A connection pool is an object managing a set of connections and allowing their use in functions needing one. Because the time to establish a new connection can be relatively long, keeping connections open can reduce latency.
This page explains a few basic concepts of Psycopg connection pool’s
behaviour. Please refer to the
ConnectionPool object API for details about
the pool operations.
The connection pool objects are distributed in a package separate
from the main
psycopg package: use
pip install "psycopg[pool]" or
install psycopg_pool to make the
psycopg_pool package available. See
Installing the connection pool.
Pool life cycle#
A simple way to use the pool is to create a single instance of it, as a global object, and to use this object in the rest of the program, allowing other functions, modules, threads to use it:
# module db.py in your program from psycopg_pool import ConnectionPool pool = ConnectionPool(conninfo, **kwargs) # the pool starts connecting immediately. # in another module from .db import pool def my_function(): with pool.connection() as conn: conn.execute(...)
Ideally you may want to call
close() when the use of the
pool is finished. Failing to call
close() at the end of the program is not
terribly bad: probably it will just result in some warnings printed on stderr.
However, if you think that it’s sloppy, you could use the
atexit module to
close() called at the end of the program.
If you want to avoid starting to connect to the database at import time, and
want to wait for the application to be ready, you can create the pool using
open=False, and call the
close() methods when the conditions are right. Certain
frameworks provide callbacks triggered when the program is started and stopped
(for instance FastAPI startup/shutdown events): they are perfect to
initiate and terminate the pool operations:
pool = ConnectionPool(conninfo, open=False, **kwargs) @app.on_event("startup") def open_pool(): pool.open() @app.on_event("shutdown") def close_pool(): pool.close()
Creating a single pool as a global variable is not the mandatory use: your program can create more than one pool, which might be useful to connect to more than one database, or to provide different types of connections, for instance to provide separate read/write and read-only connections. The pool also acts as a context manager and is open and closed, if necessary, on entering and exiting the context block:
from psycopg_pool import ConnectionPool with ConnectionPool(conninfo, **kwargs) as pool: run_app(pool) # the pool is now closed
When the pool is open, the pool’s background workers start creating the
min_size connections, while the constructor (or the
method) returns immediately. This allows the program some leeway to start
before the target database is up and running. However, if your application is
misconfigured, or the network is down, it means that the program will be able
to start, but the threads requesting a connection will fail with a
PoolTimeout only after the timeout on
expired. If this behaviour is not desirable (and you prefer your program to
crash hard and fast, if the surrounding conditions are not right, because
something else will respawn it) you should call the
method after creating the pool, or call
open(wait=True): these methods will
block until the pool is full, or will raise a
PoolTimeout exception if the
pool isn’t ready within the allocated time.
Connections life cycle#
The pool background workers create connections according to the parameters
connection_class passed to
constructor, invoking something like
**kwargs). Once a connection is created it is also passed to the
configure() callback, if provided, after which it is put in the pool (or
passed to a client requesting it, if someone is already knocking at the door).
If a connection expires (it passes
max_lifetime), or is returned to the pool
in broken state, or is found closed by
check()), then the
pool will dispose of it and will start a new connection attempt in the
Using connections from the pool#
The pool can be used to request connections from multiple threads or
concurrent tasks - it is hardly useful otherwise! If more connections than the
ones available in the pool are requested, the requesting threads are queued
and are served a connection as soon as one is available, either because
another client has finished using it or because the pool is allowed to grow
min_size) and a new connection is ready.
The main way to use the pool is to obtain a connection using the
connection() context, which returns a
with my_pool.connection() as conn: conn.execute("what you want")
connection() context behaves like the
context: at the end of the block, if there is a transaction open, it will be
committed, or rolled back if the context is exited with as exception.
At the end of the block the connection is returned to the pool and shouldn’t
be used anymore by the code which obtained it. If a
reset() function is
specified in the pool constructor, it is called on the connection before
returning it to the pool. Note that the
reset() function is called in a
worker thread, so that the thread which used the connection can keep its
execution without being slowed down by it.
Pool connection and sizing#
A pool can have a fixed size (specifying no
min_size) or a dynamic size (when
min_size). In both
cases, as soon as the pool is created, it will try to acquire
connections in the background.
If an attempt to create a connection fails, a new attempt will be made soon
after, using an exponential backoff to increase the time between attempts,
until a maximum of
reconnect_timeout is reached. When that happens, the pool
will call the
reconnect_failed() function, if provided to the pool, and just
start a new connection attempt. You can use this function either to send
alerts or to interrupt the program and allow the rest of your infrastructure
to restart it.
