Python

Storm 0.13

Yesterday, Thomas rolled the 0.13 release of Storm, which can be downloaded from Launchpad.  Storm is the object relational mapper for Python used by Launchpad and Landscape, so it is capable of supporting quite large scale applications.  It is seven months since the last release, so there is a lot of improvements.  Here are a few simple statistics:

0.12 0.13 Change
Tarball size (KB) 117 155 38
Mainline revisions 213 262 49
Revisions in ancestry 552 875 323

So it is a fairly significant update by any of these metrics.  Among the new features are:

  • Infrastructure for tracing the SQL statements issued by Storm.  Sample tracer implementations are provided to implement bounded statement run times and for logging statements (both features used for QA of Launchpad).
  • A validation framework.  The property constructors take a validator keyword argument, which should be a function taking arguments (object, attr_name, value) and return the value to set.  If the function raises an exception, it can prevent a value from being set.  By returning something different to its third argument it can transform values.
  • The find() and ResultSet API has been extended to make it possible to generate queries that use GROUP BY and HAVING.  The primary use case for result sets that contain an object plus some aggregates associated with that object.
  • Some core parts of Storm have been accelerated through a C extension.  This code is turned off by default, but can be enabled by defining the STORM_CEXTENSIONS environment variable to 1.  While it is disabled by default, it is pretty stable.  Barring any serious problems reported over the next release cycle, I’d expect it to be enabled by default for the next release.
  • The minimum dependencies of the storm.zope.zstorm module have been reduced to just the zope.interface and transaction modules.  This makes it easier to use the per-thread store management code and global transaction management outside of Zope apps (e.g. for integrating with Django).

It doesn’t include my Django integration code though, since that isn’t fully baked.  I’ll post some more about that later.

Using Storm with Django

I’ve been playing around with Django a bit for work recently, which has been interesting to see what choices they’ve made differently to Zope 3.  There were a few things that surprised me:

  • The ORM and database layer defaults to autocommit mode rather than using transactions.  This seems like an odd choice given that all the major free databases support transactions these days.  While autocommit might work fine when a web application is under light use, it is a recipe for problems at higher loads.  By using transactions that last for the duration of the request, the testing you do is more likely to help with the high load situations.
  • While there is a middleware class to enable request-duration transactions, it only covers the database connection.  There is no global transaction manager to coordinate multiple DB connections or other resources.
  • The ORM appears to only support a single connection for a request.  While this is the most common case and should be easy to code with, allowing an application to expand past this limit seems prudent.
  • The tutorial promotes schema generation from Python models, which I feel is the wrong choice for any application that is likely to evolve over time (i.e. pretty much every application).  I’ve written about this previously and believe that migration based schema management is a more workable solution.
  • It poorly reinvents thread local storage in a few places.  This isn’t too surprising for things that existed prior to Python 2.4, and probably isn’t a problem for its default mode of operation.

Other than these things I’ve noticed so far, it looks like a nice framework.

Integrating Storm

I’ve been doing a bit of work to make it easy to use Storm with Django.  I posted some initial details on the mailing list.  The initial code has been published on Launchpad but is not yet ready to merge. Some of the main details include:

  • A middleware class that integrates the Zope global transaction manager (which requires just the zope.interface and transaction packages).  There doesn’t appear to be any equivalent functionality in Django, and this made it possible to reuse the existing integration code (an approach that has been taken to use Storm with Pylons).  It will also make it easier to take advantage of other future improvements (e.g. only committing stores that are used in a transaction, two phase commit).
  • Stores can be configured through the application’s Django settings file, and are managed as long lived per-thread connections.
  • A simple get_store(name) function is provided for accessing per-thread stores within view code.

What this doesn’t do yet is provide much integration with existing Django functionality (e.g. django.contrib.admin).  I plan to try and get some of these bits working in the near future.

How not to do thread local storage with Python

The Python standard library contains a function called thread.get_ident().  It will return an integer that uniquely identifies the current thread at that point in time.  On most UNIX systems, this will be the pthread_t value returned by pthread_self(). At first look, this might seem like a good value to key a thread local storage dictionary with.  Please don’t do that.

The value uniquely identifies the thread only as long as it is running.  The value can be reused after the thread exits.  On my system, this happens quite reliably with the following sample program printing the same ID ten times:

import thread, threading

def foo():
    print 'Thread ID:', thread.get_ident()

for i in range(10):
    t = threading.Thread(target=foo)
    t.start()
    t.join()

If the return value of thread.get_ident() was used to key thread local storage, all ten threads would share the same storage. This is not generally considered to be desirable behaviour.

Assuming that you can depend on Python 2.4 (released 3.5 years ago), then just use a threading.local object. It will result in simpler code, correctly handle serially created threads, and you won’t hold onto TLS data past the exit of a thread.

You will save yourself (or another developer) a lot of time at some point in the future. Debugging these problems is not fun when you combine code doing proper TLS with other code doing broken TLS.

Psycopg migrated to Bazaar

Last week we moved psycopg from Subversion to Bazaar.  I did the migration using Gustavo Niemeyer‘s svn2bzr tool with a few tweaks to map the old Subversion committer IDs to the email address form conventionally used by Bazaar.

The tool does a good job of following tree copies and create related Bazaar branches.  It doesn’t have any special handling for stuff in the tags/ directory (it produces new branches, as it does for other tree copies).  To get real Bazaar tags, I wrote a simple post-processing script to calculate the heads of all the branches in a tags/ directory and set them as tags in another branch (provided those revisions occur in its ancestry).  This worked pretty well except for a few revisions synthesised by a previous cvs2svn migration.  As these tags were from pretty old psycopg 1 releases I don’t know how much it matters.

As there is no code browsing set up on initd.org yet, I set up mirrors of the 2.0.x and 1.x branches on Launchpad to do this:

It is pretty cool having access to the entire revision history locally, and should make it easier to maintain full credit for contributions from non-core developers.

Psycopg2 2.0.7 Released

Yesterday Federico released version 2.0.7 of psycopg2 (a Python database adapter for PostgreSQL).  I made a fair number of the changes in this release to make it more usable for some of Canonical‘s applications.  The new release should work with the development version of Storm, and shouldn’t be too difficult to get everything working with other frameworks.

Some of the improvements include:

  • Better selection of exceptions based on the SQLSTATE result field.  This causes a number of errors that were reported as ProgrammingError to use a more appropriate exception (e.g. DataError, OperationalError, InternalError).  This was the change that broke Storm’s test suite as it was checking for ProgrammingError on some queries that were clearly not programming errors.
  • Proper error reporting for commit() and rollback(). These methods now use the same error reporting code paths as execute(), so an integrity error on commit() will now raise IntegrityError rather than OperationalError.
  • The compile-time switch that controls whether the display_size member of Cursor.description is calculated is now turned off by default.  The code was quite expensive and the field is of limited use (and not provided by a number of other database adapters).
  • New QueryCanceledError and TransactionRollbackError exceptions.  The first is useful for handling queries that are canceled by statement_timeout.  The second provides a convenient way to catch serialisation failures and deadlocks: errors that indicate the transaction should be retried.
  • Fixes for a few memory leaks and GIL misuses. One of the leaks was in the notice processing code that could be particularly problematic for long-running daemon processes.
  • Better test coverage and a driver script to run the entire test suite in one go.  The tests should all pass too, provided your database cluster uses unicode (there was a report just before the release of one test failing for a LATIN1 cluster).

If you’re using previous versions of psycopg2, I’d highly recommend upgrading to this release.

Future work will probably involve support for the DB-API two phase commit extension, but I don’t know when I’ll have time to get to that.