- ... because they should immediately transform to werewolves!
In principle you could achieve this with the existing search functions combined with a lot of loops
and if statements. But for something non-standard like this, querying the database directly will be
much more efficient.
Evennia uses [Django](https://www.djangoproject.com/) to handle its connection to the database.
A [django queryset](https://docs.djangoproject.com/en/3.0/ref/models/querysets/) represents a database query. One can add querysets together to build ever-more complicated queries. Only when you are trying to use the results of the queryset will it actually call the database.
The normal way to build a queryset is to define what class of entity you want to search by getting its `.objects` resource, and then call various methods on that. We've seen variants of this before:
all_weapons = Weapon.objects.all()
This is now a queryset representing all instances of `Weapon`. If `Weapon` had a subclass `Cannon` and we only wanted the cannons, we would do
all_cannons = Cannon.objects.all()
Note that `Weapon` and `Cannon` are _different_ typeclasses. This means that you won't find any `Weapon`-typeclassed results in `all_cannons`. Vice-versa, you won't find any `Cannon`-typeclassed results in `all_weapons`. This may not be what you expect.
If you want to get all entities with typeclass `Weapon` _as well_ as all the subclasses of `Weapon`, such as `Cannon`, you need to use the `_family` type of query:
```{sidebar} _family
The `all_family` and `filter_family` (as well as `get_family` for getting exactly one result) are Evennia-specific. They are not part of regular Django.
```
really_all_weapons = Weapon.objects.all_family()
This result now contains both `Weapon` and `Cannon` instances (and any other
entities whose typeclasses inherit at any distance from `Weapon`, like `Musket` or
`Sword`).
To limit your search by other criteria than the Typeclass you need to use `.filter`
(or `.filter_family`) instead:
roses = Flower.objects.filter(db_key="rose")
This is a queryset representing all flowers having a `db_key` equal to `"rose"`.
Since this is a queryset you can keep adding to it; this will act as an `AND` condition.
It's important to note that we haven't called the database yet! Not until we
actually try to examine the result will the database be called. Here the
database is called when we try to loop over it (because now we need to actually
get results out of it to be able to loop):
for rose in local_non_red_roses:
print(rose)
From now on, the queryset is _evaluated_ and we can't keep adding more queries to it - we'd need to create a new queryset if we wanted to find some other result. Other ways to evaluate the queryset is to print it, convert it to a list with `list()` and otherwise try to access its results.
```{sidebar} database fields
Each database table have only a few fields. For `DefaultObject`, the most common ones are `db_key`, `db_location` and `db_destination`. When accessing them they are normally accessed just as `obj.key`, `obj.location` and `obj.destination`. You only need to remember the `db_` when using them in database queries. The object description, `obj.db.desc` is not such a hard-coded field, but one of many
Attributes attached to the Object.
```
Note how we use `db_key` and `db_location`. This is the actual names of these database fields. By convention Evennia uses `db_` in front of every database field. When you use the normal Evennia search helpers and objects you can skip the `db_` but here we are calling the database directly and need to use the 'real' names.
Here are the most commonly used methods to use with the `objects` managers:
- `filter` - query for a listing of objects based on search criteria. Gives empty queryset if none
were found.
- `get` - query for a single match - raises exception if none were found, or more than one was
found.
- `all` - get all instances of the particular type.
- `filter_family` - like `filter`, but search all subclasses as well.
- `get_family` - like `get`, but search all subclasses as well.
- `all_family` - like `all`, but return entities of all subclasses as well.
> All of Evennia search functions use querysets under the hood. The `evennia.search_*` functions actually return querysets (we have just been treating them as lists so far). This means you could in principle add a `.filter` query to the result of `evennia.search_object` to further refine the search.
The Django field query language uses `__` similarly to how Python uses `.` to access resources. This is because `.` is not allowed in a function keyword.
This will find all flowers whose name contains the string `"rose"`, like `"roses"`, `"wild rose"` etc. The `i` in the beginning makes the search case-insensitive. Other useful variations to use are `__istartswith` and `__iendswith`. You can also use `__gt`, `__ge` for "greater-than"/"greater-or-equal-than" comparisons (same for `__lt` and `__le`). There is also `__in`:
Don't confuse database fields with [Attributes](../../../Components/Attributes.md) you set via `obj.db.attr = 'foo'` or `obj.attributes.add()`. Attributes are custom database entities *linked* to an object. They are not separate fields *on* that object like `db_key` or `db_location` are.
While an Attribute's `db_key` is just a normal string, theor `db_value` is in fact a serialized piece of data. This means that cannot query this with additional operators. So if you use e.g. `db_attributes__db_value__iexact=2`, you'll get an error. While Attributes are very flexible, this is their drawback - their stored value is not possible to directly query with advanced query methods beyond finding the exact match.
You can't query for an Attribute `db_value` quite as freely as other data-types. This is because Attributes can store any Python entity and is actually stored as _strings_ on the database side. So while you can use `db_value=2` in the above example, you will not be able to use `dbvalue__eq=2` or `__lt=2`. See [Attributes](../../../Components/Attributes.md#querying-by-attribute) for more information on dealing with Attributes.
