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://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://scott:tiger@localhost/test?encoding=utf8")

“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.

“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.

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

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://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://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 ConnectionFairy.connection attribute on that - but there should never be any need to access the non-pool-proxied DBAPI connection, as all methods are proxied through:

engine = create_engine(...)
conn = engine.connect()
conn.connection.<do DBAPI things>
cursor = conn.connection.cursor(<DBAPI specific arguments..>)

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().