")

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

Above, the `Engine.execution_options()` method creates a shallow copy of the original `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.

See also

SQLite Transaction Isolation

PostgreSQL Transaction Isolation

MySQL Transaction Isolation

SQL Server Transaction Isolation

Oracle Database Transaction Isolation

Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT - for the ORM

Using DBAPI Autocommit Allows for a Readonly Version of Transparent Reconnect - a recipe that uses DBAPI autocommit to transparently reconnect to the database for read-only operations

### Understanding the DBAPI-Level Autocommit Isolation Level

In the parent section, we introduced the concept of the `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 `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(text("<statement>"))
        connection.execute(text("<statement>"))

Above illustrates normal usage of “DBAPI autocommit” mode. There is no need to make use of methods such as `Connection.begin()` or `Connection.commit()`, as all statements are committed to the database immediately. When the block ends, the `Connection` object will revert the “autocommit” isolation level, and the DBAPI connection is released to the connection pool where the DBAPI `connection.rollback()` method will normally be invoked, but as the above statements were already committed, this rollback has no change on the state of the database.

It is important to note that “autocommit” mode persists even when the `Connection.begin()` method is called; the DBAPI will not emit any BEGIN to the database, nor will it emit COMMIT when `Connection.commit()` is called. This usage is also not an error scenario, as it is expected that the “autocommit” isolation level may be applied to code that otherwise was written assuming a transactional context; the “isolation level” is, after all, a configurational detail of the transaction itself just like any other isolation level.

In the example below, statements remain autocommitting regardless of SQLAlchemy-level transaction blocks:

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

        # this begin() does not affect the DBAPI connection, isolation stays at AUTOCOMMIT
        with connection.begin() as trans:
            connection.execute(text("<statement>"))
            connection.execute(text("<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

At the same time, even though we are using “DBAPI autocommit”, SQLAlchemy’s transactional semantics, that is, the in-Python behavior of `Connection.begin()` as well as the behavior of “autobegin”, remain in place, even though these don’t impact the DBAPI connection itself. To illustrate, the code below will raise an error, as `Connection.begin()` is being called after autobegin has already occurred:

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

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

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

The above example also demonstrates the same theme that the “autocommit” isolation level is a configurational detail of the underlying database transaction, and is independent of the begin/commit behavior of the SQLAlchemy Connection object. The “autocommit” mode will not interact with `Connection.begin()` in any way and the `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 `Connection` where DBAPI-autocommit mode can be changed independently without indicating any code changes elsewhere.

#### Changing Between Isolation Levels

TL;DR;

prefer to use individual `Connection` objects each with just one isolation level, rather than switching isolation on a single `Connection`. The code will be easier to read and less error prone.

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 `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 `Connection` checkout, the `Connection.execution_options.isolation_level` parameter must be re-applied with the previous isolation level. The previous section illustrated an attempt to call `Connection.begin()` in order to start a transaction while autocommit was taking place; we can rewrite that example to actually do so by first reverting the isolation level before we call upon `Connection.begin()`:

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

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

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

        # "commit" the autobegun "transaction"
        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(text("<statement>"))

Above, to manually revert the isolation level we made use of `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 `Connection` which already handles resetting of isolation level automatically upon checkin. The preferred way to write the above is to use two blocks

    # use an autocommit block
    with engine.connect().execution_options(isolation_level="AUTOCOMMIT") as connection:
        # run statement in autocommit mode
        connection.execute(text("<statement>"))

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

To sum up:

1. “DBAPI level autocommit” isolation level is entirely independent of the `Connection` object’s notion of “begin” and “commit”

2. use individual `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

## 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 Database 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.

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 Working with the DBAPI cursor directly.

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 `Connection.execution_options.stream_results` and `Connection.execution_options.yield_per` execution options provide access to “server side cursors” on a per-`Connection` or per-statement basis. Similar options exist when using an 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 `Connection.execution_options.yield_per` execution option configures a `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 `Connection.execution_options()` method on `Connection` or on a statement using the `Executable.execution_options()` method.

New in version 1.4.40: `Connection.execution_options.yield_per` as a Core-only option is new as of SQLAlchemy 1.4.40; for prior 1.4 versions, use `Connection.execution_options.stream_results` directly in combination with `Result.yield_per()`.

Using this option is equivalent to manually setting the `Connection.execution_options.stream_results` option, described in the next section, and then invoking the `Result.yield_per()` method on the `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 `Connection.execution_options.yield_per` option or the `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 `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:
        with conn.execution_options(yield_per=100).execute(
            text("select * from table")
        ) as result:
            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 `Result.partitions()` method to run processing on rows in batches that match the size fetched from the server. The use of `Result.partitions()` is optional, and if the `Result` is iterated directly, a new batch of rows will be buffered for each 100 rows fetched. Calling a method such as `Result.all()` should not be used, as this will fully fetch all remaining rows at once and defeat the purpose of using `yield_per`.

Tip

The `Result` object may be used as a context manager as illustrated above. When iterating with a server-side cursor, this is the best way to ensure the `Result` object is closed, even if exceptions are raised within the iteration process.

The `Connection.execution_options.yield_per` option is portable to the ORM as well, used by a `Session` to fetch ORM objects, where it also limits the amount of ORM objects generated at once. See the section Fetching Large Result Sets with Yield Per - in the ORM Querying Guide for further background on using `Connection.execution_options.yield_per` with the ORM.

New in version 1.4.40: Added `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.

### Streaming with a dynamically growing buffer using stream_results

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

When a `Result` object delivered using the `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 `Connection.execution_options.max_row_buffer` execution option:

    with engine.connect() as conn:
        with conn.execution_options(stream_results=True, max_row_buffer=100).execute(
            text("select * from table")
        ) as result:
            for row in result:
                print(f"{row}")

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

See also

Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

`Result.partitions()`

`Result.yield_per()`

## Translation of Schema Names

To support multi-tenancy applications that distribute common sets of tables into multiple schemas, the `Connection.execution_options.schema_translate_map` execution option may be used to repurpose a set of `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 `Table` as defined by the `Table.schema` attribute is `None`. The `Connection.execution_options.schema_translate_map` can specify that all `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 `Connection.execution_options.schema_translate_map` parameter affects all DDL and SQL constructs generated from the SQL expression language, as derived from the `Table` or `Sequence` objects. It does not impact literal string SQL used via the `text()` construct nor via plain strings passed to `Connection.execute()`.

The feature takes effect only in those cases where the name of the schema is derived directly from that of a `Table` or `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 `MetaData.create_all()` or `MetaData.drop_all()` are called, and it takes effect when using table reflection given a `Table` object. However it does not affect the operations present on the `Inspector` object, as the schema name is passed to these methods explicitly.

Tip

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

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

    session = Session(schema_engine)

    ...

Warning

When using the 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 `Session` does not take current schema translate values into account for individual objects.

To use a single `Session` with multiple `schema_translate_map` configurations, the Horizontal Sharding extension may be used. See the example at Horizontal Sharding.

## SQL Compilation Caching

New in version 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 Transparent SQL Compilation Caching added to All DQL, DML Statements in Core, ORM.

SQLAlchemy includes a comprehensive caching system for the SQL compiler as well as its ORM variants. This caching system is transparent within the `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 `create_engine.query_cache_size` parameter:

    engine = create_engine(
        "postgresql+psycopg2://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.

### 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 `create_engine.echo` flag, or by using Python logging; see the section Configuring 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 select
    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.scalars(select(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 `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 `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 `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 `Values` construct as well as when using “multivalued inserts” with the `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 Disabling or using an alternate dictionary to cache some (or all) statements 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 `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 `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 `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 `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.

### 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 `Connection.execution_options.compiled_cache` option as an execution option. Execution options may be set on a statement, on an `Engine` or `Connection`, as well as when using the 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 `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())

### 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 `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 `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

New in version 1.4.5: Added the `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 `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 `Select._limit` and `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 `Select` object are not part of the cache key, so that many `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 post-compile 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 `BindParameter.render_literal_execute()` method, in conjunction with using the `Select._limit_clause` and `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 `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 which will assert that operations like SELECTs with LIMIT/OFFSET are correctly rendered and cached.

See also

Why is my application slow after upgrading to 1.4 and/or 2.x? - in the Frequently Asked Questions section

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

Deep Alchemy

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 `with_loader_criteria()` ORM option in order to provide a generic SQL fragment.

#### Synopsis

Lambda statements are constructed using the `lambda_stmt()` function, which returns an instance of `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 `StatementLambdaElement.add_criteria()` method which allows for more options.

It is assumed that the `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 `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 `select()` constructs are the prime use case for `lambda_stmt()`, DML statements such as `insert()` and `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 `lambda_stmt()` can accommodate ORM functionality completely and used directly with `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:

      >>> 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)))
      SELECT max(?, ?) AS max_1
      [generated in 0.00057s] (5, 10)
      10
      SELECT max(?, ?) AS max_1
      [cached since 0.002059s ago] (12, 8)
      12

  Above, `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 `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 `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 `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 `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 Performance performance example.

## “Insert Many Values” Behavior for INSERT statements

New in version 2.0: see Optimized ORM bulk insert now implemented for all backends other than MySQL for background on the change including sample performance tests

Tip

The insertmanyvalues feature is a transparently available performance feature which requires no end-user intervention in order for it to take place as needed. This section describes the architecture of the feature as well as how to measure its performance and tune its behavior in order to optimize the speed of bulk INSERT statements, particularly as used by the ORM.

As more databases have added support for INSERT..RETURNING, SQLAlchemy has undergone a major change in how it approaches the subject of INSERT statements where there’s a need to acquire server-generated values, most importantly server-generated primary key values which allow the new row to be referenced in subsequent operations. In particular, this scenario has long been a significant performance issue in the ORM, which relies on being able to retrieve server-generated primary key values in order to correctly populate the identity map.

With recent support for RETURNING added to SQLite and MariaDB, SQLAlchemy no longer needs to rely upon the single-row-only cursor.lastrowid attribute provided by the DBAPI for most backends; RETURNING may now be used for all SQLAlchemy-included backends with the exception of MySQL. The remaining performance limitation, that the cursor.executemany() DBAPI method does not allow for rows to be fetched, is resolved for most backends by foregoing the use of `executemany()` and instead restructuring individual INSERT statements to each accommodate a large number of rows in a single statement that is invoked using `cursor.execute()`. This approach originates from the psycopg2 fast execution helpers feature of the `psycopg2` DBAPI, which SQLAlchemy incrementally added more and more support towards in recent release series.

### Current Support

The feature is enabled for all backend included in SQLAlchemy that support RETURNING, with the exception of Oracle Database for which both the python-oracledb and cx_Oracle drivers offer their own equivalent feature. The feature normally takes place when making use of the `Insert.returning()` method of an `Insert` construct in conjunction with executemany execution, which occurs when passing a list of dictionaries to the `Connection.execute.parameters` parameter of the `Connection.execute()` or `Session.execute()` methods (as well as equivalent methods under asyncio and shorthand methods like `Session.scalars()`). It also takes place within the ORM unit of work process when using methods such as `Session.add()` and `Session.add_all()` to add rows.

For SQLAlchemy’s included dialects, support or equivalent support is currently as follows:

- SQLite - supported for SQLite versions 3.35 and above

- PostgreSQL - all supported Postgresql versions (9 and above)

- SQL Server - all supported SQL Server versions [1]

- MariaDB - supported for MariaDB versions 10.5 and above

- MySQL - no support, no RETURNING feature is present

- Oracle Database - supports RETURNING with executemany using native python-oracledb / cx_Oracle APIs, for all supported Oracle Database versions 9 and above, using multi-row OUT parameters. This is not the same implementation as “executemanyvalues”, however has the same usage patterns and equivalent performance benefits.

Changed in version 2.0.10:

[1]

”insertmanyvalues” support for Microsoft SQL Server is restored, after being temporarily disabled in version 2.0.9.

### Disabling the feature

To disable the “insertmanyvalues” feature for a given backend for an `Engine` overall, pass the `create_engine.use_insertmanyvalues` parameter as `False` to `create_engine()`:

    engine = create_engine(
        "mariadb+mariadbconnector://scott:tiger@host/db", use_insertmanyvalues=False
    )

The feature can also be disabled from being used implicitly for a particular `Table` object by passing the `Table.implicit_returning` parameter as `False`:

    t = Table(
        "t",
        metadata,
        Column("id", Integer, primary_key=True),
        Column("x", Integer),
        implicit_returning=False,
    )

The reason one might want to disable RETURNING for a specific table is to work around backend-specific limitations.

### Batched Mode Operation

The feature has two modes of operation, which are selected transparently on a per-dialect, per-`Table` basis. One is batched mode, which reduces the number of database round trips by rewriting an INSERT statement of the form:

    INSERT INTO a (data, x, y) VALUES (%(data)s, %(x)s, %(y)s) RETURNING a.id

into a “batched” form such as:

    INSERT INTO a (data, x, y) VALUES
        (%(data_0)s, %(x_0)s, %(y_0)s),
        (%(data_1)s, %(x_1)s, %(y_1)s),
        (%(data_2)s, %(x_2)s, %(y_2)s),
        ...
        (%(data_78)s, %(x_78)s, %(y_78)s)
    RETURNING a.id

where above, the statement is organized against a subset (a “batch”) of the input data, the size of which is determined by the database backend as well as the number of parameters in each batch to correspond to known limits for statement size / number of parameters. The feature then executes the INSERT statement once for each batch of input data until all records are consumed, concatenating the RETURNING results for each batch into a single large rowset that’s available from a single `Result` object.

This “batched” form allows INSERT of many rows using much fewer database round trips, and has been shown to allow dramatic performance improvements for most backends where it’s supported.

### Correlating RETURNING rows to parameter sets

New in version 2.0.10.

The “batch” mode query illustrated in the previous section does not guarantee the order of records returned would correspond with that of the input data. When used by the SQLAlchemy ORM unit of work process, as well as for applications which correlate returned server-generated values with input data, the `Insert.returning()` and `UpdateBase.return_defaults()` methods include an option `Insert.returning.sort_by_parameter_order` which indicates that “insertmanyvalues” mode should guarantee this correspondence. This is not related to the order in which records are actually INSERTed by the database backend, which is not assumed under any circumstances; only that the returned records should be organized when received back to correspond to the order in which the original input data was passed.

When the `Insert.returning.sort_by_parameter_order` parameter is present, for tables that use server-generated integer primary key values such as `IDENTITY`, PostgreSQL `SERIAL`, MariaDB `AUTO_INCREMENT`, or SQLite’s `ROWID` scheme, “batch” mode may instead opt to use a more complex INSERT..RETURNING form, in conjunction with post-execution sorting of rows based on the returned values, or if such a form is not available, the “insertmanyvalues” feature may gracefully degrade to “non-batched” mode which runs individual INSERT statements for each parameter set.

For example, on SQL Server when an auto incrementing `IDENTITY` column is used as the primary key, the following SQL form is used:

    INSERT INTO a (data, x, y)
    OUTPUT inserted.id, inserted.id AS id__1
    SELECT p0, p1, p2 FROM (VALUES
        (?, ?, ?, 0), (?, ?, ?, 1), (?, ?, ?, 2),
        ...
        (?, ?, ?, 77)
    ) AS imp_sen(p0, p1, p2, sen_counter) ORDER BY sen_counter

A similar form is used for PostgreSQL as well, when primary key columns use SERIAL or IDENTITY. The above form does not guarantee the order in which rows are inserted. However, it does guarantee that the IDENTITY or SERIAL values will be created in order with each parameter set [2]. The “insertmanyvalues” feature then sorts the returned rows for the above INSERT statement by incrementing integer identity.

For the SQLite database, there is no appropriate INSERT form that can correlate the production of new ROWID values with the order in which the parameter sets are passed. As a result, when using server-generated primary key values, the SQLite backend will degrade to “non-batched” mode when ordered RETURNING is requested. For MariaDB, the default INSERT form used by insertmanyvalues is sufficient, as this database backend will line up the order of AUTO_INCREMENT with the order of input data when using InnoDB [3].

For a client-side generated primary key, such as when using the Python `uuid.uuid4()` function to generate new values for a `Uuid` column, the “insertmanyvalues” feature transparently includes this column in the RETURNING records and correlates its value to that of the given input records, thus maintaining correspondence between input records and result rows. From this, it follows that all backends allow for batched, parameter-correlated RETURNING order when client-side-generated primary key values are used.

The subject of how “insertmanyvalues” “batch” mode determines a column or columns to use as a point of correspondence between input parameters and RETURNING rows is known as an insert sentinel, which is a specific column or columns that are used to track such values. The “insert sentinel” is normally selected automatically, however can also be user-configuration for extremely special cases; the section Configuring Sentinel Columns describes this.

For backends that do not offer an appropriate INSERT form that can deliver server-generated values deterministically aligned with input values, or for `Table` configurations that feature other kinds of server generated primary key values, “insertmanyvalues” mode will make use of non-batched mode when guaranteed RETURNING ordering is requested.

