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<a name="queryset.queryset"></a>
# queryset.queryset
<a name="queryset.queryset.QuerySet"></a>
## QuerySet Objects
```python
class QuerySet()
```
Main class to perform database queries, exposed on each model as objects attribute.
<a name="queryset.queryset.QuerySet.__init__"></a>
#### \_\_init\_\_
```python
| __init__(model_cls: Type["Model"] = None, filter_clauses: List = None, exclude_clauses: List = None, select_related: List = None, limit_count: int = None, offset: int = None, columns: Dict = None, exclude_columns: Dict = None, order_bys: List = None, prefetch_related: List = None, limit_raw_sql: bool = False) -> None
```
<a name="queryset.queryset.QuerySet.__get__"></a>
#### \_\_get\_\_
```python
| __get__(instance: Optional[Union["QuerySet", "QuerysetProxy"]], owner: Union[Type["Model"], Type["QuerysetProxy"]]) -> "QuerySet"
```
<a name="queryset.queryset.QuerySet.model_meta"></a>
#### model\_meta
```python
| @property
| model_meta() -> "ModelMeta"
```
Shortcut to model class Meta set on QuerySet model.
**Returns**:
`(model Meta class)`: Meta class of the model
<a name="queryset.queryset.QuerySet.model"></a>
#### model
```python
| @property
| model() -> Type["Model"]
```
Shortcut to model class set on QuerySet.
**Returns**:
`(Type[Model])`: model class
<a name="queryset.queryset.QuerySet._prefetch_related_models"></a>
#### \_prefetch\_related\_models
```python
| async _prefetch_related_models(models: Sequence[Optional["Model"]], rows: List) -> Sequence[Optional["Model"]]
```
Performs prefetch query for selected models names.
**Arguments**:
- `models (List[Model])`: list of already parsed main Models from main query
- `rows (List[sqlalchemy.engine.result.RowProxy])`: database rows from main query
**Returns**:
`(List[Model])`: list of models with prefetch models populated
<a name="queryset.queryset.QuerySet._process_query_result_rows"></a>
#### \_process\_query\_result\_rows
```python
| _process_query_result_rows(rows: List) -> Sequence[Optional["Model"]]
```
Process database rows and initialize ormar Model from each of the rows.
**Arguments**:
- `rows (List[sqlalchemy.engine.result.RowProxy])`: list of database rows from query result
**Returns**:
`(List[Model])`: list of models
<a name="queryset.queryset.QuerySet.check_single_result_rows_count"></a>
#### check\_single\_result\_rows\_count
```python
| @staticmethod
| check_single_result_rows_count(rows: Sequence[Optional["Model"]]) -> None
```
Verifies if the result has one and only one row.
**Arguments**:
- `rows (List[Model])`: one element list of Models
<a name="queryset.queryset.QuerySet.database"></a>
#### database
```python
| @property
| database() -> databases.Database
```
Shortcut to models database from Meta class.
**Returns**:
`(databases.Database)`: database
<a name="queryset.queryset.QuerySet.table"></a>
#### table
```python
| @property
| table() -> sqlalchemy.Table
```
Shortcut to models table from Meta class.
**Returns**:
`(sqlalchemy.Table)`: database table
<a name="queryset.queryset.QuerySet.build_select_expression"></a>
#### build\_select\_expression
```python
| build_select_expression(limit: int = None, offset: int = None, order_bys: List = None) -> sqlalchemy.sql.select
```
Constructs the actual database query used in the QuerySet.
If any of the params is not passed the QuerySet own value is used.
**Arguments**:
- `limit (int)`: number to limit the query
- `offset (int)`: number to offset by
- `order_bys (List)`: list of order-by fields names
**Returns**:
`(sqlalchemy.sql.selectable.Select)`: built sqlalchemy select expression
<a name="queryset.queryset.QuerySet.filter"></a>
#### filter
```python
| filter(_exclude: bool = False, **kwargs: Any) -> "QuerySet"
```
Allows you to filter by any `Model` attribute/field
as well as to fetch instances, with a filter across an FK relationship.
You can use special filter suffix to change the filter operands:
* exact - like `album__name__exact='Malibu'` (exact match)
* iexact - like `album__name__iexact='malibu'` (exact match case insensitive)
* contains - like `album__name__contains='Mal'` (sql like)
* icontains - like `album__name__icontains='mal'` (sql like case insensitive)
* in - like `album__name__in=['Malibu', 'Barclay']` (sql in)
* gt - like `position__gt=3` (sql >)
* gte - like `position__gte=3` (sql >=)
* lt - like `position__lt=3` (sql <)
* lte - like `position__lte=3` (sql <=)
* startswith - like `album__name__startswith='Mal'` (exact start match)
* istartswith - like `album__name__istartswith='mal'` (case insensitive)
* endswith - like `album__name__endswith='ibu'` (exact end match)
* iendswith - like `album__name__iendswith='IBU'` (case insensitive)
**Arguments**:
- `_exclude (bool)`: flag if it should be exclude or filter
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(QuerySet)`: filtered QuerySet
<a name="queryset.queryset.QuerySet.exclude"></a>
#### exclude
```python
| exclude(**kwargs: Any) -> "QuerySet"
```
Works exactly the same as filter and all modifiers (suffixes) are the same,
but returns a *not* condition.
