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://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.

tip

When using an Engine with multiple Python processes, such as when using os.fork or Python multiprocessing, it’s important that the engine is initialized per process. See Using Connection Pools with Multiprocessing or os.fork() for details.

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, and the connection is ready for its next use.

Deprecated since version 2.0: The CursorResult object is replaced in SQLAlchemy 2.0 with a newly refined object known as Result.

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, including the variety of SQL expression constructs described in SQL Expression Language Tutorial (1.x API).

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.

The Connection object provides a Connection.begin() method which returns a Transaction object. Like the Connection itself, this object is usually used within a Python with: block so that its scope is managed:

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

The above block can be stated more simply by using the Engine.begin() method of Engine:

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

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

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

Nesting of Transaction Blocks

Deprecated since version 1.4: The “transaction nesting” feature of SQLAlchemy is a legacy feature that is deprecated in the 1.4 release and will be removed in SQLAlchemy 2.0. The pattern has proven to be a little too awkward and complicated, unless an application makes more of a first-class framework around the behavior. See the following subsection Arbitrary Transaction Nesting as an Antipattern.

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

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

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

Arbitrary Transaction Nesting as an Antipattern

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

The most ideal way to organize an application would have a single, or at least very few, points at which the “beginning” and “commit” of all database transactions is demarcated. This is also the general idea discussed in terms of the ORM at When do I construct a Session, when do I commit it, and when do I close it?. To adapt the example from the previous section to this practice looks like:

# method_a calls method_b
def method_a(connection):
    method_b(connection)
# method_b uses the connection and assumes the transaction
# is external
def method_b(connection):
    connection.execute(text("insert into mytable values ('bat', 'lala')"))
    connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})
# open a Connection inside of a transaction and call method_a
with engine.begin() as conn:
    method_a(conn)

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

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

Migrating from the “nesting” pattern

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

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

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

The above contextmanager would be used as:

# method_a starts a transaction and calls method_b
def method_a(connection):
    with transaction(connection):  # open a transaction
        method_b(connection)
# method_b either starts a transaction, or uses the one already
# present
def method_b(connection):
    with transaction(connection):  # open a transaction
        connection.execute(text("insert into mytable values ('bat', 'lala')"))
        connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})
# open a Connection and call method_a
with engine.connect() as conn:
    method_a(conn)

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

import contextlib
def connectivity(engine):
    connection = None
    @contextlib.contextmanager
    def connect():
        nonlocal connection
        if connection is None:
            connection = engine.connect()
            with connection:
                with connection.begin():
                    yield connection
        else:
            yield connection
    return connect

Using the above would look like:

# method_a passes along connectivity context, at the same time
# it chooses to establish a connection by calling "with"
def method_a(connectivity):
    with connectivity():
        method_b(connectivity)
# method_b also wants to use a connection from the context, so it
# also calls "with:", but also it actually uses the connection.
def method_b(connectivity):
    with connectivity() as connection:
        connection.execute(text("insert into mytable values ('bat', 'lala')"))
        connection.execute(mytable.insert(), {"col1": "bat", "col2": "lala"})
# create a new connection/transaction context object and call
# method_a
method_a(connectivity(engine))

The above context manager acts not only as a “transaction” context but also as a context that manages having an open connection against a particular Engine. When using the ORM Session, this connectivty management is provided by the Session itself. An overview of ORM connectivity patterns is at Managing Transactions.

See also

Migrating from the “subtransaction” pattern - ORM version

Library Level (e.g. emulated) Autocommit

Deprecated since version 1.4: The “autocommit” feature of SQLAlchemy Core is deprecated and will not be present in version 2.0 of SQLAlchemy. DBAPI-level AUTOCOMMIT is now widely available which offers superior performance and occurs transparently. See Library-level (but not driver level) “Autocommit” removed from both Core and ORM for background.

