Connection Pooling¶
A connection pool is a standard technique used to maintain long running connections in memory for efficient re-use, as well as to provide management for the total number of connections an application might use simultaneously.
Particularly for server-side web applications, a connection pool is the standard way to maintain a “pool” of active database connections in memory which are reused across requests.
SQLAlchemy includes several connection pool implementations
which integrate with the Engine
. They can also be used
directly for applications that want to add pooling to an otherwise
plain DBAPI approach.
Connection Pool Configuration¶
The Engine
returned by the
create_engine()
function in most cases has a QueuePool
integrated, pre-configured with reasonable pooling defaults. If
you’re reading this section only to learn how to enable pooling - congratulations!
You’re already done.
The most common QueuePool
tuning parameters can be passed
directly to create_engine()
as keyword arguments:
pool_size
, max_overflow
, pool_recycle
and
pool_timeout
. For example:
engine = create_engine("postgresql://me@localhost/mydb", pool_size=20, max_overflow=0)
In the case of SQLite, the SingletonThreadPool
or
NullPool
are selected by the dialect to provide
greater compatibility with SQLite’s threading and locking
model, as well as to provide a reasonable default behavior
to SQLite “memory” databases, which maintain their entire
dataset within the scope of a single connection.
All SQLAlchemy pool implementations have in common
that none of them “pre create” connections - all implementations wait
until first use before creating a connection. At that point, if
no additional concurrent checkout requests for more connections
are made, no additional connections are created. This is why it’s perfectly
fine for create_engine()
to default to using a QueuePool
of size five without regard to whether or not the application really needs five connections
queued up - the pool would only grow to that size if the application
actually used five connections concurrently, in which case the usage of a
small pool is an entirely appropriate default behavior.
Switching Pool Implementations¶
The usual way to use a different kind of pool with create_engine()
is to use the poolclass
argument. This argument accepts a class
imported from the sqlalchemy.pool
module, and handles the details
of building the pool for you. Common options include specifying
QueuePool
with SQLite:
from sqlalchemy.pool import QueuePool
engine = create_engine("sqlite:///file.db", poolclass=QueuePool)
Disabling pooling using NullPool
:
from sqlalchemy.pool import NullPool
engine = create_engine(
"postgresql+psycopg2://scott:tiger@localhost/test", poolclass=NullPool
)
Using a Custom Connection Function¶
See the section Custom DBAPI connect() arguments / on-connect routines for a rundown of the various connection customization routines.
Constructing a Pool¶
To use a Pool
by itself, the creator
function is
the only argument that’s required and is passed first, followed
by any additional options:
import sqlalchemy.pool as pool
import psycopg2
def getconn():
c = psycopg2.connect(user="ed", host="127.0.0.1", dbname="test")
return c
mypool = pool.QueuePool(getconn, max_overflow=10, pool_size=5)
DBAPI connections can then be procured from the pool using the
Pool.connect()
function. The return value of this method is a DBAPI
connection that’s contained within a transparent proxy:
# get a connection
conn = mypool.connect()
# use it
cursor_obj = conn.cursor()
cursor_obj.execute("select foo")
The purpose of the transparent proxy is to intercept the close()
call,
such that instead of the DBAPI connection being closed, it is returned to the
pool:
# "close" the connection. Returns
# it to the pool.
conn.close()
The proxy also returns its contained DBAPI connection to the pool when it is garbage collected, though it’s not deterministic in Python that this occurs immediately (though it is typical with cPython). This usage is not recommended however and in particular is not supported with asyncio DBAPI drivers.
Reset On Return¶
The pool also includes the a “reset on return” feature which will call the
rollback()
method of the DBAPI connection when the connection is returned
to the pool. This is so that any existing
transaction on the connection is removed, not only ensuring that no existing
state remains on next usage, but also so that table and row locks are released
as well as that any isolated data snapshots are removed. This rollback()
occurs in most cases even when using an Engine
object,
except in the case when the Connection
can guarantee
that a rollback()
has been called immediately before the connection
is returned to the pool.
For most DBAPIs, the call to rollback()
is very inexpensive and if the
DBAPI has already completed a transaction, the method should be a no-op.
However, for DBAPIs that incur performance issues with rollback()
even if
there’s no state on the connection, this behavior can be disabled using the
reset_on_return
option of Pool
. The behavior is safe
to disable under the following conditions:
If the database does not support transactions at all, such as using MySQL with the MyISAM engine, or the DBAPI is used in autocommit mode only, the behavior can be disabled.
