Connections / Engines

How do I configure logging?

See Configuring Logging.

How do I pool database connections? Are my connections pooled?

SQLAlchemy performs application-level connection pooling automatically in most cases. With the exception of SQLite, a Engine object refers to a QueuePool as a source of connectivity.

For more detail, see Engine Configuration and Connection Pooling.

How do I pass custom connect arguments to my database API?

The create_engine() call accepts additional arguments either directly via the connect_args keyword argument:

e = create_engine(
    "mysql+mysqldb://scott:tiger@localhost/test", connect_args={"encoding": "utf8"}
)

Or for basic string and integer arguments, they can usually be specified in the query string of the URL:

e = create_engine("mysql+mysqldb://scott:tiger@localhost/test?encoding=utf8")

See also

Custom DBAPI connect() arguments / on-connect routines

“MySQL Server has gone away”

The primary cause of this error is that the MySQL connection has timed out and has been closed by the server. The MySQL server closes connections which have been idle a period of time which defaults to eight hours. To accommodate this, the immediate setting is to enable the create_engine.pool_recycle setting, which will ensure that a connection which is older than a set amount of seconds will be discarded and replaced with a new connection when it is next checked out.

For the more general case of accommodating database restarts and other temporary loss of connectivity due to network issues, connections that are in the pool may be recycled in response to more generalized disconnect detection techniques. The section Dealing with Disconnects provides background on both “pessimistic” (e.g. pre-ping) and “optimistic” (e.g. graceful recovery) techniques. Modern SQLAlchemy tends to favor the “pessimistic” approach.

See also

Dealing with Disconnects

“Commands out of sync; you can’t run this command now” / “This result object does not return rows. It has been closed automatically”

The MySQL drivers have a fairly wide class of failure modes whereby the state of the connection to the server is in an invalid state. Typically, when the connection is used again, one of these two error messages will occur. The reason is because the state of the server has been changed to one in which the client library does not expect, such that when the client library emits a new statement on the connection, the server does not respond as expected.

In SQLAlchemy, because database connections are pooled, the issue of the messaging being out of sync on a connection becomes more important, since when an operation fails, if the connection itself is in an unusable state, if it goes back into the connection pool, it will malfunction when checked out again. The mitigation for this issue is that the connection is invalidated when such a failure mode occurs so that the underlying database connection to MySQL is discarded. This invalidation occurs automatically for many known failure modes and can also be called explicitly via the Connection.invalidate() method.

There is also a second class of failure modes within this category where a context manager such as with session.begin_nested(): wants to “roll back” the transaction when an error occurs; however within some failure modes of the connection, the rollback itself (which can also be a RELEASE SAVEPOINT operation) also fails, causing misleading stack traces.

Originally, the cause of this error used to be fairly simple, it meant that a multithreaded program was invoking commands on a single connection from more than one thread. This applied to the original “MySQLdb” native-C driver that was pretty much the only driver in use. However, with the introduction of pure Python drivers like PyMySQL and MySQL-connector-Python, as well as increased use of tools such as gevent/eventlet, multiprocessing (often with Celery), and others, there is a whole series of factors that has been known to cause this problem, some of which have been improved across SQLAlchemy versions but others which are unavoidable:

• Sharing a connection among threads - This is the original reason these kinds of errors occurred. A program used the same connection in two or more threads at the same time, meaning multiple sets of messages got mixed up on the connection, putting the server-side session into a state that the client no longer knows how to interpret. However, other causes are usually more likely today.

• Sharing the filehandle for the connection among processes - This usually occurs when a program uses os.fork() to spawn a new process, and a TCP connection that is present in th parent process gets shared into one or more child processes. As multiple processes are now emitting messages to essentially the same filehandle, the server receives interleaved messages and breaks the state of the connection.

  This scenario can occur very easily if a program uses Python’s “multiprocessing” module and makes use of an Engine that was created in the parent process. It’s common that “multiprocessing” is in use when using tools like Celery. The correct approach should be either that a new Engine is produced when a child process first starts, discarding any Engine that came down from the parent process; or, the Engine that’s inherited from the parent process can have it’s internal pool of connections disposed by calling Engine.dispose().

• Greenlet Monkeypatching w/ Exits - When using a library like gevent or eventlet that monkeypatches the Python networking API, libraries like PyMySQL are now working in an asynchronous mode of operation, even though they are not developed explicitly against this model. A common issue is that a greenthread is interrupted, often due to timeout logic in the application. This results in the GreenletExit exception being raised, and the pure-Python MySQL driver is interrupted from its work, which may have been that it was receiving a response from the server or preparing to otherwise reset the state of the connection. When the exception cuts all that work short, the conversation between client and server is now out of sync and subsequent usage of the connection may fail. SQLAlchemy as of version 1.1.0 knows how to guard against this, as if a database operation is interrupted by a so-called “exit exception”, which includes GreenletExit and any other subclass of Python BaseException that is not also a subclass of Exception, the connection is invalidated.

