Adding OData support to Django with Pyslet: First Thoughts

A couple of weeks ago I got an interesting tweet from @d34dl0ck, here it is:

This got me thinking, but as I know very little about Django I had to do a bit of research first. Here's my read-back of what Django's data layer does in the form of a concept mapping from OData to Django. In this table the objects are listed in containment order and the use case of using OData to expose data managed by a Django-based website is assumed. (See below for thoughts on consuming OData in Django as if it were a data source.)

OData ConceptDjango ConceptPyslet Concept
DataServices Django website: the purpose of OData is to provide access to your application's data-layer through a standard API for machine-to-machine communication rather than through an HTML-based web view for human consumption. Instance of the DataServices class, typically parsed from a metadata XML file.
Schema No direct equivalent. In OData, the purpose of the schema is to provide a namespace in which definitions of the other elements take place. In Django this information will be spread around your Python source code in the form of class definitions that support the remaining concepts. Instance of the Schema class, typically parsed from a metadata XML file.
EntityContainer The database. An OData service can define multiple containers but there is always a default container - something that corresponds closely with the way Django links to multiple databases. Most OData services probably only define a single container and I would expect that most Django applications use the default database. If you do define custom database routers to map different models to different databases then that information would need to be represented in the corresponding Schema(s). In Pyslet, an EntityContainer is defined by an instance of the EntityContainer class but this instance is handed to a storage layer during application startup and this storage layer class binds concrete implementations of the data access API to the EntitySets it contains.
EntitySet Your model class. A model class maps to a table in the Django database. In OData the metadata file contains the information about which container contains an EntitySet and the EntityType definition in that file contains the actual definitions of the types and field names. In contrast, in Django these are defined using class attributes in the Python code. Pyslet sticks closely to the OData API here and parses definitions from the metadata file. As a result an EntitySet instance is created that represents this part of the model and it is up to the object responsible for interfacing to the storage layer to provide concrete bindings.
Entity An instance of a model class. An instance of the Entity object, typically instantiated by the storage object bound to the EntitySet.

Where do you start?

Step 1: As you can see from the above table, Pyslet depends fairly heavily on the metadata file so a good way to start would be to create a metadata file that corresponds to the parts of your Django data model you want to expose. You have some freedom here but if you are messing about with multiple databases in Django it makes sense to organise these as separate entity containers. You can't create relationships across containers in Pyslet which mirrors the equivalent restriction in Django.

Step 2: You now need to provide a storage object that maps Pyslet's DAL onto the Django DAL. This involves creating a sub-class of the EntityCollection object from Pyslet. To get a feel for the API my suggestion would be to create a class for a specific model initially and then, once this is working, consider how you might use Python's built-in introspection to write a more general object.

To start with, you don't need to do too much. EntityCollection objects are just like dictionaries but you only need to override itervalues and __getitem__ to get some sort of implementation going. There are simple wrappers that will (inefficiently) handle ordering and filtering for you to start with so itervalues can be very simple...

def itervalues(self):
    return self.OrderEntities(

All you need to do is write the entityGenerator method (the name is up to you) and yield Entity instances from your Django model. This looks pretty simple in Django, something like Customer.objects.all() where Customer is the name of a model class would appear to return all customer instances. You need to yield an Entity object from Pyslet's DAL for each customer instance and populate the property values from the fields of the returned model instance.

Implementing __getitem__ is probably also very easy, especially when you are using simple keys. Something like Customer.objects.get(pk=1) and then a similar mapping to the above seems like it would work for implementing basic resource look up by key. Look at the in-memory collection class implementation for the details of how to check the filter and populate the field values, it's in pyslet/odata2/memds.py.

Probably the hardest part of defining an EntityCollection object is getting the constructor right. You'll want to pass through the Model class from Django so that you can make calls like the above:

def __init__(self,djangoModel,**kwArgs):

Step 3: Load the metadata from a file, then bind your EntityCollection class or classes to the EntitySets. Something like this might work:

import pyslet.odata2.metadata as edmx
with open('DjangoAppMetadata.xml','rb') as f:
# customers is an EntitySet instance

The Customer object here is your Django model object for Customers and the DjangoCollection object is the EntityCollection object you created in Step 2. Each time someone opens the customers entity set a new DjangoCollection object will be created and Customer will be passed as the djangoModel parameter.

Step 4: Test that the model is working by using the interpreter or a simple script to open the customers object (the EntitySet) and make queries with the Pyslet DAL API. If it works, you can wrap it with an OData server class and just hook the resulting wsgi object to your web server and you have hacked something together.

