Welcome, guest | Sign In | My Account | Store | Cart

Notice! PyPM is being replaced with the ActiveState Platform, which enhances PyPM’s build and deploy capabilities. Create your free Platform account to download ActivePython or customize Python with the packages you require and get automatic updates.

pypm install dm.zodb.asynchronous

How to install dm.zodb.asynchronous

  1. Download and install ActivePython
  2. Open Command Prompt
  3. Type pypm install dm.zodb.asynchronous
 Python 2.7Python 3.2Python 3.3
Windows (32-bit)
1.0.2 Available View build log
1.0 Available View build log
Windows (64-bit)
1.0.2 Available View build log
1.0 Available View build log
Mac OS X (10.5+)
1.0.2 Available View build log
1.0 Available View build log
Linux (32-bit)
1.0.2 Available View build log
1.0 Available View build log
Linux (64-bit)
1.0.2 Available View build log
1.0 Available View build log
Lastest release
version 1.0.2 on May 23rd, 2012

The ZODB is a mostly easy to use object oriented database -- especially, when used within a framework which provides transaction management (such as Zope). Nice features are the almost transparent persistency (modified objects are automatically stored when the transaction is committed) and the absence of locking requirements (due to an optimistic concurrency control). However, the ZODB becomes a bit difficult when operations need to be performed asynchronously, i.e. in a separate thread.

This package contains some utilities to make it easier to implement asynchronous access to the ZODB. Some of those can be helpful, too, in a synchronous environment.


The package depends on decorator, transaction, dm.transaction.aborthook and ZODB3 (>= 3.8). The ZODB dependancy is not declared in setup.py because I need the package in an environment where the ZODB is not used eggified.

The module zope2 depends on Zope 2 (>= 2.10). This dependancy, too, is not declared (for identical reasons).


The package consists of modules transactional, scheduler, context and zope2.

Detailed information can be found in the source via docstrings.


transactional contains decorators which provide transaction management in environments where this is not provided by the framework. They can be useful even in a synchronous environment (e.g. a script environment). The transaction management comprises automatic retry after concurrency problems (which the ZODB indicates by so a called ConflictError).

Its main content is the decorator transactional, a particular instance of the class TransactionManager. transactional (and other instances of TransactionManager) declare a function or method to be transactional: before the function is called, a new transaction is begun (a potentially pending transaction aborted), metadata is registered for the transaction and when the function returns the transaction is either committed (no exception) or aborted (exception). If the exception was a ConflictError, the call is retried up to a configurable number of times after configurable delays.

transactional (and other instances of TransactionManager) have effect only at the top level, as the ZODB does not support fully nested transactions (it can, however, partially emulated nested transactions by so called "savepoint"s). Nested calls (inside the same transaction) simply call the decorated function/method. The decorators recognize only their own transaction management: if the transaction is managed on higher level, this is not recognized and control is taken over.


In this section, we set up a simple example that demonstrates how transactional (and other instances of TransactionManager) is used and what it does.

transactional manages transactions. Therefore, it is useful to be able to monitor transaction management. We use after commit hooks (directly provided by transaction) and abort hooks (provided by dm.transaction.aborthook). With them, we define the auxiliary function register_hooks which will monitor transaction aborts and commits. We also set up logging to see logging messages.

>>> import transaction
>>> def register_hooks(text):
...   """register transaction hooks such that we can monitor transaction operation"""
...   def show(status, type):
...     print "transaction %s:" % type, text
...   T = transaction.get() # current transaction
...   T.addAfterCommitHook(show, ("commit",))
...   T.addAfterAbortHook(show, (False, "abort"))
>>> from logging import basicConfig
>>> basicConfig()

We now define two simple transactional functions f and g with f calling g and then call f.

>>> @transactional
... def f(a=1, b=2):
...   register_hooks("f")
...   print "f:", a, b
...   g(2*a)
...   print "after g call"
...   return a + b
>>> @transactional
... def g(x):
...   register_hooks("g")
...   print "g:", x
>>> f()
f: 1 2
g: 2
after g call
transaction commit: f
transaction commit: g

The output tells us, that transaction commit hooks have been called. This means that some transaction has been commited. In addition, the g transaction commit hook was not called at the end of the g call but at the end of the f call. This means that the g call has not introduced its own transaction level but participates on that of f -- even though, g has be declared transactional. When we call g directly, we see that in this case, it gets its own transaction control.