So if you use `filter(name='John')` which is `where name = 'John'` in SQL,
the `exclude(name='John')` equals to `where name <> 'John'`
Note that all conditions are joined so if you pass multiple values it
becomes a union of conditions.
`exclude(name='John', age>=35)` will become
`where not (name='John' and age>=35)`
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(QuerySet)`: filtered QuerySet
<a name="queryset.queryset.QuerySet.select_related"></a>
#### select\_related
```python
| select_related(related: Union[List, str]) -> "QuerySet"
```
Allows to prefetch related models during the same query.
**With `select_related` always only one query is run against the database**,
meaning that one (sometimes complicated) join is generated and later nested
models are processed in python.
To fetch related model use `ForeignKey` names.
To chain related `Models` relation use double underscores between names.
**Arguments**:
- `related (Union[List, str])`: list of relation field names, can be linked by '__' to nest
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.prefetch_related"></a>
#### prefetch\_related
```python
| prefetch_related(related: Union[List, str]) -> "QuerySet"
```
Allows to prefetch related models during query - but opposite to
`select_related` each subsequent model is fetched in a separate database query.
**With `prefetch_related` always one query per Model is run against the
database**, meaning that you will have multiple queries executed one
after another.
To fetch related model use `ForeignKey` names.
To chain related `Models` relation use double underscores between names.
**Arguments**:
- `related (Union[List, str])`: list of relation field names, can be linked by '__' to nest
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.fields"></a>
#### fields
```python
| fields(columns: Union[List, str, Set, Dict]) -> "QuerySet"
```
With `fields()` you can select subset of model columns to limit the data load.
Note that `fields()` and `exclude_fields()` works both for main models
(on normal queries like `get`, `all` etc.)
as well as `select_related` and `prefetch_related`
models (with nested notation).
You can select specified fields by passing a `str, List[str], Set[str] or
dict` with nested definition.
To include related models use notation
`{related_name}__{column}[__{optional_next} etc.]`.
`fields()` can be called several times, building up the columns to select.
If you include related models into `select_related()` call but you won't specify
columns for those models in fields - implies a list of all fields for
those nested models.
Mandatory fields cannot be excluded as it will raise `ValidationError`,
to exclude a field it has to be nullable.
Pk column cannot be excluded - it's always auto added even if
not explicitly included.
You can also pass fields to include as dictionary or set.
To mark a field as included in a dictionary use it's name as key
and ellipsis as value.
To traverse nested models use nested dictionaries.
To include fields at last level instead of nested dictionary a set can be used.
To include whole nested model specify model related field name and ellipsis.
**Arguments**:
- `columns (Union[List, str, Set, Dict])`: columns to include
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.exclude_fields"></a>
#### exclude\_fields
```python
| exclude_fields(columns: Union[List, str, Set, Dict]) -> "QuerySet"
```
With `exclude_fields()` you can select subset of model columns that will
be excluded to limit the data load.
It's the opposite of `fields()` method so check documentation above
to see what options are available.
Especially check above how you can pass also nested dictionaries
and sets as a mask to exclude fields from whole hierarchy.
Note that `fields()` and `exclude_fields()` works both for main models
(on normal queries like `get`, `all` etc.)
as well as `select_related` and `prefetch_related` models
(with nested notation).
Mandatory fields cannot be excluded as it will raise `ValidationError`,
to exclude a field it has to be nullable.
Pk column cannot be excluded - it's always auto added even
if explicitly excluded.
**Arguments**:
- `columns (Union[List, str, Set, Dict])`: columns to exclude
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.order_by"></a>
#### order\_by
```python
| order_by(columns: Union[List, str]) -> "QuerySet"
```
With `order_by()` you can order the results from database based on your
choice of fields.
You can provide a string with field name or list of strings with fields names.
Ordering in sql will be applied in order of names you provide in order_by.
By default if you do not provide ordering `ormar` explicitly orders by
all primary keys
If you are sorting by nested models that causes that the result rows are
unsorted by the main model `ormar` will combine those children rows into
one main model.
The main model will never duplicate in the result
To order by main model field just provide a field name
To sort on nested models separate field names with dunder '__'.
You can sort this way across all relation types -> `ForeignKey`,
reverse virtual FK and `ManyToMany` fields.
To sort in descending order provide a hyphen in front of the field name
**Arguments**:
- `columns (Union[List, str])`: columns by which models should be sorted
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.exists"></a>
#### exists
```python
| async exists() -> bool
```
Returns a bool value to confirm if there are rows matching the given criteria
(applied with `filter` and `exclude` if set).
**Returns**:
`(bool)`: result of the check
<a name="queryset.queryset.QuerySet.count"></a>
#### count
```python
| async count() -> int
```
Returns number of rows matching the given criteria
(applied with `filter` and `exclude` if set before).
