I just replaced my entire tech stack

with Postgress. Modern software

engineering has basically become a

subscription management simulator. We've

been gaslit by cloud vendors into

believing that to build even a basic

application, we need to stitch together

a fragile distributed web of highly

specialized microservices. You wire up a

Reddus instance for caching, a CFKA

cluster for background jobs, elastic

search just to power a simple search bar

and a dedicated vector database for that

one AI feature you tacked on. By the

time you finally deploy your app to your

highly demanding user base of yourself

and your mom, you're paying a dozen

different Y Combinatorbacked SAS

startups just to keep the lights on. It

is an overengineered, wildly overpriced

trap. But what if I told you that you

could take almost all of those shiny

cloud dependencies, toss them directly

into the incinerator and replace them

with a single piece of boring

30-year-old open-source software? The

dirty little secret the tech industry

doesn't want you to know is that one

battle tested tool cannibalize your

entire architecture. Today we're

stripping your stack down to one

unstoppable source of truth, PostgreSQL.

Here's how you use Postgress to replace

literally everything. Before we start

violently dismantling your current

architecture, let's look at the weapon

that we're using. At its core,

Postcrestql is an open- source object

relational database system that has been

in active development for over three

decades. Out of the box, it gives you

rocksolid acid compliance, meaning when

your cheap cloud server inevitably

crashes, your user data isn't corrupted.

But the real reason it cannibalize your

entire stack is its extensibility. It

doesn't just store standard rows and

columns. It supports advanced custom

data types, multi-dimensional arrays,

geometric shapes, and key value stores.

This architectural flexibility has led

to a massive ecosystem of thirdparty

extensions. It's basically the Skyrim of

databases, a rockolid foundation that

you can aggressively mod until it does

exactly what you want. Here is how you

use it to replace everything. One of the

great debates among web developers is

SQL versus NoSQL. And the core selling

point of NoSQL is handling unstructured

data. You no longer need a separate

database like MongoDB just to do this.

Postgress offers deeply integrated

native support for JSON through its JSON

B data type which fundamentally changes

how data is processed. The B stands for

binary. Unlike standard text storage

that must be parsed every time a query

is run, JSON B converts your JSON

payload into a decomposed binary format

at the moment of insertion. The true

magic unlocks when you apply a gin or

generalized inverted index to this

column. An inverted index works exactly

like the index at the back of a

textbook. Instead of scanning every

database row looking for a specific key,

the index maps the keys directly to the

row IDs where they exist. This allows

you to query deeply nested JSON

properties instantly and join those

documents with traditional relational

tables in a single asset compliant

transaction. You get the exact schema

flexibility of NoSQL without sacrificing

data integrity. Provisioning Rabbit MQ

or Reddus purely for reliable task

distribution introduces massive

architectural overhead. But building a

queue in a standard SQL database usually

leads to deadlocks. Postgress solved

this elegantly with its native

concurrency control, specifically the

four update skip locked clause. When

building a background worker system, the

traditional problem is that two workers

might try to grab the same pending job

row at the exact same time. One locks it

and the other gets stuck waiting. Adding

skip locked changes the physics of the

query. It instructs the database engine,

grab the first available row, lock it so

no one else can touch it. But if you hit

a row that is already locked by another

worker, don't wait. Just skip it and

grab the next one. This turns a standard

relational table into a highly

concurrent weight-free message cue

capable of processing thousands of jobs

per second. While specialized tools like

Elastic Search are mandatory for

globally distributed log analysis, using

them just to power a search bar in your

app is massive overkill. Postgress is

fully equipped to power advanced full

text search directly by stripping

language down to its mechanical roots

using TS vector and TS query. When you

insert text into a TS vector column,

Postgress parses it, removes useless

stop words, and applies linguistic

stemming. So a word like running simply

becomes run. Furthermore, you can apply

the pg triagram extension for fuzzy

matching, the ability to find accurate

results even when a user makes a typo.

It does this using triagrams, which

simply breaks words down into

three-letter chunks. If a user misspells

Postgrql as Postgress with two S's, the

database doesn't look for an exact

match. It finds the overlapping

three-letter patterns and returns the

correct result anyway, giving you a

highly performant typo tolerant search

engine without syncing data to a

secondary cluster. If you're building an

AI app, you might consider paying for a

vector database like Pine Cone. But

keeping vector data separate from your

relational data creates an architectural

nightmare known as the hybrid search

problem. If you need to find documents

semantically similar to a user prompt,

but only if they were authored by a

specific user last week, querying two

different databases and cross

referencing the results over a network

is incredibly slow. You can handle this

entirely within Postgress using the PG

vector extension. It allows you to store

highdimensional arrays right next to

your core application data and supports

HNSW or hierarchical navigable small

world indexes. HNSW is a graph-based

algorithm for approximate nearest

neighbor search that organizes vectors

into a multi-layered structure acting as

a highdimensional skip list. It allows

for fast scalable vector searches by

starting at a top layer with few

longrange connections and moving to

lower denser layers to refine the

search. This minimizes the number of

distance calculations needed, allowing

the database to rapidly navigate through

neighborhoods of similar data points to

find approximate nearest neighbors in

milliseconds. Ultimately, you can

execute this complex vector math

natively while simultaneously applying

strict relational filters. We've been

talking a lot about how powerful

Postgress is, but let's be honest,

provisioning, scaling, and managing

testing environments for it can still be

a massive headache, and that's exactly

where today's sponsor, Neon, comes in.