>>> g(1)
g: 1
transaction commit: g

Should a transactional method raise an exception, the transaction is aborted and the exception is propagated:

>>> @transactional
... def raise_exception():
...   register_hooks("raise exception")
...   print "raise exception"
...   raise ValueError()
>>>> raise_exception()
raise exception
transaction abort: raise exception
Traceback (most recent call last):

In case of a ConflictError, the call is automatically retried (in a new transaction). Retrial may be repeated (how often is controlled by a TransactionManager attribute) with increasing randomly chosen delays between retries (also controlled by TransactionManager attributes).

For demonstrational purposes, we define a class for with the first call raises ConflictError and the second call succeeds. Therefore, the first retrial succeeds and the example will not show further retrials.

>>> class ConflictRaiser(object):
...   raised = False
...   @transactional
...   def __call__(self):
...     register_hooks("conflict raiser")
...     if self.raised: print "conflict raiser returns without exception"
...     else:
...       print "conflict raiser raises `ConflictError`"
...       self.raised = True
...       from ZODB.POSException import ConflictError
...       raise ConflictError()
>>> cr = ConflictRaiser()
>>> cr()
conflict raiser raises `ConflictError`
transaction abort: conflict raiser
ERROR:dm.zodb.asynchronous.transactional:retrying __call__
Traceback (most recent call last):
ConflictError: database conflict error
conflict raiser returns without exception
transaction commit: conflict raiser

This module defines the class TransactionalScheduler which supports the following use case: some context starts an operation in a separate thread and then terminates; a different context later checks whether the operation has completed and if so processes the results. The use case arises for example in a web application (such as Zope) for long running operations which should be processed asynchronously (in a separate thread) rather than inline (in the originating request) to provide useful partial results or feedback immediately. Later results are fetched and presented e.g. via dynamic (AJAX, Web 2) techniques.

The initial schedule returns an identifier which can later be used to check for and access results.

The function becomes nontrivial when the operation must access the ZODB. The ZODB forbids a thread to access persistent objects loaded in a separate thread. Therefore, persistent objects accessed asynchronously must be reloaded from the ZODB via a new thread specific connection. Without special measures, the asynchronous operation may not see modifications to persistent objects performed by the context which has scheduled the asynchonous operation (as they become available only after the transaction has committed). TransactionScheduler uses the after-commit hook of ZODB transactions to start the asynchronous operation ensuring that modifications are seen.

When the result of an asynchronous operation is fetched, its deletion is automatically scheduled at transaction commit. A deletion timeout controls deletion of results which got "forgotten".


This example demonstrates the working of the TransactionalScheduler. We set up logging, a scheduler (s) and a simple function (show) with prints something and returns something so that we can monitor when it is called.

>>> from transaction import abort, commit
>>> from logging import basicConfig
>>> basicConfig()
>>> import dm.zodb.asynchronous.scheduler
>>> s = dm.zodb.asynchronous.scheduler.TransactionalScheduler()
>>> def show(*args, **kw):
...   print "show:", args, kw
...   return "ok"

The scheduling returns an id which can be used to learn about the operation's fate via a get_result call. If get_result returns None, the schedule is unknown (probably lost); False means known but not yet complete. Finally, get_result may return a tuple return-value, exception.

After a new schedule, the schedule is known but not yet complete.

>>> sid = s.schedule(show, 1, 2, a="a")
>>> s.get_result(sid)

A transaction abort deletes the schedule.

>>> abort()
>>> s.get_result(sid)

If the transaction is commited, the scheduled operation is called.

>>> sid = s.schedule(show, 1, 2, a="a")
>>> commit()
show: (1, 2) {'a': 'a'}

After the completion, get_result returns its result. A transaction abort does not delete the result. However, a commit will.

>>> s.get_result(sid)
('ok', None)
>>> abort()
>>> s.get_result(sid)
('ok', None)
>>> commit()
>>> s.get_result(sid)

We now schedule exc, a function which raises an exception.

def exc(): raise Exception() ... >>> sid = s.schedule(exc) >>> commit() >>> ERROR:dm.zodb.asynchronous.scheduler:exception in call of <function exc at 0xb687fb54> Traceback (most recent call last): ... Exception

Note: The output above comes from the logging; the commit does not output anything by itself.

Again, get_result provides information about the result.

>>> s.get_result(sid)
(None, <exceptions.Exception instance at 0xb687c50c>)

This module defines the class PersistentContext. It can be used to pass persistent objects from one (thread) context to another one. As described in the scheduler section, persistent objects cannot simply be passed on: instead the target context must reload them from a (new) connection associated with the target. PersistentContext records the databases and oids associated with the persistent objects and facilitates the reloading inside the target.

