Dictionaries in Python

Python’s way of storing key-value pairs, a fundamental data structure in computer science. The data type is summarized in the official documentation as “an unordered set of key: value pairs, with the requirement that the keys are unique”. Dictionaries can be indexed by any immutable data type and the stored values accessed in the following ways:

value = d.get[key]

value = d.get(key)

value = d.get(key, "no data")

Whereas using [key] will return a KeyError if the key does not exist, the .get method will either return None, or a default value if specified as an optional parameter. Values within nested dictionaries, such as deserialized JSON data, can be accessed by the successive use of [key] or .get(key):

sales = {'data':{'orders':{'january':240}}}

sales['data']['orders']['january']

sales.get('data').get('orders').get('january')

sales['data']['orders'].get('january')

The following are all valid ways of creating dictionaries:

my_dict = {'key1': 'value1', 'key2': 'value2'}

my_dict = dict(key1='value1',key2='value2')

my_dict = {x: x**2 for x in values}

my_dict = dict(zip(keys, values))

When the keys are simple strings, it can be useful to pass in the keys as keywords to the dict() constructor. This is the most performant way of creating dictionaries and useful for the generation of arbitrary keys and values. Using the zip function inside the dict() constructor is particularly useful for creating dictionaries from lists of keys and values.

Dictionaries are unordered, except in Python 3.6+. To store the insertion order of keys, the dictionary sub-class OrderedDict can be used after importing it from the collections module in the standard library.

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Avoiding multi-table inheritance in Django Models

Model inheritance does not have a natural translation to relational database architecture and so models in Django should be designed in order to avoid impact on database performance. When there is no need for the base model to be translated into a table abstract inheritance should be used instead of multi-table inheritance.

Given the following model:

class Person(Model):
  name = CharField()
  …

class Employee(Person):
  department = CharField()
  …

Two tables will be created and what looks like a simple query to the Employee child class will actually involve a join automatically being created. The same example with abstract = True in the Meta class allows abstract inheritance:

class Person(Model):
  name = CharField()
  …

class Meta:
  abstract = True

class Employee(Person):
  department = CharField()
  …

By putting abstract = True, the extra table for the base model is not created and the fields within the base model are automatically created for each child model. This avoids unnecessary joins being created to access those fields. This way of using model inheritance also avoids repetition of code within the child classes.

Quickly get memcached working in Python Django

As with most frameworks, the Django framework for Python can make use of caching to greatly improve performance for many common requests. Here we will look at using memcached as it enjoys good Django support and production use although there is also Redis support which definitely improves on memcached in some aspects such as data persistence.

  1. The first step is to install memcached on your server:
  2. RedHat Linux:

    yum install memcached

    Ubuntu / Debian Linux:

    apt-get install memcached
  3. Let Django know how to access memcached:
  4. In Django’s settings.py file, add the following line:

    'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache'
  5. Load the cache within your application
  6. from django.core.cache import cache
  7. Save the value to the cache
  8. cache.set('exampleValue',exampleValue)
  9. Retrieve the value from the cache
  10. exampleValue = cache.get('exampleValue')

The beauty being that exampleValue can be anything from a computed / database retrieved value to large blocks of static text or a URL etc.

The only problem with caches is they don’t always contain the data you expect, what if the value got flushed or hasn’t yet been stored? Lets rewrite step 5 to handle the event of the value not being available in the cache:

exampleValue = cache.get('exampleValue')
if not exampleValue:
     exampleValue = exampleValueLookup
     cache.set('exampleValue',exampleValue)

Here we see the value exampleValue being retrieved with a backup regeneration if the value has not been set. In a real application this would usually be encapsulated in a getExampleValue function or somewhere appropriate.