# SQLAlchemy 조인 종류 ORM 을 사용하다보면 어떤 방법으로 연관된 엔티티들을 조회할 방식에 대해 고민을 많이 하게된다. Python 을 사용하면 주로 SQLAlchemy 를 주 ORM 으로 많이 사용하게 되는데, SQLAlchemy 에는 크게 3가지 정도의 연관관계 로딩 방법이 존재한다. 오늘은 그 중 `selectinload` , `joinedload` , `subqueryload` 에 대해서 알아보려고 한다. ## 모델링 일단 테스트를 위한 엔티티 생성은 아래와 같이 조금 복잡하지만 실무에 가까운 모델로 작업해보려고 한다. * **5개 모델**: User, Post, Comment, Category, Tag * **5개 일대다 관계**: User→Posts, User→Comments, Category→Posts, Post→Comments, Comment→Comments * **3개 다대다 관계**: Post↔Tag, User↔Post(좋아요), User↔User(팔로워) * **총 8개 관계**로 복잡한 조인 테스트 가능 ## 간단한 조인 만약 우리가 User 가 작성한 Post 를 모두 가져오려면 어떻게 SQLAlchemy 를 통해 쿼리를 작성하면 될까? 일단은 첫번째로 `joinedload` 를 사용해보려고 한다. 공식문서만 보고도 아래와 같이 쉽게 쿼리 작성이 가능하다. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).options(joinedload(User.posts)).where(User.id == 1) user = session.execute(stmt).unique().scalars().one_or_none() print(user) ``` 작성된 DSL 을 보면 `unique` 함수가 붙어 있는걸 알수 있다. 이게 당장은 의문일 수 있지만 아래 쿼리를 보면 해결되니 한번 같이 보도록 하자. 이 경우 작성되는 쿼리는 아래와 같이 작성된다. ```sql SELECT users.id, users.username, users.email, users.full_name, users.bio, users.is_active, users.created_at, users.updated_at, posts_1.id AS id_1, posts_1.title, posts_1.slug, posts_1.content, posts_1.published, posts_1.view_count, posts_1.created_at AS created_at_1, posts_1.updated_at AS updated_at_1, posts_1.author_id, posts_1.category_id FROM users LEFT OUTER JOIN posts AS posts_1 ON users.id = posts_1.author_id WHERE users.id = %(id_2)s ``` 이렇게 쿼리를 날리게 되면 데이터는 어떻게 나올까? ```markdown | id | username | email | full\_name | bio | is\_active | created\_at | updated\_at | id\_1 | title | slug | content | published | view\_count | created\_at\_1 | updated\_at\_1 | author\_id | category\_id | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | amyaguilar | alewis@example.com | Andrew James | Entire collection Democrat education first. Senior thousand might than news play. | true | 2025-08-31 07:50:11.956596 | 2025-09-30 12:15:50.381909 | 36 | Someone vote line certainly benefit movement bag trade | pretty-maintain | Citizen power expert finish. Just PM improve oil task camera sell. Particularly course what wonder.
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Something break executive cut together hit law. | true | 3827 | 2025-06-11 18:54:02.573873 | 2025-09-30 12:15:50.722608 | 1 | 1 | ``` 데이터를 보면 **동일한 user 데이터(우리가 조회한 user 1) 가** 유저가 작성한 포스트의 개수만큼 반복되는 것을 확인할수 있다. 즉, 기본적으로 **LEFT OUTER JOIN** 으로 동작한다는 것이다. 이렇게 되면 어떤 문제가 있을까? 받아온 쿼리 byte 를 serialize 하다가 동일한 user 의 데이터가 여러번 생기게 될수 있다. 따라서, SQLAlchemy 에서는 이를 `unique` 로 제어한다. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).options(joinedload(User.posts)).limit(1000) users = session.execute(stmt).unique().scalars().all() all_posts = [post for user in users for post in user.posts] print(len(all_posts)) # 500 ``` 만약 위와 같은 쿼리로 200명의 유저를 Post 와 함께 Join 하게 된다면 얼마만큼의 시간이 걸릴까? 데이터가 500개로 아직 크지 않아서 약 `40.84ms` 가 소요되었다. 성능상으로 크게 문제가 없어보이지만 다르게 생각하면 바뀌지 않는 data 에 대한 byte 를 끊임없이 소모함을 알수 있다. 참고로 모든 쿼리 결과는 application 에 load 될때 메모리에 올라가므로, 이러한 부분들을 고려해야 한다. 사실 저 정보에서 유저정보는 유니크하기 때문에 굳이 더 받아올 필요가 없다. 더 좋은 방식이 없을까? 이럴때 SQLAlchemy 에서 권장하는 `selectinload` 를 써볼수 있다. ## selectinload 이건 뭐지? 싶을 수 있지만 말 그대로 `SELECT IN …` 쿼리를 이용해서 로드하는 방식이다. 일단 공식문서에 따라 코드로 작성해보면 아래와 같이 작성해볼수 있다. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).options(selectinload(User.posts)).limit(1000) users = session.execute(stmt).scalars().all() all_posts = [post for user in users for post in user.posts] print(len(all_posts)) # 500 ``` 이 방법은 처음에 User 를 먼져 로딩해온다. User 를 먼져 로딩해오고 그 이후에 id 로 `select in` **쿼리**를 날려야 하기때문이다. ```sql SELECT users.id, users.username, users.email, users.full_name, users.bio, users.is_active, users.created_at, users.updated_at FROM users LIMIT %(param_1)s ``` 이렇게 해서 가져온 id 를 통해서 다시 post 테이블에 쿼리를 날리게 된다. ```sql SELECT posts.author_id AS posts_author_id, posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.category_id AS posts_category_id FROM posts WHERE posts.author_id IN (1, 2, 3, ...) ``` 이 방식의 장점은 무엇일까? 