Post hack

You'll want to look at Pyslet's expression objects and figure out how to map these onto the query objects used by Django. Although OData provides a rich query syntax you don't need to support it all, just reject stuff you don't want to implement. Simple queries look like they'd map to things you can pass to the filter method in Django fairly easily. In fact, one of the problems with OData is that it is very general - almost SQL over the web - and your application's data layer is probably optimised for some queries and not others. Do you want to allow people to search your zillion-record table using a query that forces a full table scan? Probably not.

You'll also want to look at navigation properties which map fairly neatly to the relationship fields. The Django DAL and Pyslet's DAL are not miles apart here so you should be able to create NavigationCollection objects (equivalent to the class you created in Step 2 above) for these. At this point, the power of OData will begin to come alive for you.

Making it Django-like

I'm not an expert on what is and is not Django like but I did notice that there is a Feed concept for exposing RSS in Django. If the post hack process has left you with a useful implementation then some sort of OData equivalent object might be a useful addition. Given that Django tends to do much of the heavy lifting you could think about providing an OData feed object. It probably isn't too hard to auto-generate the metadata from something like class attributes on such an object. Pyslet's OData server is a wsgi application so provided Django can route requests to it you'll probably end up with something that is fairly nicely integrated - even if it can't do that out of the box it should be trivial to provide a simple Django request handler that fakes a wsgi call.

Consuming OData

Normally you think of consuming OData as being easier than providing it but for Django you'd be tempted to consider exposing OData as a data source, perhaps as an auxiliary database containing some models that are externally stored. This would allow you to use the power of Django to create an application which mashed up data from OData sources as if that data were stored in a locally accessible database.

This appears to be a more ambitious project: Django non-rel appears to be a separate project and it isn't clear how easy it would be to intermingle data coming form an OData source with data coming from local databases. It is unlikely that you'd want to use OData for all data in your application. The alternative might be to try and write a Python DB API interface for Pyslet's DAL and then get Django treating it like a proper database. That would mean parsing SQL, which is nasty, but it might be the lesser of two evils.

Of course, there's nothing stopping you using Pyslet's builtin OData client class directly in your code to augment your custom views with data pulled from an external source. One of the features of Pyslet's OData client is that it treats the remote server like a data source, keeping persistent HTTP connections open, managing multi-threaded access and and pipelining requests to improve throughput. That should make it fairly easy to integrate into your Django application.


A Dictionary-like Python interface for OData Part III: a SQL-backed OData Server

This is the third and last part of a series of three posts that introduce my OData framework for Python. To recap:

  1. In Part I I introduced a new data access layer I've written for Python that is modelled on the conventions of OData. In that post I validated the API by writing a concrete implementation in the form of an OData client.
  2. In Part II I used the same API and wrote a concrete implementation using a simple in-memory storage model. I also introduced the OData server functionality to expose the API via the OData protocol.
  3. In this part, I conclude this mini-series with a quick look at another concrete implementation of the API which wraps Python's DB API allowing you to store data in a SQL environment.

As before, you can download the source code from the QTIMigration Tool & Pyslet home page. I wrote a brief tutorial on using the SQL backed classes to take care of some of the technical details.

Rain or Shine?

To make this project a little more interesting I went looking for a real data set to play with. I'm a bit of a weather watcher at home and for almost 20 years I've enjoyed using a local weather station run by a research group at the University of Cambridge. The group is currently part of the Cambridge Computer Laboratory and the station has moved to the William Gates building.

The Database

The SQL implementation comes in two halves. The base classes are as close to standard SQL as I could get and then a small 'shim' sits over the top which binds to a specific database implementation. The Python DB API takes you most of the way, including helping out with the correct form of parameterisation to use. For this example project I used SQLite because the driver is typically available in Python implementations straight out of the box.

I wrote the OData-style metadata document first and used it to automatically generate the CREATE TABLE commands but in most cases you'll probably have an existing database or want to edit the generated scripts and run them by hand. The main table in my schema got created from this SQL:

CREATE TABLE "DataPoints" (
    "Temperature" REAL,
    "Humidity" SMALLINT,
    "DewPoint" REAL,
    "Pressure" SMALLINT,
    "WindSpeed" REAL,
    "WindDirection" TEXT,
    "WindSpeedMax" REAL,
    "SunRainStart" REAL,
    "Sun" REAL,
    "Rain" REAL,
    "DataPointNotes_ID" INTEGER,
    PRIMARY KEY ("TimePoint"),
    CONSTRAINT "DataPointNotes" FOREIGN KEY ("DataPointNotes_ID") REFERENCES "Notes"("ID"))

To expose the database via my new data-access-layer API you just load the XML metadata, create a SQL container object containing the concrete implementation and then you can access the data in exactly the same way as I did in Part's I and II. The code that consumes the API doesn't need to know if the data source is an OData client, an in memory dummy source or a full-blown SQL database. Once I'd loaded the data, here is a simple session with the Python interpreter that shows you the API in action.

