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Wednesday 7th March 2012
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MongoDB and Python
by Rick Copeland and Bernie Hackett
This intermediate-level class will teach you techniques using the popular NoSQL database MongoDB, its driver PyMongo, and the object-document mapper Ming to write maintainable, high-performance, and scalable applications. We will cover everything you need to become an effective Ming/MongoDB developer from basic PyMongo queries to high-level object-document mapping setups in Ming.
The class will begin with a brief overview of MongoDB and its Python driver PyMongo. We will cover basic operations using PyMongo, including data manipulation, querying, and GridFS. Students will install MongoDB and PyMongo as part of this section.
We will then describe the design philosophy and setup of Ming, a SQLAlchemy-inspired object-document mapper (ODM) for MongoDB developed at SourceForge.
Next we will cover the base-level implementation of Ming, including schema design, the session and datastore, lazy migrations, data polymorphism, and GridFS support. We will also cover effective MongoDB index design, querying, and updating techniques, and how to use these with Ming. Students will install Ming as a part of this section, and have exercises covering schema design, lazy migrations, and GridFS.
The final segment will cover the object-document mapper portion of Ming. We will cover the unit of work design pattern, object relations, ODM-level polymorphism, and how to drop down to the base layer (or even down to pymongo) when you really need to. This section will include exercises in designing your ODM model and effectively using the unit-of-work session.
This talk targets Python 2.6-2.7 and MongoDB 2.0. Students should have Python 2.6 or 2.7 installed on their machines prior to the class and should be comfortable using virtualenv and pip or easy_install to install packages.
Thursday 8th March 2012
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Advanced Python I
Have your Python skills have hit a plateau? Come learn from Python core developer and consultant Raymond Hettinger about the tips and tricks needed to move up to the next level.
This tutorial will work through a series of real-world examples, showing how an understanding of the tools built into the Python interpreter or included in the standard library can be combined to solve difficult problems clearly and Pythonically. We will also discuss when and how to reach beyond the standard library when needed to address difficult algorithmic and optimization problems. This can be taken as a stand-alone session or in conjunction with the second session; the two sessions will be complementary.
Friday 9th March 2012
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Code Generation in Python: Dismantling Jinja
For many DSLs such as templating languages it's important to use code generation to achieve acceptable performance in Python. The current version of Jinja went through many different iterations to end up where it is currently. This talk walks through the design of Jinja2's compiler infrastructure and why it works the way it works and how one can use newer Python features for better results.
Why Code Generation?
It seems like the general consensus for code generation in many dynamic language communities is: eval is evil, do not use it. However if done properly code generation solves a lot of problems easily, securely and with much better performance than an interpreter written on top of an interpreted language like Python.Code generation is what powers most template languages in Python, what powers object relational mappers and more. It is also an excellent tool to simplify debugging.
Why Codegen is no Silver Bullet
Just because you generate code does not mean you're faster than an interpreter written in Python. This part of the talk focuses on why compiling Django templates to Python bytecode does not automatically make it fast.Design of Jinja2
Jinja2 underwent multiple design iterations, most of which were made to either improve performance or debug-ability. The internals however are largely undocumented and confusing unless you're familiar with the code. In it however are a few gems hidden and interesting tricks to make code generation work in the best possible way.Python's Support for Code Generation
Over the years Python's support for code generation was steadily improved with different ways to access the abstract syntax tree and to compiling it back to bytecode. This section highlights some alternative ways to do code generation that are not yet fully implemented in Jinja2 but are otherwise widely used. -
Apache Cassandra and Python
by Jeremiah Jordan
Using Apache Cassandra from Python is easy to do. This talk will cover setting up and using a local development instance of Cassandra from Python. It will cover using the low level thrift interface, as well as using the higher level pycassa library.
- Very brief intro to Apache Cassandra
- What is Apache Cassandra and where do I get it?
- Using the Cassandra CLI to setup a keyspace (table) to hold our data
- Installing the Cassandra thrift API module
- Using Cassandra from the thrift API
- Connecting
- Writing
- Reading
- Batch operations
- Installing the pycassa module
- Using Cassandra from the pycassa module
- Connecting
- Reading
- Writing
- Batch operations
- Indexing in Cassandra
- Automatic vs Rolling your own
- Using Composite Columns
- Setting them up from the CLI
- How to using them from pycassa
- Lessons learned
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Make Sure Your Programs Crash
by Moshe Zadka
With Python, segmentation faults and the like simply don't happen -- programs do not crash. However, the world is a messy, chaotic place. What happens when your programs crash? I will talk about how to make sure that your application survives crashes, reboots and other nasty problems.