If more than
min_size connections are requested concurrently, new ones are
created, up to
max_size. Note that the connections are always created by the
background workers, not by the thread asking for the connection: if a client
requests a new connection, and a previous client terminates its job before the
new connection is ready, the waiting client will be served the existing
connection. This is especially useful in scenarios where the time to establish
a connection dominates the time for which the connection is used (see this
analysis, for instance).
If a pool grows above
min_size, but its usage decreases afterwards, a number
of connections are eventually closed: one every time a connection is unused
max_idle time specified in the pool constructor.
What’s the right size for the pool?#
Big question. Who knows. However, probably not as large as you imagine. Please take a look at this analysis for some ideas.
Something useful you can do is probably to use the
get_stats() method and monitor the behaviour of your program
to tune the configuration parameters. The size of the pool can also be changed
at runtime using the
Null connection pools#
New in version 3.1.
Sometimes you may want leave the choice of using or not using a connection pool as a configuration parameter of your application. For instance, you might want to use a pool if you are deploying a “large instance” of your application and can dedicate it a handful of connections; conversely you might not want to use it if you deploy the application in several instances, behind a load balancer, and/or using an external connection pool process such as PgBouncer.
Switching between using or not using a pool requires some code change, because
ConnectionPool API is different from the normal
function and because the pool can perform additional connection configuration
configure parameter) that, if the pool is removed, should be
performed in some different code path of your application.
psycopg_pool 3.1 package introduces the
This class has the same interface, and largely the same behaviour, of the
ConnectionPool, but doesn’t create any connection beforehand. When a
connection is returned, unless there are other clients already waiting, it
is closed immediately and not kept in the pool state.
A null pool is not only a configuration convenience, but can also be used to
regulate the access to the server by a client program. If
max_size is set to
a value greater than 0, the pool will make sure that no more than
connections are created at any given time. If more clients ask for further
connections, they will be queued and served a connection as soon as a previous
client has finished using it, like for the basic pool. Other mechanisms to
throttle client requests (such as
max_waiting) are respected
Queued clients will be handed an already established connection, as soon
as a previous client has finished using it (and after the pool has
returned it to idle state and called
reset() on it, if necessary).
Because normally (i.e. unless queued) every client will be served a new connection, the time to obtain the connection is paid by the waiting client; background workers are not normally involved in obtaining new connections.
The state of the connection is verified when a connection is returned to the pool: if a connection is broken during its usage it will be discarded on return and a new connection will be created.
The health of the connection is not checked when the pool gives it to a client.
Why not? Because doing so would require an extra network roundtrip: we want to save you from its latency. Before getting too angry about it, just think that the connection can be lost any moment while your program is using it. As your program should already be able to cope with a loss of a connection during its process, it should be able to tolerate to be served a broken connection: unpleasant but not the end of the world.
The health of the connection is not checked when the connection is in the pool.
Does the pool keep a watchful eye on the quality of the connections inside it? No, it doesn’t. Why not? Because you will do it for us! Your program is only a big ruse to make sure the connections are still alive…
Not (entirely) trolling: if you are using a connection pool, we assume that you are using and returning connections at a good pace. If the pool had to check for the quality of a broken connection before your program notices it, it should be polling each connection even faster than your program uses them. Your database server wouldn’t be amused…
Can you do something better than that? Of course you can, there is always a
better way than polling. You can use the same recipe of Detecting disconnections,
reserving a connection and using a thread to monitor for any activity
happening on it. If any activity is detected, you can call the pool
check() method, which will run a quick check on each
connection in the pool, removing the ones found in broken state, and using the
background workers to replace them with fresh ones.
If you set up a similar check in your program, in case the database connection
is temporarily lost, we cannot do anything for the threads which had taken
already a connection from the pool, but no other thread should be served a
broken connection, because
check() would empty the pool and refill it with
working connections, as soon as they are available.
Faster than you can say poll. Or pool.
The pool can return information about its usage using the methods
pop_stats(). Both methods
return the same values, but the latter reset the counters after its use. The
values can be sent to a monitoring system such as Graphite or Prometheus.
The following values should be provided, but please don’t consider them as a rigid interface: it is possible that they might change in the future. Keys whose value is 0 may not be returned.
Current value for
Current value for
Number of connections currently managed by the pool (in the pool, given to clients, being prepared)
Number of connections currently idle in the pool
Number of requests currently waiting in a queue to receive a connection
Total usage time of the connections outside the pool
Number of connections requested to the pool
Number of requests queued because a connection wasn’t immediately available in the pool
Total time in the queue for the clients waiting
Number of connection requests resulting in an error (timeouts, queue full…)
Number of connections returned to the pool in a bad state
Number of connection attempts made by the pool to the server
Total time spent to establish connections with the server
Number of failed connection attempts
Number of connections lost identified by