Add benchmarking test suite and greatly improve performance in a few cases (#948)

* Add benchmarking test suite

* Improve amortized time of model relation loads with a large number of rows

* Improve performance of loading models with many related models

* Improve performance of loading models with many related models to O(N)ish

* Fix bug where N model creation with shared related model would build in N^2 time

* Lower blocking time for queryset results

* Add docstrings and streamline hash code

Co-authored-by: haydeec1 <Eric.Haydel@jhuapl.edu>

benchmarks/test_benchmark_get.py

@@ -0,0 +1,102 @@
+import random
+import string
+from typing import List
+
+import pytest
+import pytest_asyncio
+
+from benchmarks.conftest import Author, Book, Publisher
+
+pytestmark = pytest.mark.asyncio
+
+
+@pytest_asyncio.fixture()
+async def books(author: Author, publisher: Publisher, num_models: int):
+    books = [
+        Book(
+            author=author,
+            publisher=publisher,
+            title="".join(random.sample(string.ascii_letters, 5)),
+            year=random.randint(0, 2000),
+        )
+        for _ in range(0, num_models)
+    ]
+    await Book.objects.bulk_create(books)
+    return books
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_get_all(aio_benchmark, num_models: int, authors_in_db: List[Author]):
+    @aio_benchmark
+    async def get_all(authors: List[Author]):
+        return await Author.objects.all()
+
+    authors = get_all(authors_in_db)
+    for idx, author in enumerate(authors_in_db):
+        assert authors[idx].id == author.id
+
+
+@pytest.mark.parametrize("num_models", [10, 20, 40])
+async def test_get_all_with_related_models(
+    aio_benchmark, num_models: int, author: Author, books: List[Book]
+):
+    @aio_benchmark
+    async def get_with_related(author: Author):
+        return await Author.objects.select_related("books").all(id=author.id)
+
+    authors = get_with_related(author)
+    assert len(authors[0].books) == num_models
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_get_one(aio_benchmark, num_models: int, authors_in_db: List[Author]):
+    @aio_benchmark
+    async def get_one(authors: List[Author]):
+        return await Author.objects.get(id=authors[0].id)
+
+    author = get_one(authors_in_db)
+    assert author == authors_in_db[0]
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_get_or_none(aio_benchmark, num_models: int, authors_in_db: List[Author]):
+    @aio_benchmark
+    async def get_or_none(authors: List[Author]):
+        return await Author.objects.get_or_none(id=authors[0].id)
+
+    author = get_or_none(authors_in_db)
+    assert author == authors_in_db[0]
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_get_or_create_when_get(
+    aio_benchmark, num_models: int, authors_in_db: List[Author]
+):
+    @aio_benchmark
+    async def get_or_create(authors: List[Author]):
+        author, created = await Author.objects.get_or_create(id=authors[0].id)
+        assert not created
+        return author
+
+    author = get_or_create(authors_in_db)
+    assert author == authors_in_db[0]
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_first(aio_benchmark, num_models: int, authors_in_db: List[Author]):
+    @aio_benchmark
+    async def first():
+        return await Author.objects.first()
+
+    author = first()
+    assert author == authors_in_db[0]
+
+
+@pytest.mark.parametrize("num_models", [250, 500, 1000])
+async def test_exists(aio_benchmark, num_models: int, authors_in_db: List[Author]):
+    @aio_benchmark
+    async def check_exists(authors: List[Author]):
+        return await Author.objects.filter(id=authors[0].id).exists()
+
+    exists = check_exists(authors_in_db)
+    assert exists