Neon is a fully managed serverless

Postgress platform built specifically

for the cloud. They've fundamentally

re-engineered Postgress by separating

compute from storage, which unlocks

features you just can't get with a

traditional setup. My absolute favorite

part about Neon is database branching.

Just like you branch your code in Git,

Neon lets you instantly branch your

Postgress database. Want to test a risky

schema migration or a complex query?

Just click a button, spin up a copy of

your database in seconds with all of its

data, and run your tests. If you mess

up, your prod database remains

completely untouched. It completely

changes how you handle dev and staging

environments. Plus, because Neon is true

serverless, it automatically scales

compute based on your application's

workload and scales down to zero when

it's not in use. You don't have to

overprovision servers, meaning you only

pay for exactly what you use. Whether

you're building a weekend side project

or a hightraic application, Neon makes

Postgress feel modern, fast, and

frictionless. Click the link in the

description to sign up and deploy your

first serverless Postgress database on

Neon for free in just seconds. A huge

thanks to Neon for sponsoring this

video. Now, back to even more Postgress.

If you're building applications that

rely heavily on maps or routing,

Postgress isn't just an alternative. It

is the undisputed industry gold

standard. The PostGIS extension

transforms Postgress into a spatial

powerhouse using the gist or generalized

search tree index. If you ask the

database to find all coffee shops within

a complex geographic polygon, doing raw

mathematical distance calculations on

every coordinate would crash the server.

Instead, a generalized search tree draws

simple overlapping bounding boxes around

your geographic shapes. The database

first checks these simple boxes

instantly discarding millions of data

points that aren't even close and only

performs the heavy precise geometric

math on the handful of points that

remain. This routinely outperforms

standalone GIS systems. On the other

hand, when handling massive volumes of

telemetry or event logs, developers

reach for time series databases.

Postgress handles this natively through

declarative partitioning and the highly

underutilized brin or block range index.

Instead of storing billions of logs in

one massive table, partitioning

transparently splits your data into

physical daily or monthly chunks. As

long as your logs are inserted

sequentially, the brin index is a

superpower. Instead of indexing every

single row like a massive bloated B

tree, it only stores the minimum and

maximum timestamps for physical blocks

of data on the disk. When you query for

a specific time range, Postgress reads

the brin index, instantly skips millions

of physical disk pages that don't

contain your target timestamps, and

scans only the tiny fraction that do.

How about for complex dashboards? The

knee-jerk reaction is to pipe data into

expensive data warehouses like

snowflake. You can bypass this by

leveraging Postgress materialized views.

A standard view runs its underlying

query from scratch every time a user

hits the dashboard, crashing your

database under load. A materialized view

runs the heavy aggregation just once and

physically saves that calculated result

to the disk. To prevent stale data,

Postgress uses the refresh materialized

view concurrently command provided your

view has a unique index. It calculates

the fresh analytics entirely in the

background, compares the differences and

seamlessly hot swaps the updated rows

into place without ever locking out your

end users. For years, we've blindly

accepted that you need to write and

maintain thousands of lines of

boilerplate Node.js JS or Python code

just to shuttle JSON between your

database and your front end. You can

incinerate this entire middleware layer

using tools like post REST or the PG

GraphQL extension. Instead of manually

writing a new controller and endpoint

every time you add a database table,

these tools analyze your schema and

automatically generate a fully

documented highly performant REST or

GraphQL API on the fly. And before you

panic about security, Postgress handles

that natively, too. By leveraging rowle

security, you can write strict

cryptographic policies directly in the

database that guarantee a user can only

ever read or write their own specific

rows based on their authentication

token. Your database securely becomes

your entire backend, eliminating the

need for a fleet of API servers. While

the just use Postgress philosophy is

incredibly powerful, you shouldn't

entirely abandon your critical thinking.

It isn't a silver bullet. Postgress

scales vertically with exceptional

grace, but horizontally sharding a

monolithic database to handle extreme

scale introduces immense complexity. If

your application actually needs to

ingest millions of telemetry events per

second or requires submillisecond

in-memory caching for millions of

concurrent websockets, you absolutely

must adopt specialized distributed

tools. However, until you cross that

threshold of massive enterprise scale,

leaning on the core battle tested

mechanics of Postgress to run your

entire stack is arguably the smartest

and most cost-effective engineering

decision you can make. Seriously, to

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you have to build hard things. That's

why I highly recommend Code Crafters.

Instead of building basic apps, they

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write your own working versions of

Reddus, Git, CFKA, Docker, and even

modern AI tools like Claude Code. It

completely changes how you understand

software. Check the description for a

link that automatically applies a 40%

discount to your account. Also in the

description is a link to my free

newsletter where I share exclusive deep

dives on system design and real world

backend development. The stuff you won't

find in basic coding tutorials.