See also

> [2]

> - Microsoft SQL Server rationale

>   “INSERT queries that use SELECT with ORDER BY to populate rows guarantees how identity values are computed but not the order in which the rows are inserted.” https://learn.microsoft.com/en-us/sql/t-sql/statements/insert-transact-sql?view=sql-server-ver16#limitations-and-restrictions

> - PostgreSQL batched INSERT Discussion

>   Original description in 2018 https://www.postgresql.org/message-id/29386.1528813619@sss.pgh.pa.us

>   Follow up in 2023 - https://www.postgresql.org/message-id/be108555-da2a-4abc-a46b-acbe8b55bd25%40app.fastmail.com

> [3]

- MariaDB AUTO_INCREMENT behavior (using the same InnoDB engine as MySQL):

  https://dev.mysql.com/doc/refman/8.0/en/innodb-auto-increment-handling.html

  https://dba.stackexchange.com/a/72099

### Non-Batched Mode Operation

For `Table` configurations that do not have client side primary key values, and offer server-generated primary key values (or no primary key) that the database in question is not able to invoke in a deterministic or sortable way relative to multiple parameter sets, the “insertmanyvalues” feature when tasked with satisfying the `Insert.returning.sort_by_parameter_order` requirement for an `Insert` statement may instead opt to use non-batched mode.

In this mode, the original SQL form of INSERT is maintained, and the “insertmanyvalues” feature will instead run the statement as given for each parameter set individually, organizing the returned rows into a full result set. Unlike previous SQLAlchemy versions, it does so in a tight loop that minimizes Python overhead. In some cases, such as on SQLite, “non-batched” mode performs exactly as well as “batched” mode.

### Statement Execution Model

For both “batched” and “non-batched” modes, the feature will necessarily invoke multiple INSERT statements using the DBAPI `cursor.execute()` method, within the scope of single call to the Core-level `Connection.execute()` method, with each statement containing up to a fixed limit of parameter sets. This limit is configurable as described below at Controlling the Batch Size. The separate calls to `cursor.execute()` are logged individually and also individually passed along to event listeners such as `ConnectionEvents.before_cursor_execute()` (see Logging and Events below).

#### Configuring Sentinel Columns

In typical cases, the “insertmanyvalues” feature in order to provide INSERT..RETURNING with deterministic row order will automatically determine a sentinel column from a given table’s primary key, gracefully degrading to “row at a time” mode if one cannot be identified. As a completely optional feature, to get full “insertmanyvalues” bulk performance for tables that have server generated primary keys whose default generator functions aren’t compatible with the “sentinel” use case, other non-primary key columns may be marked as “sentinel” columns assuming they meet certain requirements. A typical example is a non-primary key `Uuid` column with a client side default such as the Python `uuid.uuid4()` function. There is also a construct to create simple integer columns with a a client side integer counter oriented towards the “insertmanyvalues” use case.

Sentinel columns may be indicated by adding `Column.insert_sentinel` to qualifying columns. The most basic “qualifying” column is a not-nullable, unique column with a client side default, such as a UUID column as follows:

    import uuid

    from sqlalchemy import Column
    from sqlalchemy import FetchedValue
    from sqlalchemy import Integer
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy import Uuid

    my_table = Table(
        "some_table",
        metadata,
        # assume some arbitrary server-side function generates
        # primary key values, so cannot be tracked by a bulk insert
        Column("id", String(50), server_default=FetchedValue(), primary_key=True),
        Column("data", String(50)),
        Column(
            "uniqueid",
            Uuid(),
            default=uuid.uuid4,
            nullable=False,
            unique=True,
            insert_sentinel=True,
        ),
    )

When using ORM Declarative models, the same forms are available using the `mapped_column` construct:

    import uuid

    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column

    class Base(DeclarativeBase):
        pass

    class MyClass(Base):
        __tablename__ = "my_table"

        id: Mapped[str] = mapped_column(primary_key=True, server_default=FetchedValue())
        data: Mapped[str] = mapped_column(String(50))
        uniqueid: Mapped[uuid.UUID] = mapped_column(
            default=uuid.uuid4, unique=True, insert_sentinel=True
        )

While the values generated by the default generator must be unique, the actual UNIQUE constraint on the above “sentinel” column, indicated by the `unique=True` parameter, itself is optional and may be omitted if not desired.

There is also a special form of “insert sentinel” that’s a dedicated nullable integer column which makes use of a special default integer counter that’s only used during “insertmanyvalues” operations; as an additional behavior, the column will omit itself from SQL statements and result sets and behave in a mostly transparent manner. It does need to be physically present within the actual database table, however. This style of `Column` may be constructed using the function `insert_sentinel()`:

    from sqlalchemy import Column
    from sqlalchemy import Integer
    from sqlalchemy import String
    from sqlalchemy import Table
    from sqlalchemy import Uuid
    from sqlalchemy import insert_sentinel

    Table(
        "some_table",
        metadata,
        Column("id", Integer, primary_key=True),
        Column("data", String(50)),
        insert_sentinel("sentinel"),
    )

When using ORM Declarative, a Declarative-friendly version of `insert_sentinel()` is available called `orm_insert_sentinel()`, which has the ability to be used on the Base class or a mixin; if packaged using `declared_attr()`, the column will apply itself to all table-bound subclasses including within joined inheritance hierarchies:

    from sqlalchemy.orm import declared_attr
    from sqlalchemy.orm import DeclarativeBase
    from sqlalchemy.orm import Mapped
    from sqlalchemy.orm import mapped_column
    from sqlalchemy.orm import orm_insert_sentinel

    class Base(DeclarativeBase):
        @declared_attr
        def _sentinel(cls) -> Mapped[int]:
            return orm_insert_sentinel()

    class MyClass(Base):
        __tablename__ = "my_table"

        id: Mapped[str] = mapped_column(primary_key=True, server_default=FetchedValue())
        data: Mapped[str] = mapped_column(String(50))

    class MySubClass(MyClass):
        __tablename__ = "sub_table"

        id: Mapped[str] = mapped_column(ForeignKey("my_table.id"), primary_key=True)

    class MySingleInhClass(MyClass):
        pass

In the example above, both “my_table” and “sub_table” will have an additional integer column named “_sentinel” that can be used by the “insertmanyvalues” feature to help optimize bulk inserts used by the ORM.

### Controlling the Batch Size

A key characteristic of “insertmanyvalues” is that the size of the INSERT statement is limited on a fixed max number of “values” clauses as well as a dialect-specific fixed total number of bound parameters that may be represented in one INSERT statement at a time. When the number of parameter dictionaries given exceeds a fixed limit, or when the total number of bound parameters to be rendered in a single INSERT statement exceeds a fixed limit (the two fixed limits are separate), multiple INSERT statements will be invoked within the scope of a single `Connection.execute()` call, each of which accommodate for a portion of the parameter dictionaries, known as a “batch”. The number of parameter dictionaries represented within each “batch” is then known as the “batch size”. For example, a batch size of 500 means that each INSERT statement emitted will INSERT at most 500 rows.

It’s potentially important to be able to adjust the batch size, as a larger batch size may be more performant for an INSERT where the value sets themselves are relatively small, and a smaller batch size may be more appropriate for an INSERT that uses very large value sets, where both the size of the rendered SQL as well as the total data size being passed in one statement may benefit from being limited to a certain size based on backend behavior and memory constraints. For this reason the batch size can be configured on a per-`Engine` as well as a per-statement basis. The parameter limit on the other hand is fixed based on the known characteristics of the database in use.

The batch size defaults to 1000 for most backends, with an additional per-dialect “max number of parameters” limiting factor that may reduce the batch size further on a per-statement basis. The max number of parameters varies by dialect and server version; the largest size is 32700 (chosen as a healthy distance away from PostgreSQL’s limit of 32767 and SQLite’s modern limit of 32766, while leaving room for additional parameters in the statement as well as for DBAPI quirkiness). Older versions of SQLite (prior to 3.32.0) will set this value to 999. MariaDB has no established limit however 32700 remains as a limiting factor for SQL message size.

The value of the “batch size” can be affected `Engine` wide via the `create_engine.insertmanyvalues_page_size` parameter. Such as, to affect INSERT statements to include up to 100 parameter sets in each statement:

    e = create_engine("sqlite://", insertmanyvalues_page_size=100)

The batch size may also be affected on a per statement basis using the `Connection.execution_options.insertmanyvalues_page_size` execution option, such as per execution:

    with e.begin() as conn:
        result = conn.execute(
            table.insert().returning(table.c.id),
            parameterlist,
            execution_options={"insertmanyvalues_page_size": 100},
        )

Or configured on the statement itself:

    stmt = (
        table.insert()
        .returning(table.c.id)
        .execution_options(insertmanyvalues_page_size=100)
    )
    with e.begin() as conn:
        result = conn.execute(stmt, parameterlist)

### Logging and Events

The “insertmanyvalues” feature integrates fully with SQLAlchemy’s statement logging as well as cursor events such as `ConnectionEvents.before_cursor_execute()`. When the list of parameters is broken into separate batches, each INSERT statement is logged and passed to event handlers individually. This is a major change compared to how the psycopg2-only feature worked in previous 1.x series of SQLAlchemy, where the production of multiple INSERT statements was hidden from logging and events. Logging display will truncate the long lists of parameters for readability, and will also indicate the specific batch of each statement. The example below illustrates an excerpt of this logging:

    INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
    [generated in 0.00177s (insertmanyvalues) 1/10 (unordered)] ('d0', 0, 0, 'd1',  ...
    INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
    [insertmanyvalues 2/10 (unordered)] ('d100', 100, 1000, 'd101', ...

    ...

    INSERT INTO a (data, x, y) VALUES (?, ?, ?), ... 795 characters truncated ...  (?, ?, ?), (?, ?, ?) RETURNING id
    [insertmanyvalues 10/10 (unordered)] ('d900', 900, 9000, 'd901', ...

When non-batch mode takes place, logging will indicate this along with the insertmanyvalues message:

    ...

    INSERT INTO a (data, x, y) VALUES (?, ?, ?) RETURNING id
    [insertmanyvalues 67/78 (ordered; batch not supported)] ('d66', 66, 66)
    INSERT INTO a (data, x, y) VALUES (?, ?, ?) RETURNING id
    [insertmanyvalues 68/78 (ordered; batch not supported)] ('d67', 67, 67)
    INSERT INTO a (data, x, y) VALUES (?, ?, ?) RETURNING id
    [insertmanyvalues 69/78 (ordered; batch not supported)] ('d68', 68, 68)
    INSERT INTO a (data, x, y) VALUES (?, ?, ?) RETURNING id
    [insertmanyvalues 70/78 (ordered; batch not supported)] ('d69', 69, 69)

    ...

See also

Configuring Logging

### Upsert Support

The PostgreSQL, SQLite, and MariaDB dialects offer backend-specific “upsert” constructs `insert()`, `insert()` and `insert()`, which are each `Insert` constructs that have an additional method such as `on_conflict_do_update()` or ``on_duplicate_key()`. These constructs also support “insertmanyvalues” behaviors when they are used with RETURNING, allowing efficient upserts with RETURNING to take place.

## Engine Disposal

The `Engine` refers to a connection pool, which means under normal circumstances, there are open database connections present while the `Engine` object is still resident in memory. When an `Engine` is garbage collected, its connection pool is no longer referred to by that `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 `Engine` will hold onto open database connections assuming it uses the normally default pool implementation of `QueuePool`.

The `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 `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 `Engine` can be explicitly disposed using the `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 `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 `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 `Engine` object is copied to the child process, `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 `Engine.dispose.close` parameter set to False in this case. See the section Using Connection Pools with Multiprocessing or os.fork() for more background on this use case.

- Within test suites or multitenancy scenarios where many ad-hoc, short-lived `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 `Engine.dispose()` is called, those connections are no longer associated with that `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 `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 `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 Switching Pool Implementations for guidelines on how to disable pooling.

See also

Connection Pooling

Using Connection Pools with Multiprocessing or os.fork()

## Working with Driver SQL and Raw DBAPI Connections

The introduction on using `Connection.execute()` made use of the `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 `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 DBAPI) without any intervention from the `text()` construct, the `Connection.exec_driver_sql()` method may be used:

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

New in version 1.4: Added the `Connection.exec_driver_sql()` method.

### Working with the DBAPI cursor directly

There are some cases where SQLAlchemy does not provide a genericized way at accessing some 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 `Connection` object directly. It is present using the `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 `Connection` object, it is best to make use of the `Connection` object for most features such as transaction control as well as calling the `Connection.close()` method; if these operations are performed on the DBAPI connection directly, the owning `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 `Connection`, a DBAPI connection is also available without the need to procure a `Connection` first, using the `Engine.raw_connection()` method of `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 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.

See also

How do I get at the raw DBAPI connection when using an Engine? - 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.

### 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 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 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 `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 `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.cfg` as follows:

       [options.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:

    [options.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

Object Name | Description
--- | ---
Connection | Provides high-level functionality for a wrapped DB-API connection.
CreateEnginePlugin | A set of hooks intended to augment the construction of an `Engine` object based on entrypoint names in a URL.
Engine | Connects a `Pool` and `Dialect` together to provide a source of database connectivity and behavior.
ExceptionContext | Encapsulate information about an error condition in progress.
NestedTransaction | Represent a ‘nested’, or SAVEPOINT transaction.
RootTransaction | Represent the “root” transaction on a `Connection`.
Transaction | Represent a database transaction in progress.
TwoPhaseTransaction | Represent a two-phase transaction.

class sqlalchemy.engine.Connection

  Provides high-level functionality for a wrapped DB-API connection.

  The `Connection` object is procured by calling the `Engine.connect()` method of the `Engine` object, and provides services for execution of SQL statements as well as transaction control.

  The Connection object is not thread-safe. While a Connection can be shared among threads using properly synchronized access, it is still possible that the underlying DBAPI connection may not support shared access between threads. Check the DBAPI documentation for details.

  Members

  __init__(), begin(), begin_nested(), begin_twophase(), close(), closed, commit(), connection, default_isolation_level, detach(), exec_driver_sql(), execute(), execution_options(), get_execution_options(), get_isolation_level(), get_nested_transaction(), get_transaction(), in_nested_transaction(), in_transaction(), info, invalidate(), invalidated, rollback(), scalar(), scalars(), schema_for_object()

  The Connection object represents a single DBAPI connection checked out from the connection pool. In this state, the connection pool has no affect upon the connection, including its expiration or timeout state. For the connection pool to properly manage connections, connections should be returned to the connection pool (i.e. `connection.close()`) whenever the connection is not in use.

  Class signature

  class `sqlalchemy.engine.Connection` (`sqlalchemy.engine.interfaces.ConnectionEventsTarget`, `sqlalchemy.inspection.Inspectable`)

  method `sqlalchemy.engine.Connection.` __init__(engine: Engine, connection: PoolProxiedConnection | None = None, _has_events: bool | None = None, _allow_revalidate: bool = True, _allow_autobegin: bool = True)

    Construct a new Connection.

  method `sqlalchemy.engine.Connection.` begin() → RootTransaction

    Begin a transaction prior to autobegin occurring.

    E.g.:

        with engine.connect() as conn:
            with conn.begin() as trans:
                conn.execute(table.insert(), {"username": "sandy"})

    The returned object is an instance of `RootTransaction`. This object represents the “scope” of the transaction, which completes when either the `Transaction.rollback()` or `Transaction.commit()` method is called; the object also works as a context manager as illustrated above.

    The `Connection.begin()` method begins a transaction that normally will be begun in any case when the connection is first used to execute a statement. The reason this method might be used would be to invoke the `ConnectionEvents.begin()` event at a specific time, or to organize code within the scope of a connection checkout in terms of context managed blocks, such as:

        with engine.connect() as conn:
            with conn.begin():
                conn.execute(...)
                conn.execute(...)

            with conn.begin():
                conn.execute(...)
                conn.execute(...)

    The above code is not fundamentally any different in its behavior than the following code which does not use `Connection.begin()`; the below style is known as “commit as you go” style:

        with engine.connect() as conn:
            conn.execute(...)
            conn.execute(...)
            conn.commit()

            conn.execute(...)
            conn.execute(...)
            conn.commit()

    From a database point of view, the `Connection.begin()` method does not emit any SQL or change the state of the underlying DBAPI connection in any way; the Python DBAPI does not have any concept of explicit transaction begin.

    See also

    Working with Transactions and the DBAPI - in the SQLAlchemy Unified Tutorial

    `Connection.begin_nested()` - use a SAVEPOINT

    `Connection.begin_twophase()` - use a two phase /XID transaction

    `Engine.begin()` - context manager available from `Engine`

  method `sqlalchemy.engine.Connection.` begin_nested() → NestedTransaction

    Begin a nested transaction (i.e. SAVEPOINT) and return a transaction handle that controls the scope of the SAVEPOINT.

    E.g.:

        with engine.begin() as connection:
            with connection.begin_nested():
                connection.execute(table.insert(), {"username": "sandy"})

    The returned object is an instance of `NestedTransaction`, which includes transactional methods `NestedTransaction.commit()` and `NestedTransaction.rollback()`; for a nested transaction, these methods correspond to the operations “RELEASE SAVEPOINT <name>” and “ROLLBACK TO SAVEPOINT <name>”. The name of the savepoint is local to the `NestedTransaction` object and is generated automatically. Like any other `Transaction`, the `NestedTransaction` may be used as a context manager as illustrated above which will “release” or “rollback” corresponding to if the operation within the block were successful or raised an exception.