• Rollbacks / SAVEPOINT releases failing - Some classes of error cause the connection to be unusable within the context of a transaction, as well as when operating in a “SAVEPOINT” block. In these cases, the failure on the connection has rendered any SAVEPOINT as no longer existing, yet when SQLAlchemy, or the application, attempts to “roll back” this savepoint, the “RELEASE SAVEPOINT” operation fails, typically with a message like “savepoint does not exist”. In this case, under Python 3 there will be a chain of exceptions output, where the ultimate “cause” of the error will be displayed as well. Under Python 2, there are no “chained” exceptions, however recent versions of SQLAlchemy will attempt to emit a warning illustrating the original failure cause, while still throwing the immediate error which is the failure of the ROLLBACK.

How Do I “Retry” a Statement Execution Automatically?

The documentation section Dealing with Disconnects discusses the strategies available for pooled connections that have been disconnected since the last time a particular connection was checked out. The most modern feature in this regard is the create_engine.pre_ping parameter, which allows that a “ping” is emitted on a database connection when it’s retrieved from the pool, reconnecting if the current connection has been disconnected.

It’s important to note that this “ping” is only emitted before the connection is actually used for an operation. Once the connection is delivered to the caller, per the Python DBAPI specification it is now subject to an autobegin operation, which means it will automatically BEGIN a new transaction when it is first used that remains in effect for subsequent statements, until the DBAPI-level connection.commit() or connection.rollback() method is invoked.

In modern use of SQLAlchemy, a series of SQL statements are always invoked within this transactional state, assuming DBAPI autocommit mode is not enabled (more on that in the next section), meaning that no single statement is automatically committed; if an operation fails, the effects of all statements within the current transaction will be lost.

The implication that this has for the notion of “retrying” a statement is that in the default case, when a connection is lost, the entire transaction is lost. There is no useful way that the database can “reconnect and retry” and continue where it left off, since data is already lost. For this reason, SQLAlchemy does not have a transparent “reconnection” feature that works mid-transaction, for the case when the database connection has disconnected while being used. The canonical approach to dealing with mid-operation disconnects is to retry the entire operation from the start of the transaction, often by using a custom Python decorator that will “retry” a particular function several times until it succeeds, or to otherwise architect the application in such a way that it is resilient against transactions that are dropped that then cause operations to fail.

There is also the notion of extensions that can keep track of all of the statements that have proceeded within a transaction and then replay them all in a new transaction in order to approximate a “retry” operation. SQLAlchemy’s event system does allow such a system to be constructed, however this approach is also not generally useful as there is no way to guarantee that those DML statements will be working against the same state, as once a transaction has ended the state of the database in a new transaction may be totally different. Architecting “retry” explicitly into the application at the points at which transactional operations begin and commit remains the better approach since the application-level transactional methods are the ones that know best how to re-run their steps.

Otherwise, if SQLAlchemy were to provide a feature that transparently and silently “reconnected” a connection mid-transaction, the effect would be that data is silently lost. By trying to hide the problem, SQLAlchemy would make the situation much worse.

However, if we are not using transactions, then there are more options available, as the next section describes.

Using DBAPI Autocommit Allows for a Readonly Version of Transparent Reconnect

With the rationale for not having a transparent reconnection mechanism stated, the preceding section rests upon the assumption that the application is in fact using DBAPI-level transactions. As most DBAPIs now offer native “autocommit” settings, we can make use of these features to provide a limited form of transparent reconnect for read only, autocommit only operations. A transparent statement retry may be applied to the cursor.execute() method of the DBAPI, however it is still not safe to apply to the cursor.executemany() method of the DBAPI, as the statement may have consumed any portion of the arguments given.

Warning

The following recipe should not be used for operations that write data. Users should carefully read and understand how the recipe works and test failure modes very carefully against the specifically targeted DBAPI driver before making production use of this recipe. The retry mechanism does not guarantee prevention of disconnection errors in all cases.