바로 위에서 언급했듯이 동일한 user 에 대한 정보를 N 번 반복해서 소비하지 않는 다는 점이다. 만약 Join 을 통해 반복되는 데이터가 크다면 이는 네트워크의 대역폭을 절감해주는 역할 또한 해줄수 있다. 따라서, SQLAlchemy 에서 가장 권장하는 방법이며 필자도 이 방법을 가장 많이 쓴다. 아주 간단하고 좋아보이는 방법이지만 무슨 문제가 있을까? 만약에 User 에 연관된 `post`, `liked_post`, `comments`, `following` 등의 엔티티를 함께 로드해야 된다고 해보자. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).options( selectinload(User.posts).selectinload(Post.comments), selectinload(User.comments), selectinload(User.liked_posts), selectinload(User.following) ).limit(1000) users = session.execute(stmt).scalars().all() ``` 이렇게 되면 총 몇개의 쿼리가 나갈까? 아마도 User 한번 각 연관관계들 별로 한번씩 쿼리가 발생하여 총 6번의 쿼리가 나갈 것이다. ```sql SELECT users.id, users.username, users.email, users.full_name, users.bio, users.is_active, users.created_at, users.updated_at FROM users LIMIT 1000 SELECT users_1.id AS users_1_id, posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.author_id AS posts_author_id, posts.category_id AS posts_category_id FROM users AS users_1 JOIN post_likes AS post_likes_1 ON users_1.id = post_likes_1.user_id JOIN posts ON posts.id = post_likes_1.post_id WHERE users_1.id IN (1, 2, 3, ...) SELECT users_1.id AS users_1_id, users.id AS users_id, users.username AS users_username, users.email AS users_email, users.full_name AS users_full_name, users.bio AS users_bio, users.is_active AS users_is_active, users.created_at AS users_created_at, users.updated_at AS users_updated_at FROM users AS users_1 JOIN followers AS followers_1 ON users_1.id = followers_1.follower_id JOIN users ON users.id = followers_1.followed_id WHERE users_1.id IN (1, 2, 3, ...) SELECT posts.author_id AS posts_author_id, posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.category_id AS posts_category_id FROM posts WHERE posts.author_id IN (1, 2, 3, ...) SELECT comments.author_id AS comments_author_id, comments.id AS comments_id, comments.content AS comments_content, comments.is_approved AS comments_is_approved, comments.created_at AS comments_created_at, comments.updated_at AS comments_updated_at, comments.post_id AS comments_post_id, comments.parent_id AS comments_parent_id FROM comments WHERE comments.author_id IN (1, 2, 3, ...) sqlalchemy.engine.Engine SELECT comments.post_id AS comments_post_id, comments.id AS comments_id, comments.content AS comments_content, comments.is_approved AS comments_is_approved, comments.created_at AS comments_created_at, comments.updated_at AS comments_updated_at, comments.author_id AS comments_author_id, comments.parent_id AS comments_parent_id FROM comments WHERE comments.post_id IN (1, 2, 3, ...) # 여기는 post_id 이다. ``` 엔티티를 한번에 가져와서 사용하기 때문에 SQLAlchemy 공식문서에 나와있듯이 대부분의 상황에서 효율적이다. `joinedload` 에서 문제가 되었던 중복 데이터의 컨슘또한 해결이 되었다. 하지만 항상 정답인 것은 없으므로 단점을 한번 생각해봐야 한다. 첫번째로 가장 쉽게 보이는 단점은 아래와 같다. ### 네트워크 왕복 횟수 증가 기존 **joinedload** 에서는 모든 데이터를 한번에 가져오므로 하나의 쿼리로 충족이 가능하다. 다만, 위와 같이 연관관계가 복잡해지고, 더 많은 데이터를 가져올수록 더 많은 중복이 발생하므로 이는 데이터의 크기를 급격하게 증가시킬수 있다. 따라서 이 부분에서도 간단하게 `selectinload` 를 사용하는 것이 성능이 좋을 것이다. ### 부모가 10000 개인데 자식은 10개인 경우? 극단적인 상황을 생각해보자 만약 부모 row 는 10000 개인데 자식 N 개에 대한 row 는 자식당 10개 정도라고 해보자. 그렇다면 우리는 자식 10N 개를 조회하기 위해 10000N 개의 PK 를 IN 절에 넣게된다. 