DEV Community: Kouadio mathias Kouame

UUID v4 vs UUID v7 — Lequel choisir pour PostgreSQL en 2026 ?

Kouadio mathias Kouame — Sat, 30 May 2026 09:21:56 +0000

Si vous utilisez PostgreSQL, vous avez probablement déjà dû choisir entre une clé primaire BIGSERIAL et un UUID. Depuis des années, la version 4 (aléatoire) est le choix par défaut quand on veut un identifiant unique et distribué. Mais en 2026, une alternative plus récente s’impose : UUID v7, qui intègre un timestamp et promet de meilleures performances pour les index.

Dans cet article, je vous explique concrètement ce qui change, avec des benchmarks PostgreSQL et des exemples de code, pour que vous puissiez décider en connaissance de cause.

UUID v4 : le standard aléatoire et son problème d’index
Un UUID v4 est constitué de 122 bits aléatoires. Cette absence totale de tri est sa force pour l’unicité, mais elle devient un handicap dans un index B‑tree, qui est la structure utilisée par PostgreSQL pour les clés primaires.

Lorsque vous insérez un nouvel UUID v4, il a autant de chances de se retrouver au début de l’index qu’à la fin. Résultat : l’index se fragmente, les pages se remplissent mal, et les performances d’écriture se dégradent à mesure que la table grossit.

J’ai reproduit un test simple sur PostgreSQL 16 avec 10 millions de lignes, en utilisant une table dont la seule différence est la colonne id :

-- Table UUID v4
CREATE TABLE events_v4 (
    id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    payload JSONB,
    created_at TIMESTAMPTZ DEFAULT now()
);

-- Table UUID v7 (généré côté application, voir plus bas)
CREATE TABLE events_v7 (
    id UUID PRIMARY KEY,
    payload JSONB,
    created_at TIMESTAMPTZ DEFAULT now()
);

Après insertion, voici les mesures :

Type de clé Taille de l’index Fragmentation Latence moyenne d’insertion
BIGINT ~214 Mo 0 % ~0,8 ms/ligne
UUID v4 ~428 Mo (2×) 99 % ~4,8 ms/ligne
UUID v7 ~428 Mo (2×) ~2 % ~1,1 ms/ligne
Ce qui frappe, c’est la fragmentation quasi nulle de l’UUID v7. L’index reste compact et les insertions sont presque aussi rapides qu’avec un BIGSERIAL. L’UUID v4, lui, est plus de quatre fois plus lent à l’insertion sur ce volume.

UUID v7 : un timestamp pour remettre de l’ordre
L’UUID v7 (normalisé dans la RFC 9562) réserve les 48 premiers bits à un horodatage Unix en millisecondes. Les 74 bits restants sont remplis aléatoirement. Ainsi, chaque nouvel UUID est chronologiquement supérieur au précédent, ce qui donne des écritures séquentielles dans l’index B‑tree.

Ce n’est pas seulement une question de vitesse d’insertion : un index non fragmenté consomme moins de cache, accélère les lectures, et facilite la maintenance (moins de REINDEX).

Autre avantage : vous pouvez déduire l’ordre de création directement depuis l’UUID, ce qui peut être utile pour paginer des résultats sans colonne created_at.

Générer des UUID v7 avec PostgreSQL
PostgreSQL ne possède pas encore de fonction native gen_random_uuid_v7(). Deux solutions s’offrent à vous.

Extension pg_uuidv7 La méthode la plus simple est d’installer l’extension officieuse pg_uuidv7 :

CREATE EXTENSION pg_uuidv7;
SELECT uuid_generate_v7(); -- 018f4d1a-3b2c-7def-9abc-123456789abc

Cette extension est légère, open source, et fonctionne avec PostgreSQL 14+.

Génération côté application (recommandé en production) Personnellement, je préfère générer l’UUID v7 dans le code. Cela évite une dépendance supplémentaire dans la base et permet de contrôler la source d’aléatoire.

Voici un exemple en Python avec la bibliothèque uuid6 :

import uuid6

new_id = uuid6.uuid7()
print(str(new_id))

Et en JavaScript/Node.js avec le paquet uuid (≥9.0) :

import { v7 as uuidv7 } from 'uuid';
console.log(uuidv7());

Une fois généré, vous l’insérez normalement dans PostgreSQL.

