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Generic Proxy and Pool Classes for Python

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Proxy Pattern Pool

Generic Proxy and Pool Classes for Python.

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This module provides two classes:

  • Proxy implements the proxy pattern, i.e. all calls to methods on the proxy are forwarded to an internally wrapped object. This allows to solve the classic chicken-and-egg importation and initialization possibly circular-dependency issue with Python modules:

    # File ""
    db = Proxy()
    def init_app(config):
        db.set_obj(initialization from config)
    # File ""
    import database
    from database import db  # db is a proxy to nothing
    # delayed initialization
    # db is now a proxy to the initialized object
  • Pool implements a thread-safe pool of things which can be used to store expensive-to-create objects such as database connections. The above proxy object creates a pool automatically depending on its parameters.

    Call db._ret_obj() to return the object to the pool when done with it.


The Proxy class manages accesses to one or more objects, possibly using a Pool, depending on the expected scope of said objects.

The Proxy constructors expects the following parameters:

  • obj one single object SHARED between all threads.
  • fun one function called for object creation, each time it is needed, for THREAD and VERSATILE scopes.
  • scope object scope as defined by Proxy.Scope:
    • SHARED one shared object (process level)
    • THREAD one object per thread (threading implementation)
    • WERKZEUG one object per greenlet (werkzeug implementation)
    • EVENTLET one object per greenlet (eventlet implementation)
    • GEVENT one object per greenlet (gevent implementation)
    • VERSATILE same as WERKZEUG default is SHARED or THREAD depending on whether an object of a function was passed for the object.
  • set_name the name of a function to set the proxy contents, default is set. This parameter allows to avoid collisions with the proxied methods. It is used as a prefix to have set_obj and set_fun functions which allow to reset the internal obj or fun.
  • log_level set logging level, default None means no setting.
  • max_size of pool, default None means no pooling.
  • max_size and all other parameters are forwarded to Pool.

When max_size is not None, a Pool is created to store the created objects so as to reuse them. It is the responsability of the user to return the object when not needed anymore by calling _ret_obj explicitely. This is useful for code which keeps creating new threads, eg werkzeug. For a database connection, a good time to do that is just after a commit.

The proxy has a _has_obj method to test whether an object is available without extracting anything from the pool: this is useful to test whether returning the object is needed in some error handling pattern.

The Pool class manage a pool of objects in a thread-safe way. Its constructor expects the following parameters:

  • fun how to create a new object; the function is passed the creation number.
  • max_size maximum size of pool, 0 for unlimited.
  • min_size minimum size of pool.
  • timeout maximum time to wait for something.
  • max_use after how many usage to discard an object.
  • max_avail_delay when to discard an unused object.
  • max_using_delay when to warn about object kept for a long time.
  • max_using_delay_kill when to kill objects kept for a long time.
  • health_freq run health check this every house keeper rounds.
  • hk_delay force house keeping delay.
  • log_level set logging level, default None means no setting.
  • opener function to call when creating an object, default None means no call.
  • getter function to call when getting an object, default None means no call.
  • retter function to call when returning an object, default None means no call.
  • closer function to call when discarding an object, default None means no call.
  • stats function to call to generate a JSON-compatible structure for stats.
  • health function to call to check for an available object health.
  • tracer object debug helper, default None means less debug.

Objects are created on demand by calling fun when needed.

Proxy Example

Here is an example of a flask application with blueprints and a shared resource.

First, a shared module holds a proxy to a yet unknown object:

# file ""
from ProxyPatternPool import Proxy
stuff = Proxy()
def init_app(stuff):

This shared object is used by module with a blueprint:

# file ""
from Flask import Blueprint
from Shared import stuff
sub = Blueprint()

def get_stuff():
    return str(stuff), 200

Then the application itself can load and initialize both modules in any order without risk of having some unitialized stuff imported:

# file ""
from flask import Flask
app = Flask("stuff")

from SubStuff import sub
app.register_blueprint(sub, url_prefix="/sub")

import Shared
Shared.init_app("hello world!")


This module is rhetorical: because of the GIL Python is quite bad as a parallel language, so the point of creating threads which will mostly not really run in parallel is moot, thus the point of having a clever pool of stuff to be shared by these thread is even mooter!

Shared object must be returned to the pool to avoid depleting resources. This may require some active cooperation from the infrastructure which may or may not be reliable. Consider monitoring your resources to detect unexpected status, eg database connections remaining idle in transaction and the like.

See Also:

  • Psycopg Pool for pooling Postgres database connexions.
  • Eventlet db_pool for pooling MySQL or Postgres database connexions.
  • Discussion about database pool sizing (spoiler: small is beautiful).


This code is Public Domain.


Sources, documentation and issues are hosted on GitHub. Install package from PyPI. See version details.

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