Note

This section discusses the feature within SQLAlchemy that automatically invokes the .commit() method on a DBAPI connection, however this is against a DBAPI connection that is itself transactional. For true AUTOCOMMIT, see the next section Setting Transaction Isolation Levels including DBAPI Autocommit.

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

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

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

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

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

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

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. When using this mode, the DBAPI does not use a transaction under any circumstances. SQLAlchemy methods like .begin(), .commit() and .rollback() pass silently and have no effect.

Instead, each statement invoked upon the connection will commit any changes automatically; it sometimes also means that the connection itself will use fewer server-side database resources. For this reason and others, “autocommit” mode is often desirable for non-transactional applications that need to read individual tables or rows outside the scope of a true ACID transaction.

SQLAlchemy dialects should support these isolation levels as well as autocommit to as great a degree as possible. The levels are set via family of “execution_options” parameters and methods that are throughout the Core, such as the 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(<statement>)

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

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

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

An application that frequently chooses to run operations within different isolation levels may wish to create multiple “sub-engines” of a lead 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://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.

Note

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

See also

SQLite Transaction Isolation

PostgreSQL Transaction Isolation

MySQL Transaction Isolation

SQL Server 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

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

A limited number of dialects have explicit support for the concept of “server side cursors” vs. “buffered cursors”. While a server side cursor implies a variety of different capabilities, within SQLAlchemy’s engine and dialect implementation, it refers only to whether or not a particular set of results is fully buffered in memory before they are fetched from the cursor, using a method such as cursor.fetchall(). SQLAlchemy has no direct support for cursor behaviors such as scrolling; to make use of these features for a particular DBAPI, use the cursor directly as documented at Working with Driver SQL and Raw DBAPI Connections.

Some DBAPIs, such as the cx_Oracle DBAPI, exclusively use server side cursors internally. All result sets are essentially unbuffered across the total span of a result set, utilizing only a smaller buffer that is of a fixed size such as 100 rows at a time.

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.

To make use of a server side cursor for a particular execution, the Connection.execution_options.stream_results option is used, which may be called on the Connection object, on the statement object, or in the ORM-level contexts mentioned below.

When using this option for a statement, it’s usually appropriate to use a method like Result.partitions() to work on small sections of the result set at a time, while also fetching enough rows for each pull so that the operation is efficient:

with engine.connect() as conn:
    result = conn.execution_options(stream_results=True).execute(text("select * from table"))
    for partition in result.partitions(100):
        _process_rows(partition)

If the Result 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. This can be affected using the max_row_buffer execution option:

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

The size of the buffer may also be set to a fixed size using the Result.yield_per() method. Calling this method with a number of rows will cause all result-fetching methods to work from buffers of the given size, only fetching new rows when the buffer is empty:

with engine.connect() as conn:
    result = conn.execution_options(stream_results=True).execute(text("select * from table"))
    for row in result.yield_per(100):
        _process_row(row)

The stream_results option is also available with the ORM. When using the ORM, either the Result.yield_per() or Result.partitions() methods should be used to set the number of ORM rows to be buffered each time while yielding:

with orm.Session(engine) as session:
    result = session.execute(
        select(User).order_by(User_id).execution_options(stream_results=True),
    )
    for partition in result.partitions(100):
        _process_rows(partition)

Note

ORM result sets currently must make use of Result.yield_per() or Result.partitions() in order to achieve streaming ORM results. If either of these methods are not used to set the number of rows to fetch before yielding, the entire result is fetched before rows are yielded. This may change in a future release so that the automatic buffer size used by Connection takes place for ORM results as well.

When using a 1.x style ORM query with Query, yield_per is available via Query.yield_per() - this also sets the stream_results execution option:

for row in session.query(User).yield_per(100):
    # process row

Connectionless Execution, Implicit Execution

Deprecated since version 2.0: The features of “connectionless” and “implicit” execution in SQLAlchemy are deprecated and will be removed in version 2.0. See “Implicit” and “Connectionless” execution, “bound metadata” removed for background.