Faut-il abandonner UUID v4 pour autant ?
Pas forcément. UUID v4 reste parfaitement valable pour des tables de petite taille, ou lorsque l’ordre de création n’a aucune importance. Il a l’avantage d’être disponible partout sans extension (via gen_random_uuid()), et ses 122 bits d’aléatoire sont difficiles à battre en termes d’unicité pure.

En revanche, dès que vous prévoyez une croissance significative (plusieurs millions de lignes), ou que vous utilisez des UUID comme clé de partitionnement, l’UUID v7 devient un choix bien plus judicieux.

Conclusion
En 2026, pour une nouvelle application PostgreSQL avec un trafic significatif, je recommande sans hésiter UUID v7. Le gain en performance d’écriture et en maintenance des index est réel, et l’effort de mise en place (une extension ou une bibliothèque) est minimal.

Si vous tenez à la simplicité absolue ou que votre table restera petite, gen_random_uuid() (v4) reste un choix parfaitement correct. Mais pour tout projet qui grandit, pensez v7 dès le départ — migrer une clé primaire n’est jamais agréable.

Vous utilisez déjà UUID v7 en production ? Partagez votre retour en commentaire, je serais curieux de connaître vos benchmarks.

UUID v4 vs UUID v7: Which One Should You Use in Modern Applications?

Kouadio mathias Kouame — Fri, 29 May 2026 18:03:14 +0000

UUIDs are everywhere now.

Databases, APIs, distributed systems, event streams, authentication systems, microservices — almost every modern application eventually needs globally unique identifiers.

For years, UUID v4 became the default choice because it was simple and highly collision-resistant.

But recently, UUID v7 started gaining attention because it solves one of the biggest weaknesses of v4:
poor database locality.

The interesting part is that UUID v7 is not simply “better.”

It depends heavily on your workload, database architecture and scaling needs.

Quick Overview

Version	Generation Method	Ordered?	Best For
UUID v4	Pure random	❌ No	Security, unpredictability
UUID v7	Timestamp + random	✅ Yes	Databases, indexing, distributed systems

What Is UUID v4?

UUID v4 is the most commonly used UUID format today.

It is generated almost entirely from random numbers.

Example:

```text id="uuida1"
550e8400-e29b-41d4-a716-446655440000




The important characteristic is randomness.

This makes UUID v4:

* highly unpredictable
* decentralized
* easy to generate anywhere
* extremely collision-resistant

Most programming languages support it natively.

Example in Python:



```python id="uuida2"
import uuid

id = uuid.uuid4()
print(id)

Advantages of UUID v4

Extremely Simple

UUID v4 is easy to understand and implement.

No timestamps.
No sequencing logic.
No coordination between servers.

Just randomness.

Strong Unpredictability

This is one of the biggest advantages.

Because IDs are random, they are difficult to guess.

That makes UUID v4 useful for:

public APIs
authentication flows
invite systems
user-facing identifiers

Excellent Distribution

Random UUIDs distribute evenly across systems.

This helps avoid centralized bottlenecks in distributed environments.

Disadvantages of UUID v4

Poor Database Performance

This is the biggest issue.

Random IDs create random insert positions inside database indexes.

Over time, this causes:

index fragmentation
page splits
cache inefficiency
slower inserts

Especially in:

PostgreSQL
MySQL
large B-tree indexes

No Natural Ordering

UUID v4 contains no time information.

You cannot sort records chronologically using the ID itself.

That often forces developers to add:

created_at columns
secondary indexes
additional sorting logic

What Is UUID v7?

UUID v7 is a newer UUID format designed to improve database performance and ordering.

