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Effective PolyModel on App Engine Standard Environment

Author(s): @devlance ,   Published: 2009-08-01

Rafe Kaplan

August 2009

The world is a complex place and it's sometimes hard to pigeon-hole ideas in to a single representational model. When writing software one would ideally like to account for variations in different categories of objects, but still be able to treat them as a single basic type. Many developers these days are used to using polymorphic class hierarchies to achieve this. App Engine provides a simple way to create polymorphic class hierarchies that can still be queried as a single kind. This article will describe how to use this method and some useful things you can do with it.

A catalog of types

Andreas is an enterprising fellow who is about to start his own online shop that sells an assortment of consumer electronics ranging from digital cameras to video players to computers. He wants to make it possible for visitors to his site to search the various products on relevant properties common to all items such as price and brand, but also let them narrow their search to more specific categories using properties specific to those items.

A simple way to model the catalog using the basic App Engine Model would be to place all the fields needed for each product in a single class and have a field indicating its type in order to determine which fields are needed.

class CatalogItem(db.Model):
    name = db.StringProperty()
    brand = db.StringProperty()
    price = db.FloatProperty(required=True)
    category = db.StringProperty(
        choices=['camera', 'video', 'computer'])

    # Camera Properties
    ram = db.IntegerProperty()
    megapixels = db.IntegerProperty()
    memory_type = db.StringProperty(choices=['pocketmem',

    # Video Player Properties
    disk_trays = db.IntegerProperty()
    output_hdmi = db.BooleanProperty()
    output_component = db.BooleanProperty()
    output_svideo = db.BooleanProperty()

    # Computer Properties
    ghz = db.FloatProperty()
    # NOTE: The ram property is shared between camera and laptop.
    hard_drive = db.IntegerProperty()

With AJAX, it's not that hard for Andreas to write forms that are customized based on what the customer is looking for. GQL also makes it easy to do the search. For example, if a customer is looking for a new 8 megapixel camera the query would probably look like:

CatalogItem.gql("WHERE category='camera' AND megapixels=8")

There's a few problems with this model the way it is. At a very low level it's hard to determine what properties should be used for a given catalog category. Calling the properties method on a given CatalogItem instance will retrieve all properties for all categories. If Andreas wanted to use reflection to show him the properties of his catalog in the Google Cloud Console, he would see all the properties for that item whether it uses it or not. Another problem is the fact that cameras and computers share the same field, but it might have different meanings. For example, camera RAM might be measured in megabytes, whereas computer memory in gigabytes. They're not really the same property.

Meet polyModel

Instead of placing all products in to a single class, it is possible to define each category as its own class that fits naturally in to a product category hierarchy. To do this, create a subclass of PolyModel instead of Model. Let's see how Andreas redesigns the basic product model:

from google.appengine.ext import db
from google.appengine.ext.db import polymodel

class CatalogItem(polymodel.PolyModel):
    name = db.StringProperty()
    brand = db.StringProperty()
    price = db.FloatProperty(required=True)

    def category(self):
        return self.class_name().lower()

class Camera(CatalogItem):
    megapixels = db.IntegerProperty()
    ram = db.IntegerProperty(
        validator=lambda ram: ram >= 128 and ram <= 2048)
    memory_type = db.StringProperty(choices=['pocketmem',

class Video(CatalogItem):
    disk_trays = db.IntegerProperty()
    output_hdmi = db.BooleanProperty()
    output_component = db.BooleanProperty()
    output_svideo = db.BooleanProperty()

class Computer(CatalogItem):
    ghz = db.FloatProperty()
    ram = db.FloatProperty(
        validator=lambda ram: ram >= 1.0 and ram <= 8.0)
    hard_drive = db.IntegerProperty()

There are a few things to notice about the new class definitions. For one, there is no longer a need to explicitly set the products category. This can be calculated automatically (if needed) by calling class_name() on a PolyModel instance. But since it is not a persistant property, how does one query for products of a particular category? How this is done is one of the most powerful and important features of PolyModel. Using all() or gql() on a PolyModel class will perform a query over all instances of that class and all instances of all of its sub-classes. This feature of PolyModel is called "polymorphic queries". So for example, this queries all Computers that run at or over 2.0 GHz:

Computer.gql('WHERE ghz > 2.0')

Another thing to notice is that each category is independently responsible for maintaining its own properties. Recall that originally that the Computer category and the Camera category had to share the same ram property. Now, Camera.ram and Computer.ram are completely different properties on different classes and therefore can have completely different definitions that even have different constraints, as illustrated. The other way to have done this in the non-polymorphic model would have been to give each ram property its own name, like computer_ram and camera_ram. As the model grows, there are more potential property name conflicts that can pop up. In this respect, using a polymorphic class can act as a little name space for different categories of your model.

Creating a polymorphic property

Andreas is bright and innovative and has some ideas that he can add to his online store to distinguish himself from his competitors. With so many products with so many features it can be very difficult for people to decide which product is the best, or which ones are of good value. To help the customer choose which he wants to add an automatically calculated "Quality Rating" field based on the number and sophistication of features on an item and a secret mathematical formula of his own devising.

The problem here is that how the calculation is done depends on the type of object in the catalog. For example, he needs to calculate the Quality Rating of a camera based on the megapixels and the supported memory types, video players on the kinds of outputs it has and number of disk trays, and computers on speed, memory and storage capacity. "Fortunately", Andreas thinks, "computer science has provided us with a special tool for just this kind of situation!" Andreas is of course thinking of the if statement:

class CatalogItem(db.Model):
    # ...

    quality_rating = db.FloatProperty()

    def set_quality_rating(self):
        self.quality_rating = self.calculate_quality_rating()

    def calculate_quality_rating(self):
        raise NotImplementedError(
            'Need to define this for each category')

class Camera(CatalogItem):
    # ...

    def calculate_quality_rating(self):
        memory_type_score = {
            'pocketmem': 1.3,
            'datarod': 1.0,
            'fastchip': 0.9
        return self.megapixels * memory_type_score + self.ram

class Video(CatalogItem):
    # ...

    def calculate_quality_rating(self):
        output_hdmi_score = 0.0
        output_component_score = 0.0
        output_svideo_score = 0.0
        if self.output_hdmi: output_hdmi_score = 3.0
        if self.output_component: output_component_score = 2.0
        if self.output_video: output_svideo_score = 1.0
        features = (output_hdmi_score +
                    output_component_score +
        return self.disk_trays * features

class Computer(CatalogItem):
    # ...

    def calculate_quality_rating(self):
        return self.ghz * self.ram + self.hard_drive

And thus, Andreas goes out and builds his new website. But there's something worth noting here.

Further refinements

Very soon, as he looks over his proposed inventory, it becomes apparent to Andreas that he needs to continue to narrow the searches over the products he has. For example, he sells two kinds of computers, desktops and laptops. When shopping for laptops, suddenly the computers weight and battery life becomes very important. He could just add a weight and battery life to the computer category, but most folks don't usually care how much a desktop weighs, and battery life is altogether meaningless! Furthermore, most people come looking specifically for a desktop or a laptop, so it would make sense to separate desktops and laptops in to their own categories. On the other hand, some customers might very well have not chosen which kind of computer they want and would like to compare the relative merits between desktops and laptops. Placing desktops and laptops in to separate categories means they forget their original association as a related category. Andreas should be able to write a kind of GQL query that would allow someone to search on both category types. Is there a way?

There is.

