Django QuerySets: Chained vs Evaluated

The Django ORM feels like magic. But how does it work internally? It's actually three layers doing very specific jobs. Layer 1: Query -> When a QuerySet is built, Django creates a Query object internally. -> This object is a structured, database-agnostic representation of the intended operation. -> All this is stored as Python data structures. No SQL yet. This is why QuerySets are chainable. Layer 2: SQL Compiler -> At evaluation time, Django takes the Query object and hands it to a SQLCompiler. -> Compiler translates Python representation into actual SQL. -> This is where database-specific syntax is handled. The same Query object produces different SQL depending on which backend is active. Layer 3: Connection -> The compiled SQL string is handed to the database Connection. -> This manages the actual connection to the database, sends the query, and returns raw results. -> Django then maps those raw results back into model instances. The ORM is not a black box. It's a pipeline and knowing it means knowing exactly where to look when something goes wrong. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

A view works fine in development. Slows down in staging. Crawls in production. What is the infamous N+1 query problem? TL;DR - N+1 is a query that looks like one DB call but fires hundreds. How does it happen? 1. One query fetches a list of objects. 2. Then inside the loop, a related object is accessed. 3. Django doesn't have it. So it fetches it. Once per row. So, 10 orders → 11 queries. 100 orders → 101 queries. No errors. No warnings. The reason is lazy evaluation of queries as discussed in last post. How to identify N+1 query problem? Django Debug Toolbar shows exact query counts per request, use it to check the query count. How to fix this issue? - Fetch related data upfront in single query. - select_related for ForeignKey and OneToOne relationships. Uses a SQL JOIN. One query in total. - prefetch_related for ManyToMany and reverse ForeignKey. Uses a second query, joins in Python. The loop stays identical. The query count drops from N+1 to 2 at most. N+1 is not a Django problem. It's a misunderstanding of when evaluation happens and how it works! I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

Your Django code looks clean. Your database is screaming 1,000 orders. 1,001 database queries. Per page load. Per user. That's the N+1 problem — and it's completely invisible until production is on fire. Here's what's actually happening: When you loop through orders and access order.items.all(), Django fires a brand new SQL query for every single order. 10 orders = 11 queries. 10,000 orders = 10,001 queries. Your DB melts. Your users wait. The fix is one line:  Order.objects.prefetch_related('items') But here's what most tutorials don't tell you — prefetch_related doesn't use a SQL JOIN. It runs 2 separate queries, then builds an in-memory dictionary and does O(1) lookups in Python. Always 2 queries. Whether you have 10 orders or 10 million. And the sneaky part? It can silently break. Call .filter() on the related manager inside your loop and the cache is bypassed — N+1 comes right back, and you'd never know. I wrote a deep dive covering: → How Django joins data in Python (not SQL) → The 3 edge cases that kill your prefetch silently → Production strategies for millions of users Full article 👇 https://lnkd.in/dnipxQpP #Django #Backend #Python #WebDevelopment #SoftwareEngineering

One extra line in your Django queryset can turn a 20ms API into a 2s one. ⚡ I ran into this while optimizing an API that was fetching related data inside a loop. Everything worked. But response time kept increasing as data grew. The issue was the classic N+1 query problem. Django gives two powerful tools to solve it. 🔹 select_related Uses SQL JOIN Best for ForeignKey or OneToOne Fetches everything in a single query 🔹 prefetch_related Runs separate queries and combines results in Python Best for ManyToMany or reverse relationships Prevents large JOIN explosions The key difference is not syntax. It is how the data is fetched. Golden rule 👇 Single relationship → select_related Multiple relationship → prefetch_related Getting this wrong does not just slow things down. It can quietly break performance as your data grows. In my experience, fixing N+1 queries is one of the fastest ways to improve API performance in Django. Which one do you end up using more in your projects? #Django #Python #PostgreSQL #BackendDevelopment #DatabaseOptimization #SoftwareEngineering

Every Django ORM query does the same thing by default. It fetches every column by default. For a table with 30 columns including JSON blobs or large text fields, every query carries significant unnecessary weight. The data is fetched from the database, sent over the network, and loaded into Python model instances. Solution - 1. only() - fetch exactly these fields. only('id', 'email') tells Django, generate a model instance with only these fields loaded. 2. defer() - fetch everything except these fields. defer('bio', 'profile_picture') is the inverse, fetch all columns except the ones specified. The deferred field trap - Accessing a deferred field on an instance doesn't raise an error. Django silently fires an additional query to fetch that field, once per instance accessed. The rule - only() and defer() require discipline. Know which fields are accessed downstream. Takeaways - → Performance: Wide tables with heavy columns see significant query weight reduction → Trap: Accessing deferred fields silently fires additional queries, N+1 in disguise → Alternative: values() and values_list() skip model instantiation entirely for raw data Telling ORM explicitly is the difference between a query that transfers 2KB and one that transfers 200KB. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