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

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

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

Given a table as below:

from sqlalchemy import MetaData, Table, Column, Integer
meta = MetaData()
users_table = Table('users', meta,
    Column('id', Integer, primary_key=True),
    Column('name', String(50))
)

Explicit execution delivers the SQL text or constructed SQL expression to the Connection.execute() method of Connection:

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

Explicit, connectionless execution delivers the expression to the Engine.execute() method of Engine:

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

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

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

Above, we associate an Engine with a MetaData object using the special attribute MetaData.bind. The select() construct produced from the Table object has a method Executable.execute(), which will search for an Engine that’s “bound” to the Table.

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

• SQL statement objects gain an Executable.execute() method which automatically locates a “bind” with which to execute themselves.

• The ORM Session object supports using “bound metadata” in order to establish which Engine should be used to invoke SQL statements on behalf of a particular mapped class, though the Session also features its own explicit system of establishing complex Engine/ mapped class configurations.

• The MetaData.create_all(), MetaData.drop_all(), Table.create(), Table.drop(), and “autoload” features all make usage of the bound Engine automatically without the need to pass it explicitly.

Note

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

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

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

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

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

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

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,
    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)
...

New in version 1.1.

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://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 String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy.orm import Session
Base = declarative_base()
class A(Base):
    __tablename__ = "a"
    id = Column(Integer, primary_key=True)
    data = Column(String)
    bs = relationship("B")
class B(Base):
    __tablename__ = "b"
    id = Column(Integer, primary_key=True)
    a_id = Column(ForeignKey("a.id"))
    data = Column(String)
e = create_engine("sqlite://", echo=True)
Base.metadata.create_all(e)
s = Session(e)
s.add_all(
    [A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()]), A(bs=[B(), B(), B()])]
)
s.commit()
for a_rec in s.query(A):
    print(a_rec.bs)

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

• [raw sql] - the driver or the end-user emitted raw SQL using 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())

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 LamdaElement 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 += s.where(table.c.x > parameter)
      else:
          stmt += 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 cachable 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 contine 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.

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.

• 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.

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.

Some recipes for DBAPI connection use follow.

Calling Stored Procedures

For stored procedures with special syntactical or parameter concerns, DBAPI-level callproc may be used:

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

Multiple Result Sets

Multiple result set support is available from a raw DBAPI cursor using the nextset method:

connection = engine.raw_connection()
try:
    cursor = connection.cursor()
    cursor.execute("select * from table1; select * from table2")
    results_one = cursor.fetchall()
    cursor.nextset()
    results_two = cursor.fetchall()
    cursor.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.py as follows:

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

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

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

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

Registering Dialects In-Process

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

from sqlalchemy.dialects import registry
registry.register("mysql.foodialect", "myapp.dialect", "MyMySQLDialect")

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

Connection / Engine API

class sqlalchemy.engine.``Connection(engine, connection=None, close_with_result=False, _branch_from=None, _execution_options=None, _dispatch=None, _has_events=None)

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

This is the SQLAlchemy 1.x.x version of the Connection class. For the 2.0 style version, which features some API differences, see 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.

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.Connectable)

• method sqlalchemy.engine.Connection.__init__(engine, connection=None, close_with_result=False, _branch_from=None, _execution_options=None, _dispatch=None, _has_events=None)

  Construct a new Connection.

• method sqlalchemy.engine.Connection.begin()

  Begin a transaction and return a transaction handle.

  The returned object is an instance of Transaction. This object represents the “scope” of the transaction, which completes when either the Transaction.rollback() or Transaction.commit() method is called.

  Nested calls to begin() on the same Connection will return new Transaction objects that represent an emulated transaction within the scope of the enclosing transaction, that is:

  trans = conn.begin()   # outermost transaction
  trans2 = conn.begin()  # "nested"
  trans2.commit()        # does nothing
  trans.commit()         # actually commits

  Calls to Transaction.commit() only have an effect when invoked via the outermost Transaction object, though the Transaction.rollback() method of any of the Transaction objects will roll back the transaction.

