update documentation, optimize for memory saving, update docs for select_related, fields and exclude_fields, bump version
This commit is contained in:
@ -45,7 +45,7 @@ Ormar is built with:
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* [`SQLAlchemy core`][sqlalchemy-core] for query building.
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* [`SQLAlchemy core`][sqlalchemy-core] for query building.
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* [`databases`][databases] for cross-database async support.
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* [`databases`][databases] for cross-database async support.
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* [`pydantic`][pydantic] for data validation.
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* [`pydantic`][pydantic] for data validation.
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* typing_extensions for python 3.6 - 3.7
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* `typing_extensions` for python 3.6 - 3.7
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### Migrations
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### Migrations
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@ -53,7 +53,8 @@ Because ormar is built on SQLAlchemy core, you can use [`alembic`][alembic] to p
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database migrations.
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database migrations.
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**ormar is still under development:** We recommend pinning any dependencies with `ormar~=0.4.0`
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**ormar is still under development:**
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We recommend pinning any dependencies (with i.e. `ormar~=0.5.2`)
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### Quick Start
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### Quick Start
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@ -157,6 +158,7 @@ assert len(tracks) == 1
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* `filter(**kwargs) -> QuerySet`
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* `filter(**kwargs) -> QuerySet`
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* `exclude(**kwargs) -> QuerySet`
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* `exclude(**kwargs) -> QuerySet`
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* `select_related(related: Union[List, str]) -> QuerySet`
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* `select_related(related: Union[List, str]) -> QuerySet`
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* `prefetch_related(related: Union[List, str]) -> QuerySet`
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* `limit(limit_count: int) -> QuerySet`
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* `limit(limit_count: int) -> QuerySet`
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* `offset(offset: int) -> QuerySet`
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* `offset(offset: int) -> QuerySet`
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* `count() -> int`
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* `count() -> int`
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@ -165,6 +167,7 @@ assert len(tracks) == 1
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* `exclude_fields(columns: Union[List, str, set, dict]) -> QuerySet`
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* `exclude_fields(columns: Union[List, str, set, dict]) -> QuerySet`
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* `order_by(columns:Union[List, str]) -> QuerySet`
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* `order_by(columns:Union[List, str]) -> QuerySet`
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#### Relation types
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#### Relation types
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* One to many - with `ForeignKey(to: Model)`
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* One to many - with `ForeignKey(to: Model)`
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@ -45,7 +45,7 @@ Ormar is built with:
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* [`SQLAlchemy core`][sqlalchemy-core] for query building.
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* [`SQLAlchemy core`][sqlalchemy-core] for query building.
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* [`databases`][databases] for cross-database async support.
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* [`databases`][databases] for cross-database async support.
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* [`pydantic`][pydantic] for data validation.
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* [`pydantic`][pydantic] for data validation.
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* typing_extensions for python 3.6 - 3.7
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* `typing_extensions` for python 3.6 - 3.7
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### Migrations
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### Migrations
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@ -53,7 +53,8 @@ Because ormar is built on SQLAlchemy core, you can use [`alembic`][alembic] to p
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database migrations.
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database migrations.
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**ormar is still under development:** We recommend pinning any dependencies with `ormar~=0.4.0`
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**ormar is still under development:**
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We recommend pinning any dependencies (with i.e. `ormar~=0.5.2`)
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### Quick Start
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### Quick Start
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@ -157,6 +158,7 @@ assert len(tracks) == 1
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* `filter(**kwargs) -> QuerySet`
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* `filter(**kwargs) -> QuerySet`
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* `exclude(**kwargs) -> QuerySet`
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* `exclude(**kwargs) -> QuerySet`
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* `select_related(related: Union[List, str]) -> QuerySet`
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* `select_related(related: Union[List, str]) -> QuerySet`
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* `prefetch_related(related: Union[List, str]) -> QuerySet`
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* `limit(limit_count: int) -> QuerySet`
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* `limit(limit_count: int) -> QuerySet`
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* `offset(offset: int) -> QuerySet`
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* `offset(offset: int) -> QuerySet`
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* `count() -> int`
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* `count() -> int`
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164
docs/queries.md
164
docs/queries.md
@ -253,11 +253,22 @@ notes = await Track.objects.exclude(position_gt=3).all()
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`select_related(related: Union[List, str]) -> QuerySet`
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`select_related(related: Union[List, str]) -> QuerySet`
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Allows to prefetch related models.