Handling crashes is divided into two parts -- resilience (making sure that your software maintains correctness in the face of crashes) and speed of recovery (optimizing the time it takes back to get back to full working condition). I will talk about techniques to allow for resilience -- separating master data from cache data, minimizing the amount of master data, using atomic file operations, using databases and persisting structures in the right order. Then I will talk about speedy recovery techniques, among them process separation, working while restarting and more. I will conclude with surveying the options in testing all of these things so that the crashes are made to happen in the development/testing environment.
Outline:
- Ways Python programs can crash
- Infinite loops
- Getting stuck
- Memory leaks
- Exceptions
- Catching exceptions considered scary
- Threads dead-locks
- Minimizing effects of a crash
- Atomic file operations
- Databases
- Vertical process splitting
- Horizontal process splitting
- Limiting process lifetime
- Detecting crashes
- Process death
- Process inresponsiveness
- Test communication
- Helper checker processes
- Restarting processes
- Minimize master data
- Boot-up speed
- Order of start-up and communication
- Testing by killing processes
- Testing by pausing processes
- Conclusions
- Python processes can still crash
- Plan for crashes
- Test your plan for crashes
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Python Metaprogramming for Mad Scientists and Evil Geniuses
by Walker Hale
This talk covers the power and metaprogramming features of Python that cater to mad scientists and evil geniuses. This will also be of interest to others who just want to use of Python in a more power (hungry) way. The core concept is that you can synthesize functions, classes and modules without a direct correspondence to source code. You can also mutate third-party objects and apps.
This talk covers the power and metaprogramming features of Python that cater to mad scientists and evil geniuses. This will also be of interest to others who just want to use of Python in a more power (hungry) way.
Users of Python are not limited to the usual model of a one-to-one correspondence between source code and live objects. Python allows you to synthesize functions, classes and modules without a direct correspondence to source code. You can mutate third-party objects, classes, modules and applications through monkey patching -- changing their behavior without altering their source code. You can even "chop-up" third-party objects to create new objects from the pieces. Find out how to unleash your inner Mad Scientist!
Thesis: Python is an ideal language for both:
- Mad Scientists
- Evil Geniuses
- Mad Scientist versus Evil Genius
- Mad Scientist: creating new things because it's cool
- Evil Genius: practical applications
- Typical Mad Science Goals
- Create new living code objects from scraps without corresponding source code.
- Mutate third-party code to suite our purposes without modifying the third-party source code.
- Synthetics
- Synthetic Functions
- Synthetic Classes
- Synthetic Modules
- Applications of Synthetics
- Monkey Patching
- Monkey Patching Modules
- Monkey Patching Classes
- Monkey Patching Instances
- sitecustomize.py
- Dealing with Angry Villagers
- Limitations: When not to do this
- For the Evil Geniuses
Although most of the material is presented from the point of view of the Mad Scientist, it is equally useful to the Evil Genius.
Since the Python community prides itself on diversity, I should emphasize that the sane, the non-evil, and "do-gooders" are all welcome.
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Introspecting Running Python Processes
by Adam Lowry
Understanding the internal state of a running system can be vital to maintaining a high performance, stable system, but conventional approaches such as logging and error handling only expose so much. This talk will touch on how to instrument Python programs in order to observe the state of the system, measure performance, and identify ongoing problems.
Something is wrong with your web application. The time it’s taking to serve requests is growing. Your logs don’t contain enough. Your database appears bored. How do you know what’s going wrong?
In high-performance production servers it’s vital to know as much about the internals of your system as possible. Traditionally this is done by simple methods like logging anything of potential interest or sending error emails with unexpected exceptions. These methods are insufficient, both due to the level of noise inherent in such systems and because of the difficulty in anticipating what metrics are important during an incident.
Environments such as the JVM and .Net VM have advanced tools for communicating with the VM and for applications to expose internal state, but CPython has lacked similar tooling.
This talk will cover what options CPython application developers have for introspecting their programs; new tools for instrumenting, exposing, and compiling performance and behavior metrics; and techniques for diagnosing runtime issues without restarting the process.
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