See the module docstrings for details, especially about the restrictions and risks.

An example is shown in the section "Typical Usage Example".

Note: PersistentContext does not retain any acquisition context. This means (among others) that the Zope2 security mechanism will fail and that the target thread will not have access to the request object (a good thing as it gets closed asynchronously). Thus, there are still severe limitations of what you can do in an asynchronous operation.


This module contains adaptations of facilities defined in the other modules to a Zope2 environment. For example, there is an adapted TransactionManager (and derived transactional decorator) which provides transaction metadata in the way typical for the Zope 2 framework. There is also a PersistantContext implementation which automatically determines the root database using Zope 2 implementation details.

Typical Usage Example

As mentioned in section scheduler, the package can be used in (e.g.) a Zope 2 environment when some operation takes too much time to be performed inline (in the same request). In this case, one can execute it in a separate thread and look for its results in a following (new) request. We present now a simple example.

We define a simple asynchronous_operation, for demonstrational purposes. In real life, the scheduler would probably be global, e.g. provided by a so called "utility".

>>> import transaction
>>> from dm.zodb.asynchronous.zope2 import transactional, PersistentContext
>>> from dm.zodb.asynchronous.scheduler import TransactionalScheduler
>>> @transactional
... def asynchronous_operation(context):
...   print "asynchronous_operation"
...   return (context["param"].x, context[0].x)
>>> scheduler = TransactionalScheduler()

We simulate now a request which schedules asynchronous_operation and allows it to access the app object (the Zope2 root object) via PersistentContext. PersistentContext supports both positional as well as keyword parameters. For demonstational purposes, we pass app both positional as well as via the keyword param. Inside asynchronous_operation, subscription is used to access the persistent objects; an integer index accesses positional arguments, an str index the keyword arguments.

The scheduling returns an id which (in real life) would somehow be stored (e.g. in the user session or (better) be incorporated inside the generated response and be used as parameter of a followup request). The assignment to app.x is used to demonstrate that asynchronous_operation sees modifications performed in the original request (even when they happen after the scheduling).

At the end of the initial request, there will be either a transaction.abort() or a transaction.commit(). In the former case, the schedule will be removed and asynchronous_operation not started. In the latter case, asynchronous_operation will start.

>>> sid = scheduler.schedule(asynchronous_operation,
...                          PersistentContext(app, param=app)
...                          )
>>> print sid
>>> app.x = 1
>>> transaction.commit()

We look now at the followup request. Things must somehow have been set up that it can access the same scheduler (usually done via an utility). Somehow, the followup request has learned of the schedule id (from the user session or via a request parameter). With this information, it can check the fate of the asynchronous operation, process the result and commit.

>>> r = scheduler.get_result(sid)
>>> if r is None: print "lost schedule"
... elif not r: print "operation not yet complete"
... else:
...   (rv, exc) = r
...   if exc is not None:
...     # the asynchronous operation has raised *exc*.
...     # Do not reraise it! It belongs to a different context.
...     # If you raise a different exception, you might want
...     #   to call ``scheduler.remove(sid)``; otherwise, the schedule
...     #   gets removed only after timeout.
...     # Usually, you would not raise an exception but only provide information
...     # about the failure of the asynchronous operation
...     print "exceptioon: ", exc
...     #return process_exception(exc)
...   else:
...     print rv
...     #return process_return_value(rv)
(1, 1)
>>> transaction.commit()

The code snippet above has an extended comment about exception handling from asynchronous_operation (in our trivial exemple, there will be no exception). Note that a failing asynchronous operation does not mean that the current request has failed. The purpose of the current request is to inform us about the fate of the asynchronous operation, not to perform this operation. Therefore, a failure of the asynchronous operation usually should result in the success of the current request (no exception) -- with appropriate information that the asynchronous operation has failed. In our exemple, we have decorated asynchronous_request with transactional. This way, it handles transaction management correctly in case of errors (the transaction gets aborted when the asynchronous operation should fail).

Subscribe to package updates

Last updated May 23rd, 2012

Download Stats

Last month:1

What does the lock icon mean?

Builds marked with a lock icon are only available via PyPM to users with a current ActivePython Business Edition subscription.

Need custom builds or support?

ActivePython Enterprise Edition guarantees priority access to technical support, indemnification, expert consulting and quality-assured language builds.

Plan on re-distributing ActivePython?

Get re-distribution rights and eliminate legal risks with ActivePython OEM Edition.