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Allows to prefetch related models during the same query.
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**With `select_related` always only one query is run against the database**, meaning that one
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(sometimes complicated) join is generated and later nested models are processed in python.
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To fetch related model use `ForeignKey` names.
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To fetch related model use `ForeignKey` names.
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To chain related `Models` relation use double underscore.
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To chain related `Models` relation use double underscores between names.
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!!!note
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If you are coming from `django` note that `ormar` `select_related` differs -> in `django` you can `select_related`
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only singe relation types, while in `ormar` you can select related across `ForeignKey` relation,
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reverse side of `ForeignKey` (so virtual auto generated keys) and `ManyToMany` fields (so all relations as of current version).
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!!!note
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To control which model fields to select use `fields()` and `exclude_fields()` `QuerySet` methods.
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```python
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```python
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album = await Album.objects.select_related("tracks").all()
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album = await Album.objects.select_related("tracks").all()
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@ -286,6 +297,147 @@ Exactly the same behavior is for Many2Many fields, where you put the names of Ma
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Something like `Track.object.select_related("album").filter(album__name="Malibu").offset(1).limit(1).all()`
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Something like `Track.object.select_related("album").filter(album__name="Malibu").offset(1).limit(1).all()`
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### prefetch_related
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`prefetch_related(related: Union[List, str]) -> QuerySet`
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Allows to prefetch related models during query - but opposite to `select_related` each
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subsequent model is fetched in a separate database query.
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**With `prefetch_related` always one query per Model is run against the database**,
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meaning that you will have multiple queries executed one after another.
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To fetch related model use `ForeignKey` names.
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To chain related `Models` relation use double underscores between names.
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!!!note
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To control which model fields to select use `fields()` and `exclude_fields()` `QuerySet` methods.
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```python
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album = await Album.objects.prefetch_related("tracks").all()
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# will return album will all columns tracks
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```
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You can provide a string or a list of strings
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```python
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classes = await SchoolClass.objects.prefetch_related(
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["teachers__category", "students"]).all()
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# will return classes with teachers and teachers categories
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# as well as classes students
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```
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Exactly the same behavior is for Many2Many fields, where you put the names of Many2Many fields and the final `Models` are fetched for you.
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!!!warning
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If you set `ForeignKey` field as not nullable (so required) during
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all queries the not nullable `Models` will be auto prefetched, even if you do not include them in select_related.
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!!!note
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All methods that do not return the rows explicitly returns a QueySet instance so you can chain them together
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So operations like `filter()`, `select_related()`, `limit()` and `offset()` etc. can be chained.
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Something like `Track.object.select_related("album").filter(album__name="Malibu").offset(1).limit(1).all()`
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### select_related vs prefetch_related
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Which should you use -> `select_related` or `prefetch_related`?
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Well, it really depends on your data. The best answer is try yourself and see which one performs faster/better in your system constraints.
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What to keep in mind:
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#### Performance
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**Number of queries**:
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`select_related` always executes one query against the database, while `prefetch_related` executes multiple queries.
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Usually the query (I/O) operation is the slowest one but it does not have to be.
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**Number of rows**:
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Imagine that you have 10 000 object in one table A and each of those objects have 3 children in table B,
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and subsequently each object in table B has 2 children in table C. Something like this:
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```
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Model C
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/
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Model B - Model C
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/
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Model A - Model B - Model C
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\ \
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\ Model C
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\
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Model B - Model C
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\
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Model C
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```
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That means that `select_related` will always return 60 000 rows (10 000 * 3 * 2) later compacted to 10 000 models.
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How many rows will return `prefetch_related`?
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Well, that depends, if each of models B and C is unique it will return 10 000 rows in first query, 30 000 rows
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(each of 3 children of A in table B are unique) in second query and 60 000 rows (each of 2 children of model B
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in table C are unique) in 3rd query.
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In this case `select_related` seems like a better choice, not only it will run one query comparing to 3 of
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`prefetch_related` but will also return 60 000 rows comparing to 100 000 of `prefetch_related` (10+30+60k).