A simple retry mechanism may be applied to the DBAPI level cursor.execute() method by making use of the DialectEvents.do_execute() and DialectEvents.do_execute_no_params() hooks, which will be able to intercept disconnections during statement executions. It will not intercept connection failures during result set fetch operations, for those DBAPIs that don’t fully buffer result sets. The recipe requires that the database support DBAPI level autocommit and is not guaranteed for particular backends. A single function reconnecting_engine() is presented which applies the event hooks to a given Engine object, returning an always-autocommit version that enables DBAPI-level autocommit. A connection will transparently reconnect for single-parameter and no-parameter statement executions:

import time
from sqlalchemy import event
def reconnecting_engine(engine, num_retries, retry_interval):
    def _run_with_retries(fn, context, cursor_obj, statement, *arg, **kw):
        for retry in range(num_retries + 1):
            try:
                fn(cursor_obj, statement, context=context, *arg)
            except engine.dialect.dbapi.Error as raw_dbapi_err:
                connection = context.root_connection
                if engine.dialect.is_disconnect(raw_dbapi_err, connection, cursor_obj):
                    if retry > num_retries:
                        raise
                    engine.logger.error(
                        "disconnection error, retrying operation",
                        exc_info=True,
                    )
                    connection.invalidate()
                    # use SQLAlchemy 2.0 API if available
                    if hasattr(connection, "rollback"):
                        connection.rollback()
                    else:
                        trans = connection.get_transaction()
                        if trans:
                            trans.rollback()
                    time.sleep(retry_interval)
                    context.cursor = cursor_obj = connection.connection.cursor()
                else:
                    raise
            else:
                return True
    e = engine.execution_options(isolation_level="AUTOCOMMIT")
    @event.listens_for(e, "do_execute_no_params")
    def do_execute_no_params(cursor_obj, statement, context):
        return _run_with_retries(
            context.dialect.do_execute_no_params, context, cursor_obj, statement
        )
    @event.listens_for(e, "do_execute")
    def do_execute(cursor_obj, statement, parameters, context):
        return _run_with_retries(
            context.dialect.do_execute, context, cursor_obj, statement, parameters
        )
    return e

Given the above recipe, a reconnection mid-transaction may be demonstrated using the following proof of concept script. Once run, it will emit a SELECT 1 statement to the database every five seconds:

from sqlalchemy import create_engine
from sqlalchemy import select
if __name__ == "__main__":
    engine = create_engine("mysql+mysqldb://scott:tiger@localhost/test", echo_pool=True)
    def do_a_thing(engine):
        with engine.begin() as conn:
            while True:
                print("ping: %s" % conn.execute(select([1])).scalar())
                time.sleep(5)
    e = reconnecting_engine(
        create_engine("mysql+mysqldb://scott:tiger@localhost/test", echo_pool=True),
        num_retries=5,
        retry_interval=2,
    )
    do_a_thing(e)

Restart the database while the script runs to demonstrate the transparent reconnect operation:

$ python reconnect_test.py
ping: 1
ping: 1
disconnection error, retrying operation
Traceback (most recent call last):
  ...
MySQLdb._exceptions.OperationalError: (2006, 'MySQL server has gone away')
2020-10-19 16:16:22,624 INFO sqlalchemy.pool.impl.QueuePool Invalidate connection <_mysql.connection open to 'localhost' at 0xf59240>
ping: 1
ping: 1
...

The above recipe is tested for SQLAlchemy 1.4.

Why does SQLAlchemy issue so many ROLLBACKs?

SQLAlchemy currently assumes DBAPI connections are in “non-autocommit” mode - this is the default behavior of the Python database API, meaning it must be assumed that a transaction is always in progress. The connection pool issues connection.rollback() when a connection is returned. This is so that any transactional resources remaining on the connection are released. On a database like PostgreSQL or MSSQL where table resources are aggressively locked, this is critical so that rows and tables don’t remain locked within connections that are no longer in use. An application can otherwise hang. It’s not just for locks, however, and is equally critical on any database that has any kind of transaction isolation, including MySQL with InnoDB. Any connection that is still inside an old transaction will return stale data, if that data was already queried on that connection within isolation. For background on why you might see stale data even on MySQL, see https://dev.mysql.com/doc/refman/5.1/en/innodb-transaction-model.html

I’m on MyISAM - how do I turn it off?

The behavior of the connection pool’s connection return behavior can be configured using reset_on_return:

from sqlalchemy import create_engine
from sqlalchemy.pool import QueuePool
engine = create_engine(
    "mysql+mysqldb://scott:tiger@localhost/myisam_database",
    pool=QueuePool(reset_on_return=False),
)

I’m on SQL Server - how do I turn those ROLLBACKs into COMMITs?

reset_on_return accepts the values commit, rollback in addition to True, False, and None. Setting to commit will cause a COMMIT as any connection is returned to the pool:

engine = create_engine(
    "mssql+pyodbc://scott:tiger@mydsn", pool=QueuePool(reset_on_return="commit")
)

I am using multiple connections with a SQLite database (typically to test transaction operation), and my test program is not working!