    Nested transactions require SAVEPOINT support in the underlying database, else the behavior is undefined. SAVEPOINT is commonly used to run operations within a transaction that may fail, while continuing the outer transaction. E.g.:

        from sqlalchemy import exc

        with engine.begin() as connection:
            trans = connection.begin_nested()
            try:
                connection.execute(table.insert(), {"username": "sandy"})
                trans.commit()
            except exc.IntegrityError:  # catch for duplicate username
                trans.rollback()  # rollback to savepoint

            # outer transaction continues
            connection.execute(...)

    If `Connection.begin_nested()` is called without first calling `Connection.begin()` or `Engine.begin()`, the `Connection` object will “autobegin” the outer transaction first. This outer transaction may be committed using “commit-as-you-go” style, e.g.:

        with engine.connect() as connection:  # begin() wasn't called

            with connection.begin_nested():  # will auto-"begin()" first
                connection.execute(...)
            # savepoint is released

            connection.execute(...)

            # explicitly commit outer transaction
            connection.commit()

            # can continue working with connection here

    Changed in version 2.0: `Connection.begin_nested()` will now participate in the connection “autobegin” behavior that is new as of 2.0 / “future” style connections in 1.4.

    See also

    `Connection.begin()`

    Using SAVEPOINT - ORM support for SAVEPOINT

  method `sqlalchemy.engine.Connection.` begin_twophase(xid: Any | None = None) → TwoPhaseTransaction

    Begin a two-phase or XA transaction and return a transaction handle.

    The returned object is an instance of `TwoPhaseTransaction`, which in addition to the methods provided by `Transaction`, also provides a `TwoPhaseTransaction.prepare()` method.

    Parameters:

      xid – the two phase transaction id. If not supplied, a random id will be generated.

    See also

    `Connection.begin()`

    `Connection.begin_twophase()`

  method `sqlalchemy.engine.Connection.` close() → None

    Close this `Connection`.

    This results in a release of the underlying database resources, that is, the DBAPI connection referenced internally. The DBAPI connection is typically restored back to the connection-holding `Pool` referenced by the `Engine` that produced this `Connection`. Any transactional state present on the DBAPI connection is also unconditionally released via the DBAPI connection’s `rollback()` method, regardless of any `Transaction` object that may be outstanding with regards to this `Connection`.

    This has the effect of also calling `Connection.rollback()` if any transaction is in place.

    After `Connection.close()` is called, the `Connection` is permanently in a closed state, and will allow no further operations.

  attribute `sqlalchemy.engine.Connection.` closed

    Return True if this connection is closed.

  method `sqlalchemy.engine.Connection.` commit() → None

    Commit the transaction that is currently in progress.

    This method commits the current transaction if one has been started. If no transaction was started, the method has no effect, assuming the connection is in a non-invalidated state.

    A transaction is begun on a `Connection` automatically whenever a statement is first executed, or when the `Connection.begin()` method is called.

    Note

    The `Connection.commit()` method only acts upon the primary database transaction that is linked to the `Connection` object. It does not operate upon a SAVEPOINT that would have been invoked from the `Connection.begin_nested()` method; for control of a SAVEPOINT, call `NestedTransaction.commit()` on the `NestedTransaction` that is returned by the `Connection.begin_nested()` method itself.

  attribute `sqlalchemy.engine.Connection.` connection

    The underlying DB-API connection managed by this Connection.

    This is a SQLAlchemy connection-pool proxied connection which then has the attribute `_ConnectionFairy.dbapi_connection` that refers to the actual driver connection.

    See also

    Working with Driver SQL and Raw DBAPI Connections

  attribute `sqlalchemy.engine.Connection.` default_isolation_level

    The initial-connection time isolation level associated with the `Dialect` in use.

    This value is independent of the `Connection.execution_options.isolation_level` and `Engine.execution_options.isolation_level` execution options, and is determined by the `Dialect` when the first connection is created, by performing a SQL query against the database for the current isolation level before any additional commands have been emitted.

    Calling this accessor does not invoke any new SQL queries.

    See also

    `Connection.get_isolation_level()` - view current actual isolation level

    `create_engine.isolation_level` - set per `Engine` isolation level

    `Connection.execution_options.isolation_level` - set per `Connection` isolation level

  method `sqlalchemy.engine.Connection.` detach() → None

    Detach the underlying DB-API connection from its connection pool.

    E.g.:

        with engine.connect() as conn:
            conn.detach()
            conn.execute(text("SET search_path TO schema1, schema2"))

            # work with connection

        # connection is fully closed (since we used "with:", can
        # also call .close())

    This `Connection` instance will remain usable. When closed (or exited from a context manager context as above), the DB-API connection will be literally closed and not returned to its originating pool.

    This method can be used to insulate the rest of an application from a modified state on a connection (such as a transaction isolation level or similar).

  method `sqlalchemy.engine.Connection.` exec_driver_sql(statement: str, parameters: _DBAPIAnyExecuteParams | None = None, execution_options: CoreExecuteOptionsParameter | None = None) → CursorResult[Any]

    Executes a string SQL statement on the DBAPI cursor directly, without any SQL compilation steps.

    This can be used to pass any string directly to the `cursor.execute()` method of the DBAPI in use.

    Parameters:

      - statement – The statement str to be executed. Bound parameters must use the underlying DBAPI’s paramstyle, such as “qmark”, “pyformat”, “format”, etc.

      - parameters – represent bound parameter values to be used in the execution. The format is one of: a dictionary of named parameters, a tuple of positional parameters, or a list containing either dictionaries or tuples for multiple-execute support.

    Returns:

      a `CursorResult`.

      E.g. multiple dictionaries:

          conn.exec_driver_sql(
              "INSERT INTO table (id, value) VALUES (%(id)s, %(value)s)",
              [{"id": 1, "value": "v1"}, {"id": 2, "value": "v2"}],
          )

      Single dictionary:

          conn.exec_driver_sql(
              "INSERT INTO table (id, value) VALUES (%(id)s, %(value)s)",
              dict(id=1, value="v1"),
          )

      Single tuple:

          conn.exec_driver_sql(
              "INSERT INTO table (id, value) VALUES (?, ?)", (1, "v1")
          )

      Note

      The `Connection.exec_driver_sql()` method does not participate in the `ConnectionEvents.before_execute()` and `ConnectionEvents.after_execute()` events. To intercept calls to `Connection.exec_driver_sql()`, use `ConnectionEvents.before_cursor_execute()` and `ConnectionEvents.after_cursor_execute()`.

      See also

      PEP 249

  method `sqlalchemy.engine.Connection.` execute(statement: Executable, parameters: _CoreAnyExecuteParams | None = None, *, execution_options: CoreExecuteOptionsParameter | None = None) → CursorResult[Any]

    Executes a SQL statement construct and returns a `CursorResult`.

    Parameters:

      - statement –

        The statement to be executed. This is always an object that is in both the `ClauseElement` and `Executable` hierarchies, including:

        - `Select`

        - `Insert`, `Update`, `Delete`

        - `TextClause` and `TextualSelect`

        - `DDL` and objects which inherit from `ExecutableDDLElement`

      - parameters – parameters which will be bound into the statement. This may be either a dictionary of parameter names to values, or a mutable sequence (e.g. a list) of dictionaries. When a list of dictionaries is passed, the underlying statement execution will make use of the DBAPI `cursor.executemany()` method. When a single dictionary is passed, the DBAPI `cursor.execute()` method will be used.

      - execution_options – optional dictionary of execution options, which will be associated with the statement execution. This dictionary can provide a subset of the options that are accepted by `Connection.execution_options()`.

    Returns:

      a `Result` object.

  method `sqlalchemy.engine.Connection.` execution_options(**opt: Any) → Connection

    Set non-SQL options for the connection which take effect during execution.

    This method modifies this `Connection` in-place; the return value is the same `Connection` object upon which the method is called. Note that this is in contrast to the behavior of the `execution_options` methods on other objects such as `Engine.execution_options()` and `Executable.execution_options()`. The rationale is that many such execution options necessarily modify the state of the base DBAPI connection in any case so there is no feasible means of keeping the effect of such an option localized to a “sub” connection.

    Changed in version 2.0: The `Connection.execution_options()` method, in contrast to other objects with this method, modifies the connection in-place without creating copy of it.

    As discussed elsewhere, the `Connection.execution_options()` method accepts any arbitrary parameters including user defined names. All parameters given are consumable in a number of ways including by using the `Connection.get_execution_options()` method. See the examples at `Executable.execution_options()` and `Engine.execution_options()`.

    The keywords that are currently recognized by SQLAlchemy itself include all those listed under `Executable.execution_options()`, as well as others that are specific to `Connection`.

    Parameters:

      - compiled_cache –

        Available on: `Connection`, `Engine`.

        A dictionary where `Compiled` objects will be cached when the `Connection` compiles a clause expression into a `Compiled` object. This dictionary will supersede the statement cache that may be configured on the `Engine` itself. If set to None, caching is disabled, even if the engine has a configured cache size.

        Note that the ORM makes use of its own “compiled” caches for some operations, including flush operations. The caching used by the ORM internally supersedes a cache dictionary specified here.

      - logging_token –

        Available on: `Connection`, `Engine`, `Executable`.

        Adds the specified string token surrounded by brackets in log messages logged by the connection, i.e. the logging that’s enabled either via the `create_engine.echo` flag or via the `logging.getLogger("sqlalchemy.engine")` logger. This allows a per-connection or per-sub-engine token to be available which is useful for debugging concurrent connection scenarios.

        New in version 1.4.0b2.

        See also

        Setting Per-Connection / Sub-Engine Tokens - usage example

        `create_engine.logging_name` - adds a name to the name used by the Python logger object itself.

      - isolation_level –

        Available on: `Connection`, `Engine`.

        Set the transaction isolation level for the lifespan of this `Connection` object. Valid values include those string values accepted by the `create_engine.isolation_level` parameter passed to `create_engine()`. These levels are semi-database specific; see individual dialect documentation for valid levels.

        The isolation level option applies the isolation level by emitting statements on the DBAPI connection, and necessarily affects the original Connection object overall. The isolation level will remain at the given setting until explicitly changed, or when the DBAPI connection itself is released to the connection pool, i.e. the `Connection.close()` method is called, at which time an event handler will emit additional statements on the DBAPI connection in order to revert the isolation level change.

        Note

        The `isolation_level` execution option may only be established before the `Connection.begin()` method is called, as well as before any SQL statements are emitted which would otherwise trigger “autobegin”, or directly after a call to `Connection.commit()` or `Connection.rollback()`. A database cannot change the isolation level on a transaction in progress.

        Note

        The `isolation_level` execution option is implicitly reset if the `Connection` is invalidated, e.g. via the `Connection.invalidate()` method, or if a disconnection error occurs. The new connection produced after the invalidation will not have the selected isolation level re-applied to it automatically.

        See also

        Setting Transaction Isolation Levels including DBAPI Autocommit

        `Connection.get_isolation_level()` - view current actual level

      - no_parameters –

        Available on: `Connection`, `Executable`.

        When `True`, if the final parameter list or dictionary is totally empty, will invoke the statement on the cursor as `cursor.execute(statement)`, not passing the parameter collection at all. Some DBAPIs such as psycopg2 and mysql-python consider percent signs as significant only when parameters are present; this option allows code to generate SQL containing percent signs (and possibly other characters) that is neutral regarding whether it’s executed by the DBAPI or piped into a script that’s later invoked by command line tools.

      - stream_results –

        Available on: `Connection`, `Executable`.

        Indicate to the dialect that results should be “streamed” and not pre-buffered, if possible. For backends such as PostgreSQL, MySQL and MariaDB, this indicates the use of a “server side cursor” as opposed to a client side cursor. Other backends such as that of Oracle Database may already use server side cursors by default.

        The usage of `Connection.execution_options.stream_results` is usually combined with setting a fixed number of rows to to be fetched in batches, to allow for efficient iteration of database rows while at the same time not loading all result rows into memory at once; this can be configured on a `Result` object using the `Result.yield_per()` method, after execution has returned a new `Result`. If `Result.yield_per()` is not used, the `Connection.execution_options.stream_results` mode of operation will instead use a dynamically sized buffer which buffers sets of rows at a time, growing on each batch based on a fixed growth size up until a limit which may be configured using the `Connection.execution_options.max_row_buffer` parameter.

        When using the ORM to fetch ORM mapped objects from a result, `Result.yield_per()` should always be used with `Connection.execution_options.stream_results`, so that the ORM does not fetch all rows into new ORM objects at once.

        For typical use, the `Connection.execution_options.yield_per` execution option should be preferred, which sets up both `Connection.execution_options.stream_results` and `Result.yield_per()` at once. This option is supported both at a core level by `Connection` as well as by the ORM `Session`; the latter is described at Fetching Large Result Sets with Yield Per.

        See also

        Using Server Side Cursors (a.k.a. stream results) - background on `Connection.execution_options.stream_results`

        `Connection.execution_options.max_row_buffer`

        `Connection.execution_options.yield_per`

        Fetching Large Result Sets with Yield Per - in the ORM Querying Guide describing the ORM version of `yield_per`

      - max_row_buffer –

        Available on: `Connection`, `Executable`. Sets a maximum buffer size to use when the `Connection.execution_options.stream_results` execution option is used on a backend that supports server side cursors. The default value if not specified is 1000.

        See also

        `Connection.execution_options.stream_results`

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

      - yield_per –

        Available on: `Connection`, `Executable`. Integer value applied which will set the `Connection.execution_options.stream_results` execution option and invoke `Result.yield_per()` automatically at once. Allows equivalent functionality as is present when using this parameter with the ORM.

        New in version 1.4.40.

        See also

        Using Server Side Cursors (a.k.a. stream results) - background and examples on using server side cursors with Core.

        Fetching Large Result Sets with Yield Per - in the ORM Querying Guide describing the ORM version of `yield_per`

      - insertmanyvalues_page_size –

        Available on: `Connection`, `Engine`. Number of rows to format into an INSERT statement when the statement uses “insertmanyvalues” mode, which is a paged form of bulk insert that is used for many backends when using executemany execution typically in conjunction with RETURNING. Defaults to 1000. May also be modified on a per-engine basis using the `create_engine.insertmanyvalues_page_size` parameter.

        New in version 2.0.

        See also

        “Insert Many Values” Behavior for INSERT statements

      - schema_translate_map –

        Available on: `Connection`, `Engine`, `Executable`.

        A dictionary mapping schema names to schema names, that will be applied to the `Table.schema` element of each `Table` encountered when SQL or DDL expression elements are compiled into strings; the resulting schema name will be converted based on presence in the map of the original name.

        See also

        Translation of Schema Names

      - preserve_rowcount –

        Boolean; when True, the `cursor.rowcount` attribute will be unconditionally memoized within the result and made available via the `CursorResult.rowcount` attribute. Normally, this attribute is only preserved for UPDATE and DELETE statements. Using this option, the DBAPIs rowcount value can be accessed for other kinds of statements such as INSERT and SELECT, to the degree that the DBAPI supports these statements. See `CursorResult.rowcount` for notes regarding the behavior of this attribute.

        New in version 2.0.28.

    See also

    `Engine.execution_options()`

    `Executable.execution_options()`

    `Connection.get_execution_options()`

    ORM Execution Options - documentation on all ORM-specific execution options

  method `sqlalchemy.engine.Connection.` get_execution_options() → _ExecuteOptions

    Get the non-SQL options which will take effect during execution.

    New in version 1.3.

    See also

    `Connection.execution_options()`

  method `sqlalchemy.engine.Connection.` get_isolation_level() → Literal['SERIALIZABLE', 'REPEATABLE READ', 'READ COMMITTED', 'READ UNCOMMITTED', 'AUTOCOMMIT']

    Return the current actual isolation level that’s present on the database within the scope of this connection.

    This attribute will perform a live SQL operation against the database in order to procure the current isolation level, so the value returned is the actual level on the underlying DBAPI connection regardless of how this state was set. This will be one of the four actual isolation modes `READ UNCOMMITTED`, `READ COMMITTED`, `REPEATABLE READ`, `SERIALIZABLE`. It will not include the `AUTOCOMMIT` isolation level setting. Third party dialects may also feature additional isolation level settings.

    Note

    This method will not report on the `AUTOCOMMIT` isolation level, which is a separate dbapi setting that’s independent of actual isolation level. When `AUTOCOMMIT` is in use, the database connection still has a “traditional” isolation mode in effect, that is typically one of the four values `READ UNCOMMITTED`, `READ COMMITTED`, `REPEATABLE READ`, `SERIALIZABLE`.

    Compare to the `Connection.default_isolation_level` accessor which returns the isolation level that is present on the database at initial connection time.

    See also

    `Connection.default_isolation_level` - view default level

    `create_engine.isolation_level` - set per `Engine` isolation level

    `Connection.execution_options.isolation_level` - set per `Connection` isolation level

  method `sqlalchemy.engine.Connection.` get_nested_transaction() → NestedTransaction | None

    Return the current nested transaction in progress, if any.