>>> import pyslet.odata2.metadata as edmx
>>> import pyslet.odata2.core as core
>>> doc=edmx.Document()
>>> with open('WeatherSchema.xml','rb') as f: doc.Read(f)
>>> from pyslet.odata2.sqlds import SQLiteEntityContainer
>>> container=SQLiteEntityContainer(filePath='weather.db',containerDef=doc.root.DataServices['WeatherSchema.CambridgeWeather'])
>>> weatherData=doc.root.DataServices['WeatherSchema.CambridgeWeather.DataPoints']
>>> collection=weatherData.OpenCollection()
>>> collection.OrderBy(core.CommonExpression.OrderByFromString('WindSpeedMax desc'))
>>> collection.SetPage(5)
>>> for e in collection.iterpage(): print "%s: Max wind speed: %0.1f mph"%(unicode(e['TimePoint'].value),e['WindSpeedMax'].value*1.15078)
2002-10-27T10:30:00: Max wind speed: 85.2 mph
2004-03-20T15:30:00: Max wind speed: 82.9 mph
2007-01-18T14:30:00: Max wind speed: 80.6 mph
2004-03-20T16:00:00: Max wind speed: 78.3 mph
2005-01-08T06:00:00: Max wind speed: 78.3 mph

Notice that the container itself isn't needed when accessing the data because the SQLiteEntityContainer __init__ method takes care of binding the appropriate collection classes to the model passed in. Unfortunately the dataset doesn't go all the way back to the great storm of 1987 which is a shame as at the time I was living in a 5th floor flat perched on top of what I was reliably informed was the highest building in Cambridge not to have some form of structural support. I woke up when the building shook so much my bed moved across the floor.

Setting up a Server

I used the same technique as I did in Part II to wrap the API with an OData server and then had some real fun getting it up and running on Amazon's EC2. Pyslet requires Python 2.7 but EC2 Linux comes with Python 2.6 out of the box. Thanks to this blog article for help with getting Python 2.7 installed. I also had to build mod_wsgi from scratch in order to get it to pick up the version I wanted. Essentially here's what I did:

# Python 2.7 install
sudo yum install make automake gcc gcc-c++ kernel-devel git-core -y
sudo yum install python27-devel -y
# Apache install
#  Thanks to http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-LAMP.html
sudo yum groupinstall -y "Web Server"
sudo service httpd start
sudo chkconfig httpd on

And to get mod_wsgi working with Python2.7...

sudo bash
yum install httpd-devel -y
mkdir downloads
cd downloads
wget http://modwsgi.googlecode.com/files/mod_wsgi-3.4.tar.gz
tar -xzvf mod_wsgi-3.4.tar.gz
cd mod_wsgi-3.4
./configure --with-python=/usr/bin/python2.7
make install
# Optional check to ensure that we've got the correct Python linked
# you should see the 2.7 library linked
ldd /etc/httpd/modules/mod_wsgi.so
service httpd restart

To drive the server with mod_wsgi I used a script like this:

#! /usr/bin/env python

import logging, os.path
import pyslet.odata2.metadata as edmx
from pyslet.odata2.sqlds import SQLiteEntityContainer
from pyslet.odata2.server import ReadOnlyServer



with open(os.path.join(HOME_DIR,'WeatherSchema.xml'),'rb') as f:



def application(environ, start_response):
 return server(environ,start_response)

I'm relying on the fact that Apache is configured to run Python internally and that my server object persists between calls. I think by default mod_wsgi serialises calls to the application method but a smarter configuration with a multi-threaded daemon would be OK because the server and container objects are thread safe. There are limits to the underlying SQLite module of course so you may not gain a lot of performance this way but a proper database would help.

Try it out!

If you were watching carefully you'll see that the above script uses a public service root. So let's try the same query but this time using OData. Here it is in Firefox:

Notice that Firefox recognises that the OData feed is an Atom feed and displays the syndication title and updated date. I used the metadata document to map the temperature and the date of the observation to these (you can see they are the same data points as above by the matching dates). The windiest days are never particularly hot or cold in Cambridge because they are almost always associated with Atlantic storms and the sea temperature just doesn't change that much.

The server is hosted at http://odata.pyslet.org/weather


A Dictionary-like Python interface for OData Part II: a Memory-backed OData Server

In my previous post, A Dictionary-like Python interface for OData I introduced a new sub-package I've added to Pyslet to implement support for OData version 2. You can download the latest version of the Pyslet package from the QTI Migration Tool & Pyslet home page.