If using a SQLite :memory: database, or a version of SQLAlchemy prior to version 0.7, the default connection pool is the SingletonThreadPool, which maintains exactly one SQLite connection per thread. So two connections in use in the same thread will actually be the same SQLite connection. Make sure you’re not using a :memory: database and use NullPool, which is the default for non-memory databases in current SQLAlchemy versions.

See also

Threading/Pooling Behavior - info on PySQLite’s behavior.

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

With a regular SA engine-level Connection, you can get at a pool-proxied version of the DBAPI connection via the Connection.connection attribute on Connection, and for the really-real DBAPI connection you can call the PoolProxiedConnection.dbapi_connection attribute on that. On regular sync drivers there is usually no need to access the non-pool-proxied DBAPI connection, as all methods are proxied through:

engine = create_engine(...)
conn = engine.connect()
# pep-249 style PoolProxiedConnection (historically called a "connection fairy")
connection_fairy = conn.connection
# typically to run statements one would get a cursor() from this
# object
cursor_obj = connection_fairy.cursor()
# ... work with cursor_obj
# to bypass "connection_fairy", such as to set attributes on the
# unproxied pep-249 DBAPI connection, use .dbapi_connection
raw_dbapi_connection = connection_fairy.dbapi_connection
# the same thing is available as .driver_connection (more on this
# in the next section)
also_raw_dbapi_connection = connection_fairy.driver_connection

Changed in version 1.4.24: Added the PoolProxiedConnection.dbapi_connection attribute, which supersedes the previous PoolProxiedConnection.connection attribute which still remains available; this attribute always provides a pep-249 synchronous style connection object. The PoolProxiedConnection.driver_connection attribute is also added which will always refer to the real driver-level connection regardless of what API it presents.

Accessing the underlying connection for an asyncio driver

When an asyncio driver is in use, there are two changes to the above scheme. The first is that when using an AsyncConnection, the PoolProxiedConnection must be accessed using the awaitable method AsyncConnection.get_raw_connection(). The returned PoolProxiedConnection in this case retains a sync-style pep-249 usage pattern, and the PoolProxiedConnection.dbapi_connection attribute refers to a a SQLAlchemy-adapted connection object which adapts the asyncio connection to a sync style pep-249 API, in other words there are two levels of proxying going on when using an asyncio driver. The actual asyncio connection is available from the driver_connection attribute. To restate the previous example in terms of asyncio looks like:

async def main():
    engine = create_async_engine(...)
    conn = await engine.connect()
    # pep-249 style ConnectionFairy connection pool proxy object
    # presents a sync interface
    connection_fairy = await conn.get_raw_connection()
    # beneath that proxy is a second proxy which adapts the
    # asyncio driver into a pep-249 connection object, accessible
    # via .dbapi_connection as is the same with a sync API
    sqla_sync_conn = connection_fairy.dbapi_connection
    # the really-real innermost driver connection is available
    # from the .driver_connection attribute
    raw_asyncio_connection = connection_fairy.driver_connection
    # work with raw asyncio connection
    result = await raw_asyncio_connection.execute(...)

Changed in version 1.4.24: Added the PoolProxiedConnection.dbapi_connection and PoolProxiedConnection.driver_connection attributes to allow access to pep-249 connections, pep-249 adaption layers, and underlying driver connections using a consistent interface.

When using asyncio drivers, the above “DBAPI” connection is actually a SQLAlchemy-adapted form of connection which presents a synchronous-style pep-249 style API. To access the actual asyncio driver connection, which will present the original asyncio API of the driver in use, this can be accessed via the PoolProxiedConnection.driver_connection attribute of PoolProxiedConnection. For a standard pep-249 driver, PoolProxiedConnection.dbapi_connection and PoolProxiedConnection.driver_connection are synonymous.

You must ensure that you revert any isolation level settings or other operation-specific settings on the connection back to normal before returning it to the pool.

As an alternative to reverting settings, you can call the Connection.detach() method on either Connection or the proxied connection, which will de-associate the connection from the pool such that it will be closed and discarded when Connection.close() is called:

conn = engine.connect()
conn.detach()  # detaches the DBAPI connection from the connection pool
conn.connection.<go nuts>
conn.close()  # connection is closed for real, the pool replaces it with a new connection

How do I use engines / connections / sessions with Python multiprocessing, or os.fork()?

This is covered in the section Using Connection Pools with Multiprocessing or os.fork().