즉, 이 경우 상당히 비효율적으로 쿼리가 발생하게 된다. ```sql SELECT users.id, users.username, users.email, users.full_name, users.bio, users.is_active, users.created_at, users.updated_at FROM users LIMIT 1000 SELECT users_1.id AS users_1_id, posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.author_id AS posts_author_id, posts.category_id AS posts_category_id FROM users AS users_1 JOIN post_likes AS post_likes_1 ON users_1.id = post_likes_1.user_id JOIN posts ON posts.id = post_likes_1.post_id WHERE users_1.id IN (1, 2, 3, ..., 10000) SELECT users_1.id AS users_1_id, users.id AS users_id, users.username AS users_username, users.email AS users_email, users.full_name AS users_full_name, users.bio AS users_bio, users.is_active AS users_is_active, users.created_at AS users_created_at, users.updated_at AS users_updated_at FROM users AS users_1 JOIN followers AS followers_1 ON users_1.id = followers_1.follower_id JOIN users ON users.id = followers_1.followed_id WHERE users_1.id IN (1, 2, 3, ..., 10000) SELECT posts.author_id AS posts_author_id, posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.category_id AS posts_category_id FROM posts WHERE posts.author_id IN (1, 2, 3, ..., 10000) SELECT comments.author_id AS comments_author_id, comments.id AS comments_id, comments.content AS comments_content, comments.is_approved AS comments_is_approved, comments.created_at AS comments_created_at, comments.updated_at AS comments_updated_at, comments.post_id AS comments_post_id, comments.parent_id AS comments_parent_id FROM comments WHERE comments.author_id IN (1, 2, 3, ..., 10000) sqlalchemy.engine.Engine SELECT comments.post_id AS comments_post_id, comments.id AS comments_id, comments.content AS comments_content, comments.is_approved AS comments_is_approved, comments.created_at AS comments_created_at, comments.updated_at AS comments_updated_at, comments.author_id AS comments_author_id, comments.parent_id AS comments_parent_id FROM comments WHERE comments.post_id IN (1, 2, 3, ..., 10000) ``` 이러한 경우에는 어떠한 loading 전략(option)을 사용하면 좋을까? SQLAlchemy 에는 이러한 상황에 사용하기 위한 `subqueryload` 가 존재한다. 아주 쉽게 저 IN 절의 PK 부분을 subquery 로 대체하는 방식이다. ## subqueryload ```sql SELECT posts.id AS posts_id, posts.title AS posts_title, posts.slug AS posts_slug, posts.content AS posts_content, posts.published AS posts_published, posts.view_count AS posts_view_count, posts.created_at AS posts_created_at, posts.updated_at AS posts_updated_at, posts.author_id AS posts_author_id, posts.category_id AS posts_category_id, anon_1.users_id AS anon_1_users_id FROM ( SELECT users.id AS users_id FROM users LIMIT 1000 ) AS anon_1 JOIN posts ON anon_1.users_id = posts.author_id ``` 이렇게 되면 만약 내부의 user 가 10000명일 경우 IN 절에 대한 쿼리에 대한 용량을 줄일 수 있게 된다. ### 만약 자식 collection 에 대한 filter 를 하고 싶은 경우? 컬렉션을 로딩하다보면 이런 요구 사항들이 생기곤 한다. `유저가 작성한 포스트 중에 publish 된거만 보고 싶어` 라는 요구사항들이 생긴다. 이럴경우 `contains_eager` 를 이용하면 쉽게 구현이 가능하다. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).join(User.posts).filter(Post.published == True).options(contains_eager(User.posts)) users = session.execute(stmt).scalars().unique().all() all_posts = [post for user in users for post in user.posts] print(len(all_posts)) # 457 ``` `contains_eager` 는 join 으로 구현되어 공식문서에 나온대로 구현하면 쉽게 따라할수 있다. 이 방법은 아주 간단하게 앞에서 join 으로 가져온 데이터를 단순히 wrapping 한다고 생각하면 쉽다. 하지만 join 을 해야하므로 `selectinload` 를 쓰는 부분에서 아쉬울 수 있다. ### selectinload 에서 자식 컬렉션 필터링 다행이도, SQLAlchemy 가 업데이트 되면서 `selectinload` 에서도 가능한데 단순히 `and_` 를 사용하면 된다. ```python @measure_time def test_get_posts(): session = get_session() stmt = select(User).options( selectinload(User.posts.and_(Post.published == True)) ).limit(1000) users = session.execute(stmt).scalars().all() all_posts = [post for user in users for post in user.posts] print(len(all_posts)) # 457 ``` ## 마치며 테스트할 데이터나 테이블을 쉽게 seeding 하고 싶으면 아래 github repository 를 클론하면 된다. Vibe coding 으로 만들어졌고, 필자도 테스트할때만 코드를 바꿔가며 이용해서 하나 function 을 만들고 그냥 Query 나 성능 테스트를 진행하기에 딱 좋은 Repo 이다. [https://github.com/tmdgusya/example-sqlalchemy](https://github.com/tmdgusya/example-sqlalchemy)