update documentation, optimize for memory saving, update docs for select_related, fields and exclude_fields, bump version

This commit is contained in:
collerek
2020-11-26 09:15:13 +01:00
parent ba360974de
commit 8a75379b44
7 changed files with 213 additions and 11 deletions

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@ -45,7 +45,7 @@ Ormar is built with:
* [`SQLAlchemy core`][sqlalchemy-core] for query building.
* [`databases`][databases] for cross-database async support.
* [`pydantic`][pydantic] for data validation.
* typing_extensions for python 3.6 - 3.7
* `typing_extensions` for python 3.6 - 3.7
### Migrations
@ -53,7 +53,8 @@ Because ormar is built on SQLAlchemy core, you can use [`alembic`][alembic] to p
database migrations.
**ormar is still under development:** We recommend pinning any dependencies with `ormar~=0.4.0`
**ormar is still under development:**
We recommend pinning any dependencies (with i.e. `ormar~=0.5.2`)
### Quick Start
@ -157,6 +158,7 @@ assert len(tracks) == 1
* `filter(**kwargs) -> QuerySet`
* `exclude(**kwargs) -> QuerySet`
* `select_related(related: Union[List, str]) -> QuerySet`
* `prefetch_related(related: Union[List, str]) -> QuerySet`
* `limit(limit_count: int) -> QuerySet`
* `offset(offset: int) -> QuerySet`
* `count() -> int`
@ -165,6 +167,7 @@ assert len(tracks) == 1
* `exclude_fields(columns: Union[List, str, set, dict]) -> QuerySet`
* `order_by(columns:Union[List, str]) -> QuerySet`
#### Relation types
* One to many - with `ForeignKey(to: Model)`

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@ -45,7 +45,7 @@ Ormar is built with:
* [`SQLAlchemy core`][sqlalchemy-core] for query building.
* [`databases`][databases] for cross-database async support.
* [`pydantic`][pydantic] for data validation.
* typing_extensions for python 3.6 - 3.7
* `typing_extensions` for python 3.6 - 3.7
### Migrations
@ -53,7 +53,8 @@ Because ormar is built on SQLAlchemy core, you can use [`alembic`][alembic] to p
database migrations.
**ormar is still under development:** We recommend pinning any dependencies with `ormar~=0.4.0`
**ormar is still under development:**
We recommend pinning any dependencies (with i.e. `ormar~=0.5.2`)
### Quick Start
@ -157,6 +158,7 @@ assert len(tracks) == 1
* `filter(**kwargs) -> QuerySet`
* `exclude(**kwargs) -> QuerySet`
* `select_related(related: Union[List, str]) -> QuerySet`
* `prefetch_related(related: Union[List, str]) -> QuerySet`
* `limit(limit_count: int) -> QuerySet`
* `offset(offset: int) -> QuerySet`
* `count() -> int`