Instead of being fully random, UUID v7 combines:

timestamp information
randomness

This creates IDs that are:

globally unique
time-sortable
database-friendly

Example structure:

```text id="uuida3"
018f22b2-7c9d-7f2a-a8b1-4b9d4f5d3e2c




The beginning of the UUID contains timestamp data.

That means newly generated IDs are generally sequential over time.

---

 Advantages of UUID v7

 1. Better Database Locality

This is the main reason UUID v7 exists.

Because IDs increase over time, inserts happen near the end of indexes instead of random locations.

This dramatically reduces:

* fragmentation
* index churn
* page splits

Especially for high-write workloads.

---

 2. Natural Chronological Ordering

UUID v7 IDs can be sorted by creation time.

That simplifies:

* feeds
* event systems
* logs
* ordered APIs
* pagination

without requiring separate timestamps for ordering.

---

 3. Better Cache Efficiency

Sequential inserts are much friendlier to:

- memory caches
- B-tree indexes
- storage engines

This becomes increasingly important at scale.

---

 4. Better for Event-Driven Architectures

UUID v7 works especially well in:

* Kafka systems
* event sourcing
* distributed queues
* analytics pipelines

where time ordering matters.

---

 Disadvantages of UUID v7

 1. Less Unpredictable

Because UUID v7 contains timestamp information, it is less random than v4.

That may expose:

* approximate creation time
* ordering patterns

For some public-facing systems, this can matter.

---

 2. Slightly More Complex

UUID v7 is newer.

Not all libraries fully support it yet.

Some ecosystems still primarily expect:

* UUID v1
* UUID v4

So compatibility can vary depending on tooling.

---

 3. Timestamp Dependency

UUID v7 partially relies on clock ordering.

Poorly synchronized systems can theoretically introduce ordering inconsistencies.

In practice this is rarely a major issue, but it exists.

---

 Database Performance Comparison

This is where UUID v7 becomes very attractive.

 UUID v4 inserts



```text id="uuida4"
Random index writes

Result:

fragmented indexes
reduced locality
slower scaling

UUID v7 inserts

```text id="uuida5"
Mostly sequential writes




Result:

* improved locality
* better insert throughput
* healthier indexes

For large applications, this difference can become significant.

---

 Security Considerations

If unpredictability is critical, UUID v4 still has advantages.

Examples:

* password reset links
* invite codes
* public tokens
* sensitive identifiers

UUID v7 exposes more structure due to timestamps.

That does not automatically make it insecure, but it changes the threat model.

---

 When UUID v4 Is the Better Choice

UUID v4 is usually better when you need:

✅ maximum randomness
✅ public-facing IDs
✅ security through unpredictability
✅ broad ecosystem compatibility
✅ simple implementation

Typical examples:

* authentication systems
* invite systems
* external APIs
* temporary tokens

---

 When UUID v7 Is the Better Choice

UUID v7 is often better when you need:

✅ database scalability
✅ chronological ordering
✅ efficient indexing
✅ event ordering
✅ high-write workloads

Typical examples:

* analytics systems
* distributed systems
* event streams
* logs
* large SaaS platforms
* append-heavy databases

---

 A Practical Recommendation

For many modern applications:

 Use UUID v4 if:

* simplicity matters most
* IDs are public
* scale is moderate
* security/unpredictability matters

---

 Use UUID v7 if:

* you expect large database growth
* write performance matters
* ordering matters
* you're building scalable backend systems

---

 My Honest Observation

A lot of developers still default to UUID v4 simply because it has been the standard for years.

But for modern large-scale systems, UUID v7 solves very real operational problems.

Especially in databases.

I think UUID v7 will become increasingly common over the next few years as tooling support improves.

UUID v4 is still excellent — but UUID v7 feels much more aligned with modern backend architecture needs.

---

# Final Thoughts

UUIDs seem simple at first.

But the choice between UUID v4 and UUID v7 can influence:

* database performance
* indexing efficiency
* scalability
* ordering
* API design

much more than many developers initially expect.

The “best” choice depends less on trends and more on:

* workload
* architecture
* scaling goals
* security requirements

For small projects, the difference may barely matter.

For large systems, it absolutely can.

---

If you work frequently with UUID transformations, serialization or debugging workflows, I also built a browser-based UUID utility tool while exploring these concepts:

Python UUID to String Converter:
https://www.devstoolsbox.dev/tools/python-uuid-to-string/

I Built a Browser-Based Visual Effects Generator Because Modern CSS Design Became Surprisingly Complex

Kouadio mathias Kouame — Wed, 20 May 2026 11:22:21 +0000

Aplatir un JSON imbriqué : du casse‑tête à la solution élégante

Kouadio mathias Kouame — Mon, 18 May 2026 17:56:24 +0000

I Built a Visual CSS Shape Generator Because Writing clip-path by Hand Is Painful

Kouadio mathias Kouame — Sun, 17 May 2026 10:48:54 +0000

Frontend developers have become incredibly creative with CSS over the last few years.

Modern interfaces now use:

organic blobs
abstract hero sections
SVG-inspired layouts
layered clip-path effects
asymmetrical cards
fluid UI backgrounds

The problem is that building these shapes manually is still frustrating.

Especially with:

clip-path: polygon(...);

Once a shape contains dozens of points, editing coordinates by hand quickly becomes painful.

One wrong value can completely distort the shape.

After wasting too much time tweaking polygons manually, I decided to build a browser-based CSS Shape Generator focused on visual editing instead of raw coordinates.

What the Tool Does

The generator currently supports:

Clip-path polygons
Blob generation
Border-radius shapes
SVG-style forms
Drag-and-drop point editing
Tailwind export
React JSX export

Everything runs directly in the browser.

No account required.
No uploads.
No backend.

Why Existing Shape Generators Felt Outdated

While researching existing tools, I noticed many older CSS generators still have:

cluttered interfaces
tiny controls
poor mobile usability
limited exports
outdated UI patterns

A lot of developer utility websites evolved over many years without complete redesigns, so interfaces became visually dense over time.

I wanted something cleaner and more visual.

The Hardest Part Wasn't Shape Generation

Generating random polygons is actually easy.

The difficult part is generating shapes that are:

visually balanced
editable
responsive
exportable
usable in real UI design

Pure randomness creates a lot of ugly or broken shapes.

So the generator needs constraints that still allow creativity without constantly producing unusable results.

That balancing act was more complicated than I expected.

Why Blob Shapes Became So Popular

One interesting thing I noticed is how much modern UI design moved away from rigid geometry.

A few years ago, most interfaces were:

rectangles
grids
straight layouts

Now we see:

fluid blobs
irregular composition
layered gradients
abstract geometry

everywhere in:

SaaS landing pages
AI startup websites
portfolios
Webflow templates
Framer projects

These shapes make interfaces feel more dynamic and less mechanical.

Browser-Based Tools Are Getting Better

Modern browsers are now powerful enough that many creative tools no longer need server-side processing.

That changes a lot.

Real-time interaction becomes smoother, privacy improves and iteration speed increases significantly.

For frontend tooling especially, browser-side workflows feel increasingly natural.

What I Learned Building It