To recap, I've decided to set about writing my own data access layer for Python that is modelled on the conventions of OData. I've validated the API by writing a concrete implementation in the form of an OData client. In this post I'll introduce the next step in the process which is a simple alternative implementation that uses a different underlying storage model, in other words, an implementation which uses something other than a remote OData server. I'll then expose this implementation as an OData server to validate that my data access layer API works from both perspectives.


Unlike other frameworks for implementing OData services Pyslet starts with the metadata model, it is not automatically generated from your code, you must write it yourself. This differs from the object-first approach taken by other frameworks, illustrated here:

This picture is typical of a project using something like Microsoft's WCF. Essentially, there's a two-step process. You use something like Microsoft's entity framework to generate classes from a database schema, customise the classes a little and then the metadata model is auto-generated from your code model. Of course, you can go straight to code and implement your own code model that implements the appropriate queryable interface but this would typically be done for a specific model.

Contrast this with the approach taken by Pyslet where the entities are not model-specific classes. For example, when modelling the Northwind service there is no Python class called Product as there would be in the approach taken by other frameworks. Instead there is a generalised implementation of Entity which behaves like a dictionary. The main difference is probably that you'll use supplier['Phone'] instead of simply supplier.phone or, if you'd have gone down the getter/setter route, supplier.GetPhone(). In my opinion, this works better than a tighter binding for a number of reasons, but particularly because it makes the user more mindful of when data access is happening and when it isn't.

Using a looser binding also helps prevent the type of problems I had during the development of the QTI specification. Lots of people were using Java and JAXB to autogenerate classes from the XML specification (cf autogenerating classes from a database schema) but the QTI model contained a class attribute on most elements to allow for stylesheet support. This class attribute prevented auto-generation because class is a reserved word in the Java language. Trying to fix this up after auto-generation would be madness but fixing it up before turns out to be a little tricky and this glitch seriously damaged the specification's user-experience. We got over it, but I'm wary now and when modelling OData I stepped back from a tighter binding, in part, to prevent hard to fix glitches like the use of Python reserved words as property names.

Allocating Storage

For this blog post I'm using a lightweight in-memory data storage implementation which can be automatically provisioned from the metadata document and I'm going to cheat by making a copy of the metadata document used by the Northwind service. Exposing OData the Pyslet way is a little more work if you already have a SQL database containing your data because I don't have a tool that auto-generates the metadata document from the SQL database schema. Automating the other direction is easy, but more on that in Part III.

I used my web browser to grab a copy of http://services.odata.org/V2/Northwind/Northwind.svc/$metadata and saved it to a file called Northwind.xml. I can then load the model from the interpreter:

>>> import pyslet.odata2.metadata as edmx
>>> doc=edmx.Document()
>>> f=open('Northwind.xml')
>>> doc.Read(f)
>>> f.close()

This special Document class ensures that the model is loaded with the special Pyslet element implementations. The Products entity set can be looked up directly but at the moment it's empty!

>>> productSet=doc.root.DataServices['ODataWeb.Northwind.Model.NorthwindEntities.Products']
>>> products=productSet.OpenCollection()
>>> len(products)
>>> products.close()

This isn't surprising, there is nothing in the metadata model itself which binds it to the data service at services.odata.org. The model isn't linked to any actual storage for the data. By default, the model behaves as if it is bound to an empty read-only data store.

To help me validate that my API can be used for something other than talking to real OData services I've created an object that provisions storage for an EntityContainer (that's like a database in OData) using standard Python dictionaries. By passing the definition of an EntityContainer to the object's constructor I create a binding between the model and this new data store.

>>> from pyslet.odata2.memds import InMemoryEntityContainer
>>> container=InMemoryEntityContainer(doc.root.DataServices['ODataWeb.Northwind.Model.NorthwindEntities'])
>>> products=productSet.OpenCollection()
>>> len(products)

The collection of products is still empty but it is now writeable. I'm going to cheat again to illustrate this by borrowing some code from the previous blog post to open an OData client connected to the real Northwind service.

>>> from pyslet.odata2.client import Client
>>> c=Client("http://services.odata.org/V2/Northwind/Northwind.svc/")
>>> nwProducts=c.feeds['Products'].OpenCollection()

Here's a simple loop to copy the products from the real service into my own collection. It's a bit clumsy in the interpreter but careful typing pays off:

>>> for nwProduct in nwProducts.itervalues():
...   product=collection.CopyEntity(nwProduct)
...   product.SetKey(nwProduct.Key())
...   collection.InsertEntity(product)
>>> len(collection)

To emphasise the difference between my in-memory collection and the live OData service I'll add another record to my copy of this entity set. Fortunately most of the fields are marked as Nullable in the model so to save my fingers I'll just set those that aren't.

