How to optimize SQLAlchemy queries in Python 3.x for large datasets?
I've been working on this all day and Currently developing a data-driven application using SQLAlchemy with a PostgreSQL backend, and I'm struggling with query performance as the dataset grows. My current implementation fetches data like this: ```python from sqlalchemy import create_engine, select from sqlalchemy.orm import sessionmaker from mymodels import MyModel engine = create_engine('postgresql://user:password@localhost/mydb') Session = sessionmaker(bind=engine) session = Session() query = select(MyModel).filter(MyModel.some_field == 'value') data = session.execute(query).scalars().all() ``` This basic query works fine for smaller datasets, but as the number of records increases, the response time is noticeably lagging. I've tried adding indexes to the `some_field`, but it hasn't made much difference. Looking for the best way to optimize these queries, I've also considered using `EXPLAIN ANALYZE` to evaluate performance. The output suggested a sequential scan, which seems inefficient. I then modified my query to use pagination: ```python page_number = 1 page_size = 100 query = select(MyModel).filter(MyModel.some_field == 'value').offset((page_number - 1) * page_size).limit(page_size) data = session.execute(query).scalars().all() ``` While this approach helped a bit, I'm still not satisfied with the speed. Additionally, I suspect that loading relationships could be part of the issue, as I often need related data from another table. Is there a recommended practice for efficiently loading related entities or batching these queries? Would using `joinedload` or `subqueryload` help reduce the number of queries executed? Any suggestions for optimizing this type of workload in SQLAlchemy would be highly appreciated. Additionally, if there's a way to profile query performance directly within SQLAlchemy, that would be beneficial too. I recently upgraded to Python 3.11.