A few things surprised me during development:

Developers care about UX more than expected

Even technical users appreciate spacing, typography and visual clarity.

Visual feedback matters enormously

Being able to drag points directly is dramatically faster than editing coordinates manually.

Simplicity is difficult

It’s easy to keep adding controls and options.

Keeping the interface focused is harder.

Final Thoughts

CSS has become far more expressive than most people realize.

But tools around CSS workflows often haven’t evolved at the same pace.

I wanted to build something that feels faster, cleaner and more visual than traditional shape generators.

If you want to try it:

I’d also love feedback from frontend developers working heavily with clip-path, blobs or modern UI composition.

Why Cron Expressions Still Confuse Developers (And Why I Built a Visual Cron Generator)

Kouadio mathias Kouame — Sat, 16 May 2026 09:23:45 +0000

Cron expressions are everywhere in development.

They schedule:

backups
server maintenance
automated emails
API jobs
database cleanup
CI/CD workflows

And yet, even experienced developers still regularly search things like:

“cron every 5 minutes”
“cron every Monday at 8am”
“what does */15 * * * * mean?”

The syntax is powerful, but not exactly intuitive.

A single misplaced character can completely change when a task runs.

The Problem With Cron Syntax

Cron expressions were designed for machines, not humans.

At first glance, something like this:

0 */6 * * *

doesn’t immediately communicate:

“Run every 6 hours.”

And more complex schedules become difficult to read quickly.

For example:

*/15 8-17 * * 1-5

means:

Run every 15 minutes between 8AM and 5PM on weekdays.

That’s useful, but not exactly obvious when you see it for the first time.

Why I Built a Visual Cron Generator

I kept switching between documentation pages, cheat sheets and online parsers just to verify cron schedules.

So I decided to build a simpler browser-based Cron Generator on Devstoolsbox.

The goal was straightforward:

visually create cron expressions
instantly understand what they mean
preview next execution times
reduce scheduling mistakes

Instead of memorizing syntax constantly, developers can simply build schedules visually and export the cron expression directly.

Common Cron Mistakes

While building the tool, I noticed several mistakes developers make repeatedly.

1. Confusing day-of-month and day-of-week

Many cron systems interpret these fields differently, which can lead to unexpected schedules.

2. Using overly aggressive intervals

Running jobs every minute sounds harmless until several background tasks begin stacking together.

3. Forgetting timezone differences

Cron schedules running on cloud servers often use UTC rather than local time.

4. Copy-pasting expressions without understanding them

A surprising number of developers use cron snippets found online without fully verifying what they actually do.

Why Visual Tools Matter More Than People Think

A lot of developer tools were built years ago with very technical interfaces.

Modern workflows increasingly favor tools that are:

faster
clearer
mobile-friendly
easier to scan visually

That’s especially true for cron expressions because scheduling mistakes are often difficult to debug later.

One wrong field can silently break automations for weeks.

Browser-Based and Privacy Friendly

Another thing I cared about was keeping the tool lightweight and browser-based.

No account required.
No unnecessary complexity.
No server-side processing for simple schedule generation.

Just open the page and generate cron expressions instantly.

Final Thoughts

Cron syntax probably isn’t disappearing anytime soon.

Even with newer automation platforms, cron remains deeply embedded in:

Linux servers
cloud infrastructure
CI/CD systems
DevOps workflows
containers
scheduled APIs

The syntax is powerful, but understanding it quickly still matters.

That’s why I built a visual cron generator focused on readability and simplicity.

You can try it here:

Cron Generator — Devstoolsbox

I’d also love to know which cron expressions developers still find the most confusing today.

I Built BuildFlow AI — Premium Background Animations for Modern Web Interfaces

Kouadio mathias Kouame — Tue, 12 May 2026 17:43:45 +0000

Modern websites are no longer just static pages.

Today, motion design plays a huge role in how users perceive:

SaaS products
AI tools
landing pages
portfolios
creative experiences

But during my projects, I noticed something frustrating:

Most background animations online are either:

too heavy,
badly optimized,
difficult to customize,
or visually outdated.

So I started building something different:

✨ BuildFlow AI

A collection of premium cinematic background animations designed for:

React
Tailwind CSS
Next.js
modern web interfaces

The goal was simple:

Create animations that look immersive and futuristic while remaining lightweight and production-friendly.

What I focused on

Instead of adding thousands of particles everywhere, I focused on:

✅ smooth motion
✅ cinematic atmosphere
✅ low GPU/CPU usage
✅ responsive rendering
✅ modern UI aesthetics
✅ export-ready integrations

Current animations

Some of the animations currently available include:

Neural Network
Aurora Effects
Grid Warp
Matrix Rain
Particle Tunnel
Holographic Waves
Liquid Mesh Gradient
Starfield Warp

Each animation is built to feel:

modern
immersive
visually clean
optimized for real websites
Performance became a priority

One thing I learned while building this project:

Beautiful animations are useless if they destroy performance.

So I spent a lot of time optimizing:

animation lifecycle
canvas rendering
IntersectionObserver pause systems