>>> product=collection.NewEntity()
>>> product.SetKey(100)
>>> product['ProductName'].SetFromValue("The one and only Pyslet")
>>> product['Discontinued'].SetFromValue(False)
>>> collection.InsertEntity(product)
>>> len(collection)

Now I can do everything I can with the OData client using my copy of the service, I'll filter the entities to make it easier to see:

>>> import pyslet.odata2.core as core
>>> filter=core.CommonExpression.FromString("substringof('one',ProductName)")
>>> collection.Filter(filter)
>>> for p in collection.itervalues(): print p.Key(), p['ProductName'].value
21 Sir Rodney's Scones
32 Mascarpone Fabioli
100 The one and only Pyslet

I can access my own data store using the same API that I used to access a remote OData service in the previous post. In that post, I also claimed that it was easy to wrap my own implementations of this API to expose it as an OData service.

Exposing an OData Server

My OData server class implements the wsgi protocol so it is easy to link it up to a simple http server and tell it to handle a single request.

>>> from pyslet.odata2.server import Server
>>> server=Server("http://localhost:8081/")
>>> server.SetModel(doc)
>>> from wsgiref.simple_server import make_server
>>> httpServer=make_server('',8081,server)
>>> httpServer.handle_request()

My interpreter session is hanging at this point waiting for a single HTTP connection. The Northwind service doesn't have any feed customisations on the Products feed and, as we slavishly copied it, the Atom-view in the browser is a bit boring so I used the excellent JSONView plugin for Firefox and the following URL to hit my service:

http://localhost:8081/Products?$filter=substringof('one',ProductName)&$orderby=ProductID desc&$format=json

This is the same filter as I used in the interpreter before but I've added an ordering and specified my preference for JSON format. Here's the result.

As I did this, Python's simple server object logged the following output to my console: - - [24/Feb/2014 11:17:05] "GET /Products?$filter=substringof(%27one%27,ProductName)&$orderby=ProductID%20desc&$format=json HTTP/1.1" 200 1701

The in-memory data store is a bit of a toy, though some more useful applications might be possible. In the OData documentation I go through a tutorial on how to create a lightweight memory-cache of key-value pairs exposed as an OData service. I'm not really suggestion using it in a production environment to replace memcached. What this implementation is really useful for is developing and testing applications that consume the DAL API without needing to be connected to the real data source. Also, it can be wrapped in the OData Server class as shown above and used to provide a more realistic mock of an actual service for testing that your consumer application still works when the data service is remote. I've used it in Pyslet's unit-tests this way.

In the third and final part of this Python and OData series I'll cover a more interesting implementation of the API using the SQLite database.


A Dictionary-like Python interface for OData


This blog post introduces some new modules that I've added to the Pyslet package I wrote. Pyslet's purpose is providing support for Standards for Learning, Education and Training in Python. The new modules implement the OData protocol by providing a dictionary-like interface. You can download pyslet from the QTIMigration Tool & Pyslet home page. There is some documentation linked from the main Pyslet wiki. This blog article is as good a way as any to get you started.

The Problem

Python has a database API which does a good job but it is not the whole solution for data access. Embedding SQL statements in code, grappling with the complexities of parameterization and dealing with individual database quirks makes it useful to have some type of layer between your web app and the database API so that you can tweak your code as you move between data sources.

If SQL has failed to be a really interoperable standard then perhaps OData, the new kid on the block, can fill the vacuum. The standard is sometimes referred to as "ODBC over the web" so it is definitely in this space (after all, who runs their database on the same server as their web app these days?).

My Solution

To solve this problem I decided to set about writing my own data access layer that would be modeled on the conventions of OData but that used some simple concepts in Python. I decided to go down the dictionary-like route, rather than simulating objects with attributes, because I find the code more transparent that way. Implementing methods like __getitem__, __setitem__ and itervalues keeps the data layer abstraction at arms length from the basic python machinery. It is a matter of taste. See what you think.

The vision here is to write a single API (represented by a set of base classes) that can be implemented in different ways to access different data sources. There are three steps:

  1. An implementation that uses the OData protocol to talk to a remote OData service.
  2. An implementation that uses python dictionaries to create a transient in-memory data service for testing.
  3. An implementation that uses the python database API to access a real database.

This blog post is mainly about the first step, which should validate the API as being OData-like and set the groundwork for the others which I'll describe in subsequent blog posts. Incidentally, it turns out to be fairly easy to write an OData server that exposes a data service written to this API, more on that in future posts.

Quick Tutorial

The client implementation uses Python's logging module to provide logging. To make it easier to see what is going on during this walk through I'm going to turn logging up from the default "WARN" to "INFO":

>>> import logging
>>> logging.basicConfig(level=logging.INFO)

To create a new OData client you simply instantiate a Client object passing the URL of the OData service root. Notice that, during construction, the Client object downloads the list of feeds followed by the metadata document. The metadata document is used extensively by this module and is loaded into a DOM-like representation.