  See also

  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()

  Begin a nested transaction and return a transaction handle.

  The returned object is an instance of NestedTransaction.

  Nested transactions require SAVEPOINT support in the underlying database. Any transaction in the hierarchy may commit and rollback, however the outermost transaction still controls the overall commit or rollback of the transaction of a whole.

  See also

  Connection.begin()

  Connection.begin_twophase()

• method sqlalchemy.engine.Connection.begin_twophase(xid=None)

  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()

  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.

  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.connect(close_with_result=False)

  Returns a branched version of this Connection.

  Deprecated since version 1.4: The Connection.connect() method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. (Background on SQLAlchemy 2.0 at: Migrating to SQLAlchemy 2.0)

  The Connection.close() method on the returned Connection can be called and this Connection will remain open.

  This method provides usage symmetry with Engine.connect(), including for usage with context managers.

• attribute sqlalchemy.engine.Connection.connection

  The underlying DB-API connection managed by this Connection.

  See also

  Working with Driver SQL and Raw DBAPI Connections

• attribute sqlalchemy.engine.Connection.default_isolation_level

  The default isolation level assigned to this Connection.

  This is the isolation level setting that the Connection has when first procured via the Engine.connect() method. This level stays in place until the Connection.execution_options.isolation_level is used to change the setting on a per-Connection basis.

  Unlike Connection.get_isolation_level(), this attribute is set ahead of time from the first connection procured by the dialect, so SQL query is not invoked when this accessor is called.

  New in version 0.9.9.

  See also

  Connection.get_isolation_level() - view current 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()

  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, parameters=None, execution_options=None)

  Executes a SQL statement construct and returns a CursorResult.

  • 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.

      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, \multiparams, **params*)

  Executes a SQL statement construct and returns a CursorResult.

  • Parameters

    • statement –

      The statement to be executed. May be one of:

      • a plain string (deprecated)

      • any ClauseElement construct that is also a subclass of Executable, such as a select() construct

      • a FunctionElement, such as that generated by func, will be automatically wrapped in a SELECT statement, which is then executed.

      • a DDLElement object

      • a DefaultGenerator object

      • a Compiled object

      Deprecated since version 2.0: passing a string to Connection.execute() is deprecated and will be removed in version 2.0. Use the text() construct with Connection.execute(), or the Connection.exec_driver_sql() method to invoke a driver-level SQL string.

    • *multiparams/**params –

      represent bound parameter values to be used in the execution. Typically, the format is either a collection of one or more dictionaries passed to *multiparams:

      conn.execute(
          table.insert(),
          {"id":1, "value":"v1"},
          {"id":2, "value":"v2"}
      )

      …or individual key/values interpreted by **params:

      conn.execute(
          table.insert(), id=1, value="v1"
      )

      In the case that a plain SQL string is passed, and the underlying DBAPI accepts positional bind parameters, a collection of tuples or individual values in *multiparams may be passed:

      conn.execute(
          "INSERT INTO table (id, value) VALUES (?, ?)",
          (1, "v1"), (2, "v2")
      )
      conn.execute(
          "INSERT INTO table (id, value) VALUES (?, ?)",
          1, "v1"
      )

      Note above, the usage of a question mark “?” or other symbol is contingent upon the “paramstyle” accepted by the DBAPI in use, which may be any of “qmark”, “named”, “pyformat”, “format”, “numeric”. See pep-249 for details on paramstyle.

      To execute a textual SQL statement which uses bound parameters in a DBAPI-agnostic way, use the text() construct.

      Deprecated since version 2.0: use of tuple or scalar positional parameters is deprecated. All params should be dicts or sequences of dicts. Use exec_driver_sql() to execute a plain string with tuple or scalar positional parameters.