    New in version 1.4.

  method `sqlalchemy.engine.Connection.` get_transaction() → RootTransaction | None

    Return the current root transaction in progress, if any.

    New in version 1.4.

  method `sqlalchemy.engine.Connection.` in_nested_transaction() → bool

    Return True if a transaction is in progress.

  method `sqlalchemy.engine.Connection.` in_transaction() → bool

    Return True if a transaction is in progress.

  attribute `sqlalchemy.engine.Connection.` info

    Info dictionary associated with the underlying DBAPI connection referred to by this `Connection`, allowing user-defined data to be associated with the connection.

    The data here will follow along with the DBAPI connection including after it is returned to the connection pool and used again in subsequent instances of `Connection`.

  method `sqlalchemy.engine.Connection.` invalidate(exception: BaseException | None = None) → None

    Invalidate the underlying DBAPI connection associated with this `Connection`.

    An attempt will be made to close the underlying DBAPI connection immediately; however if this operation fails, the error is logged but not raised. The connection is then discarded whether or not close() succeeded.

    Upon the next use (where “use” typically means using the `Connection.execute()` method or similar), this `Connection` will attempt to procure a new DBAPI connection using the services of the `Pool` as a source of connectivity (e.g. a “reconnection”).

    If a transaction was in progress (e.g. the `Connection.begin()` method has been called) when `Connection.invalidate()` method is called, at the DBAPI level all state associated with this transaction is lost, as the DBAPI connection is closed. The `Connection` will not allow a reconnection to proceed until the `Transaction` object is ended, by calling the `Transaction.rollback()` method; until that point, any attempt at continuing to use the `Connection` will raise an `InvalidRequestError`. This is to prevent applications from accidentally continuing an ongoing transactional operations despite the fact that the transaction has been lost due to an invalidation.

    The `Connection.invalidate()` method, just like auto-invalidation, will at the connection pool level invoke the `PoolEvents.invalidate()` event.

    Parameters:

      exception – an optional `Exception` instance that’s the reason for the invalidation. is passed along to event handlers and logging functions.

    See also

    More on Invalidation

  attribute `sqlalchemy.engine.Connection.` invalidated

    Return True if this connection was invalidated.

    This does not indicate whether or not the connection was invalidated at the pool level, however

  method `sqlalchemy.engine.Connection.` rollback() → None

    Roll back the transaction that is currently in progress.

    This method rolls back the current transaction if one has been started. If no transaction was started, the method has no effect. If a transaction was started and the connection is in an invalidated state, the transaction is cleared using this method.

    A transaction is begun on a `Connection` automatically whenever a statement is first executed, or when the `Connection.begin()` method is called.

    Note

    The `Connection.rollback()` method only acts upon the primary database transaction that is linked to the `Connection` object. It does not operate upon a SAVEPOINT that would have been invoked from the `Connection.begin_nested()` method; for control of a SAVEPOINT, call `NestedTransaction.rollback()` on the `NestedTransaction` that is returned by the `Connection.begin_nested()` method itself.

  method `sqlalchemy.engine.Connection.` scalar(statement: Executable, parameters: _CoreSingleExecuteParams | None = None, *, execution_options: CoreExecuteOptionsParameter | None = None) → Any

    Executes a SQL statement construct and returns a scalar object.

    This method is shorthand for invoking the `Result.scalar()` method after invoking the `Connection.execute()` method. Parameters are equivalent.

    Returns:

      a scalar Python value representing the first column of the first row returned.

  method `sqlalchemy.engine.Connection.` scalars(statement: Executable, parameters: _CoreAnyExecuteParams | None = None, *, execution_options: CoreExecuteOptionsParameter | None = None) → ScalarResult[Any]

    Executes and returns a scalar result set, which yields scalar values from the first column of each row.

    This method is equivalent to calling `Connection.execute()` to receive a `Result` object, then invoking the `Result.scalars()` method to produce a `ScalarResult` instance.

    Returns:

      a `ScalarResult`

    New in version 1.4.24.

  method `sqlalchemy.engine.Connection.` schema_for_object(obj: HasSchemaAttr) → str | None

    Return the schema name for the given schema item taking into account current schema translate map.

class sqlalchemy.engine.CreateEnginePlugin

  A set of hooks intended to augment the construction of an `Engine` object based on entrypoint names in a URL.

  The purpose of `CreateEnginePlugin` is to allow third-party systems to apply engine, pool and dialect level event listeners without the need for the target application to be modified; instead, the plugin names can be added to the database URL. Target applications for `CreateEnginePlugin` include:

  - connection and SQL performance tools, e.g. which use events to track number of checkouts and/or time spent with statements

  - connectivity plugins such as proxies

  A rudimentary `CreateEnginePlugin` that attaches a logger to an `Engine` object might look like:

      import logging

      from sqlalchemy.engine import CreateEnginePlugin
      from sqlalchemy import event

      class LogCursorEventsPlugin(CreateEnginePlugin):
          def __init__(self, url, kwargs):
              # consume the parameter "log_cursor_logging_name" from the
              # URL query
              logging_name = url.query.get(
                  "log_cursor_logging_name", "log_cursor"
              )

              self.log = logging.getLogger(logging_name)

          def update_url(self, url):
              "update the URL to one that no longer includes our parameters"
              return url.difference_update_query(["log_cursor_logging_name"])

          def engine_created(self, engine):
              "attach an event listener after the new Engine is constructed"
              event.listen(engine, "before_cursor_execute", self._log_event)

          def _log_event(
              self,
              conn,
              cursor,
              statement,
              parameters,
              context,
              executemany,
          ):

              self.log.info("Plugin logged cursor event: %s", statement)

  Plugins are registered using entry points in a similar way as that of dialects:

      entry_points = {
          "sqlalchemy.plugins": [
              "log_cursor_plugin = myapp.plugins:LogCursorEventsPlugin"
          ]
      }

  A plugin that uses the above names would be invoked from a database URL as in:

      from sqlalchemy import create_engine

      engine = create_engine(
          "mysql+pymysql://scott:tiger@localhost/test?"
          "plugin=log_cursor_plugin&log_cursor_logging_name=mylogger"
      )

  The `plugin` URL parameter supports multiple instances, so that a URL may specify multiple plugins; they are loaded in the order stated in the URL:

      engine = create_engine(
          "mysql+pymysql://scott:tiger@localhost/test?"
          "plugin=plugin_one&plugin=plugin_twp&plugin=plugin_three"
      )

  The plugin names may also be passed directly to `create_engine()` using the `create_engine.plugins` argument:

      engine = create_engine(
          "mysql+pymysql://scott:tiger@localhost/test", plugins=["myplugin"]
      )

  New in version 1.2.3: plugin names can also be specified to `create_engine()` as a list

  A plugin may consume plugin-specific arguments from the `URL` object as well as the `kwargs` dictionary, which is the dictionary of arguments passed to the `create_engine()` call. “Consuming” these arguments includes that they must be removed when the plugin initializes, so that the arguments are not passed along to the `Dialect` constructor, where they will raise an `ArgumentError` because they are not known by the dialect.

  As of version 1.4 of SQLAlchemy, arguments should continue to be consumed from the `kwargs` dictionary directly, by removing the values with a method such as `dict.pop`. Arguments from the `URL` object should be consumed by implementing the `CreateEnginePlugin.update_url()` method, returning a new copy of the `URL` with plugin-specific parameters removed:

      class MyPlugin(CreateEnginePlugin):
          def __init__(self, url, kwargs):
              self.my_argument_one = url.query["my_argument_one"]
              self.my_argument_two = url.query["my_argument_two"]
              self.my_argument_three = kwargs.pop("my_argument_three", None)

          def update_url(self, url):
              return url.difference_update_query(
                  ["my_argument_one", "my_argument_two"]
              )

  Arguments like those illustrated above would be consumed from a `create_engine()` call such as:

      from sqlalchemy import create_engine

      engine = create_engine(
          "mysql+pymysql://scott:tiger@localhost/test?"
          "plugin=myplugin&my_argument_one=foo&my_argument_two=bar",
          my_argument_three="bat",
      )

  Changed in version 1.4: The `URL` object is now immutable; a `CreateEnginePlugin` that needs to alter the `URL` should implement the newly added `CreateEnginePlugin.update_url()` method, which is invoked after the plugin is constructed.

  For migration, construct the plugin in the following way, checking for the existence of the `CreateEnginePlugin.update_url()` method to detect which version is running:

      class MyPlugin(CreateEnginePlugin):
          def __init__(self, url, kwargs):
              if hasattr(CreateEnginePlugin, "update_url"):
                  # detect the 1.4 API
                  self.my_argument_one = url.query["my_argument_one"]
                  self.my_argument_two = url.query["my_argument_two"]
              else:
                  # detect the 1.3 and earlier API - mutate the
                  # URL directly
                  self.my_argument_one = url.query.pop("my_argument_one")
                  self.my_argument_two = url.query.pop("my_argument_two")

              self.my_argument_three = kwargs.pop("my_argument_three", None)

          def update_url(self, url):
              # this method is only called in the 1.4 version
              return url.difference_update_query(
                  ["my_argument_one", "my_argument_two"]
              )

  See also

  The URL object is now immutable - overview of the `URL` change which also includes notes regarding `CreateEnginePlugin`.

  Members

  __init__(), engine_created(), handle_dialect_kwargs(), handle_pool_kwargs(), update_url()

  When the engine creation process completes and produces the `Engine` object, it is again passed to the plugin via the `CreateEnginePlugin.engine_created()` hook. In this hook, additional changes can be made to the engine, most typically involving setup of events (e.g. those defined in Core Events).

  method `sqlalchemy.engine.CreateEnginePlugin.` __init__(url: URL, kwargs: Dict[str, Any])

    Construct a new `CreateEnginePlugin`.

    The plugin object is instantiated individually for each call to `create_engine()`. A single `Engine` will be passed to the `CreateEnginePlugin.engine_created()` method corresponding to this URL.

    Parameters:

      - url –

        the `URL` object. The plugin may inspect the `URL` for arguments. Arguments used by the plugin should be removed, by returning an updated `URL` from the `CreateEnginePlugin.update_url()` method.

        Changed in version 1.4: The `URL` object is now immutable, so a `CreateEnginePlugin` that needs to alter the `URL` object should implement the `CreateEnginePlugin.update_url()` method.

      - kwargs – The keyword arguments passed to `create_engine()`.

  method `sqlalchemy.engine.CreateEnginePlugin.` engine_created(engine: Engine) → None

    Receive the `Engine` object when it is fully constructed.

    The plugin may make additional changes to the engine, such as registering engine or connection pool events.

  method `sqlalchemy.engine.CreateEnginePlugin.` handle_dialect_kwargs(dialect_cls: Type[Dialect], dialect_args: Dict[str, Any]) → None

    parse and modify dialect kwargs

  method `sqlalchemy.engine.CreateEnginePlugin.` handle_pool_kwargs(pool_cls: Type[Pool], pool_args: Dict[str, Any]) → None

    parse and modify pool kwargs

  method `sqlalchemy.engine.CreateEnginePlugin.` update_url(url: URL) → URL

    Update the `URL`.

    A new `URL` should be returned. This method is typically used to consume configuration arguments from the `URL` which must be removed, as they will not be recognized by the dialect. The `URL.difference_update_query()` method is available to remove these arguments. See the docstring at `CreateEnginePlugin` for an example.

    New in version 1.4.

class sqlalchemy.engine.Engine

  Connects a `Pool` and `Dialect` together to provide a source of database connectivity and behavior.

  An `Engine` object is instantiated publicly using the `create_engine()` function.

  See also

  Engine Configuration

  Working with Engines and Connections

  Members

  begin(), clear_compiled_cache(), connect(), dispose(), driver, engine, execution_options(), get_execution_options(), name, raw_connection(), update_execution_options()

  Class signature

  class `sqlalchemy.engine.Engine` (`sqlalchemy.engine.interfaces.ConnectionEventsTarget`, `sqlalchemy.log.Identified`, `sqlalchemy.inspection.Inspectable`)

  method `sqlalchemy.engine.Engine.` begin() → Iterator[Connection]

    Return a context manager delivering a `Connection` with a `Transaction` established.

    E.g.:

        with engine.begin() as conn:
            conn.execute(text("insert into table (x, y, z) values (1, 2, 3)"))
            conn.execute(text("my_special_procedure(5)"))

    Upon successful operation, the `Transaction` is committed. If an error is raised, the `Transaction` is rolled back.

    See also

    `Engine.connect()` - procure a `Connection` from an `Engine`.

    `Connection.begin()` - start a `Transaction` for a particular `Connection`.

  method `sqlalchemy.engine.Engine.` clear_compiled_cache() → None

    Clear the compiled cache associated with the dialect.

    This applies only to the built-in cache that is established via the `create_engine.query_cache_size` parameter. It will not impact any dictionary caches that were passed via the `Connection.execution_options.compiled_cache` parameter.

    New in version 1.4.

  method `sqlalchemy.engine.Engine.` connect() → Connection

    Return a new `Connection` object.

    The `Connection` acts as a Python context manager, so the typical use of this method looks like:

        with engine.connect() as connection:
            connection.execute(text("insert into table values ('foo')"))
            connection.commit()

    Where above, after the block is completed, the connection is “closed” and its underlying DBAPI resources are returned to the connection pool. This also has the effect of rolling back any transaction that was explicitly begun or was begun via autobegin, and will emit the `ConnectionEvents.rollback()` event if one was started and is still in progress.

    See also

    `Engine.begin()`

  method `sqlalchemy.engine.Engine.` dispose(close: bool = True) → None

    Dispose of the connection pool used by this `Engine`.

    A new connection pool is created immediately after the old one has been disposed. The previous connection pool is disposed either actively, by closing out all currently checked-in connections in that pool, or passively, by losing references to it but otherwise not closing any connections. The latter strategy is more appropriate for an initializer in a forked Python process.

    Parameters:

      close –

      if left at its default of `True`, has the effect of fully closing all currently checked in database connections. Connections that are still checked out will not be closed, however they will no longer be associated with this `Engine`, so when they are closed individually, eventually the `Pool` which they are associated with will be garbage collected and they will be closed out fully, if not already closed on checkin.

      If set to `False`, the previous connection pool is de-referenced, and otherwise not touched in any way.

    New in version 1.4.33: Added the `Engine.dispose.close` parameter to allow the replacement of a connection pool in a child process without interfering with the connections used by the parent process.

    See also

    Engine Disposal

    Using Connection Pools with Multiprocessing or os.fork()

  attribute `sqlalchemy.engine.Engine.` driver

    Driver name of the `Dialect` in use by this `Engine`.

  attribute `sqlalchemy.engine.Engine.` engine

    Returns this `Engine`.

    Used for legacy schemes that accept `Connection` / `Engine` objects within the same variable.

  method `sqlalchemy.engine.Engine.` execution_options(**opt: Any) → OptionEngine

    Return a new `Engine` that will provide `Connection` objects with the given execution options.

    The returned `Engine` remains related to the original `Engine` in that it shares the same connection pool and other state:

    - The `Pool` used by the new `Engine` is the same instance. The `Engine.dispose()` method will replace the connection pool instance for the parent engine as well as this one.

    - Event listeners are “cascaded” - meaning, the new `Engine` inherits the events of the parent, and new events can be associated with the new `Engine` individually.

    - The logging configuration and logging_name is copied from the parent `Engine`.

    The intent of the `Engine.execution_options()` method is to implement schemes where multiple `Engine` objects refer to the same connection pool, but are differentiated by options that affect some execution-level behavior for each engine. One such example is breaking into separate “reader” and “writer” `Engine` instances, where one `Engine` has a lower isolation level setting configured or is even transaction-disabled using “autocommit”. An example of this configuration is at Maintaining Multiple Isolation Levels for a Single Engine.

    Another example is one that uses a custom option `shard_id` which is consumed by an event to change the current schema on a database connection:

        from sqlalchemy import event
        from sqlalchemy.engine import Engine

        primary_engine = create_engine("mysql+mysqldb://")
        shard1 = primary_engine.execution_options(shard_id="shard1")
        shard2 = primary_engine.execution_options(shard_id="shard2")

        shards = {"default": "base", "shard_1": "db1", "shard_2": "db2"}

        @event.listens_for(Engine, "before_cursor_execute")
        def _switch_shard(conn, cursor, stmt, params, context, executemany):
            shard_id = conn.get_execution_options().get("shard_id", "default")
            current_shard = conn.info.get("current_shard", None)

            if current_shard != shard_id:
                cursor.execute("use %s" % shards[shard_id])
                conn.info["current_shard"] = shard_id

    The above recipe illustrates two `Engine` objects that will each serve as factories for `Connection` objects that have pre-established “shard_id” execution options present. A `ConnectionEvents.before_cursor_execute()` event handler then interprets this execution option to emit a MySQL `use` statement to switch databases before a statement execution, while at the same time keeping track of which database we’ve established using the `Connection.info` dictionary.

    See also

    `Connection.execution_options()` - update execution options on a `Connection` object.

    `Engine.update_execution_options()` - update the execution options for a given `Engine` in place.