**Returns**:
`(int)`: number of rows
<a name="queryset.queryset.QuerySet.update"></a>
#### update
```python
| async update(each: bool = False, **kwargs: Any) -> int
```
Updates the model table after applying the filters from kwargs.
You have to either pass a filter to narrow down a query or explicitly pass
each=True flag to affect whole table.
**Arguments**:
- `each (bool)`: flag if whole table should be affected if no filter is passed
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(int)`: number of updated rows
<a name="queryset.queryset.QuerySet.delete"></a>
#### delete
```python
| async delete(each: bool = False, **kwargs: Any) -> int
```
Deletes from the model table after applying the filters from kwargs.
You have to either pass a filter to narrow down a query or explicitly pass
each=True flag to affect whole table.
**Arguments**:
- `each (bool)`: flag if whole table should be affected if no filter is passed
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(int)`: number of deleted rows
<a name="queryset.queryset.QuerySet.limit"></a>
#### limit
```python
| limit(limit_count: int, limit_raw_sql: bool = None) -> "QuerySet"
```
You can limit the results to desired number of parent models.
To limit the actual number of database query rows instead of number of main
models use the `limit_raw_sql` parameter flag, and set it to `True`.
**Arguments**:
- `limit_raw_sql (bool)`: flag if raw sql should be limited
- `limit_count (int)`: number of models to limit
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.offset"></a>
#### offset
```python
| offset(offset: int, limit_raw_sql: bool = None) -> "QuerySet"
```
You can also offset the results by desired number of main models.
To offset the actual number of database query rows instead of number of main
models use the `limit_raw_sql` parameter flag, and set it to `True`.
**Arguments**:
- `limit_raw_sql (bool)`: flag if raw sql should be offset
- `offset (int)`: numbers of models to offset
**Returns**:
`(QuerySet)`: QuerySet
<a name="queryset.queryset.QuerySet.first"></a>
#### first
```python
| async first(**kwargs: Any) -> "Model"
```
Gets the first row from the db ordered by primary key column ascending.
**Raises**:
- `NoMatch`: if no rows are returned
- `MultipleMatches`: if more than 1 row is returned.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(Model)`: returned model
<a name="queryset.queryset.QuerySet.get"></a>
#### get
```python
| async get(**kwargs: Any) -> "Model"
```
Get's the first row from the db meeting the criteria set by kwargs.
If no criteria set it will return the last row in db sorted by pk.
Passing a criteria is actually calling filter(**kwargs) method described below.
**Raises**:
- `NoMatch`: if no rows are returned
- `MultipleMatches`: if more than 1 row is returned.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(Model)`: returned model
<a name="queryset.queryset.QuerySet.get_or_create"></a>
#### get\_or\_create
```python
| async get_or_create(**kwargs: Any) -> "Model"
```
Combination of create and get methods.
Tries to get a row meeting the criteria fro kwargs
and if `NoMatch` exception is raised
it creates a new one with given kwargs.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(Model)`: returned or created Model
<a name="queryset.queryset.QuerySet.update_or_create"></a>
#### update\_or\_create
```python
| async update_or_create(**kwargs: Any) -> "Model"
```
Updates the model, or in case there is no match in database creates a new one.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(Model)`: updated or created model
<a name="queryset.queryset.QuerySet.all"></a>
#### all
```python
| async all(**kwargs: Any) -> Sequence[Optional["Model"]]
```
Returns all rows from a database for given model for set filter options.
Passing kwargs is a shortcut and equals to calling `filter(**kwrags).all()`.
If there are no rows meeting the criteria an empty list is returned.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(List[Model])`: list of returned models
<a name="queryset.queryset.QuerySet.create"></a>
#### create
```python
| async create(**kwargs: Any) -> "Model"
```
Creates the model instance, saves it in a database and returns the updates model
(with pk populated if not passed and autoincrement is set).
The allowed kwargs are `Model` fields names and proper value types.
**Arguments**:
- `kwargs (Any)`: fields names and proper value types
**Returns**:
`(Model)`: created model
<a name="queryset.queryset.QuerySet.bulk_create"></a>
#### bulk\_create
```python
| async bulk_create(objects: List["Model"]) -> None
```
Performs a bulk update in one database session to speed up the process.
Allows you to create multiple objects at once.
A valid list of `Model` objects needs to be passed.
Bulk operations do not send signals.
**Arguments**:
- `objects (List[Model])`: list of ormar models already initialized and ready to save.
<a name="queryset.queryset.QuerySet.bulk_update"></a>
#### bulk\_update
```python
| async bulk_update(objects: List["Model"], columns: List[str] = None) -> None
```
Performs bulk update in one database session to speed up the process.
Allows to update multiple instance at once.
All `Models` passed need to have primary key column populated.
You can also select which fields to update by passing `columns` list
as a list of string names.
Bulk operations do not send signals.
**Arguments**:
- `objects (List[Model])`: list of ormar models
- `columns (List[str])`: list of columns to update