If the pool itself doesn’t maintain a connection after it’s checked in, such as when using
NullPool
, the behavior can be disabled.Otherwise, it must be ensured that:
the application ensures that all
Connection
objects are explicitly closed out using a context manager (i.e.with
block) or atry/finally
style blockconnections are never allowed to be garbage collected before being explicitly closed.
the DBAPI connection itself, e.g.
connection.connection
, is not used directly, or the application ensures that.rollback()
is called on this connection before releasing it back to the connection pool.
The “reset on return” step may be logged using the logging.DEBUG
log level along with the sqlalchemy.pool
logger, or by setting
echo_pool='debug'
with create_engine()
.
Pool Events¶
Connection pools support an event interface that allows hooks to execute
upon first connect, upon each new connection, and upon checkout and
checkin of connections. See PoolEvents
for details.
Dealing with Disconnects¶
The connection pool has the ability to refresh individual connections as well as its entire set of connections, setting the previously pooled connections as “invalid”. A common use case is allow the connection pool to gracefully recover when the database server has been restarted, and all previously established connections are no longer functional. There are two approaches to this.
Disconnect Handling - Pessimistic¶
The pessimistic approach refers to emitting a test statement on the SQL connection at the start of each connection pool checkout, to test that the database connection is still viable. Typically, this is a simple statement like “SELECT 1”, but may also make use of some DBAPI-specific method to test the connection for liveness.
The approach adds a small bit of overhead to the connection checkout process, however is otherwise the most simple and reliable approach to completely eliminating database errors due to stale pooled connections. The calling application does not need to be concerned about organizing operations to be able to recover from stale connections checked out from the pool.
It is critical to note that the pre-ping approach does not accommodate for
connections dropped in the middle of transactions or other SQL operations. If
the database becomes unavailable while a transaction is in progress, the
transaction will be lost and the database error will be raised. While the
Connection
object will detect a “disconnect” situation and
recycle the connection as well as invalidate the rest of the connection pool
when this condition occurs, the individual operation where the exception was
raised will be lost, and it’s up to the application to either abandon the
operation, or retry the whole transaction again. If the engine is
configured using DBAPI-level autocommit connections, as described at
Setting Transaction Isolation Levels including DBAPI Autocommit, a connection may be reconnected transparently
mid-operation using events. See the section How Do I “Retry” a Statement Execution Automatically? for
an example.
Pessimistic testing of connections upon checkout is achievable by
using the Pool.pre_ping
argument, available from create_engine()
via the create_engine.pool_pre_ping
argument:
engine = create_engine("mysql+pymysql://user:pw@host/db", pool_pre_ping=True)
The “pre ping” feature will normally emit SQL equivalent to “SELECT 1” each time a connection is checked out from the pool; if an error is raised that is detected as a “disconnect” situation, the connection will be immediately recycled, and all other pooled connections older than the current time are invalidated, so that the next time they are checked out, they will also be recycled before use.
If the database is still not available when “pre ping” runs, then the initial connect will fail and the error for failure to connect will be propagated normally. In the uncommon situation that the database is available for connections, but is not able to respond to a “ping”, the “pre_ping” will try up to three times before giving up, propagating the database error last received.
Note
the “SELECT 1” emitted by “pre-ping” is invoked within the scope of the connection pool / dialect, using a very short codepath for minimal Python latency. As such, this statement is not logged in the SQL echo output, and will not show up in SQLAlchemy’s engine logging.
New in version 1.2: Added “pre-ping” capability to the Pool
class.
Custom / Legacy Pessimistic Ping¶
Before create_engine.pool_pre_ping
was added, the “pre-ping”
approach historically has been performed manually using
the ConnectionEvents.engine_connect()
engine event.
The most common recipe for this is below, for reference
purposes in case an application is already using such a recipe, or special
behaviors are needed:
from sqlalchemy import exc
from sqlalchemy import event
from sqlalchemy import select
some_engine = create_engine(...)
@event.listens_for(some_engine, "engine_connect")
def ping_connection(connection, branch):
if branch:
# "branch" refers to a sub-connection of a connection,
# we don't want to bother pinging on these.
return
# turn off "close with result". This flag is only used with
# "connectionless" execution, otherwise will be False in any case
save_should_close_with_result = connection.should_close_with_result
connection.should_close_with_result = False
try:
# run a SELECT 1. use a core select() so that
# the SELECT of a scalar value without a table is
# appropriately formatted for the backend
connection.scalar(select(1))
except exc.DBAPIError as err:
# catch SQLAlchemy's DBAPIError, which is a wrapper
# for the DBAPI's exception. It includes a .connection_invalidated
# attribute which specifies if this connection is a "disconnect"
# condition, which is based on inspection of the original exception
# by the dialect in use.
if err.connection_invalidated:
# run the same SELECT again - the connection will re-validate
# itself and establish a new connection. The disconnect detection
# here also causes the whole connection pool to be invalidated
# so that all stale connections are discarded.
connection.scalar(select(1))
else:
raise
finally:
# restore "close with result"
connection.should_close_with_result = save_should_close_with_result
The above recipe has the advantage that we are making use of SQLAlchemy’s
facilities for detecting those DBAPI exceptions that are known to indicate
a “disconnect” situation, as well as the Engine
object’s ability
to correctly invalidate the current connection pool when this condition
occurs and allowing the current Connection
to re-validate onto
a new DBAPI connection.