• method sqlalchemy.engine.Connection.execution_options(\*opt*)

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

  For a “future” style connection, this method returns this same Connection object with the new options added.

  For a legacy connection, this method returns a copy of this Connection which references the same underlying DBAPI connection, but also defines the given execution options which will take effect for a call to execute(). As the new Connection references the same underlying resource, it’s usually a good idea to ensure that the copies will be discarded immediately, which is implicit if used as in:

  result = connection.execution_options(stream_results=True).\
                      execute(stmt)

  Note that any key/value can be passed to Connection.execution_options(), and it will be stored in the _execution_options dictionary of the Connection. It is suitable for usage by end-user schemes to communicate with event listeners, for example.

  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

    • autocommit –

      Available on: Connection, statement. When True, a COMMIT will be invoked after execution when executed in ‘autocommit’ mode, i.e. when an explicit transaction is not begun on the connection. Note that this is library level, not DBAPI level autocommit. The DBAPI connection will remain in a real transaction unless the “AUTOCOMMIT” isolation level is used.

      Deprecated since version 1.4: The “autocommit” execution option is deprecated and will be removed in SQLAlchemy 2.0. See Library-level (but not driver level) “Autocommit” removed from both Core and ORM for discussion.

    • compiled_cache –

      Available on: Connection. 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.

      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.

      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, not just the copy that is returned by the call to Connection.execution_options() method. The isolation level will remain at the given setting until the DBAPI connection itself is returned to the connection pool, i.e. the Connection.close() method on the original Connection is called, where an event handler will emit additional statements on the DBAPI connection in order to revert the isolation level change.

      Warning

      The isolation_level execution option should not be used when a transaction is already established, that is, the Connection.begin() method or similar has been called. A database cannot change the isolation level on a transaction in progress, and different DBAPIs and/or SQLAlchemy dialects may implicitly roll back or commit the transaction, or not affect the connection at all.

      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 isolation level re-applied to it automatically.

      See also

      create_engine.isolation_level - set per Engine isolation level

      Connection.get_isolation_level() - view current level

      SQLite Transaction Isolation

      PostgreSQL Transaction Isolation

      MySQL Transaction Isolation

      SQL Server Transaction Isolation

      Setting Transaction Isolation Levels / DBAPI AUTOCOMMIT - for the ORM

    • no_parameters – 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, statement. Indicate to the dialect that results should be “streamed” and not pre-buffered, if possible. This is a limitation of many DBAPIs. The flag is currently understood within a subset of dialects within the PostgreSQL and MySQL categories, and may be supported by other third party dialects as well.

      See also

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

    • schema_translate_map –

      Available on: Connection, Engine. 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.

      New in version 1.1.

      See also

      Translation of Schema Names

See also
[`Engine.execution_options()`](#sqlalchemy.engine.Engine.execution_options "sqlalchemy.engine.Engine.execution_options")
[`Executable.execution_options()`]($fc2d211e9d1454ca.md#sqlalchemy.sql.expression.Executable.execution_options "sqlalchemy.sql.expression.Executable.execution_options")
[`Connection.get_execution_options()`](#sqlalchemy.engine.Connection.get_execution_options "sqlalchemy.engine.Connection.get_execution_options")

• method sqlalchemy.engine.Connection.get_execution_options()

  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()

  Return the current isolation level assigned to this Connection.

  This will typically be the default isolation level as determined by the dialect, unless if the Connection.execution_options.isolation_level feature has been used to alter the isolation level on a per-Connection basis.

  This attribute will typically perform a live SQL operation 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. Compare to the Connection.default_isolation_level accessor which returns the dialect-level setting without performing a SQL query.

  New in version 0.9.9.

  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()

  Return the current nested transaction in progress, if any.

  New in version 1.4.

• method sqlalchemy.engine.Connection.get_transaction()

  Return the current root transaction in progress, if any.

  New in version 1.4.