>>> from pyslet.odata2.client import Client
>>> c=Client("http://services.odata.org/V2/Northwind/Northwind.svc/")
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/ HTTP/1.1
INFO:root:Finished Response, status 200
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/$metadata HTTP/1.1
INFO:root:Finished Response, status 200

Client objects have a feeds attribute that is a plain dictionary mapping the exposed feeds (by name) onto EntitySet objects. These objects are part of the metadata model but serve a special purpose in the API as they can be opened (a bit like files or directories) to gain access to the (collections of) entities themselves. Collection objects can be used in the with statement and that's normally how you'd use them but I'm sticking with the interactive terminal for now.

>>> products=c.feeds['Products'].OpenCollection()
>>> for p in products: print p
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products HTTP/1.1
INFO:root:Finished Response, status 200
... [and so on]
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products?$skiptoken=20 HTTP/1.1
INFO:root:Finished Response, status 200
... [and so on]

The products collection behaves like a dictionary, iterating through it iterates through the keys in the dictionary. In this case these are the keys of the entities in the collection of products in Microsoft's sample Northwind data service. Notice that the client logs several requests to the server interspersed with the printed output. That's because the server is limiting the maximum page size and the client is following the page links provided. These calls are made as you iterate through the collection allowing you to iterate through very large collections without loading everything in to memory.

The keys alone are of limited interest, let's try a similar loop but this time we'll print the product names as well:

>>> for k,p in products.iteritems(): print k,p['ProductName'].value
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products HTTP/1.1
INFO:root:Finished Response, status 200
1 Chai
2 Chang
3 Aniseed Syrup
20 Sir Rodney's Marmalade
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products?$skiptoken=20 HTTP/1.1
INFO:root:Finished Response, status 200
21 Sir Rodney's Scones
22 Gustaf's Knäckebröd
23 Tunnbröd
76 Lakkalikööri
77 Original Frankfurter grüne Soße

Sir Rodney's Scones sound interesting, we can grab an individual record just as we normally would from a dictionary, by using its key.

>>> scones=products[21]
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products(21) HTTP/1.1
INFO:root:Finished Response, status 200
>>> for k,v in scones.DataItems(): print k,v.value
ProductID 21
ProductName Sir Rodney's Scones
SupplierID 8
CategoryID 3
QuantityPerUnit 24 pkgs. x 4 pieces
UnitPrice 10.0000
UnitsInStock 3
UnitsOnOrder 40
ReorderLevel 5
Discontinued False

The scones object is an Entity object. It too behaves like a dictionary. The keys are the property names and the values are one of SimpleValue, Complex or DeferredValue. In the snippet above I've used a variation of iteritems which iterates only through the data properties, excluding the navigation properties. In this model, there are no complex properties. The simple values have a value attribute which contains a python representation of the value.

Deferred values (navigation properties) can be used to navigate between Entities. Although deferred values can be opened just like EntitySets, if the model dictates that at most 1 entity can be linked a convenience method called GetEntity can be used to open the collection and read the entity in one call. In this case, a product can have at most one supplier.

>>> supplier=scones['Supplier'].GetEntity()
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products(21)/Supplier HTTP/1.1
INFO:root:Finished Response, status 200
>>> for k,v in supplier.DataItems(): print k,v.value
SupplierID 8
CompanyName Specialty Biscuits, Ltd.
ContactName Peter Wilson
ContactTitle Sales Representative
Address 29 King's Way
City Manchester
Region None
PostalCode M14 GSD
Country UK
Phone (161) 555-4448
Fax None
HomePage None

Continuing with the dictionary-like theme, attempting to load a non existent entity results in a KeyError:

>>> p=products[211]
INFO:root:Sending request to services.odata.org
INFO:root:GET /V2/Northwind/Northwind.svc/Products(211) HTTP/1.1
INFO:root:Finished Response, status 404
Traceback (most recent call last):
  File "", line 1, in 
  File "/Library/Python/2.7/site-packages/pyslet/odata2/client.py", line 165, in __getitem__
 raise KeyError(key)
KeyError: 211

Finally, when we're done, it is a good idea to close the open collection. If we'd used the with statement this step would have been done automatically for us of course.

>>> products.close()


Currently the client only supports OData version 2. Version 3 has now been published and I do intend to update the classes to speak version 3 at some point. If you try and connect to a version 3 service the client will complain when it tries to load the metadata document. There are ways around this limitation, if you are interested add a comment to this post and I'll add some documentation.

