by Andy McKay
Mozilla's add-ons site serves a few billion hits a day through Python. This talk covers how the site is built, how it scales, the fun we had on the way and tools that you can use from it.
by Jim Baker
Jython is arguably the best Python implementation to target concurrent
code. Jython has no GIL, it leverages the Java platform to provide
robust support for concurrency in its runtime, and it enables access
to a set of high-level abstractions from Java. This talk will walk
through a series of motivating examples that emphasize Java integration.
by Lynn Root
Are you a woman wanting to break into the engineering field? Or do you know any women wanting to learn how to code, but don’t know how to help them? Perhaps our nerdy ladies are a bit shy too ask for help. That’s okay! I’ll give you some tools to nudge you in the right direction.
In this session, I’ll be talking about how I increased women’s engagement in the Python community though demonstrating how versatile the language is with practical projects, by creating a safe space for women to learn and to fail, and by encouraging involvement beyond the classroom.
by Wesley Chun
Twitter and Google+ are examples of the highly active and popular social media scene today. Using Python, you can easily execute searches on both. All you need is a client library (easily found), and you can get going without a lot of effort!
Have you ever needed to work on applications in two different versions of the same language at the same time? There are many ways to do this, most of which involve a lot of mechanics or are custom to a single language or both.
by Eric Wills
Metaclasses are a commonly misunderstood and misrepresented topic in Python. This session will introduce metaclasses, explore the added functionality they provide, and look at some real world examples of metaclasses as we use them in server-side web development at Vizme.
by Tom Lee
Ever wondered how you might experiment with your own syntax changes to the Python programming language? Learn about the internals of Python as I dissect the steps required to introduce a new keyword to the language.
PostgreSQL 9.1 introduced ‘Foreign Data Wrappers’ (FDW) – a partial implementation of the SQL/MED standard for handling access to remote data sources. FDW allows PostgreSQL to expose remote data as foreign tables which then behave similarly to native PostgreSQL tables, in particular, allowing remote data to be queried with SQL statements.
This session provides an overview of Foreign Data Wrappers, looks at the native interface for writing FDWs in C, and contrasts this with Multicorn, an open source framework that allows FDWs to be developed in Python. We will show a real-world Python FDW that retrieves business data from salesforce.com, with a sample client application that demonstrates how foreign data can be combined with data held in native PostgreSQL tables using a simple SQL JOIN.
16th–20th July 2012