Disconnect Handling - Optimistic¶
When pessimistic handling is not employed, as well as when the database is
shutdown and/or restarted in the middle of a connection’s period of use within
a transaction, the other approach to dealing with stale / closed connections is
to let SQLAlchemy handle disconnects as they occur, at which point all
connections in the pool are invalidated, meaning they are assumed to be
stale and will be refreshed upon next checkout. This behavior assumes the
Pool
is used in conjunction with a Engine
.
The Engine
has logic which can detect
disconnection events and refresh the pool automatically.
When the Connection
attempts to use a DBAPI connection, and an
exception is raised that corresponds to a “disconnect” event, the connection
is invalidated. The Connection
then calls the Pool.recreate()
method, effectively invalidating all connections not currently checked out so
that they are replaced with new ones upon next checkout. This flow is
illustrated by the code example below:
from sqlalchemy import create_engine, exc
e = create_engine(...)
c = e.connect()
try:
# suppose the database has been restarted.
c.execute(text("SELECT * FROM table"))
c.close()
except exc.DBAPIError, e:
# an exception is raised, Connection is invalidated.
if e.connection_invalidated:
print("Connection was invalidated!")
# after the invalidate event, a new connection
# starts with a new Pool
c = e.connect()
c.execute(text("SELECT * FROM table"))
The above example illustrates that no special intervention is needed to refresh the pool, which continues normally after a disconnection event is detected. However, one database exception is raised, per each connection that is in use while the database unavailability event occurred. In a typical web application using an ORM Session, the above condition would correspond to a single request failing with a 500 error, then the web application continuing normally beyond that. Hence the approach is “optimistic” in that frequent database restarts are not anticipated.
Setting Pool Recycle¶
An additional setting that can augment the “optimistic” approach is to set the pool recycle parameter. This parameter prevents the pool from using a particular connection that has passed a certain age, and is appropriate for database backends such as MySQL that automatically close connections that have been stale after a particular period of time:
from sqlalchemy import create_engine
e = create_engine("mysql://scott:tiger@localhost/test", pool_recycle=3600)
Above, any DBAPI connection that has been open for more than one hour will be invalidated and replaced,
upon next checkout. Note that the invalidation only occurs during checkout - not on
any connections that are held in a checked out state. pool_recycle
is a function
of the Pool
itself, independent of whether or not an Engine
is in use.
More on Invalidation¶
The Pool
provides “connection invalidation” services which allow
both explicit invalidation of a connection as well as automatic invalidation
in response to conditions that are determined to render a connection unusable.
“Invalidation” means that a particular DBAPI connection is removed from the
pool and discarded. The .close()
method is called on this connection
if it is not clear that the connection itself might not be closed, however
if this method fails, the exception is logged but the operation still proceeds.
When using a Engine
, the Connection.invalidate()
method is
the usual entrypoint to explicit invalidation. Other conditions by which
a DBAPI connection might be invalidated include:
a DBAPI exception such as
OperationalError
, raised when a method likeconnection.execute()
is called, is detected as indicating a so-called “disconnect” condition. As the Python DBAPI provides no standard system for determining the nature of an exception, all SQLAlchemy dialects include a system calledis_disconnect()
which will examine the contents of an exception object, including the string message and any potential error codes included with it, in order to determine if this exception indicates that the connection is no longer usable. If this is the case, the_ConnectionFairy.invalidate()
method is called and the DBAPI connection is then discarded.When the connection is returned to the pool, and calling the
connection.rollback()
orconnection.commit()
methods, as dictated by the pool’s “reset on return” behavior, throws an exception. A final attempt at calling.close()
on the connection will be made, and it is then discarded.When a listener implementing
PoolEvents.checkout()
raises theDisconnectionError
exception, indicating that the connection won’t be usable and a new connection attempt needs to be made.
All invalidations which occur will invoke the PoolEvents.invalidate()
event.
Using FIFO vs. LIFO¶
The QueuePool
class features a flag called
QueuePool.use_lifo
, which can also be accessed from
create_engine()
via the flag create_engine.pool_use_lifo
.
