What is Django's database optimization with query analysis?

Advanced Django query optimization techniques: 1. Django Debug Toolbar: shows all SQL queries per request with timing, duplicates. Essential for development: pip install django-debug-toolbar. 2. Query counting and timing: from django.db import connection, reset_queries from django.conf import settings settings.DEBUG = True reset_queries() # your view code print(len(connection.queries)) print(connection.queries) # list of SQL + execution time. 3. Explain query plan: qs = Article.objects.filter(is_published=True) print(qs.query) # Shows SQL qs.explain() # PostgreSQL EXPLAIN output. 4. only() and defer(): Article.objects.only("title", "published_at") # SELECT only these Article.objects.defer("content") # SELECT everything EXCEPT content. Only fetch what you need — especially avoid large text fields in list views. 5. values() and values_list(): Article.objects.values("title", "author__name") # dict list Article.objects.values_list("id", "title") # tuple list Article.objects.values_list("id", flat=True) # flat list of ids. Returns dicts/tuples not model instances — faster for read-only access. 6. bulk operations: Article.objects.bulk_create([Article(...), ...], batch_size=500) Article.objects.bulk_update(articles, ["title", "status"], batch_size=500) Article.objects.filter(...).update(is_published=True) Article.objects.filter(...).delete(). 7. aggregate vs annotate: aggregate returns a single value over QuerySet; annotate adds a computed value per row. 8. iterator(): for article in Article.objects.iterator(chunk_size=1000): — memory-efficient for processing large querysets without loading all into RAM.