The client only speaks XML so if your service only speaks JSON it won't work at the moment. Most of the JSON code is done and tested so adding it shouldn't be a big issue if you are interested.

The client can be used to both read and write to a service, and there are even ways of passing basic authentication credentials. However, if calling an https URL it doesn't do certificate validation at the moment so be warned as your security could be compromised. Python 2.7 does now support certification validation using OpenSLL so this could change quite easily I think.

Moving to Python 3 is non-trivial - let me know if you are interested. I have taken the first steps (running unit tests with "python -3Wd" to force warnings) and, as much as possible, the code is ready for migration. I haven't tried it yet though and I know that some of the older code (we're talking 10-15 years here) is a bit sensitive to the raw/unicode string distinction.

The documentation is currently about 80% accurate and only about 50% useful. Trending upwards though.

Downloading and Installing Pyslet

Pyslet is pure-python. If you are only interested in OData you don't need any other modules, just Python 2.7 and a reasonable setuptools to help you install it. I just upgraded my machine to Mavericks which effectively reset my Python environment. Here's what I did to get Pyslet running.

  1. Installed setuptools
  2. Downloaded the pyslet package tgz and unpacked it (download from here)
  3. Ran python setup.py install


Some lessons are hard! Ten years or so ago I wrote a migration tool to convert QTI version 1 to QTI version 2 format. I wrote it as a Python script and used it to validate the work the project team were doing on the version 2 specification itself. Realising that most people holding QTI content weren't able to easily run a Python script (especially on Windows PCs) my co-chair Pierre Gorissen wrote a small Windows-wrapper for the script using the excellent wxPython and published an installer via his website. From then on, everyone referred to it as "Pierre's migration tool". I'm not bitter, the lesson was clear. No point in writing the tool if you don't package it up in the way people want to use it.

This sentiment brings me to the latest developments with the tool. A few years back I wrote (and blogged about) a module for writing Basic LTI tools in Python. I did this partly to prove that LTI really was simple (I wrote the entire module on a single flight to the US) but also because I believed that the LTI specification was really on to something useful. LTI has been a huge success and offers a quick route for tool developers to gain access to users of learning management systems. It seems obvious that the next version of the QTI Migration Tool should be an LTI tool but moving from a desktop app to a server-based web-app means that I need a data access layer that can persist data and be smarter about things like multiple threads and processes.


Deleting from iCalendar Without Notifying the Organizer - At Last!

Being a bit of a laggard I only just upgraded my Mac to Mavericks a few days ago.  If only I'd known they had fixed the number one most annoying thing about iCalendar in OS X I'd have upgraded ages ago.

Yes, you can now delete an event from your calendar without notifying the organizer.  This has caused me serious pain in the past.  Events sometimes arrive to the wrong email address or get accidentally put in the wrong calendar and previously you had to just leave them there for fear of sending a stupid "Steve declined your event" type email.

Thank you!


Transforming QTI v2 into (X)HTML 5

At a recent IMS meeting I again mentioned that transforming QTI v2 into HTML shouldn't be too difficult and that I'd already made a start on this project many years ago. I even mentioned it in an earlier blog post: Semantic Markup in HTML. To my shame, I got a comment on that post which called my bluff and I haven't posted my work - until now! I won't bore you with the excuses, day job, etc. I should also warn you that these files are in no way complete. However, they do solve most of the hard problems in my opinion and could be built out to cover the rest of the interaction types fairly easily.

If you want to sing along with this blog post you should look at the files in the following directory of the QTI migration source repository: https://code.google.com/p/qtimigration/source/browse/#svn%2Ftrunk%2Fqti2html. In there you'll find a collection of XSL files.


The goal of this project was to see how easy it would be to transform QTI version 2 files into HTML 5 in such a way that the HTML 5 was an alternative representation of the complete QTI information model. The goal was not to create a rendering which would work in an assessment delivery engine but to create a rendering that would store all the information about an item and render it in a sensible way, perhaps in a way suitable for a reviewer to view. I was partly inspired by a comment from Dick Bacon, of SToMP fame. He said it would be nice to see everything from the question all in one go, including feedback that is initially hidden and so on. It sort of gave me the idea to do the XSL this way.

Let's see what this stylesheet does to the most basic QTI v2 example, here's the command I ran on my Mac:

xsltproc qti2html.xsl choice.xml > choice.xhtml

And here's how the resulting file looks in Firefox:

The first thing you'll notice is that there is no HTML form in sight. You can't interact with this page, it is static text. But remember the goal of this stylesheet is to represent the QTI information completely. Let's look at the generated HTML source:

<html xmlns="http://www.w3.org/1999/xhtml" xmlns:qti="http://www.imsglobal.org/xsd/imsqti_v2p1">
        <title>Unattended Luggage</title>
        <meta name="qti.identifier" content="choice"/>
        <meta name="qti.adaptive" content="false"/>
        <meta name="qti.timeDependent" content="false"/>
        <style type="text/css">
        <!-- removed for brevity -->

To start with, the head element contains some useful meta tags with names starting "qti.", this allows us to capture basic information that would normally be in the root element of the item.