• method sqlalchemy.engine.Connection.in_nested_transaction()

  Return True if a transaction is in progress.

• method sqlalchemy.engine.Connection.in_transaction()

  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=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.

• method sqlalchemy.engine.Connection.run_callable(callable_, \args, **kwargs*)

  Given a callable object or function, execute it, passing a Connection as the first argument.

  Deprecated since version 1.4: The Connection.run_callable() method is deprecated and will be removed in a future release. Invoke the callable function directly, passing the Connection.

  The given *args and **kwargs are passed subsequent to the Connection argument.

  This function, along with Engine.run_callable(), allows a function to be run with a Connection or Engine object without the need to know which one is being dealt with.

• method sqlalchemy.engine.Connection.scalar(object_, \multiparams, **params*)

  Executes and returns the first column of the first row.

  The underlying result/cursor is closed after execution.

• method sqlalchemy.engine.Connection.schema_for_object(obj)

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

• method sqlalchemy.engine.Connection.transaction(callable_, \args, **kwargs*)

  Execute the given function within a transaction boundary.

  Deprecated since version 1.4: The Connection.transaction() method is deprecated and will be removed in a future release. Use the Engine.begin() context manager instead.

  The function is passed this Connection as the first argument, followed by the given *args and **kwargs, e.g.:

  def do_something(conn, x, y):
      conn.execute(text("some statement"), {'x':x, 'y':y})
  conn.transaction(do_something, 5, 10)

  The operations inside the function are all invoked within the context of a single Transaction. Upon success, the transaction is committed. If an exception is raised, the transaction is rolled back before propagating the exception.

  Note

  The transaction() method is superseded by the usage of the Python with: statement, which can be used with Connection.begin():

  with conn.begin():
      conn.execute(text("some statement"), {'x':5, 'y':10})

  As well as with Engine.begin():

  with engine.begin() as conn:
      conn.execute(text("some statement"), {'x':5, 'y':10})

  See also

  Engine.begin() - engine-level transactional context

  Engine.transaction() - engine-level version of Connection.transaction()

class sqlalchemy.engine.``CreateEnginePlugin(url, kwargs)

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.

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).

New in version 1.1.

• method sqlalchemy.engine.CreateEnginePlugin.__init__(url, kwargs)

  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)

  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, dialect_args)

  parse and modify dialect kwargs

• method sqlalchemy.engine.CreateEnginePlugin.handle_pool_kwargs(pool_cls, pool_args)

  parse and modify pool kwargs

• method sqlalchemy.engine.CreateEnginePlugin.update_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(pool, dialect, url, logging_name=None, echo=None, query_cache_size=500, execution_options=None, hide_parameters=False)

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

This is the SQLAlchemy 1.x version of Engine. For the 2.0 style version, which includes some API differences, see Engine.

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

See also

Engine Configuration

Working with Engines and Connections

Class signature

class sqlalchemy.engine.Engine (sqlalchemy.engine.Connectable, sqlalchemy.log.Identified)

• method sqlalchemy.engine.Engine.begin(close_with_result=False)

  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.

  The close_with_result flag is normally False, and indicates that the Connection will be closed when the operation is complete. When set to True, it indicates the Connection is in “single use” mode, where the CursorResult returned by the first call to Connection.execute() will close the Connection when that CursorResult has exhausted all result rows.

  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()

  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.query_cache parameter.

  New in version 1.4.

• method sqlalchemy.engine.Engine.connect(close_with_result=False)

  Return a new Connection object.

  The Connection object is a facade that uses a DBAPI connection internally in order to communicate with the database. This connection is procured from the connection-holding Pool referenced by this Engine. When the Connection.close() method of the Connection object is called, the underlying DBAPI connection is then returned to the connection pool, where it may be used again in a subsequent call to Engine.connect().

• method sqlalchemy.engine.Engine.dispose()

  Dispose of the connection pool used by this Engine.

  This 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.