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But what if each Model A has exactly the same 3 models B and each models C has exactly same models C? `select_related`
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will still return 60 000 rows, while `prefetch_related` will return 10 000 for model A, 3 rows for model B and 2 rows for Model C.
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So in total 10 006 rows. Now depending on the structure of models (i.e. if it has long Text() fields etc.) `prefetch_related`
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might be faster despite it needs to perform three separate queries instead of one.
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#### Memory
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`ormar` is a mini ORM meaning that it does not keep a registry of already loaded models.
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That means that in `select_related` example above you will always have 10 000 Models A, 30 000 Models B
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(even if the unique number of rows in db is 3 - processing of `select_related` spawns **new** child models for each parent model).
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And 60 000 Models C.
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If the same Model B is shared by rows 1, 10, 100 etc. and you update one of those, the rest of rows
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that share the same child will **not** be updated on the spot.
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If you persist your changes into the database the change **will be available only after reload
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(either each child separately or the whole query again)**.
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That means that `select_related` will use more memory as each child is instantiated as a new object - obviously using it's own space.
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!!!note
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This might change in future versions if we decide to introduce caching.
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!!!warning
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By default all children (or event the same models loaded 2+ times) are completely independent, distinct python objects, despite that they represent the same row in db.
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They will evaluate to True when compared, so in example above:
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```python
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# will return True if child1 of both rows is the same child db row
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row1.child1 == row100.child1
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# same here:
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model1 = await Model.get(pk=1)
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model2 = await Model.get(pk=1) # same pk = same row in db
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# will return `True`
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model1 == model2
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```
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but
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```python
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# will return False (note that id is a python `builtin` function not ormar one).
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id(row1.child1) == (ro100.child1)
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# from above - will also return False
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id(model1) == id(model2)
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```
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On the contrary - with `prefetch_related` each unique distinct child model is instantiated
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only once and the same child models is shared across all parent models.
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That means that in `prefetch_related` example above if there are 3 distinct models in table B and 2 in table C,
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there will be only 5 children nested models shared between all model A instances. That also means that if you update
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any attribute it will be updated on all parents as they share the same child object.
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|
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### limit
|
### limit
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|
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@ -352,6 +504,10 @@ has_sample = await Book.objects.filter(title='Sample').exists()
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|
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With `fields()` you can select subset of model columns to limit the data load.
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With `fields()` you can select subset of model columns to limit the data load.
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!!!note
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Note that `fields()` and `exclude_fields()` works both for main models (on normal queries like `get`, `all` etc.)
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as well as `select_related` and `prefetch_related` models (with nested notation).
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Given a sample data like following:
|
Given a sample data like following:
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|
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```python
|
```python
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@ -433,6 +589,10 @@ It's the opposite of `fields()` method so check documentation above to see what
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Especially check above how you can pass also nested dictionaries and sets as a mask to exclude fields from whole hierarchy.
|
Especially check above how you can pass also nested dictionaries and sets as a mask to exclude fields from whole hierarchy.
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|
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|
!!!note
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|
Note that `fields()` and `exclude_fields()` works both for main models (on normal queries like `get`, `all` etc.)
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|
as well as `select_related` and `prefetch_related` models (with nested notation).
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|
|
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Below you can find few simple examples:
|
Below you can find few simple examples:
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|
|
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```python hl_lines="47 48 60 61 67"
|
```python hl_lines="47 48 60 61 67"
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@ -1,3 +1,13 @@
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|
# 0.5.2
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|
|
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|
* Added `prefetch_related` method to load subsequent models in separate queries.