Every Django ORM query does the same thing by default. It fetches every column by default. For a table with 30 columns including JSON blobs or large text fields, every query carries significant unnecessary weight. The data is fetched from the database, sent over the network, and loaded into Python model instances. Solution - 1. only() - fetch exactly these fields. only('id', 'email') tells Django, generate a model instance with only these fields loaded. 2. defer() - fetch everything except these fields. defer('bio', 'profile_picture') is the inverse, fetch all columns except the ones specified. The deferred field trap - Accessing a deferred field on an instance doesn't raise an error. Django silently fires an additional query to fetch that field, once per instance accessed. The rule - only() and defer() require discipline. Know which fields are accessed downstream. Takeaways - → Performance: Wide tables with heavy columns see significant query weight reduction → Trap: Accessing deferred fields silently fires additional queries, N+1 in disguise → Alternative: values() and values_list() skip model instantiation entirely for raw data Telling ORM explicitly is the difference between a query that transfers 2KB and one that transfers 200KB. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

Our first integration, Django, is in the Django Newsletter this week. ParadeDB is now officially available from the Python / Django ecosystem via `pip install django-paradedb` Source: https://lnkd.in/g7gyYsD5

Build search and RAG apps with boring technology. Django + Postgres. Most times technology that you already know is the best.

select_related and prefetch_related both solve N+1 query problem. But they do it in completely different ways. Most developers pick one, it works, and they never think about why. How it works? select_related - the SQL JOIN approach 1. Django modifies the original query. 2. Instead of SELECT * FROM orders, it becomes SELECT * FROM orders JOIN users ON orders.user_id = users.id. 3. One round trip to the database. Everything arrives together. 4. Works only for ForeignKey and OneToOne relationships. prefetch_related - the two query approach 1. Django runs the original query first. Gets all orders. Then runs a second query. 2. SELECT * FROM items WHERE id IN (1, 2, 3, ...) - fetching all related objects at once. 3. Then joins them together in Python, in memory. 4. Works for ManyToMany and reverse ForeignKey. The choice is not about preference. It's about relationship type. Takeaway - → Performance: select_related saves a round trip, ideal for simple FK lookups → Correctness: prefetch_related avoids duplicate rows that JOINs create on many relationships → Flexibility: Both can be combined on the same QuerySet without conflict The ORM abstracts SQL, but understanding what SQL it generates is what separates good from great Django engineers. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

bulk_create and bulk_update don't behave like regular Django saves. Most developers find out the hard way or never realise it! The assumption - they're just faster versions of calling .save() in a loop. Same behaviour, better performance. save() on a single instance does several things - 1. runs pre_save and post_save signals 2. calls full_clean() for validation, handles auto-generated fields 3. returns the saved instance with its new primary key bulk_create and bulk_update bypass all of it. No signals. No validation. No per-instance hooks. Django hands a list of objects directly to the database and walks away. bulk_create - the PK problem ~ By default, bulk_create returns instances without primary keys populated(in Python object) - unless update_conflicts or returning_fields is explicitly set. ~ ignore_conflicts=True silently swallows insert failures - no exception, no log, no signal. A uniqueness violation disappears without a trace. bulk_update - what it can't do ~ bulk_update requires an explicit list of fields. Miss a field - it doesn't update. ~ It cannot update fields using expressions - no F(), no computed values. ~ And like bulk_create - no post_save signals fire. Anything listening for model changes never knows. The performance gain is real - 1000 inserts in one query vs 1000 round trips. But the tradeoffs are real too. Takeaway — -> bulk_create / bulk_update - no signals, no validation, no per-instance hooks -> bulk_create → PKs not populated in Python object by default on PostgreSQL, not at all on MySQL -> ignore_conflicts=True → silent failure, uniqueness violations disappear without exception -> bulk_update → explicit fields only, no F() expressions, missed fields silently skip Have you been bitten by missing signals after a bulk operation? How did you handle downstream consistency? #Python #Django #BackendDevelopment #SoftwareEngineering