For queries using `OR` and `NOT` we need Django's [Q object](https://docs.djangoproject.com/en/4.1/topics/db/queries/#complex-lookups-with-q-objects). It is imported from Django directly:
from django.db.models import Q
The `Q` is an object that is created with the same arguments as `.filter`, for example
Q(db_key="foo")
You can then use this `Q` instance as argument in a `filter`:
q1 = Q(db_key="foo")
Character.objects.filter(q1)
# this is the same as
Character.objects.filter(db_key="foo")
The useful thing about `Q` is that these objects can be chained together with special symbols (bit operators): `|` for `OR` and `&` for `AND`. A tilde `~` in front negates the expression inside the `Q` and thus
works like `NOT`.
q1 = Q(db_key="Dalton")
q2 = Q(db_location=prison)
Character.objects.filter(q1 | ~q2)
Would get all Characters that are either named "Dalton" _or_ which is _not_ in prison. The result is a mix
Let us expand our original werewolf query. Not only do we want to find all Characters in a moonlit room with a certain level of `lycanthropy` - we decide that if they have been _newly bitten_, they should also turn, _regardless_ of their lycanthropy level (more dramatic that way!).
`recently_bitten`". With an OR-query like this it's possible to find the same
Character via different paths, so we add `.distinct()` at the end. This makes
sure that there is only one instance of each Character in the result.
## Annotations
What if we wanted to filter on some condition that isn't represented easily by a
field on the object? An example would wanting to find rooms only containing _five or more objects_.
We *could* do it like this (don't actually do it this way!):
```python
from typeclasses.rooms import Room
all_rooms = Rooms.objects.all()
rooms_with_five_objects = []
for room in all_rooms:
if len(room.contents) >= 5:
rooms_with_five_objects.append(room)
```
```{sidebar} list.append, extend and .pop
Use `mylist.append(obj)` to add new items to a list. Use `mylist.extend(another_list))` or `list1 + list2` to merge two lists together. Use `mylist.pop()` to remove an item from the end or `.pop(0)` to remove from the beginning of the list. Remember all indices start from `0` in Python.
```
Above we get _all_ rooms and then use `list.append()` to keep adding the right
rooms to an ever-growing list. This is _not_ a good idea, once your database
grows this will be unnecessarily compute-intensive. It's much better to query the
database directly
_Annotations_ allow you to set a 'variable' inside the query that you can then
access from other parts of the query. Let's do the same example as before
directly in the database:
```{code-block} python
:linenos:
:emphasize-lines: 6,8
from typeclasses.rooms import Room
from django.db.models import Count
rooms = (
Room.objects
.annotate(
num_objects=Count('locations_set'))
.filter(num_objects__gte=5)
)
```
```{sidebar} locations_set
Note the use of `locations_set` in that `Count`. The `*s_set` is a back-reference automatically created by Django. In this case it allows you to find all objects that *has the current object as location*.
```
`Count` is a Django class for counting the number of things in the database.
- **Line 6-7**: Here we first create an annotation `num_objects` of type `Count`. It creates an in-database function that will count the number of results inside the database. The fact annotation means that now `num_objects` is avaiable to be used in other parts of the query.
- **Line 8** We filter on this annotation, using the name `num_objects` as something we
can filter for. We use `num_objects__gte=5` which means that `num_objects`
should be greater than or equal to 5.
Annotations can be a little harder to get one's head around but much more efficient than lopping over all objects in Python.
## F-objects
What if we wanted to compare two dynamic parameters against one another in a
query? For example, what if instead of having 5 or more objects, we only wanted
objects that had a bigger inventory than they had tags (silly example, but ...)?
This can be with Django's [F objects](https://docs.djangoproject.com/en/4.1/ref/models/expressions/#f-expressions). So-called F expressions allow you to do a query that looks at a value of each object in the database.
Here we used `.annotate` to create two in-query 'variables' `num_objects` and `num_tags`. We then directly use these results in the filter. Using `F()` allows for also the right-hand-side of the filter condition to be calculated on the fly, completely within the database.
Suppose you used tags to mark someone belonging to an organization. Now you want to make a list and need to get the membership count of every organization all at once.
The `.annotate`, `.values_list`, and `.order_by` queryset methods are useful for this. Normally when you run a `.filter`, what you get back is a bunch of full typeclass instances, like roses or swords. Using `.values_list` you can instead choose to only get back certain properties on objects. The `.order_by` method finally allows for sorting the results according to some criterion:
```{code-block} python
:linenos:
:emphasize-lines: 6,7,8,9
from django.db.models import Count
from typeclasses.rooms import Room
result = (
Character.objects
.filter(db_tags__db_category="organization")
.annotate(tagcount=Count('id'))
.order_by('-tagcount'))
.values_list('db_tags__db_key', "tagcount")
```
Here we fetch all Characters who ...
- **Line 6**: ... has a tag of category "organization" on them
- **Line 7**:... along the way we count how many different Characters (each `id` is unique) we find for each organization and store it in a 'variable' `tagcount` using `.annotate` and `Count`
- **Line 8**: ... we use this count to sort the result in descending order of `tagcount` (descending because there is a minus sign, default is increasing order but we want the most popular organization to be first).
- **Line 9**: ... and finally we make sure to only return exactly the properties we want, namely the name of the organization tag and how many matches we found for that organization. For this we use the `values_list` method on the queryset. This will evaluate the queryset immediately.
The result will be a list of tuples ordered in descending order by the number of matches,