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@ -253,11 +253,22 @@ notes = await Track.objects.exclude(position_gt=3).all()
`select_related(related: Union[List, str]) -> QuerySet`
Allows to prefetch related models.
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 underscore.
To chain related `Models` relation use double underscores between names.
!!!note
If you are coming from `django` note that `ormar` `select_related` differs -> in `django` you can `select_related`
only singe relation types, while in `ormar` you can select related across `ForeignKey` relation,
reverse side of `ForeignKey` (so virtual auto generated keys) and `ManyToMany` fields (so all relations as of current version).
!!!note
To control which model fields to select use `fields()` and `exclude_fields()` `QuerySet` methods.
```python
album = await Album.objects.select_related("tracks").all()
@ -286,6 +297,147 @@ Exactly the same behavior is for Many2Many fields, where you put the names of Ma
Something like `Track.object.select_related("album").filter(album__name="Malibu").offset(1).limit(1).all()`
### prefetch_related
`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.
!!!note
To control which model fields to select use `fields()` and `exclude_fields()` `QuerySet` methods.
```python
album = await Album.objects.prefetch_related("tracks").all()
# will return album will all columns tracks
```
You can provide a string or a list of strings
```python
classes = await SchoolClass.objects.prefetch_related(
["teachers__category", "students"]).all()
# will return classes with teachers and teachers categories
# as well as classes students
```
Exactly the same behavior is for Many2Many fields, where you put the names of Many2Many fields and the final `Models` are fetched for you.
!!!warning
If you set `ForeignKey` field as not nullable (so required) during
all queries the not nullable `Models` will be auto prefetched, even if you do not include them in select_related.
!!!note
All methods that do not return the rows explicitly returns a QueySet instance so you can chain them together
So operations like `filter()`, `select_related()`, `limit()` and `offset()` etc. can be chained.
Something like `Track.object.select_related("album").filter(album__name="Malibu").offset(1).limit(1).all()`
### select_related vs prefetch_related
Which should you use -> `select_related` or `prefetch_related`?
Well, it really depends on your data. The best answer is try yourself and see which one performs faster/better in your system constraints.
What to keep in mind:
#### Performance
**Number of queries**:
`select_related` always executes one query against the database, while `prefetch_related` executes multiple queries.
Usually the query (I/O) operation is the slowest one but it does not have to be.
**Number of rows**:
Imagine that you have 10 000 object in one table A and each of those objects have 3 children in table B,
and subsequently each object in table B has 2 children in table C. Something like this:
```
Model C
/
Model B - Model C
/
Model A - Model B - Model C
\ \
\ Model C
\
Model B - Model C
\
Model C
```
That means that `select_related` will always return 60 000 rows (10 000 * 3 * 2) later compacted to 10 000 models.
How many rows will return `prefetch_related`?
Well, that depends, if each of models B and C is unique it will return 10 000 rows in first query, 30 000 rows
(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
in table C are unique) in 3rd query.
In this case `select_related` seems like a better choice, not only it will run one query comparing to 3 of
`prefetch_related` but will also return 60 000 rows comparing to 100 000 of `prefetch_related` (10+30+60k).
But what if each Model A has exactly the same 3 models B and each models C has exactly same models C? `select_related`
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.
So in total 10 006 rows. Now depending on the structure of models (i.e. if it has long Text() fields etc.) `prefetch_related`
might be faster despite it needs to perform three separate queries instead of one.
#### Memory
`ormar` is a mini ORM meaning that it does not keep a registry of already loaded models.
That means that in `select_related` example above you will always have 10 000 Models A, 30 000 Models B
(even if the unique number of rows in db is 3 - processing of `select_related` spawns **new** child models for each parent model).
And 60 000 Models C.
If the same Model B is shared by rows 1, 10, 100 etc. and you update one of those, the rest of rows
that share the same child will **not** be updated on the spot.
If you persist your changes into the database the change **will be available only after reload
(either each child separately or the whole query again)**.
That means that `select_related` will use more memory as each child is instantiated as a new object - obviously using it's own space.
!!!note
This might change in future versions if we decide to introduce caching.
!!!warning
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.
They will evaluate to True when compared, so in example above:
```python
# will return True if child1 of both rows is the same child db row
row1.child1 == row100.child1
# same here:
model1 = await Model.get(pk=1)
model2 = await Model.get(pk=1) # same pk = same row in db
# will return `True`
model1 == model2
```
but
```python
# will return False (note that id is a python `builtin` function not ormar one).
id(row1.child1) == (ro100.child1)
# from above - will also return False
id(model1) == id(model2)
```
On the contrary - with `prefetch_related` each unique distinct child model is instantiated
only once and the same child models is shared across all parent models.
That means that in `prefetch_related` example above if there are 3 distinct models in table B and 2 in table C,
there will be only 5 children nested models shared between all model A instances. That also means that if you update
any attribute it will be updated on all parents as they share the same child object.
### limit
@ -352,6 +504,10 @@ has_sample = await Book.objects.filter(title='Sample').exists()
With `fields()` you can select subset of model columns to limit the data load.
!!!note
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).
Given a sample data like following:
```python
@ -433,6 +589,10 @@ It's the opposite of `fields()` method so check documentation above to see what
Especially check above how you can pass also nested dictionaries and sets as a mask to exclude fields from whole hierarchy.
!!!note
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).
Below you can find few simple examples:
```python hl_lines="47 48 60 61 67"

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@ -1,3 +1,13 @@
# 0.5.2
* Added `prefetch_related` method to load subsequent models in separate queries.
* Update docs
# 0.5.1
* Switched to github actions instead of travis
* Update badges in the docs
# 0.5.0
* 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
Undefined = UndefinedType()
__version__ = "0.5.1"
__version__ = "0.5.2"
__all__ = [
"Integer",
"BigInteger",

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@ -145,20 +145,25 @@ class PrefetchQuery:
field_name = model.get_related_field_name(target_field=target_field)
children = self.already_extracted.get(target_model, {}).get(field_name, {})
models = self.already_extracted.get(target_model, {}).get("pk_models", {})
self._set_children_on_model(
model=model, related=related, children=children, model_id=model_id
model=model,
related=related,
children=children,
model_id=model_id,
models=models,
)
return model
@staticmethod
def _set_children_on_model(
model: "Model", related: str, children: Dict, model_id: int
model: "Model", related: str, children: Dict, model_id: int, models: Dict
) -> None:
for key, child_models in children.items():
if key == model_id:
for child in child_models:
setattr(model, related, child)
setattr(model, related, models.get(child))
async def _prefetch_related_models(
self, models: Sequence["Model"], rows: List
@ -338,6 +343,11 @@ class PrefetchQuery:
model=instance, prefetch_dict=prefetch_dict,
)
field_db_name = target_model.get_column_alias(field_name)
models = self.already_extracted[target_model.get_name()].setdefault(
"pk_models", {}
)
if instance.pk not in models:
models[instance.pk] = instance
self.already_extracted[target_model.get_name()].setdefault(
field_name, dict()
).setdefault(row[field_db_name], []).append(instance)
).setdefault(row[field_db_name], set()).add(instance.pk)

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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].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
async def test_prefetch_related_empty():