ormar/docs/api/query-set/query-set.md

<a name="queryset.queryset"></a>
# queryset.queryset

<a name="queryset.queryset.QuerySet"></a>
## QuerySet Objects

```python
class QuerySet(Generic[T])
```

Main class to perform database queries, exposed on each model as objects attribute.

<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["T"]
```

Shortcut to model class set on QuerySet.

**Returns**:

`(Type[Model])`: model class

<a name="queryset.queryset.QuerySet.rebuild_self"></a>
#### rebuild\_self

```python
 | rebuild_self(filter_clauses: List = None, exclude_clauses: List = None, select_related: List = None, limit_count: int = None, offset: int = None, excludable: "ExcludableItems" = None, order_bys: List = None, prefetch_related: List = None, limit_raw_sql: bool = None, proxy_source_model: Optional[Type["Model"]] = None) -> "QuerySet"
```

Method that returns new instance of queryset based on passed params,
all not passed params are taken from current values.

<a name="queryset.queryset.QuerySet._prefetch_related_models"></a>
#### \_prefetch\_related\_models

```python
 | async _prefetch_related_models(models: List[Optional["T"]], rows: List) -> List[Optional["T"]]
```

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) -> List[Optional["T"]]
```

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._resolve_filter_groups"></a>
#### \_resolve\_filter\_groups

```python
 | _resolve_filter_groups(groups: Any) -> Tuple[List[FilterGroup], List[str]]
```

Resolves filter groups to populate FilterAction params in group tree.

**Arguments**:

- `groups (Any)`: tuple of FilterGroups

**Returns**:

`(Tuple[List[FilterGroup], List[str]])`: list of resolver groups

<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["T"]]) -> 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(*args: Any, *, _exclude: bool = False, **kwargs: Any) -> "QuerySet[T]"
```

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)
*  isnull - like `album__name__isnull=True` (sql is null)
(isnotnull `album__name__isnull=False` (sql is not null))
*  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(*args: Any, **kwargs: Any) -> "QuerySet[T]"
```

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[T]"
```

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.select_all"></a>
#### select\_all

```python
 | select_all(follow: bool = False) -> "QuerySet[T]"
```

By default adds only directly related models.

If follow=True is set it adds also related models of related models.

To not get stuck in an infinite loop as related models also keep a relation
to parent model visited models set is kept.

That way already visited models that are nested are loaded, but the load do not
follow them inside. So Model A -> Model B -> Model C -> Model A -> Model X
will load second Model A but will never follow into Model X.
Nested relations of those kind need to be loaded manually.

**Arguments**:

- `follow (bool)`: flag to trigger deep save -
by default only directly related models are saved
with follow=True also related models of related models are saved

**Returns**:

`(Model)`: reloaded Model

<a name="queryset.queryset.QuerySet.prefetch_related"></a>
#### prefetch\_related

```python
 | prefetch_related(related: Union[List, str]) -> "QuerySet[T]"
```

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], _is_exclude: bool = False) -> "QuerySet[T]"
```

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**:

- `_is_exclude (bool)`: flag if it's exclude or include operation
- `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[T]"
```

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, OrderAction]) -> "QuerySet[T]"
```

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.max"></a>
#### max

```python
 | async max(columns: Union[str, List[str]]) -> Any
```

Returns max value of columns for rows matching the given criteria
(applied with `filter` and `exclude` if set before).

**Returns**:

`(Any)`: max value of column(s)

<a name="queryset.queryset.QuerySet.min"></a>
#### min

```python
 | async min(columns: Union[str, List[str]]) -> Any
```

Returns min value of columns for rows matching the given criteria
(applied with `filter` and `exclude` if set before).

**Returns**:

`(Any)`: min value of column(s)

<a name="queryset.queryset.QuerySet.sum"></a>
#### sum

```python
 | async sum(columns: Union[str, List[str]]) -> Any
```

Returns sum value of columns for rows matching the given criteria
(applied with `filter` and `exclude` if set before).

**Returns**:

`(int)`: sum value of columns

<a name="queryset.queryset.QuerySet.avg"></a>
#### avg

```python
 | async avg(columns: Union[str, List[str]]) -> Any
```

Returns avg value of columns for rows matching the given criteria
(applied with `filter` and `exclude` if set before).

**Returns**:

`(Union[int, float, List])`: avg value of columns

<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(*args: Any, *, 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.paginate"></a>
#### paginate

```python
 | paginate(page: int, page_size: int = 20) -> "QuerySet[T]"
```

You can paginate the result which is a combination of offset and limit clauses.
Limit is set to page size and offset is set to (page-1) * page_size.

**Arguments**:

- `page_size (int)`: numbers of items per page
- `page (int)`: page number

**Returns**:

`(QuerySet)`: QuerySet

<a name="queryset.queryset.QuerySet.limit"></a>
#### limit

```python
 | limit(limit_count: int, limit_raw_sql: bool = None) -> "QuerySet[T]"
```

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[T]"
```

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(*args: Any, **kwargs: Any) -> "T"
```

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_or_none"></a>
#### get\_or\_none

```python
 | async get_or_none(*args: Any, **kwargs: Any) -> Optional["T"]
```

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.

If not match is found None will be 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(*args: Any, **kwargs: Any) -> "T"
```

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(*args: Any, **kwargs: Any) -> "T"
```

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) -> "T"
```

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(*args: Any, **kwargs: Any) -> List[Optional["T"]]
```

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) -> "T"
```

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["T"]) -> 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["T"], 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