    `Engine.get_execution_options()`

  method `sqlalchemy.engine.Engine.` get_execution_options() → _ExecuteOptions

    Get the non-SQL options which will take effect during execution.

    See also

    `Engine.execution_options()`

  attribute `sqlalchemy.engine.Engine.` name

    String name of the `Dialect` in use by this `Engine`.

  method `sqlalchemy.engine.Engine.` raw_connection() → PoolProxiedConnection

    Return a “raw” DBAPI connection from the connection pool.

    The returned object is a proxied version of the DBAPI connection object used by the underlying driver in use. The object will have all the same behavior as the real DBAPI connection, except that its `close()` method will result in the connection being returned to the pool, rather than being closed for real.

    This method provides direct DBAPI connection access for special situations when the API provided by `Connection` is not needed. When a `Connection` object is already present, the DBAPI connection is available using the `Connection.connection` accessor.

    See also

    Working with Driver SQL and Raw DBAPI Connections

  method `sqlalchemy.engine.Engine.` update_execution_options(**opt: Any) → None

    Update the default execution_options dictionary of this `Engine`.

    The given keys/values in **opt are added to the default execution options that will be used for all connections. The initial contents of this dictionary can be sent via the `execution_options` parameter to `create_engine()`.

    See also

    `Connection.execution_options()`

    `Engine.execution_options()`

class sqlalchemy.engine.ExceptionContext

  Encapsulate information about an error condition in progress.

  Members

  chained_exception, connection, cursor, dialect, engine, execution_context, invalidate_pool_on_disconnect, is_disconnect, is_pre_ping, original_exception, parameters, sqlalchemy_exception, statement

  This object exists solely to be passed to the `DialectEvents.handle_error()` event, supporting an interface that can be extended without backwards-incompatibility.

  attribute `sqlalchemy.engine.ExceptionContext.` chained_exception: BaseException | None

    The exception that was returned by the previous handler in the exception chain, if any.

    If present, this exception will be the one ultimately raised by SQLAlchemy unless a subsequent handler replaces it.

    May be None.

  attribute `sqlalchemy.engine.ExceptionContext.` connection: Connection | None

    The `Connection` in use during the exception.

    This member is present, except in the case of a failure when first connecting.

    See also

    `ExceptionContext.engine`

  attribute `sqlalchemy.engine.ExceptionContext.` cursor: DBAPICursor | None

    The DBAPI cursor object.

    May be None.

  attribute `sqlalchemy.engine.ExceptionContext.` dialect: Dialect

    The `Dialect` in use.

    This member is present for all invocations of the event hook.

    New in version 2.0.

  attribute `sqlalchemy.engine.ExceptionContext.` engine: Engine | None

    The `Engine` in use during the exception.

    This member is present in all cases except for when handling an error within the connection pool “pre-ping” process.

  attribute `sqlalchemy.engine.ExceptionContext.` execution_context: ExecutionContext | None

    The `ExecutionContext` corresponding to the execution operation in progress.

    This is present for statement execution operations, but not for operations such as transaction begin/end. It also is not present when the exception was raised before the `ExecutionContext` could be constructed.

    Note that the `ExceptionContext.statement` and `ExceptionContext.parameters` members may represent a different value than that of the `ExecutionContext`, potentially in the case where a `ConnectionEvents.before_cursor_execute()` event or similar modified the statement/parameters to be sent.

    May be None.

  attribute `sqlalchemy.engine.ExceptionContext.` invalidate_pool_on_disconnect: bool

    Represent whether all connections in the pool should be invalidated when a “disconnect” condition is in effect.

    Setting this flag to False within the scope of the `DialectEvents.handle_error()` event will have the effect such that the full collection of connections in the pool will not be invalidated during a disconnect; only the current connection that is the subject of the error will actually be invalidated.

    The purpose of this flag is for custom disconnect-handling schemes where the invalidation of other connections in the pool is to be performed based on other conditions, or even on a per-connection basis.

  attribute `sqlalchemy.engine.ExceptionContext.` is_disconnect: bool

    Represent whether the exception as occurred represents a “disconnect” condition.

    This flag will always be True or False within the scope of the `DialectEvents.handle_error()` handler.

    SQLAlchemy will defer to this flag in order to determine whether or not the connection should be invalidated subsequently. That is, by assigning to this flag, a “disconnect” event which then results in a connection and pool invalidation can be invoked or prevented by changing this flag.

    Note

    The pool “pre_ping” handler enabled using the `create_engine.pool_pre_ping` parameter does not consult this event before deciding if the “ping” returned false, as opposed to receiving an unhandled error. For this use case, the legacy recipe based on engine_connect() may be used. A future API allow more comprehensive customization of the “disconnect” detection mechanism across all functions.

  attribute `sqlalchemy.engine.ExceptionContext.` is_pre_ping: bool

    Indicates if this error is occurring within the “pre-ping” step performed when `create_engine.pool_pre_ping` is set to `True`. In this mode, the `ExceptionContext.engine` attribute will be `None`. The dialect in use is accessible via the `ExceptionContext.dialect` attribute.

    New in version 2.0.5.

  attribute `sqlalchemy.engine.ExceptionContext.` original_exception: BaseException

    The exception object which was caught.

    This member is always present.

  attribute `sqlalchemy.engine.ExceptionContext.` parameters: _DBAPIAnyExecuteParams | None

    Parameter collection that was emitted directly to the DBAPI.

    May be None.

  attribute `sqlalchemy.engine.ExceptionContext.` sqlalchemy_exception: StatementError | None

    The `sqlalchemy.exc.StatementError` which wraps the original, and will be raised if exception handling is not circumvented by the event.

    May be None, as not all exception types are wrapped by SQLAlchemy. For DBAPI-level exceptions that subclass the dbapi’s Error class, this field will always be present.

  attribute `sqlalchemy.engine.ExceptionContext.` statement: str | None

    String SQL statement that was emitted directly to the DBAPI.

    May be None.

class sqlalchemy.engine.NestedTransaction

  Represent a ‘nested’, or SAVEPOINT transaction.

  The `NestedTransaction` object is created by calling the `Connection.begin_nested()` method of `Connection`.

  When using `NestedTransaction`, the semantics of “begin” / “commit” / “rollback” are as follows:

  - the “begin” operation corresponds to the “BEGIN SAVEPOINT” command, where the savepoint is given an explicit name that is part of the state of this object.

  - The `NestedTransaction.commit()` method corresponds to a “RELEASE SAVEPOINT” operation, using the savepoint identifier associated with this `NestedTransaction`.

  - The `NestedTransaction.rollback()` method corresponds to a “ROLLBACK TO SAVEPOINT” operation, using the savepoint identifier associated with this `NestedTransaction`.

  The rationale for mimicking the semantics of an outer transaction in terms of savepoints so that code may deal with a “savepoint” transaction and an “outer” transaction in an agnostic way.

  See also

  Using SAVEPOINT - ORM version of the SAVEPOINT API.

  Members

  close(), commit(), rollback()

  Class signature

  class `sqlalchemy.engine.NestedTransaction` (`sqlalchemy.engine.Transaction`)

  method `sqlalchemy.engine.NestedTransaction.` close() → None

    inherited from the `Transaction.close()` method of `Transaction`

    Close this `Transaction`.

    If this transaction is the base transaction in a begin/commit nesting, the transaction will rollback(). Otherwise, the method returns.

    This is used to cancel a Transaction without affecting the scope of an enclosing transaction.

  method `sqlalchemy.engine.NestedTransaction.` commit() → None

    inherited from the `Transaction.commit()` method of `Transaction`

    Commit this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a COMMIT.

    - For a `NestedTransaction`, it corresponds to a “RELEASE SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

  method `sqlalchemy.engine.NestedTransaction.` rollback() → None

    inherited from the `Transaction.rollback()` method of `Transaction`

    Roll back this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a ROLLBACK.

    - For a `NestedTransaction`, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

class sqlalchemy.engine.RootTransaction

  Represent the “root” transaction on a `Connection`.

  This corresponds to the current “BEGIN/COMMIT/ROLLBACK” that’s occurring for the `Connection`. The `RootTransaction` is created by calling upon the `Connection.begin()` method, and remains associated with the `Connection` throughout its active span. The current `RootTransaction` in use is accessible via the `Connection.get_transaction` method of `Connection`.

  In 2.0 style use, the `Connection` also employs “autobegin” behavior that will create a new `RootTransaction` whenever a connection in a non-transactional state is used to emit commands on the DBAPI connection. The scope of the `RootTransaction` in 2.0 style use can be controlled using the `Connection.commit()` and `Connection.rollback()` methods.

  Members

  close(), commit(), rollback()

  Class signature

  class `sqlalchemy.engine.RootTransaction` (`sqlalchemy.engine.Transaction`)

  method `sqlalchemy.engine.RootTransaction.` close() → None

    inherited from the `Transaction.close()` method of `Transaction`

    Close this `Transaction`.

    If this transaction is the base transaction in a begin/commit nesting, the transaction will rollback(). Otherwise, the method returns.

    This is used to cancel a Transaction without affecting the scope of an enclosing transaction.

  method `sqlalchemy.engine.RootTransaction.` commit() → None

    inherited from the `Transaction.commit()` method of `Transaction`

    Commit this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a COMMIT.

    - For a `NestedTransaction`, it corresponds to a “RELEASE SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

  method `sqlalchemy.engine.RootTransaction.` rollback() → None

    inherited from the `Transaction.rollback()` method of `Transaction`

    Roll back this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a ROLLBACK.

    - For a `NestedTransaction`, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

class sqlalchemy.engine.Transaction

  Represent a database transaction in progress.

  The `Transaction` object is procured by calling the `Connection.begin()` method of `Connection`:

      from sqlalchemy import create_engine

      engine = create_engine("postgresql+psycopg2://scott:tiger@localhost/test")
      connection = engine.connect()
      trans = connection.begin()
      connection.execute(text("insert into x (a, b) values (1, 2)"))
      trans.commit()

  The object provides `rollback()` and `commit()` methods in order to control transaction boundaries. It also implements a context manager interface so that the Python `with` statement can be used with the `Connection.begin()` method:

      with connection.begin():
          connection.execute(text("insert into x (a, b) values (1, 2)"))

  The Transaction object is not threadsafe.

  Members

  close(), commit(), rollback()

  See also

  `Connection.begin()`

  `Connection.begin_twophase()`

  `Connection.begin_nested()`

  Class signature

  class `sqlalchemy.engine.Transaction` (`sqlalchemy.engine.util.TransactionalContext`)

  method `sqlalchemy.engine.Transaction.` close() → None

    Close this `Transaction`.

    If this transaction is the base transaction in a begin/commit nesting, the transaction will rollback(). Otherwise, the method returns.

    This is used to cancel a Transaction without affecting the scope of an enclosing transaction.

  method `sqlalchemy.engine.Transaction.` commit() → None

    Commit this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a COMMIT.

    - For a `NestedTransaction`, it corresponds to a “RELEASE SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

  method `sqlalchemy.engine.Transaction.` rollback() → None

    Roll back this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a ROLLBACK.

    - For a `NestedTransaction`, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

class sqlalchemy.engine.TwoPhaseTransaction

  Represent a two-phase transaction.

  A new `TwoPhaseTransaction` object may be procured using the `Connection.begin_twophase()` method.

  The interface is the same as that of `Transaction` with the addition of the `prepare()` method.

  Members

  close(), commit(), prepare(), rollback()

  Class signature

  class `sqlalchemy.engine.TwoPhaseTransaction` (`sqlalchemy.engine.RootTransaction`)

  method `sqlalchemy.engine.TwoPhaseTransaction.` close() → None

    inherited from the `Transaction.close()` method of `Transaction`

    Close this `Transaction`.

    If this transaction is the base transaction in a begin/commit nesting, the transaction will rollback(). Otherwise, the method returns.

    This is used to cancel a Transaction without affecting the scope of an enclosing transaction.

  method `sqlalchemy.engine.TwoPhaseTransaction.` commit() → None

    inherited from the `Transaction.commit()` method of `Transaction`

    Commit this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a COMMIT.

    - For a `NestedTransaction`, it corresponds to a “RELEASE SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

  method `sqlalchemy.engine.TwoPhaseTransaction.` prepare() → None

    Prepare this `TwoPhaseTransaction`.

    After a PREPARE, the transaction can be committed.

  method `sqlalchemy.engine.TwoPhaseTransaction.` rollback() → None

    inherited from the `Transaction.rollback()` method of `Transaction`

    Roll back this `Transaction`.

    The implementation of this may vary based on the type of transaction in use:

    - For a simple database transaction (e.g. `RootTransaction`), it corresponds to a ROLLBACK.

    - For a `NestedTransaction`, it corresponds to a “ROLLBACK TO SAVEPOINT” operation.

    - For a `TwoPhaseTransaction`, DBAPI-specific methods for two phase transactions may be used.

## Result Set API

Object Name | Description
--- | ---
ChunkedIteratorResult | An `IteratorResult` that works from an iterator-producing callable.
CursorResult | A Result that is representing state from a DBAPI cursor.
FilterResult | A wrapper for a `Result` that returns objects other than `Row` objects, such as dictionaries or scalar objects.
FrozenResult | Represents a `Result` object in a “frozen” state suitable for caching.
IteratorResult | A `Result` that gets data from a Python iterator of `Row` objects or similar row-like data.
MappingResult | A wrapper for a `Result` that returns dictionary values rather than `Row` values.
MergedResult | A `Result` that is merged from any number of `Result` objects.
Result | Represent a set of database results.
Row | Represent a single result row.
RowMapping | A `Mapping` that maps column names and objects to `Row` values.
ScalarResult | A wrapper for a `Result` that returns scalar values rather than `Row` values.
TupleResult | A `Result` that’s typed as returning plain Python tuples instead of rows.

class sqlalchemy.engine.ChunkedIteratorResult

  An `IteratorResult` that works from an iterator-producing callable.

  The given `chunks` argument is a function that is given a number of rows to return in each chunk, or `None` for all rows. The function should then return an un-consumed iterator of lists, each list of the requested size.

  The function can be called at any time again, in which case it should continue from the same result set but adjust the chunk size as given.

  New in version 1.4.

  Members

  yield_per()

  Class signature

  class `sqlalchemy.engine.ChunkedIteratorResult` (`sqlalchemy.engine.IteratorResult`)

  method `sqlalchemy.engine.ChunkedIteratorResult.` yield_per(num: int) → Self

    Configure the row-fetching strategy to fetch `num` rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as `Result.fetchone()` that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The `Result.yield_per()` method is generally used in conjunction with the `Connection.execution_options.stream_results` execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the `Connection.execution_options.yield_per` execution option, which will simultaneously set `Connection.execution_options.stream_results` to ensure the use of server side cursors, as well as automatically invoke the `Result.yield_per()` method to establish a fixed row buffer size at once.

    The `Connection.execution_options.yield_per` execution option is available for ORM operations, with `Session`-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with `Connection` is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.CursorResult

  A Result that is representing state from a DBAPI cursor.

  Changed in version 1.4: The `CursorResult`` class replaces the previous `ResultProxy` interface. This classes are based on the `Result` calling API which provides an updated usage model and calling facade for SQLAlchemy Core and SQLAlchemy ORM.

  Returns database rows via the `Row` class, which provides additional API features and behaviors on top of the raw data returned by the DBAPI. Through the use of filters such as the `Result.scalars()` method, other kinds of objects may also be returned.

  See also

  Using SELECT Statements - introductory material for accessing `CursorResult` and `Row` objects.

  Members

  all(), close(), columns(), fetchall(), fetchmany(), fetchone(), first(), freeze(), inserted_primary_key, inserted_primary_key_rows, is_insert, keys(), last_inserted_params(), last_updated_params(), lastrow_has_defaults(), lastrowid, mappings(), merge(), one(), one_or_none(), partitions(), postfetch_cols(), prefetch_cols(), returned_defaults, returned_defaults_rows, returns_rows, rowcount, scalar(), scalar_one(), scalar_one_or_none(), scalars(), splice_horizontally(), splice_vertically(), supports_sane_multi_rowcount(), supports_sane_rowcount(), t, tuples(), unique(), yield_per()

  Class signature

  class `sqlalchemy.engine.CursorResult` (`sqlalchemy.engine.Result`)

  method `sqlalchemy.engine.CursorResult.` all() → Sequence[Row[_TP]]

    inherited from the `Result.all()` method of `Result`

    Return all rows in a sequence.

    Closes the result set after invocation. Subsequent invocations will return an empty sequence.

    New in version 1.4.

    Returns:

      a sequence of `Row` objects.

    See also

    Using Server Side Cursors (a.k.a. stream results) - How to stream a large result set without loading it completely in python.

  method `sqlalchemy.engine.CursorResult.` close() → Any

    Close this `CursorResult`.

    This closes out the underlying DBAPI cursor corresponding to the statement execution, if one is still present. Note that the DBAPI cursor is automatically released when the `CursorResult` exhausts all available rows. `CursorResult.close()` is generally an optional method except in the case when discarding a `CursorResult` that still has additional rows pending for fetch.

    After this method is called, it is no longer valid to call upon the fetch methods, which will raise a `ResourceClosedError` on subsequent use.