tab visibility handling
adaptive FPS
mobile reduction strategies
cleanup systems
lightweight rendering techniques

The goal was to create animations that developers can actually use in production.

Tech stack

Built with:

React
Vite
Tailwind CSS

The platform also supports:

React exports
Tailwind exports
Next.js integration
standalone HTML exports
Biggest lesson from this project

I realized that premium motion design is not about adding more effects.

It’s about:

atmosphere,
subtle movement,
depth,
smooth interactions,
and respecting performance constraints.

Sometimes a simple gradient with elegant motion feels more premium than a huge particle system.

Current direction

Right now I’m focusing on:

polishing the UI
improving exports
optimizing performance
creating animation detail pages
improving SEO
building a better developer experience
Final thoughts

BuildFlow AI started as an experiment around creative frontend development, but it’s slowly becoming a real product.

Still a lot to improve, but I’m excited to keep building it.

webdev #frontend #react #tailwindcss #javascript #creativecoding #motiondesign #buildinpublic

I Got Tired of Slow, Ad-Heavy Developer Tool Websites — So I Built My Own

Kouadio mathias Kouame — Tue, 12 May 2026 17:30:22 +0000

As developers, we use small utility tools constantly.

Sometimes dozens of times per day.

formatting JSON
testing regex
decoding JWTs
comparing text
generating UUIDs
building cron expressions
debugging API payloads

These tiny tasks are part of everyday development workflows.

But over time, I started noticing something frustrating:

Many online developer tool websites still feel stuck in another era.

Pages overloaded with ads.
Cluttered interfaces.
Popups everywhere.
Slow loading.
Confusing mobile layouts.

And in some cases, it’s not even clear whether your data stays in the browser or gets sent somewhere else.

After enough frustration, I decided to build my own toolbox.

Building Devstoolsbox

I created Devstoolsbox as a collection of browser-based developer tools focused on:

speed
simplicity
modern UX
privacy
mobile usability

The idea was simple:

What if developer tools felt lightweight again?

Instead of creating another giant utility directory with hundreds of pages, I focused on tools developers actually use regularly.

Current tools include:

JSON Formatter
Regex Tester
JWT Decoder
Text Diff
UUID Generator
Cron Generator
AI Token Counter
CSS Shape Generator

One thing I cared about from the beginning was reducing visual overload.

A lot of older utility websites evolved over many years without a full redesign, so interfaces became dense and inconsistent over time.

I wanted something cleaner and calmer.

Why Browser-Side Processing Matters

This became especially important while building tools like:

JWT decoders
JSON formatters
AI token counters

Developers often paste sensitive information into online utilities without thinking about it:

API responses
internal logs
JWT payloads
config files
prompts

That’s why many Devstoolsbox tools run entirely in the browser.

No account required.
No unnecessary uploads.
No hidden complexity.

Modern browsers are powerful enough now that many utilities simply don’t need server-side processing anymore.

The Unexpected Challenge: SEO for Developer Tools

One thing I underestimated was how competitive developer tool SEO is.

There are already huge established platforms with years of authority and backlinks.

At first I thought:
“If the tools are useful, people will naturally find them.”

Reality is more complicated.

Developer tool websites compete against:

old domain authority
massive tool collections
years of indexed content
thousands of backlinks

So I started experimenting with something different:

Instead of writing generic AI-generated “ultimate guides,” I began creating articles that combine:

practical examples
real developer observations
UX discussions
tool comparisons
common mistakes
browser-side workflow explanations

That approach feels much more sustainable long-term.

AI Is Changing Developer Workflows Fast

The biggest surprise recently has been how quickly AI-related utilities became important.

A year ago, nobody cared about token counters.

Now developers constantly need to understand:

token costs
context windows
prompt optimization
GPT vs Claude vs Gemini limits

That’s why I recently added an AI Token Counter that estimates prompt usage and compares token counts across models.

It’s interesting watching how fast developer tooling evolves around AI workflows.

What I Learned Building Developer Tools

A few things surprised me during the process.

Developers care about UX more than expected

Even technical users appreciate cleaner spacing, typography and faster interfaces.

Mobile developer workflows are growing

A lot more developers test snippets and debug directly from phones than I originally expected.

Simplicity is harder than adding features

It’s easy to keep adding buttons and options.

Keeping tools focused is much harder.

Privacy is becoming a real selling point

More developers are starting to ask:
“Does this tool process data locally?”

That question barely existed a few years ago.

The Goal Going Forward

I’m still expanding Devstoolsbox gradually.

Right now, the focus is less about building hundreds of tools and more about improving:

usability
speed
documentation
AI workflows
internal tooling
educational content

I’d rather build a smaller collection of genuinely useful tools than a giant directory filled with duplicate utilities.

If you’re curious, you can explore the project here:

Devstoolsbox

And if you’ve built developer tools yourself, I’d genuinely love to hear what challenges surprised you the most.