Setting this flag to True
causes the pool’s “queue” behavior to instead be
that of a “stack”, e.g. the last connection to be returned to the pool is the
first one to be used on the next request. In contrast to the pool’s long-
standing behavior of first-in-first-out, which produces a round-robin effect of
using each connection in the pool in series, lifo mode allows excess
connections to remain idle in the pool, allowing server-side timeout schemes to
close these connections out. The difference between FIFO and LIFO is
basically whether or not its desirable for the pool to keep a full set of
connections ready to go even during idle periods:
engine = create_engine("postgreql://", pool_use_lifo=True, pool_pre_ping=True)
Above, we also make use of the create_engine.pool_pre_ping
flag
so that connections which are closed from the server side are gracefully
handled by the connection pool and replaced with a new connection.
Note that the flag only applies to QueuePool
use.
New in version 1.3.
See also
Using Connection Pools with Multiprocessing or os.fork()¶
It’s critical that when using a connection pool, and by extension when
using an Engine
created via create_engine()
, that
the pooled connections are not shared to a forked process. TCP connections
are represented as file descriptors, which usually work across process
boundaries, meaning this will cause concurrent access to the file descriptor
on behalf of two or more entirely independent Python interpreter states.
Depending on specifics of the driver and OS, the issues that arise here range from non-working connections to socket connections that are used by multiple processes concurrently, leading to broken messaging (the latter case is typically the most common).
The SQLAlchemy Engine
object refers to a connection pool of existing
database connections. So when this object is replicated to a child process,
the goal is to ensure that no database connections are carried over. There
are three general approaches to this:
Disable pooling using
NullPool
. This is the most simplistic, one shot system that prevents theEngine
from using any connection more than once:from sqlalchemy.pool import NullPool engine = create_engine("mysql://user:pass@host/dbname", poolclass=NullPool)
Call
Engine.dispose()
on any givenEngine
, passing theEngine.dispose.close
parameter with a value ofFalse
, within the initialize phase of the child process. This is so that the new process will not touch any of the parent process’ connections and will instead start with new connections. This is the recommended approach:from multiprocessing import Pool engine = create_engine("mysql+mysqldb://user:pass@host/dbname") def run_in_process(some_data_record): with engine.connect() as conn: conn.execute(text("...")) def initializer(): """ensure the parent proc's database connections are not touched in the new connection pool""" engine.dispose(close=False) with Pool(10, initializer=initializer) as p: p.map(run_in_process, data)
New in version 1.4.33: Added the
Engine.dispose.close
parameter to allow the replacement of a connection pool in a child process without interfering with the connections used by the parent process.To achieve the same “dispose without close” behavior prior to version 1.4.33 (all SQLAlchemy versions), instead of calling
Engine.dispose()
, replace thePool
directly usingPool.recreate()
:engine.pool = engine.pool.recreate()
The above code is equivalent to
engine.dispose(close=False)
with the exception that theConnectionEvents.engine_disposed()
end-user event hook is not invoked; assuming end-user code is not making use of this hook, this workaround has no other negative effects.Call
Engine.dispose()
directly before the child process is created. This will also cause the child process to start with a new connection pool, while ensuring the parent connections are not transferred to the child process:engine = create_engine("mysql://user:pass@host/dbname") def run_in_process(): with engine.connect() as conn: conn.execute(text("...")) # before process starts, ensure engine.dispose() is called engine.dispose() p = Process(target=run_in_process) p.start()
An event handler can be applied to the connection pool that tests for connections being shared across process boundaries, and invalidates them:
from sqlalchemy import event from sqlalchemy import exc import os engine = create_engine("...") @event.listens_for(engine, "connect") def connect(dbapi_connection, connection_record): connection_record.info["pid"] = os.getpid() @event.listens_for(engine, "checkout") def checkout(dbapi_connection, connection_record, connection_proxy): pid = os.getpid() if connection_record.info["pid"] != pid: connection_record.dbapi_connection = connection_proxy.dbapi_connection = None raise exc.DisconnectionError( "Connection record belongs to pid %s, " "attempting to check out in pid %s" % (connection_record.info["pid"], pid) )
Above, we use an approach similar to that described in Disconnect Handling - Pessimistic to treat a DBAPI connection that originated in a different parent process as an “invalid” connection, coercing the pool to recycle the connection record to make a new connection.
The above strategies will accommodate the case of an Engine
being shared among processes. The above steps alone are not sufficient for the
case of sharing a specific Connection
over a process boundary;
prefer to keep the scope of a particular Connection
local to a
single process (and thread). It’s additionally not supported to share any kind
of ongoing transactional state directly across a process boundary, such as an
ORM Session
object that’s begun a transaction and references
active Connection
instances; again prefer to create new
Session
objects in new processes.