Optimizing SQL Queries: Left Joins vs. Except Clause

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In relational databases, optimizing SQL queries is paramount for efficient data retrieval and processing. Two standard methods for filtering data left joins and except clauses, offer distinct approaches to achieving the desired results.

We often write code to query databases in production, and the data size might be huge. In these cases, the extra 1–2 seconds the query takes would make all the difference for the customers. Customers don’t want to wait on a screen for 2–3 seconds. The faster the developer can make the query, the better the user experience. This is why we will be looking at one of the ways to optimize the query.

This article will delve into the concepts behind left joins and except clauses, compare their usage in SQL queries, and showcase practical examples using SQLAlchemy in Python. Furthermore, we’ll explore the performance implications of each approach through benchmarking.

Understanding Left Joins and Except Clauses

Before delving into comparisons, grasping the fundamentals of left joins and except clauses is crucial.

• Left Join: A left join retrieves all records from the left table and the matched records from the right table. If there is no match, NULL values are returned for the columns of the right table.
• Except for Clause: On the other hand, the except clause returns distinct rows from the left query that are not present in the correct query.

Comparing Left Joins and Except Clauses

Let’s illustrate the differences between left joins and except clauses with a simple example. Suppose we have two tables, “orders” and “returns,” where “orders” contains all orders placed, and “returns” includes orders that were returned.

from sqlalchemy import create_engine, Column, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class Order(Base):
    __tablename__ = 'orders'
    id = Column(Integer, primary_key=True)

class Return(Base):
    __tablename__ = 'returns'
    id = Column(Integer, primary_key=True)

engine = create_engine('sqlite:///example.db')
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()

Now, let’s populate the tables with some data:

for i in range(1, 10001):
    session.add(Order(id=i))
    if i % 10 == 0:
        session.add(Return(id=i))
session.commit()

Using Left Joins

To retrieve all orders that were not returned using a left join:

from sqlalchemy.orm import aliased

orders = aliased(Order)
returns = aliased(Return)

query = session.query(orders).outerjoin(returns, orders.id == returns.id).filter(returns.id == None)

Using Except Clause

To achieve the same result use an except clause:

all_orders = session.query(Order.id)
returned_orders = session.query(Return.id)
unreturned_orders = all_orders.except_(returned_orders)

Benchmarking Performance

To assess the performance trade-offs between left joins and except clauses, we’ll measure the time taken by each approach to retrieve unreturned orders.

import time

start_time = time.time()
# Execute left join query
# Measure time taken
end_time = time.time()
left_join_time = end_time - start_time

start_time = time.time()
# Execute except clause query
# Measure time taken
end_time = time.time()
except_clause_time = end_time - start_time

print("Left Join Time:", left_join_time)
print("Except Clause Time:", except_clause_time)

Conclusion

Both left joins and except clauses offer distinct ways to filter data in SQL queries. Left joins are suitable for retrieving matched records from two tables, while except clauses excel at returning different rows from the left query not present in the correct query.

As a developer, you will be conducting experiments with data sizes comparable to production to see which of these is faster for your use case.