        <h2>Unattended Luggage</h2>
        <div class="qti-itemBody">
            <p>Look at the text in the picture.</p>
                <img src="images/sign.png" alt="NEVER LEAVE LUGGAGE UNATTENDED"/>
            <div class="qti-choiceInteraction" id="RESPONSE" data-baseType="identifier"
                data-cardinality="single" data-shuffle="false" data-maxChoices="1">
                <p class="qti-prompt">What does it say?</p>
                <ul class="qti-choiceInteraction">
                    <li class="qti-simpleChoice" data-identifier="ChoiceA" data-correct="true">You
                        must stay with your luggage at all times.</li>
                    <li class="qti-simpleChoice" data-identifier="ChoiceB">Do not let someone else
                        look after your luggage.</li>
                    <li class="qti-simpleChoice" data-identifier="ChoiceC">Remember your luggage
                        when you leave.</li>

I've abbreviated the body here but you'll see that the item body maps in to a div with a appropriate class name (this time prefixed with qti- to make styling easier). The HTML copies across pretty much unchanged but the interesting part is the div with a class of "qti-choiceInteraction". Here we've mapped the choiceInteraction in the original XML into a div and used HTML5 style data attributes to add information about the behaviour. Cardinality, etc. In essence, this div performs the role of both interaction and response variable declaration.

I chose to map the choices themselves on to an unordered list in HTML, again, using HTML5 data- attributes to provide the additional information required by QTI.


So can this format be converted back to QTI v2? Yes it can. The purpose of this stylesheet is to take the XHTML5 representation created by the previous stylesheet and turn it back in to valid QTIv2. This gives us the power to work in either representation. If we want to edit the HTML we can!

xsltproc html2qti.xsl choice.xhmtl > choice-new.xml

The output file is not identical to the input file, there are some changes to comments, white space and the order in which attributes are expressed but it is valid QTI v2 and is the same in every important respect.


Once we have an XHTML5 representation of the item it seems like it should be easy to make something more interactive. A pure HTML/JS delivery engine for QTIv2 is an attractive proposition, especially if you can use an XSL transform to get there from the original QTI file. One of the main objections to QTI I used to hear from the SCORM crowd back in the early days was that there was no player for QTI files that were put in SCORM packages. But given that most modern browsers can do XSL this objection could just disappear, at least for formative quizzes where you don't mind handing out the scoring rules embedded in the HTML. You could even tie up such an engine with calls to the SCORM API to report back scores.

Given this background, the qti.xsl file takes a first step to transforming the XHTML5 file produced with the first XSL into something that is more like an interactive question.

xsltproc qti.xsl choice.xhtml > choice_tryout.xhtml

This is what the output looks like in Firefox:

This page is interactive, if you change your choice and click OK it re-scores the question. Note that I currently have a score of 1 as I have the correct choice selected.

Future Work

These XSL files will handle some of the other variants of multi-choice, including multi-response (yes, even when interacting) but are in no way a complete method of using XSL to transform QTI. For me, the only compelling argument for using XSL is if you want to embed it in a package to force the client to do the transformation or if you are using one of those cocoon like tools that uses XSL pipelines at runtime. Realistically, writing XSL is a pig and so much time has elapsed since I wrote it that it would take an hour or two to familiarise myself with it again if I wanted to make changes.

But the key idea is the transforms enabled by the first two, which present an alternative binding for the QTI information model.


In historic vote, New Zealand bans software patents | Ars Technica

In historic vote, New Zealand bans software patents | Ars Technica

In my personal opinion, this is great news. I have had to sift through patent applications from software vendors that have clearly been created by simply sending a bunch of interface files to a lawyer to be translated in to patentese. You know the sort of thing, an API call like Student.GetName(id) becomes a 'claim' in which a string representation of a student's name is obtained from a system with a stored representation of a student's registration information, etc, etc. If we carry on as we are, someone will have to write an Eclipse plug-in that generates the patent application every time you build your software.

So this news is a ray of hope. It isn't a blanket "IP law is bad" bill but a measured way of enshrining the basic principle that software 'as such' is not an invention. There will always be the odd counterexample where something is no longer patentable that we might feel should be (and vice versa!) but ever since I've been following this debate it seems to me that the system has stayed the same not for lack of suggestions of how to make it better but because of FUD around change. Thank you NZ for being bolder.

Initially 'via Blog this'