    See also

    Working with Engines and Connections

  method `sqlalchemy.engine.CursorResult.` columns(*col_expressions: _KeyIndexType) → Self

    inherited from the `Result.columns()` method of `Result`

    Establish the columns that should be returned in each row.

    This method may be used to limit the columns returned as well as to reorder them. The given list of expressions are normally a series of integers or string key names. They may also be appropriate `ColumnElement` objects which correspond to a given statement construct.

    Changed in version 2.0: Due to a bug in 1.4, the `Result.columns()` method had an incorrect behavior where calling upon the method with just one index would cause the `Result` object to yield scalar values rather than `Row` objects. In version 2.0, this behavior has been corrected such that calling upon `Result.columns()` with a single index will produce a `Result` object that continues to yield `Row` objects, which include only a single column.

    E.g.:

        statement = select(table.c.x, table.c.y, table.c.z)
        result = connection.execute(statement)

        for z, y in result.columns("z", "y"):
            ...

    Example of using the column objects from the statement itself:

        for z, y in result.columns(
            statement.selected_columns.c.z, statement.selected_columns.c.y
        ):
            ...

    New in version 1.4.

    Parameters:

      *col_expressions – indicates columns to be returned. Elements may be integer row indexes, string column names, or appropriate `ColumnElement` objects corresponding to a select construct.

    Returns:

      this `Result` object with the modifications given.

  method `sqlalchemy.engine.CursorResult.` fetchall() → Sequence[Row[_TP]]

    inherited from the `Result.fetchall()` method of `Result`

    A synonym for the `Result.all()` method.

  method `sqlalchemy.engine.CursorResult.` fetchmany(size: int | None = None) → Sequence[Row[_TP]]

    inherited from the `Result.fetchmany()` method of `Result`

    Fetch many rows.

    When all rows are exhausted, returns an empty sequence.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch rows in groups, use the `Result.partitions()` method.

    Returns:

      a sequence of `Row` objects.

    See also

    `Result.partitions()`

  method `sqlalchemy.engine.CursorResult.` fetchone() → Row[_TP] | None

    inherited from the `Result.fetchone()` method of `Result`

    Fetch one row.

    When all rows are exhausted, returns None.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch the first row of a result only, use the `Result.first()` method. To iterate through all rows, iterate the `Result` object directly.

    Returns:

      a `Row` object if no filters are applied, or `None` if no rows remain.

  method `sqlalchemy.engine.CursorResult.` first() → Row[_TP] | None

    inherited from the `Result.first()` method of `Result`

    Fetch the first row or `None` if no row is present.

    Closes the result set and discards remaining rows.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the `Result.scalar()` method, or combine `Result.scalars()` and `Result.first()`.

    Additionally, in contrast to the behavior of the legacy ORM `Query.first()` method, no limit is applied to the SQL query which was invoked to produce this `Result`; for a DBAPI driver that buffers results in memory before yielding rows, all rows will be sent to the Python process and all but the first row will be discarded.

    See also

    ORM Query Unified with Core Select

    Returns:

      a `Row` object, or None if no rows remain.

    See also

    `Result.scalar()`

    `Result.one()`

  method `sqlalchemy.engine.CursorResult.` freeze() → FrozenResult[_TP]

    inherited from the `Result.freeze()` method of `Result`

    Return a callable object that will produce copies of this `Result` when invoked.

    The callable object returned is an instance of `FrozenResult`.

    This is used for result set caching. The method must be called on the result when it has been unconsumed, and calling the method will consume the result fully. When the `FrozenResult` is retrieved from a cache, it can be called any number of times where it will produce a new `Result` object each time against its stored set of rows.

    See also

    Re-Executing Statements - example usage within the ORM to implement a result-set cache.

  attribute `sqlalchemy.engine.CursorResult.` inserted_primary_key

    Return the primary key for the row just inserted.

    The return value is a `Row` object representing a named tuple of primary key values in the order in which the primary key columns are configured in the source `Table`.

    Changed in version 1.4.8: - the `CursorResult.inserted_primary_key` value is now a named tuple via the `Row` class, rather than a plain tuple.

    This accessor only applies to single row `insert()` constructs which did not explicitly specify `Insert.returning()`. Support for multirow inserts, while not yet available for most backends, would be accessed using the `CursorResult.inserted_primary_key_rows` accessor.

    Note that primary key columns which specify a server_default clause, or otherwise do not qualify as “autoincrement” columns (see the notes at `Column`), and were generated using the database-side default, will appear in this list as `None` unless the backend supports “returning” and the insert statement executed with the “implicit returning” enabled.

    Raises `InvalidRequestError` if the executed statement is not a compiled expression construct or is not an insert() construct.

  attribute `sqlalchemy.engine.CursorResult.` inserted_primary_key_rows

    Return the value of `CursorResult.inserted_primary_key` as a row contained within a list; some dialects may support a multiple row form as well.

    Note

    As indicated below, in current SQLAlchemy versions this accessor is only useful beyond what’s already supplied by `CursorResult.inserted_primary_key` when using the psycopg2 dialect. Future versions hope to generalize this feature to more dialects.

    This accessor is added to support dialects that offer the feature that is currently implemented by the Psycopg2 Fast Execution Helpers feature, currently only the psycopg2 dialect, which provides for many rows to be INSERTed at once while still retaining the behavior of being able to return server-generated primary key values.

    - When using the psycopg2 dialect, or other dialects that may support “fast executemany” style inserts in upcoming releases : When invoking an INSERT statement while passing a list of rows as the second argument to `Connection.execute()`, this accessor will then provide a list of rows, where each row contains the primary key value for each row that was INSERTed.

    - When using all other dialects / backends that don’t yet support this feature: This accessor is only useful for single row INSERT statements, and returns the same information as that of the `CursorResult.inserted_primary_key` within a single-element list. When an INSERT statement is executed in conjunction with a list of rows to be INSERTed, the list will contain one row per row inserted in the statement, however it will contain `None` for any server-generated values.

    Future releases of SQLAlchemy will further generalize the “fast execution helper” feature of psycopg2 to suit other dialects, thus allowing this accessor to be of more general use.

    New in version 1.4.

    See also

    `CursorResult.inserted_primary_key`

  attribute `sqlalchemy.engine.CursorResult.` is_insert

    True if this `CursorResult` is the result of a executing an expression language compiled `insert()` construct.

    When True, this implies that the `inserted_primary_key` attribute is accessible, assuming the statement did not include a user defined “returning” construct.

  method `sqlalchemy.engine.CursorResult.` keys() → RMKeyView

    inherited from the `sqlalchemy.engine._WithKeys.keys` method of `sqlalchemy.engine._WithKeys`

    Return an iterable view which yields the string keys that would be represented by each `Row`.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    The view also can be tested for key containment using the Python `in` operator, which will test both for the string keys represented in the view, as well as for alternate keys such as column objects.

    Changed in version 1.4: a key view object is returned rather than a plain list.

  method `sqlalchemy.engine.CursorResult.` last_inserted_params()

    Return the collection of inserted parameters from this execution.

    Raises `InvalidRequestError` if the executed statement is not a compiled expression construct or is not an insert() construct.

  method `sqlalchemy.engine.CursorResult.` last_updated_params()

    Return the collection of updated parameters from this execution.

    Raises `InvalidRequestError` if the executed statement is not a compiled expression construct or is not an update() construct.

  method `sqlalchemy.engine.CursorResult.` lastrow_has_defaults()

    Return `lastrow_has_defaults()` from the underlying `ExecutionContext`.

    See `ExecutionContext` for details.

  attribute `sqlalchemy.engine.CursorResult.` lastrowid

    Return the ‘lastrowid’ accessor on the DBAPI cursor.

    This is a DBAPI specific method and is only functional for those backends which support it, for statements where it is appropriate. It’s behavior is not consistent across backends.

    Usage of this method is normally unnecessary when using insert() expression constructs; the `CursorResult.inserted_primary_key` attribute provides a tuple of primary key values for a newly inserted row, regardless of database backend.

  method `sqlalchemy.engine.CursorResult.` mappings() → MappingResult

    inherited from the `Result.mappings()` method of `Result`

    Apply a mappings filter to returned rows, returning an instance of `MappingResult`.

    When this filter is applied, fetching rows will return `RowMapping` objects instead of `Row` objects.

    New in version 1.4.

    Returns:

      a new `MappingResult` filtering object referring to this `Result` object.

  method `sqlalchemy.engine.CursorResult.` merge(*others: Result[Any]) → MergedResult[Any]

    Merge this `Result` with other compatible result objects.

    The object returned is an instance of `MergedResult`, which will be composed of iterators from the given result objects.

    The new result will use the metadata from this result object. The subsequent result objects must be against an identical set of result / cursor metadata, otherwise the behavior is undefined.

  method `sqlalchemy.engine.CursorResult.` one() → Row[_TP]

    inherited from the `Result.one()` method of `Result`

    Return exactly one row or raise an exception.

    Raises `NoResultFound` if the result returns no rows, or `MultipleResultsFound` if multiple rows would be returned.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the `Result.scalar_one()` method, or combine `Result.scalars()` and `Result.one()`.

    New in version 1.4.

    Returns:

      The first `Row`.

    Raises:

      `MultipleResultsFound`, `NoResultFound`

    See also

    `Result.first()`

    `Result.one_or_none()`

    `Result.scalar_one()`

  method `sqlalchemy.engine.CursorResult.` one_or_none() → Row[_TP] | None

    inherited from the `Result.one_or_none()` method of `Result`

    Return at most one result or raise an exception.

    Returns `None` if the result has no rows. Raises `MultipleResultsFound` if multiple rows are returned.

    New in version 1.4.

    Returns:

      The first `Row` or `None` if no row is available.

    Raises:

      `MultipleResultsFound`

    See also

    `Result.first()`

    `Result.one()`

  method `sqlalchemy.engine.CursorResult.` partitions(size: int | None = None) → Iterator[Sequence[Row[_TP]]]

    inherited from the `Result.partitions()` method of `Result`

    Iterate through sub-lists of rows of the size given.

    Each list will be of the size given, excluding the last list to be yielded, which may have a small number of rows. No empty lists will be yielded.

    The result object is automatically closed when the iterator is fully consumed.

    Note that the backend driver will usually buffer the entire result ahead of time unless the `Connection.execution_options.stream_results` execution option is used indicating that the driver should not pre-buffer results, if possible. Not all drivers support this option and the option is silently ignored for those who do not.

    When using the ORM, the `Result.partitions()` method is typically more effective from a memory perspective when it is combined with use of the yield_per execution option, which instructs both the DBAPI driver to use server side cursors, if available, as well as instructs the ORM loading internals to only build a certain amount of ORM objects from a result at a time before yielding them out.

    New in version 1.4.

    Parameters:

      size – indicate the maximum number of rows to be present in each list yielded. If None, makes use of the value set by the `Result.yield_per()`, method, if it were called, or the `Connection.execution_options.yield_per` execution option, which is equivalent in this regard. If yield_per weren’t set, it makes use of the `Result.fetchmany()` default, which may be backend specific and not well defined.

    Returns:

      iterator of lists

    See also

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

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

  method `sqlalchemy.engine.CursorResult.` postfetch_cols()

    Return `postfetch_cols()` from the underlying `ExecutionContext`.

    See `ExecutionContext` for details.

    Raises `InvalidRequestError` if the executed statement is not a compiled expression construct or is not an insert() or update() construct.

  method `sqlalchemy.engine.CursorResult.` prefetch_cols()

    Return `prefetch_cols()` from the underlying `ExecutionContext`.

    See `ExecutionContext` for details.

    Raises `InvalidRequestError` if the executed statement is not a compiled expression construct or is not an insert() or update() construct.

  attribute `sqlalchemy.engine.CursorResult.` returned_defaults

    Return the values of default columns that were fetched using the `ValuesBase.return_defaults()` feature.

    The value is an instance of `Row`, or `None` if `ValuesBase.return_defaults()` was not used or if the backend does not support RETURNING.

    See also

    `ValuesBase.return_defaults()`

  attribute `sqlalchemy.engine.CursorResult.` returned_defaults_rows

    Return a list of rows each containing the values of default columns that were fetched using the `ValuesBase.return_defaults()` feature.

    The return value is a list of `Row` objects.

    New in version 1.4.

  attribute `sqlalchemy.engine.CursorResult.` returns_rows

    True if this `CursorResult` returns zero or more rows.

    I.e. if it is legal to call the methods `CursorResult.fetchone()`, `CursorResult.fetchmany()` `CursorResult.fetchall()`.

    Overall, the value of `CursorResult.returns_rows` should always be synonymous with whether or not the DBAPI cursor had a `.description` attribute, indicating the presence of result columns, noting that a cursor that returns zero rows still has a `.description` if a row-returning statement was emitted.

    This attribute should be True for all results that are against SELECT statements, as well as for DML statements INSERT/UPDATE/DELETE that use RETURNING. For INSERT/UPDATE/DELETE statements that were not using RETURNING, the value will usually be False, however there are some dialect-specific exceptions to this, such as when using the MSSQL / pyodbc dialect a SELECT is emitted inline in order to retrieve an inserted primary key value.

  attribute `sqlalchemy.engine.CursorResult.` rowcount

    Return the ‘rowcount’ for this result.

    The primary purpose of ‘rowcount’ is to report the number of rows matched by the WHERE criterion of an UPDATE or DELETE statement executed once (i.e. for a single parameter set), which may then be compared to the number of rows expected to be updated or deleted as a means of asserting data integrity.

    This attribute is transferred from the `cursor.rowcount` attribute of the DBAPI before the cursor is closed, to support DBAPIs that don’t make this value available after cursor close. Some DBAPIs may offer meaningful values for other kinds of statements, such as INSERT and SELECT statements as well. In order to retrieve `cursor.rowcount` for these statements, set the `Connection.execution_options.preserve_rowcount` execution option to True, which will cause the `cursor.rowcount` value to be unconditionally memoized before any results are returned or the cursor is closed, regardless of statement type.

    For cases where the DBAPI does not support rowcount for a particular kind of statement and/or execution, the returned value will be `-1`, which is delivered directly from the DBAPI and is part of PEP 249. All DBAPIs should support rowcount for single-parameter-set UPDATE and DELETE statements, however.

    Note

    Notes regarding `CursorResult.rowcount`:

    - This attribute returns the number of rows matched, which is not necessarily the same as the number of rows that were actually modified. For example, an UPDATE statement may have no net change on a given row if the SET values given are the same as those present in the row already. Such a row would be matched but not modified. On backends that feature both styles, such as MySQL, rowcount is configured to return the match count in all cases.

    - `CursorResult.rowcount` in the default case is only useful in conjunction with an UPDATE or DELETE statement, and only with a single set of parameters. For other kinds of statements, SQLAlchemy will not attempt to pre-memoize the value unless the `Connection.execution_options.preserve_rowcount` execution option is used. Note that contrary to PEP 249, many DBAPIs do not support rowcount values for statements that are not UPDATE or DELETE, particularly when rows are being returned which are not fully pre-buffered. DBAPIs that dont support rowcount for a particular kind of statement should return the value `-1` for such statements.

    - `CursorResult.rowcount` may not be meaningful when executing a single statement with multiple parameter sets (i.e. an executemany). Most DBAPIs do not sum “rowcount” values across multiple parameter sets and will return `-1` when accessed.

    - SQLAlchemy’s “Insert Many Values” Behavior for INSERT statements feature does support a correct population of `CursorResult.rowcount` when the `Connection.execution_options.preserve_rowcount` execution option is set to True.

    - Statements that use RETURNING may not support rowcount, returning a `-1` value instead.

    See also

    Getting Affected Row Count from UPDATE, DELETE - in the SQLAlchemy Unified Tutorial

    `Connection.execution_options.preserve_rowcount`

  method `sqlalchemy.engine.CursorResult.` scalar() → Any

    inherited from the `Result.scalar()` method of `Result`

    Fetch the first column of the first row, and close the result set.

    Returns `None` if there are no rows to fetch.

    No validation is performed to test if additional rows remain.

    After calling this method, the object is fully closed, e.g. the `CursorResult.close()` method will have been called.

    Returns:

      a Python scalar value, or `None` if no rows remain.

  method `sqlalchemy.engine.CursorResult.` scalar_one() → Any

    inherited from the `Result.scalar_one()` method of `Result`

    Return exactly one scalar result or raise an exception.

    This is equivalent to calling `Result.scalars()` and then `ScalarResult.one()`.

    See also

    `ScalarResult.one()`

    `Result.scalars()`

  method `sqlalchemy.engine.CursorResult.` scalar_one_or_none() → Any | None

    inherited from the `Result.scalar_one_or_none()` method of `Result`

    Return exactly one scalar result or `None`.

    This is equivalent to calling `Result.scalars()` and then `ScalarResult.one_or_none()`.

    See also

    `ScalarResult.one_or_none()`

    `Result.scalars()`

  method `sqlalchemy.engine.CursorResult.` scalars(index: _KeyIndexType = 0) → ScalarResult[Any]

    inherited from the `Result.scalars()` method of `Result`

    Return a `ScalarResult` filtering object which will return single elements rather than `Row` objects.