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* Update docs
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|
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|
# 0.5.1
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|
|
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|
* Switched to github actions instead of travis
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|
* Update badges in the docs
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|
|
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# 0.5.0
|
# 0.5.0
|
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|
|
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* Added save status -> you can check if model is saved with `ModelInstance.saved` property
|
* Added save status -> you can check if model is saved with `ModelInstance.saved` property
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@ -30,7 +30,7 @@ class UndefinedType: # pragma no cover
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|
|
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Undefined = UndefinedType()
|
Undefined = UndefinedType()
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|
|
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__version__ = "0.5.1"
|
__version__ = "0.5.2"
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__all__ = [
|
__all__ = [
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"Integer",
|
"Integer",
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"BigInteger",
|
"BigInteger",
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|||||||
@ -145,20 +145,25 @@ class PrefetchQuery:
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field_name = model.get_related_field_name(target_field=target_field)
|
field_name = model.get_related_field_name(target_field=target_field)
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|
|
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children = self.already_extracted.get(target_model, {}).get(field_name, {})
|
children = self.already_extracted.get(target_model, {}).get(field_name, {})
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|
models = self.already_extracted.get(target_model, {}).get("pk_models", {})
|
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self._set_children_on_model(
|
self._set_children_on_model(
|
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model=model, related=related, children=children, model_id=model_id
|
model=model,
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|
related=related,
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|
children=children,
|
||||||
|
model_id=model_id,
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|
models=models,
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||||||
)
|
)
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||||||
|
|
||||||
return model
|
return model
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|
|
||||||
@staticmethod
|
@staticmethod
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||||||
def _set_children_on_model(
|
def _set_children_on_model(
|
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model: "Model", related: str, children: Dict, model_id: int
|
model: "Model", related: str, children: Dict, model_id: int, models: Dict
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||||||
) -> None:
|
) -> None:
|
||||||
for key, child_models in children.items():
|
for key, child_models in children.items():
|
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if key == model_id:
|
if key == model_id:
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for child in child_models:
|
for child in child_models:
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setattr(model, related, child)
|
setattr(model, related, models.get(child))
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|
|
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async def _prefetch_related_models(
|
async def _prefetch_related_models(
|
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self, models: Sequence["Model"], rows: List
|
self, models: Sequence["Model"], rows: List
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@ -338,6 +343,11 @@ class PrefetchQuery:
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model=instance, prefetch_dict=prefetch_dict,
|
model=instance, prefetch_dict=prefetch_dict,
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)
|
)
|
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field_db_name = target_model.get_column_alias(field_name)
|
field_db_name = target_model.get_column_alias(field_name)
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|
models = self.already_extracted[target_model.get_name()].setdefault(
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|
"pk_models", {}
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|
)
|
||||||
|
if instance.pk not in models:
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||||||
|
models[instance.pk] = instance
|
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self.already_extracted[target_model.get_name()].setdefault(
|
self.already_extracted[target_model.get_name()].setdefault(
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||||||
field_name, dict()
|
field_name, dict()
|
||||||
).setdefault(row[field_db_name], []).append(instance)
|
).setdefault(row[field_db_name], set()).add(instance.pk)
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||||||
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|||||||
@ -183,6 +183,23 @@ async def test_prefetch_related_with_many_to_many():
|
|||||||
assert track.album.shops[0].name == 'Shop 1'
|
assert track.album.shops[0].name == 'Shop 1'
|
||||||
assert track.album.shops[0].division.name == 'Div 1'
|
assert track.album.shops[0].division.name == 'Div 1'
|
||||||
|
|
||||||
|
album2 = Album(name="Malibu 2")
|
||||||
|
await album2.save()
|
||||||
|
await album2.shops.add(shop1)
|
||||||
|
await album2.shops.add(shop2)
|
||||||
|
await Track.objects.create(album=album2, title="The Bird 2", position=1)
|
||||||
|
|
||||||
|
tracks = await Track.objects.prefetch_related(["album__shops"]).all()
|
||||||
|
assert tracks[0].album.name == 'Malibu'
|
||||||
|
assert tracks[0].album.shops[0].name == "Shop 1"
|
||||||
|
assert tracks[3].album.name == 'Malibu 2'
|
||||||
|
assert tracks[3].album.shops[0].name == "Shop 1"
|
||||||
|
|
||||||
|
assert tracks[0].album.shops[0] == tracks[3].album.shops[0]
|
||||||
|
assert id(tracks[0].album.shops[0]) == id(tracks[3].album.shops[0])
|
||||||
|
tracks[0].album.shops[0].name = 'Dummy'
|
||||||
|
assert tracks[0].album.shops[0].name == tracks[3].album.shops[0].name
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.asyncio
|
@pytest.mark.asyncio
|
||||||
async def test_prefetch_related_empty():
|
async def test_prefetch_related_empty():
|
||||||
|
|||||||
Reference in New Issue
Block a user