  A new connection pool is created immediately after the old one has been disposed. This new pool, like all SQLAlchemy connection pools, does not make any actual connections to the database until one is first requested, so as long as the Engine isn’t used again, no new connections will be made.

  See also

  Engine Disposal

• attribute sqlalchemy.engine.Engine.driver

  Driver name of the Dialect in use by this Engine.

• method sqlalchemy.engine.Engine.execute(statement, \multiparams, **params*)

  Executes the given construct and returns a CursorResult.

  Deprecated since version 1.4: The Engine.execute() method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. All statement execution in SQLAlchemy 2.0 is performed by the Connection.execute() method of Connection, or in the ORM by the Session.execute() method of Session. (Background on SQLAlchemy 2.0 at: Migrating to SQLAlchemy 2.0)

  The arguments are the same as those used by Connection.execute().

  Here, a Connection is acquired using the Engine.connect() method, and the statement executed with that connection. The returned CursorResult is flagged such that when the CursorResult is exhausted and its underlying cursor is closed, the Connection created here will also be closed, which allows its associated DBAPI connection resource to be returned to the connection pool.

• method sqlalchemy.engine.Engine.execution_options(\*opt*)

  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 “sharding” schemes where multiple Engine objects refer to the same connection pool, but are differentiated by options that would be consumed by a custom event:

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

  Above, the shard1 engine serves as a factory for Connection objects that will contain the execution option shard_id=shard1, and shard2 will produce Connection objects that contain the execution option shard_id=shard2.

  An event handler can consume the above execution option to perform a schema switch or other operation, given a connection. Below we emit a MySQL use statement to switch databases, at the same time keeping track of which database we’ve established using the Connection.info dictionary, which gives us a persistent storage space that follows the DBAPI connection:

  from sqlalchemy import event
  from sqlalchemy.engine import Engine
  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._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

  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()

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

  See also

  Engine.execution_options()

• method sqlalchemy.engine.Engine.has_table(table_name, schema=None)

  Return True if the given backend has a table of the given name.

  Deprecated since version 1.4: The Engine.has_table() method is deprecated and will be removed in a future release. Please refer to Inspector.has_table().

  See also

  Fine Grained Reflection with Inspector - detailed schema inspection using the Inspector interface.

  quoted_name - used to pass quoting information along with a schema identifier.

• attribute sqlalchemy.engine.Engine.name

  String name of the Dialect in use by this Engine.

• method sqlalchemy.engine.Engine.raw_connection(_connection=None)

  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.run_callable(callable_, \args, **kwargs*)

  Given a callable object or function, execute it, passing a Connection as the first argument.

  Deprecated since version 1.4: The Engine.run_callable() method is deprecated and will be removed in a future release. Use the Engine.connect() context manager instead.

  The given *args and **kwargs are passed subsequent to the Connection argument.

  This function, along with Connection.run_callable(), allows a function to be run with a Connection or Engine object without the need to know which one is being dealt with.

• method sqlalchemy.engine.Engine.scalar(statement, \multiparams, **params*)

  Executes and returns the first column of the first row.

  Deprecated since version 1.4: The Engine.scalar() method is considered legacy as of the 1.x series of SQLAlchemy and will be removed in 2.0. All statement execution in SQLAlchemy 2.0 is performed by the Connection.execute() method of Connection, or in the ORM by the Session.execute() method of Session; the Result.scalar() method can then be used to return a scalar result. (Background on SQLAlchemy 2.0 at: Migrating to SQLAlchemy 2.0)

  The underlying result/cursor is closed after execution.

• method sqlalchemy.engine.Engine.table_names(schema=None, connection=None)

  Return a list of all table names available in the database.

  Deprecated since version 1.4: The Engine.table_names() method is deprecated and will be removed in a future release. Please refer to Inspector.get_table_names().

  • Parameters

    • schema – Optional, retrieve names from a non-default schema.

    • connection – Optional, use a specified connection.