    E.g.:

        >>> result = conn.execute(text("select int_id from table"))
        >>> result.scalars().all()
        [1, 2, 3]

    When results are fetched from the `ScalarResult` filtering object, the single column-row that would be returned by the `Result` is instead returned as the column’s value.

    New in version 1.4.

    Parameters:

      index – integer or row key indicating the column to be fetched from each row, defaults to `0` indicating the first column.

    Returns:

      a new `ScalarResult` filtering object referring to this `Result` object.

  method `sqlalchemy.engine.CursorResult.` splice_horizontally(other)

    Return a new `CursorResult` that “horizontally splices” together the rows of this `CursorResult` with that of another `CursorResult`.

    Tip

    This method is for the benefit of the SQLAlchemy ORM and is not intended for general use.

    “horizontally splices” means that for each row in the first and second result sets, a new row that concatenates the two rows together is produced, which then becomes the new row. The incoming `CursorResult` must have the identical number of rows. It is typically expected that the two result sets come from the same sort order as well, as the result rows are spliced together based on their position in the result.

    The expected use case here is so that multiple INSERT..RETURNING statements (which definitely need to be sorted) against different tables can produce a single result that looks like a JOIN of those two tables.

    E.g.:

        r1 = connection.execute(
            users.insert().returning(
                users.c.user_name, users.c.user_id, sort_by_parameter_order=True
            ),
            user_values,
        )

        r2 = connection.execute(
            addresses.insert().returning(
                addresses.c.address_id,
                addresses.c.address,
                addresses.c.user_id,
                sort_by_parameter_order=True,
            ),
            address_values,
        )

        rows = r1.splice_horizontally(r2).all()
        assert rows == [
            ("john", 1, 1, "foo@bar.com", 1),
            ("jack", 2, 2, "bar@bat.com", 2),
        ]

    New in version 2.0.

    See also

    `CursorResult.splice_vertically()`

  method `sqlalchemy.engine.CursorResult.` splice_vertically(other)

    Return a new `CursorResult` that “vertically splices”, i.e. “extends”, the rows of this `CursorResult` with that of another `CursorResult`.

    Tip

    This method is for the benefit of the SQLAlchemy ORM and is not intended for general use.

    “vertically splices” means the rows of the given result are appended to the rows of this cursor result. The incoming `CursorResult` must have rows that represent the identical list of columns in the identical order as they are in this `CursorResult`.

    New in version 2.0.

    See also

    `CursorResult.splice_horizontally()`

  method `sqlalchemy.engine.CursorResult.` supports_sane_multi_rowcount()

    Return `supports_sane_multi_rowcount` from the dialect.

    See `CursorResult.rowcount` for background.

  method `sqlalchemy.engine.CursorResult.` supports_sane_rowcount()

    Return `supports_sane_rowcount` from the dialect.

    See `CursorResult.rowcount` for background.

  attribute `sqlalchemy.engine.CursorResult.` t

    inherited from the `Result.t` attribute of `Result`

    Apply a “typed tuple” typing filter to returned rows.

    The `Result.t` attribute is a synonym for calling the `Result.tuples()` method.

    New in version 2.0.

  method `sqlalchemy.engine.CursorResult.` tuples() → TupleResult[_TP]

    inherited from the `Result.tuples()` method of `Result`

    Apply a “typed tuple” typing filter to returned rows.

    This method returns the same `Result` object at runtime, however annotates as returning a `TupleResult` object that will indicate to PEP 484 typing tools that plain typed `Tuple` instances are returned rather than rows. This allows tuple unpacking and `__getitem__` access of `Row` objects to by typed, for those cases where the statement invoked itself included typing information.

    New in version 2.0.

    Returns:

      the `TupleResult` type at typing time.

    See also

    `Result.t` - shorter synonym

    `Row._t` - `Row` version

  method `sqlalchemy.engine.CursorResult.` unique(strategy: Callable[[Any], Any] | None = None) → Self

    inherited from the `Result.unique()` method of `Result`

    Apply unique filtering to the objects returned by this `Result`.

    When this filter is applied with no arguments, the rows or objects returned will filtered such that each row is returned uniquely. The algorithm used to determine this uniqueness is by default the Python hashing identity of the whole tuple. In some cases a specialized per-entity hashing scheme may be used, such as when using the ORM, a scheme is applied which works against the primary key identity of returned objects.

    The unique filter is applied after all other filters, which means if the columns returned have been refined using a method such as the `Result.columns()` or `Result.scalars()` method, the uniquing is applied to only the column or columns returned. This occurs regardless of the order in which these methods have been called upon the `Result` object.

    The unique filter also changes the calculus used for methods like `Result.fetchmany()` and `Result.partitions()`. When using `Result.unique()`, these methods will continue to yield the number of rows or objects requested, after uniquing has been applied. However, this necessarily impacts the buffering behavior of the underlying cursor or datasource, such that multiple underlying calls to `cursor.fetchmany()` may be necessary in order to accumulate enough objects in order to provide a unique collection of the requested size.

    Parameters:

      strategy – a callable that will be applied to rows or objects being iterated, which should return an object that represents the unique value of the row. A Python `set()` is used to store these identities. If not passed, a default uniqueness strategy is used which may have been assembled by the source of this `Result` object.

  method `sqlalchemy.engine.CursorResult.` yield_per(num: int) → Self

    Configure the row-fetching strategy to fetch `num` rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as `Result.fetchone()` that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The `Result.yield_per()` method is generally used in conjunction with the `Connection.execution_options.stream_results` execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the `Connection.execution_options.yield_per` execution option, which will simultaneously set `Connection.execution_options.stream_results` to ensure the use of server side cursors, as well as automatically invoke the `Result.yield_per()` method to establish a fixed row buffer size at once.

    The `Connection.execution_options.yield_per` execution option is available for ORM operations, with `Session`-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with `Connection` is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.FilterResult

  A wrapper for a `Result` that returns objects other than `Row` objects, such as dictionaries or scalar objects.

  `FilterResult` is the common base for additional result APIs including `MappingResult`, `ScalarResult` and `AsyncResult`.

  Members

  close(), closed, yield_per()

  Class signature

  class `sqlalchemy.engine.FilterResult` (`sqlalchemy.engine.ResultInternal`)

  method `sqlalchemy.engine.FilterResult.` close() → None

    Close this `FilterResult`.

    New in version 1.4.43.

  attribute `sqlalchemy.engine.FilterResult.` closed

    Return `True` if the underlying `Result` reports closed

    New in version 1.4.43.

  method `sqlalchemy.engine.FilterResult.` yield_per(num: int) → Self

    Configure the row-fetching strategy to fetch `num` rows at a time.

    The `FilterResult.yield_per()` method is a pass through to the `Result.yield_per()` method. See that method’s documentation for usage notes.

    New in version 1.4.40: - added `FilterResult.yield_per()` so that the method is available on all result set implementations

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.FrozenResult

  Represents a `Result` object in a “frozen” state suitable for caching.

  The `FrozenResult` object is returned from the `Result.freeze()` method of any `Result` object.

  A new iterable `Result` object is generated from a fixed set of data each time the `FrozenResult` is invoked as a callable:

      result = connection.execute(query)

      frozen = result.freeze()

      unfrozen_result_one = frozen()

      for row in unfrozen_result_one:
          print(row)

      unfrozen_result_two = frozen()
      rows = unfrozen_result_two.all()

      # ... etc

  New in version 1.4.

  See also

  Re-Executing Statements - example usage within the ORM to implement a result-set cache.

  `merge_frozen_result()` - ORM function to merge a frozen result back into a `Session`.

  Class signature

  class `sqlalchemy.engine.FrozenResult` (`typing.Generic`)

class sqlalchemy.engine.IteratorResult

  A `Result` that gets data from a Python iterator of `Row` objects or similar row-like data.

  New in version 1.4.

  Members

  closed

  Class signature

  class `sqlalchemy.engine.IteratorResult` (`sqlalchemy.engine.Result`)

  attribute `sqlalchemy.engine.IteratorResult.` closed

    Return `True` if this `IteratorResult` has been closed

    New in version 1.4.43.

class sqlalchemy.engine.MergedResult

  A `Result` that is merged from any number of `Result` objects.

  Returned by the `Result.merge()` method.

  New in version 1.4.

  Class signature

  class `sqlalchemy.engine.MergedResult` (`sqlalchemy.engine.IteratorResult`)

class sqlalchemy.engine.Result

  Represent a set of database results.

  New in version 1.4: The `Result` object provides a completely updated usage model and calling facade for SQLAlchemy Core and SQLAlchemy ORM. In Core, it forms the basis of the `CursorResult` object which replaces the previous `ResultProxy` interface. When using the ORM, a higher level object called `ChunkedIteratorResult` is normally used.

  Note

  In SQLAlchemy 1.4 and above, this object is used for ORM results returned by `Session.execute()`, which can yield instances of ORM mapped objects either individually or within tuple-like rows. Note that the `Result` object does not deduplicate instances or rows automatically as is the case with the legacy `Query` object. For in-Python de-duplication of instances or rows, use the `Result.unique()` modifier method.

  See also

  Fetching Rows - in the SQLAlchemy Unified Tutorial

  Members

  all(), close(), closed, columns(), fetchall(), fetchmany(), fetchone(), first(), freeze(), keys(), mappings(), merge(), one(), one_or_none(), partitions(), scalar(), scalar_one(), scalar_one_or_none(), scalars(), t, tuples(), unique(), yield_per()

  Class signature

  class `sqlalchemy.engine.Result` (`sqlalchemy.engine._WithKeys`, `sqlalchemy.engine.ResultInternal`)

  method `sqlalchemy.engine.Result.` all() → Sequence[Row[_TP]]

    Return all rows in a sequence.

    Closes the result set after invocation. Subsequent invocations will return an empty sequence.

    New in version 1.4.

    Returns:

      a sequence of `Row` objects.

    See also

    Using Server Side Cursors (a.k.a. stream results) - How to stream a large result set without loading it completely in python.

  method `sqlalchemy.engine.Result.` close() → None

    close this `Result`.

    The behavior of this method is implementation specific, and is not implemented by default. The method should generally end the resources in use by the result object and also cause any subsequent iteration or row fetching to raise `ResourceClosedError`.

    New in version 1.4.27: - `.close()` was previously not generally available for all `Result` classes, instead only being available on the `CursorResult` returned for Core statement executions. As most other result objects, namely the ones used by the ORM, are proxying a `CursorResult` in any case, this allows the underlying cursor result to be closed from the outside facade for the case when the ORM query is using the `yield_per` execution option where it does not immediately exhaust and autoclose the database cursor.

  attribute `sqlalchemy.engine.Result.` closed

    return `True` if this `Result` reports .closed

    New in version 1.4.43.

  method `sqlalchemy.engine.Result.` columns(*col_expressions: _KeyIndexType) → Self

    Establish the columns that should be returned in each row.

    This method may be used to limit the columns returned as well as to reorder them. The given list of expressions are normally a series of integers or string key names. They may also be appropriate `ColumnElement` objects which correspond to a given statement construct.

    Changed in version 2.0: Due to a bug in 1.4, the `Result.columns()` method had an incorrect behavior where calling upon the method with just one index would cause the `Result` object to yield scalar values rather than `Row` objects. In version 2.0, this behavior has been corrected such that calling upon `Result.columns()` with a single index will produce a `Result` object that continues to yield `Row` objects, which include only a single column.

    E.g.:

        statement = select(table.c.x, table.c.y, table.c.z)
        result = connection.execute(statement)

        for z, y in result.columns("z", "y"):
            ...

    Example of using the column objects from the statement itself:

        for z, y in result.columns(
            statement.selected_columns.c.z, statement.selected_columns.c.y
        ):
            ...

    New in version 1.4.

    Parameters:

      *col_expressions – indicates columns to be returned. Elements may be integer row indexes, string column names, or appropriate `ColumnElement` objects corresponding to a select construct.

    Returns:

      this `Result` object with the modifications given.

  method `sqlalchemy.engine.Result.` fetchall() → Sequence[Row[_TP]]

    A synonym for the `Result.all()` method.

  method `sqlalchemy.engine.Result.` fetchmany(size: int | None = None) → Sequence[Row[_TP]]

    Fetch many rows.

    When all rows are exhausted, returns an empty sequence.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch rows in groups, use the `Result.partitions()` method.

    Returns:

      a sequence of `Row` objects.

    See also

    `Result.partitions()`

  method `sqlalchemy.engine.Result.` fetchone() → Row[_TP] | None

    Fetch one row.

    When all rows are exhausted, returns None.

    This method is provided for backwards compatibility with SQLAlchemy 1.x.x.

    To fetch the first row of a result only, use the `Result.first()` method. To iterate through all rows, iterate the `Result` object directly.

    Returns:

      a `Row` object if no filters are applied, or `None` if no rows remain.

  method `sqlalchemy.engine.Result.` first() → Row[_TP] | None

    Fetch the first row or `None` if no row is present.

    Closes the result set and discards remaining rows.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the `Result.scalar()` method, or combine `Result.scalars()` and `Result.first()`.

    Additionally, in contrast to the behavior of the legacy ORM `Query.first()` method, no limit is applied to the SQL query which was invoked to produce this `Result`; for a DBAPI driver that buffers results in memory before yielding rows, all rows will be sent to the Python process and all but the first row will be discarded.

    See also

    ORM Query Unified with Core Select

    Returns:

      a `Row` object, or None if no rows remain.

    See also

    `Result.scalar()`

    `Result.one()`

  method `sqlalchemy.engine.Result.` freeze() → FrozenResult[_TP]

    Return a callable object that will produce copies of this `Result` when invoked.

    The callable object returned is an instance of `FrozenResult`.

    This is used for result set caching. The method must be called on the result when it has been unconsumed, and calling the method will consume the result fully. When the `FrozenResult` is retrieved from a cache, it can be called any number of times where it will produce a new `Result` object each time against its stored set of rows.

    See also

    Re-Executing Statements - example usage within the ORM to implement a result-set cache.

  method `sqlalchemy.engine.Result.` keys() → RMKeyView

    inherited from the `sqlalchemy.engine._WithKeys.keys` method of `sqlalchemy.engine._WithKeys`

    Return an iterable view which yields the string keys that would be represented by each `Row`.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    The view also can be tested for key containment using the Python `in` operator, which will test both for the string keys represented in the view, as well as for alternate keys such as column objects.

    Changed in version 1.4: a key view object is returned rather than a plain list.

  method `sqlalchemy.engine.Result.` mappings() → MappingResult

    Apply a mappings filter to returned rows, returning an instance of `MappingResult`.

    When this filter is applied, fetching rows will return `RowMapping` objects instead of `Row` objects.

    New in version 1.4.

    Returns:

      a new `MappingResult` filtering object referring to this `Result` object.

  method `sqlalchemy.engine.Result.` merge(*others: Result[Any]) → MergedResult[_TP]

    Merge this `Result` with other compatible result objects.

    The object returned is an instance of `MergedResult`, which will be composed of iterators from the given result objects.

    The new result will use the metadata from this result object. The subsequent result objects must be against an identical set of result / cursor metadata, otherwise the behavior is undefined.

  method `sqlalchemy.engine.Result.` one() → Row[_TP]

    Return exactly one row or raise an exception.

    Raises `NoResultFound` if the result returns no rows, or `MultipleResultsFound` if multiple rows would be returned.

    Note

    This method returns one row, e.g. tuple, by default. To return exactly one single scalar value, that is, the first column of the first row, use the `Result.scalar_one()` method, or combine `Result.scalars()` and `Result.one()`.

    New in version 1.4.

    Returns:

      The first `Row`.

    Raises:

      `MultipleResultsFound`, `NoResultFound`

    See also

    `Result.first()`

    `Result.one_or_none()`

    `Result.scalar_one()`

  method `sqlalchemy.engine.Result.` one_or_none() → Row[_TP] | None

    Return at most one result or raise an exception.

    Returns `None` if the result has no rows. Raises `MultipleResultsFound` if multiple rows are returned.

    New in version 1.4.

    Returns:

      The first `Row` or `None` if no row is available.

    Raises:

      `MultipleResultsFound`

    See also

    `Result.first()`

    `Result.one()`

  method `sqlalchemy.engine.Result.` partitions(size: int | None = None) → Iterator[Sequence[Row[_TP]]]

    Iterate through sub-lists of rows of the size given.

    Each list will be of the size given, excluding the last list to be yielded, which may have a small number of rows. No empty lists will be yielded.

    The result object is automatically closed when the iterator is fully consumed.

    Note that the backend driver will usually buffer the entire result ahead of time unless the `Connection.execution_options.stream_results` execution option is used indicating that the driver should not pre-buffer results, if possible. Not all drivers support this option and the option is silently ignored for those who do not.

    When using the ORM, the `Result.partitions()` method is typically more effective from a memory perspective when it is combined with use of the yield_per execution option, which instructs both the DBAPI driver to use server side cursors, if available, as well as instructs the ORM loading internals to only build a certain amount of ORM objects from a result at a time before yielding them out.

    New in version 1.4.