Sub-categories done right

Now that Andreas has created a class hierarchy for his online store, he can easily implement sub-categories for laptops and desktops:

class Desktop(Computer):

    slots = db.IntegerProperty()

    def calculate_quality_rating():
        rating = super(Desktop, self).calculate_quality_rating()
        return  rating * self.slots

class Laptop(Computer):

    weight = db.FloatProperty()

    def calculate_quality_rating():
        rating = super(Laptop, self).calculate_quality_rating()
        return rating / self.weight

# Let's create an example catalog of computers.
Laptop( name='The Superlight', weight=3.4, ram=1.0).put()
Laptop( name='Robusto',        weight=8.9, ram=2.0).put()
Desktop(name='Workstation D',  slots=2,    ram=2.0).put()
Desktop(name='Workhorse',      slots=8,    ram=8.0).put()

When the user wants to search for laptops that are a certain weight or less, all the application needs to do is:

>>> selected_weight = 5.0
... for laptop in Laptop.gql('WHERE weight <= :1', selected_weight):
...  print laptop.name

The Superlight

This query will only return laptops.

If the user wants to search for desktop computers with a minimum number of slots the application would do:

>>> minimum_slots = 4
... for desktop in Desktop.gql('WHERE slot >= :1', minimum_slots):
...  print desktop.name


If the user is wants to see all computers that have a certain amount of memory or more, all the application needs to do is:

>>> selected_ram = 2.0
... for computer in Computer.gql('WHERE ram >= :1', selected_ram):
...   print computer.name

Workstation D

This will query all computers, laptop and desktop alike. However, it is very easy to narrow this same query to just laptops:

>>> for laptop in Laptop.gql('WHERE ram >= :1', selected_ram):
...  print laptop.name


Using polymorphic queries allows an intuitive way to query over objects of different types that share a similar basic purpose, but allow those objects to maintain their specific structure.

Why this works

It might help to understand a little bit about how this polymorphism is implemented. All sub-classes of a given class hierarchy root share the same Google Cloud Datastore kind. To differentiate between classes within the hiearchy, the PolyModel has an extra hidden string list property, class, in the Cloud Datastore. This list, known as the class key, describes that particular object's location in the class hierarchy. Each element of this list is the name of a class, starting with the root of the hierarchy at index 0. Because queries against a list match if any elements match the query value, searching for an object using the name of one class will match the class keys of all instances that contain that class name.

For the online shop models, the class keys for each class look like this:

CatalogItem: ['CatalogItem'                             ]
Camera:      ['CatalogItem',  'Camera'                  ]
Computer:    ['CatalogItem', 'Computer'                 ]
Desktop:     ['CatalogItem', 'Computer',  'Desktop'     ]
Laptop:      ['CatalogItem', 'Computer',  'Laptop'      ]

When a call to Computer.gql or Computer.all is made an equality query is made against the class property for the class name (as defined by PolyModel.class_name()). If Andreas was working in the Cloud Console and wanted to hand write a query for all computers that 4 GB or more of RAM, he can write:

SELECT * FROM CatalogItem WHERE class = 'Laptop' and ram >= 4.0

In practice, however, outside of applications such as the Cloud Console, the class property should never be used directly. The name of the class should always be accessed using PolyModel.class_name() and the class key by PolyModel.class_key(). Avoid calling db.gql directly in production code, always using the gql class method associated with the class you would like to query.

Things to watch out for

It might be tempting to make every single class in an application a PolyModel class, even for classes that do not immediately require a subclass. However it should not normally be required to create a PolyModel class earlier so that it might be subclassed in the future. If the application sticks to using the class method version of gql and all it is future compatible to change the inheritence from Model to PolyModel later. This is because calls to gql and all on the class hierarchy root class do not attempt to query against class property.

But why not make everything a PolyModel anyway? There is not too much of a problem making everything a PolyModel, except that each instance must be stored with its class information. This value is pretty small and in practice may not cause a problem, but it is something to consider.

Also be careful not to create large class hierarchies of unrelated object types. For example, it's not wise to attempt to create a single polymorphic root class that every class in the application inherits from. The main reason not to do this is that it ensures every query in your application will require a composite index. For an application with a lot of data, this could end up taking up a lot of space disk space with these indexes for queries that give no real benefit.


The introduction of polymorphic queries to App Engine creates a very intuitive way to create collections of similar but variable Cloud Datastore types. It makes it easier to design models using standard object oriented techniques and access them according to their taxonomy. PolyModel cannot be used for everything, but with care, more complicated models can be easier to maintain and extend.

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