Implementing Advance Query Optimization in Django ORM

Django's ORM makes database interactions seamless, allowing developers to write queries in Python without raw SQL. However, as applications scale, inefficient queries can slow down performance, leading to high latency and database load.

This guide explores advanced query optimization techniques in Django ORM to go beyond basic CRUD (Create, Read, Update, Delete) operations and improve efficiency.

1. Use QuerySet Caching to Avoid Repeated Queries

Using cache reduces redundant queries for frequently accessed data.

Caching helps reduce repeated database hits.

2. Avoid .count() on Large Datasets

Using .count() on large tables can be expensive

Inefficient way:

Optimized way (.exists() is Faster)

3. Use Indexes for Faster Lookups

Indexes speed up queries on frequently filtered fields.

Add db_index=True for frequently queried fields:

4. Optimize Bulk Inserts and Updated

Performing operations on multiple records one by one is inefficient. Use bulk_create() for mass inserts.

Bulk Operations significantly reduce query execution time

5. Reduce Query Count with only() and defer()

By default, Django loads all model fields, which can be slow for large table.

# use Only() to fetch specific fields:

# Use defer() to exclude heave fields:

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