    Parameters:

      size – indicate the maximum number of rows to be present in each list yielded. If None, makes use of the value set by the `Result.yield_per()`, method, if it were called, or the `Connection.execution_options.yield_per` execution option, which is equivalent in this regard. If yield_per weren’t set, it makes use of the `Result.fetchmany()` default, which may be backend specific and not well defined.

    Returns:

      iterator of lists

    See also

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

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

  method `sqlalchemy.engine.Result.` scalar() → Any

    Fetch the first column of the first row, and close the result set.

    Returns `None` if there are no rows to fetch.

    No validation is performed to test if additional rows remain.

    After calling this method, the object is fully closed, e.g. the `CursorResult.close()` method will have been called.

    Returns:

      a Python scalar value, or `None` if no rows remain.

  method `sqlalchemy.engine.Result.` scalar_one() → Any

    Return exactly one scalar result or raise an exception.

    This is equivalent to calling `Result.scalars()` and then `ScalarResult.one()`.

    See also

    `ScalarResult.one()`

    `Result.scalars()`

  method `sqlalchemy.engine.Result.` scalar_one_or_none() → Any | None

    Return exactly one scalar result or `None`.

    This is equivalent to calling `Result.scalars()` and then `ScalarResult.one_or_none()`.

    See also

    `ScalarResult.one_or_none()`

    `Result.scalars()`

  method `sqlalchemy.engine.Result.` scalars(index: _KeyIndexType = 0) → ScalarResult[Any]

    Return a `ScalarResult` filtering object which will return single elements rather than `Row` objects.

    E.g.:

        >>> result = conn.execute(text("select int_id from table"))
        >>> result.scalars().all()
        [1, 2, 3]

    When results are fetched from the `ScalarResult` filtering object, the single column-row that would be returned by the `Result` is instead returned as the column’s value.

    New in version 1.4.

    Parameters:

      index – integer or row key indicating the column to be fetched from each row, defaults to `0` indicating the first column.

    Returns:

      a new `ScalarResult` filtering object referring to this `Result` object.

  attribute `sqlalchemy.engine.Result.` t

    Apply a “typed tuple” typing filter to returned rows.

    The `Result.t` attribute is a synonym for calling the `Result.tuples()` method.

    New in version 2.0.

  method `sqlalchemy.engine.Result.` tuples() → TupleResult[_TP]

    Apply a “typed tuple” typing filter to returned rows.

    This method returns the same `Result` object at runtime, however annotates as returning a `TupleResult` object that will indicate to PEP 484 typing tools that plain typed `Tuple` instances are returned rather than rows. This allows tuple unpacking and `__getitem__` access of `Row` objects to by typed, for those cases where the statement invoked itself included typing information.

    New in version 2.0.

    Returns:

      the `TupleResult` type at typing time.

    See also

    `Result.t` - shorter synonym

    `Row._t` - `Row` version

  method `sqlalchemy.engine.Result.` unique(strategy: Callable[[Any], Any] | None = None) → Self

    Apply unique filtering to the objects returned by this `Result`.

    When this filter is applied with no arguments, the rows or objects returned will filtered such that each row is returned uniquely. The algorithm used to determine this uniqueness is by default the Python hashing identity of the whole tuple. In some cases a specialized per-entity hashing scheme may be used, such as when using the ORM, a scheme is applied which works against the primary key identity of returned objects.

    The unique filter is applied after all other filters, which means if the columns returned have been refined using a method such as the `Result.columns()` or `Result.scalars()` method, the uniquing is applied to only the column or columns returned. This occurs regardless of the order in which these methods have been called upon the `Result` object.

    The unique filter also changes the calculus used for methods like `Result.fetchmany()` and `Result.partitions()`. When using `Result.unique()`, these methods will continue to yield the number of rows or objects requested, after uniquing has been applied. However, this necessarily impacts the buffering behavior of the underlying cursor or datasource, such that multiple underlying calls to `cursor.fetchmany()` may be necessary in order to accumulate enough objects in order to provide a unique collection of the requested size.

    Parameters:

      strategy – a callable that will be applied to rows or objects being iterated, which should return an object that represents the unique value of the row. A Python `set()` is used to store these identities. If not passed, a default uniqueness strategy is used which may have been assembled by the source of this `Result` object.

  method `sqlalchemy.engine.Result.` yield_per(num: int) → Self

    Configure the row-fetching strategy to fetch `num` rows at a time.

    This impacts the underlying behavior of the result when iterating over the result object, or otherwise making use of methods such as `Result.fetchone()` that return one row at a time. Data from the underlying cursor or other data source will be buffered up to this many rows in memory, and the buffered collection will then be yielded out one row at a time or as many rows are requested. Each time the buffer clears, it will be refreshed to this many rows or as many rows remain if fewer remain.

    The `Result.yield_per()` method is generally used in conjunction with the `Connection.execution_options.stream_results` execution option, which will allow the database dialect in use to make use of a server side cursor, if the DBAPI supports a specific “server side cursor” mode separate from its default mode of operation.

    Tip

    Consider using the `Connection.execution_options.yield_per` execution option, which will simultaneously set `Connection.execution_options.stream_results` to ensure the use of server side cursors, as well as automatically invoke the `Result.yield_per()` method to establish a fixed row buffer size at once.

    The `Connection.execution_options.yield_per` execution option is available for ORM operations, with `Session`-oriented use described at Fetching Large Result Sets with Yield Per. The Core-only version which works with `Connection` is new as of SQLAlchemy 1.4.40.

    New in version 1.4.

    Parameters:

      num – number of rows to fetch each time the buffer is refilled. If set to a value below 1, fetches all rows for the next buffer.

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.ScalarResult

  A wrapper for a `Result` that returns scalar values rather than `Row` values.

  The `ScalarResult` object is acquired by calling the `Result.scalars()` method.

  A special limitation of `ScalarResult` is that it has no `fetchone()` method; since the semantics of `fetchone()` are that the `None` value indicates no more results, this is not compatible with `ScalarResult` since there is no way to distinguish between `None` as a row value versus `None` as an indicator. Use `next(result)` to receive values individually.

  Members

  all(), close(), closed, fetchall(), fetchmany(), first(), one(), one_or_none(), partitions(), unique(), yield_per()

  Class signature

  class `sqlalchemy.engine.ScalarResult` (`sqlalchemy.engine.FilterResult`)

  method `sqlalchemy.engine.ScalarResult.` all() → Sequence[_R]

    Return all scalar values in a sequence.

    Equivalent to `Result.all()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` close() → None

    inherited from the `FilterResult.close()` method of `FilterResult`

    Close this `FilterResult`.

    New in version 1.4.43.

  attribute `sqlalchemy.engine.ScalarResult.` closed

    inherited from the `FilterResult.closed` attribute of `FilterResult`

    Return `True` if the underlying `Result` reports closed

    New in version 1.4.43.

  method `sqlalchemy.engine.ScalarResult.` fetchall() → Sequence[_R]

    A synonym for the `ScalarResult.all()` method.

  method `sqlalchemy.engine.ScalarResult.` fetchmany(size: int | None = None) → Sequence[_R]

    Fetch many objects.

    Equivalent to `Result.fetchmany()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` first() → _R | None

    Fetch the first object or `None` if no object is present.

    Equivalent to `Result.first()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` one() → _R

    Return exactly one object or raise an exception.

    Equivalent to `Result.one()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` one_or_none() → _R | None

    Return at most one object or raise an exception.

    Equivalent to `Result.one_or_none()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` partitions(size: int | None = None) → Iterator[Sequence[_R]]

    Iterate through sub-lists of elements of the size given.

    Equivalent to `Result.partitions()` except that scalar values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.ScalarResult.` unique(strategy: Callable[[Any], Any] | None = None) → Self

    Apply unique filtering to the objects returned by this `ScalarResult`.

    See `Result.unique()` for usage details.

  method `sqlalchemy.engine.ScalarResult.` yield_per(num: int) → Self

    inherited from the `FilterResult.yield_per()` method of `FilterResult`

    Configure the row-fetching strategy to fetch `num` rows at a time.

    The `FilterResult.yield_per()` method is a pass through to the `Result.yield_per()` method. See that method’s documentation for usage notes.

    New in version 1.4.40: - added `FilterResult.yield_per()` so that the method is available on all result set implementations

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.MappingResult

  A wrapper for a `Result` that returns dictionary values rather than `Row` values.

  The `MappingResult` object is acquired by calling the `Result.mappings()` method.

  Members

  all(), close(), closed, columns(), fetchall(), fetchmany(), fetchone(), first(), keys(), one(), one_or_none(), partitions(), unique(), yield_per()

  Class signature

  class `sqlalchemy.engine.MappingResult` (`sqlalchemy.engine._WithKeys`, `sqlalchemy.engine.FilterResult`)

  method `sqlalchemy.engine.MappingResult.` all() → Sequence[RowMapping]

    Return all scalar values in a sequence.

    Equivalent to `Result.all()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` close() → None

    inherited from the `FilterResult.close()` method of `FilterResult`

    Close this `FilterResult`.

    New in version 1.4.43.

  attribute `sqlalchemy.engine.MappingResult.` closed

    inherited from the `FilterResult.closed` attribute of `FilterResult`

    Return `True` if the underlying `Result` reports closed

    New in version 1.4.43.

  method `sqlalchemy.engine.MappingResult.` columns(*col_expressions: _KeyIndexType) → Self

    Establish the columns that should be returned in each row.

  method `sqlalchemy.engine.MappingResult.` fetchall() → Sequence[RowMapping]

    A synonym for the `MappingResult.all()` method.

  method `sqlalchemy.engine.MappingResult.` fetchmany(size: int | None = None) → Sequence[RowMapping]

    Fetch many objects.

    Equivalent to `Result.fetchmany()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` fetchone() → RowMapping | None

    Fetch one object.

    Equivalent to `Result.fetchone()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` first() → RowMapping | None

    Fetch the first object or `None` if no object is present.

    Equivalent to `Result.first()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` keys() → RMKeyView

    inherited from the `sqlalchemy.engine._WithKeys.keys` method of `sqlalchemy.engine._WithKeys`

    Return an iterable view which yields the string keys that would be represented by each `Row`.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    The view also can be tested for key containment using the Python `in` operator, which will test both for the string keys represented in the view, as well as for alternate keys such as column objects.

    Changed in version 1.4: a key view object is returned rather than a plain list.

  method `sqlalchemy.engine.MappingResult.` one() → RowMapping

    Return exactly one object or raise an exception.

    Equivalent to `Result.one()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` one_or_none() → RowMapping | None

    Return at most one object or raise an exception.

    Equivalent to `Result.one_or_none()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` partitions(size: int | None = None) → Iterator[Sequence[RowMapping]]

    Iterate through sub-lists of elements of the size given.

    Equivalent to `Result.partitions()` except that `RowMapping` values, rather than `Row` objects, are returned.

  method `sqlalchemy.engine.MappingResult.` unique(strategy: Callable[[Any], Any] | None = None) → Self

    Apply unique filtering to the objects returned by this `MappingResult`.

    See `Result.unique()` for usage details.

  method `sqlalchemy.engine.MappingResult.` yield_per(num: int) → Self

    inherited from the `FilterResult.yield_per()` method of `FilterResult`

    Configure the row-fetching strategy to fetch `num` rows at a time.

    The `FilterResult.yield_per()` method is a pass through to the `Result.yield_per()` method. See that method’s documentation for usage notes.

    New in version 1.4.40: - added `FilterResult.yield_per()` so that the method is available on all result set implementations

    See also

    Using Server Side Cursors (a.k.a. stream results) - describes Core behavior for `Result.yield_per()`

    Fetching Large Result Sets with Yield Per - in the ORM Querying Guide

class sqlalchemy.engine.Row

  Represent a single result row.

  The `Row` object represents a row of a database result. It is typically associated in the 1.x series of SQLAlchemy with the `CursorResult` object, however is also used by the ORM for tuple-like results as of SQLAlchemy 1.4.

  The `Row` object seeks to act as much like a Python named tuple as possible. For mapping (i.e. dictionary) behavior on a row, such as testing for containment of keys, refer to the `Row._mapping` attribute.

  See also

  Using SELECT Statements - includes examples of selecting rows from SELECT statements.

  Changed in version 1.4: Renamed `RowProxy` to `Row`. `Row` is no longer a “proxy” object in that it contains the final form of data within it, and now acts mostly like a named tuple. Mapping-like functionality is moved to the `Row._mapping` attribute. See RowProxy is no longer a “proxy”; is now called Row and behaves like an enhanced named tuple for background on this change.

  Members

  _asdict(), _fields, _mapping, _t, _tuple(), count, index, t, tuple()

  Class signature

  class `sqlalchemy.engine.Row` (`sqlalchemy.engine._py_row.BaseRow`, `collections.abc.Sequence`, `typing.Generic`)

  method `sqlalchemy.engine.Row.` _asdict() → Dict[str, Any]

    Return a new dict which maps field names to their corresponding values.

    This method is analogous to the Python named tuple `._asdict()` method, and works by applying the `dict()` constructor to the `Row._mapping` attribute.

    New in version 1.4.

    See also

    `Row._mapping`

  attribute `sqlalchemy.engine.Row.` _fields

    Return a tuple of string keys as represented by this `Row`.

    The keys can represent the labels of the columns returned by a core statement or the names of the orm classes returned by an orm execution.

    This attribute is analogous to the Python named tuple `._fields` attribute.

    New in version 1.4.

    See also

    `Row._mapping`

  attribute `sqlalchemy.engine.Row.` _mapping

    Return a `RowMapping` for this `Row`.

    This object provides a consistent Python mapping (i.e. dictionary) interface for the data contained within the row. The `Row` by itself behaves like a named tuple.

    See also

    `Row._fields`

    New in version 1.4.

  attribute `sqlalchemy.engine.Row.` _t

    A synonym for `Row._tuple()`.

    New in version 2.0.19: - The `Row._t` attribute supersedes the previous `Row.t` attribute, which is now underscored to avoid name conflicts with column names in the same way as other named-tuple methods on `Row`.

    See also

    `Result.t`

  method `sqlalchemy.engine.Row.` _tuple() → _TP

    Return a ‘tuple’ form of this `Row`.

    At runtime, this method returns “self”; the `Row` object is already a named tuple. However, at the typing level, if this `Row` is typed, the “tuple” return type will be a PEP 484 `Tuple` datatype that contains typing information about individual elements, supporting typed unpacking and attribute access.

    New in version 2.0.19: - The `Row._tuple()` method supersedes the previous `Row.tuple()` method, which is now underscored to avoid name conflicts with column names in the same way as other named-tuple methods on `Row`.

    See also

    `Row._t` - shorthand attribute notation

    `Result.tuples()`

  attribute `sqlalchemy.engine.Row.` count
  attribute `sqlalchemy.engine.Row.` index
  attribute `sqlalchemy.engine.Row.` t

    A synonym for `Row._tuple()`.

    Deprecated since version 2.0.19: The `Row.t` attribute is deprecated in favor of `Row._t`; all `Row` methods and library-level attributes are intended to be underscored to avoid name conflicts. Please use `Row._t`.

    New in version 2.0.

  method `sqlalchemy.engine.Row.` tuple() → _TP

    Return a ‘tuple’ form of this `Row`.

    Deprecated since version 2.0.19: The `Row.tuple()` method is deprecated in favor of `Row._tuple()`; all `Row` methods and library-level attributes are intended to be underscored to avoid name conflicts. Please use `Row._tuple()`.

    New in version 2.0.

class sqlalchemy.engine.RowMapping

  A `Mapping` that maps column names and objects to `Row` values.

  The `RowMapping` is available from a `Row` via the `Row._mapping` attribute, as well as from the iterable interface provided by the `MappingResult` object returned by the `Result.mappings()` method.

  `RowMapping` supplies Python mapping (i.e. dictionary) access to the contents of the row. This includes support for testing of containment of specific keys (string column names or objects), as well as iteration of keys, values, and items:

      for row in result:
          if "a" in row._mapping:
              print("Column 'a': %s" % row._mapping["a"])

          print("Column b: %s" % row._mapping[table.c.b])

  New in version 1.4: The `RowMapping` object replaces the mapping-like access previously provided by a database result row, which now seeks to behave mostly like a named tuple.

  Members

  items(), keys(), values()

  Class signature

  class `sqlalchemy.engine.RowMapping` (`sqlalchemy.engine._py_row.BaseRow`, `collections.abc.Mapping`, `typing.Generic`)

  method `sqlalchemy.engine.RowMapping.` items() → ROMappingItemsView

    Return a view of key/value tuples for the elements in the underlying `Row`.

  method `sqlalchemy.engine.RowMapping.` keys() → RMKeyView

    Return a view of ‘keys’ for string column names represented by the underlying `Row`.

  method `sqlalchemy.engine.RowMapping.` values() → ROMappingKeysValuesView

    Return a view of values for the values represented in the underlying `Row`.

class sqlalchemy.engine.TupleResult

  A `Result` that’s typed as returning plain Python tuples instead of rows.

  Since `Row` acts like a tuple in every way already, this class is a typing only class, regular `Result` is still used at runtime.

  Class signature

  class `sqlalchemy.engine.TupleResult` (`sqlalchemy.engine.FilterResult`, `sqlalchemy.util.langhelpers.TypingOnly`)