Django querysets are amazing. We use them everyday, but rarely think about the wonderful API they give us. Just some of the amazing properties which queysets have

You can get a slice queryset[i:j] out of them, only the needed objects are pulled from DB.

out of them, only the needed objects are pulled from DB. You can lookup a specifc object queryset[i] , only the required object is pulled from DB.

, only the required object is pulled from DB. You can iterate over them, for user in users_queryset , as if they were a list.

, as if they were a list. You can AND or OR them and they apply the criteria at the SQL level.

or them and they apply the criteria at the SQL level. You can use them like a boolean, if users_queryset: users_queryset.update(first_name="Batman")

You can pickle and unpickle them, even when the individual istances may not be.

You can get a useful representation of the queryset in python cli, or ipython. Even if the queryset consists of 1000s of records, only first 20 records will be printed and shown.

Querysets get all of these properties by implemnting the Python magic methods, aka the dunder methods. So why do you need these magic, dunder methods? Because they make the api much cleaned to use.

It is more intutive to say, if users_queryset: users_queryset.do_something() than if users_queryset.as_boolean: users_queryset.do_something() . It is more intutive to say queryset_1 & queryset_2 rather than queryse_1.do_and(queryset_2)

Magic methods are metods implemented by classes which have a special meaning to the Python interpretor. They always start with a __ and are sometimes called dunder method. (Dunder == double underscore).

Query and related classes implement the following methods to get the properies we listed above.

__getitem__ : For queryset[i:j] and queryset[i]

: For and __iter__ for for user in users_queryset

for __and__ and __or__ for queryset_1 & queryset_2 and queryset_1 | queryset_2

and for and __bool__ to use them like a boolean

to use them like a boolean __getstate__ and __setstate__ to pickle and unpickle them

and to pickle and unpickle them __repr__ to get a useful representation and to limit the DB hit

We will look at how Django 2.0 does it.

Implementing __getitem__

The code looks like this:

def __getitem__ ( self , k ): """Retrieve an item or slice from the set of results.""" if not isinstance ( k , ( int , slice )): raise TypeError assert (( not isinstance ( k , slice ) and ( k >= 0 )) or ( isinstance ( k , slice ) and ( k . start is None or k . start >= 0 ) and ( k . stop is None or k . stop >= 0 ))), \ "Negative indexing is not supported." if self . _result_cache is not None : return self . _result_cache [ k ] if isinstance ( k , slice ): qs = self . _chain () if k . start is not None : start = int ( k . start ) else : start = None if k . stop is not None : stop = int ( k . stop ) else : stop = None qs . query . set_limits ( start , stop ) return list ( qs )[:: k . step ] if k . step else qs

There is a lot going on here, but each if block is straightforward.

In the first of block, we ensure slice has reaonable value.

In second block, if _result_cache is filled, aka the queryset has been evaluated, we return the slice from the cache and skip hitting the db again.

is filled, aka the queryset has been evaluated, we return the slice from the cache and skip hitting the db again. If the _result_cache is not filled, we qs.query.set_limits(start, stop) which sets the limit and offset in sql.

Implementing __iter__

def __iter__ ( self ): # ... self . _fetch_all () return iter ( self . _result_cache )

Pretty strightforward, we populate the data then use builtin iter to return an iterator.

It is also instructive to look at FlatValuesListIterable.__iter__ which uses yield to implment __iter__ .

class FlatValuesListIterable ( BaseIterable ): """ Iterable returned by QuerySet.values_list(flat=True) that yields single values. """ def __iter__ ( self ): queryset = self . queryset compiler = queryset . query . get_compiler ( queryset . db ) for row in compiler . results_iter ( chunked_fetch = self . chunked_fetch , chunk_size = self . chunk_size ): yield row [ 0 ]

Implementing __and__ and __or__

The code looks like this:

def __and__ ( self , other ): self . _merge_sanity_check ( other ) if isinstance ( other , EmptyQuerySet ): return other if isinstance ( self , EmptyQuerySet ): return self combined = self . _chain () combined . _merge_known_related_objects ( other ) combined . query . combine ( other . query , sql . AND ) return combined

We d some sanity checks on the querysets, return early if one of the querysets is empty then apply SQL or using combined.query.combine(other.query, sql.AND) . The __or__ is essentially same except the SQL is changed using combined.query.combine(other.query, sql.OR)

Implementing __bool__

The code looks like this:

def __bool__ ( self ): self . _fetch_all () return bool ( self . _result_cache )

Pretty straightforward, _fetch_all() ensures that the queryset is evaluated, and _result_cache is filled. We then return the boolean equivalent of _result_cache , which means if there are any records, you will get a True .

Implementing __getstate__ and __setstate__

__getstate__ and __setstate__ look like this:

def __getstate__ ( self ): # Force the cache to be fully populated. self . _fetch_all () return { ** self . __dict__ , DJANGO_VERSION_PICKLE_KEY : get_version ()} def __setstate__ ( self , state ): msg = None pickled_version = state . get ( DJANGO_VERSION_PICKLE_KEY ) if pickled_version : current_version = get_version () if current_version != pickled_version : msg = ( "Pickled queryset instance's Django version %s does not " "match the current version %s." % ( pickled_version , current_version ) ) else : msg = "Pickled queryset instance's Django version is not specified." if msg : warnings . warn ( msg , RuntimeWarning , stacklevel = 2 ) self . __dict__ . update ( state )

While pickling, we ensure data is populated, then use self.__dict__ to get queryset representation, and return it along with Django version. While unpickling, __setstate__ ensures that a warning is raised when pickled querysets are used across Django versions.

On a related note, {**self.__dict__, DJANGO_VERSION_PICKLE_KEY: get_version()} , shows why you should move to Python 3. This syntax for merging dictionaries doesn’t work in Python2.

Implementing __repr__

The code for __repr__ , look like this

def __repr__ ( self ): data = list ( self [: REPR_OUTPUT_SIZE + 1 ]) if len ( data ) > REPR_OUTPUT_SIZE : data [ - 1 ] = "...(remaining elements truncated)..." return '<%s %r>' % ( self . __class__ . __name__ , data )

This is straightforward, but has a few nice tricks worth looking at.

self[:REPR_OUTPUT_SIZE + 1] does slicing, which because we implemented __getitem__ , does ... limit ... offset ... query.

REPR_OUTPUT_SIZE ensures that we don’t pull in the wholeyset to display data, but pulls up REPR_OUTPUT_SIZE + 1 records. On next line len(data) > REPR_OUTPUT_SIZE allows us the check if there were more records without hitting the DB.

Final thoughts

Magic, dunder methods provide a clean straightforward way to provide a clean api to your classes. Unlike their name, they don’t have any hidden magic and should be used where it makes sense.