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19.09.2026 14:35
RE_DO (@RE_DO@mstdn.jp)

“ Joplinは無料のオープンソースのノート作成およびツール提供アプリケーションでありノートに整理された多数のノートを処理... / 1件のコメント b.hatena.ne.jp/entry/s/github. htn.to/415a9NBvQf #ノートアプリ #ナレッジ管理 #Markdown #electron #OSS #Evernote #Rust #TypeScript #Node.js #React

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19.09.2026 05:49
display (@display@www.urbanmind.net)

Over the past few months, I worked on building a full-stack e-commerce platform designed to provide a smooth shopping experience while keeping the backend scalable and maintainable.

The goal was to create more than just a product listing website — I wanted to build a complete shopping system that handles product management, user accounts, payments, orders, and real-time inventory.

💡 Project Overview


The platform allows customers to:

  • Browse products by category
  • Search and filter items
  • Add products to a shopping cart
  • Manage their wishlist
  • Complete secure checkout
  • Track order status

For administrators, the system provides tools to:

  • Manage products
  • Update inventory
  • Process orders
  • Monitor sales data


🛠️ Tech Stack

Frontend


  • React.js
  • TypeScript
  • Next.js
  • Tailwind CSS


Backend


  • Node.js / NestJS
  • REST APIs
  • Authentication with JWT


Database


  • PostgreSQL
  • Redis for caching


Infrastructure


  • Docker
  • AWS
  • CI/CD pipelines


🏗️ Architecture


The application follows a modular architecture:

User → Frontend → API Gateway → Backend Services → Database

Main services include:

  • User Management
  • Product Catalog
  • Cart Service
  • Order Processing
  • Payment Integration
  • Notification System

This structure makes it easier to add new features without affecting existing functionality.

🔍 Challenges I Solved

1. Improving Product Search Performance


As the product catalog grows, searching through thousands of products can become slow.

Solutions:

✅ Added database indexing
✅ Optimized search queries
✅ Implemented caching for frequently accessed products

2. Handling Inventory Consistency


One important challenge was preventing incorrect stock levels when multiple customers purchase the same product.

I implemented:

  • Transaction-based inventory updates
  • Stock validation before checkout
  • Order status synchronization


3. Creating a Better Checkout Experience


A good checkout process should be simple and reliable.

The flow includes:

  1. Cart validation
  2. Customer information confirmation
  3. Payment processing
  4. Order creation
  5. Email notification


📈 Performance Improvements


Some optimizations included:

  • Reduced unnecessary API requests
  • Added Redis caching
  • Improved database queries
  • Optimized frontend rendering

These improvements helped create a faster and smoother shopping experience.

🚀 Future Improvements


Some features I want to explore next:

  • AI-powered product recommendations
  • Personalized shopping experiences
  • Smart inventory forecasting
  • Chat-based shopping assistant


Lessons Learned


Building an e-commerce platform taught me that successful applications are not only about writing code.

The important parts are:

  • Designing scalable architecture
  • Understanding user behavior
  • Protecting customer data
  • Creating reliable systems

A shopping platform is a combination of software engineering, business logic, and user experience design.

WebDevelopment #React #NodeJS #Ecommerce #JavaScript #TypeScript #SoftwareEngineering

#fullstack #node #react #webdev #software #coding #development #engineering #inclusive #community
Building a Modern E-Commerce Platform: From Product Search to Secure Checkout 🛒🚀

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18.09.2026 10:06
jobsfordevelopers (@jobsfordevelopers@mastodon.world)

3Pillar Global is hiring Mid Full Stack Engineer (Javascript)

🔧 #javascript #typescript #angular #react #node #express #json #sql
🌎 United States
⏰ Full-time
🏢 3Pillar Global

Job details jobsfordevelopers.com/jobs/mid
#jobalert #jobsearch #hiring

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18.09.2026 05:25
display (@display@www.urbanmind.net)

The Quest Begins (The “Why”)


I still remember the first time I opened our analytics dashboard after a feature launch and saw the page load time creeping up to six seconds. The product team was thrilled—new filters, shiny UI—but the users were staring at a spinner like they were waiting for a bus that never arrived. I dug into the logs and there it was, the classic villain: the N+1 query problem.

Picture this: we had a simple endpoint that listed blog posts with their author’s name. The code looked innocent enough:

# app/controllers/posts_controller.rb
def index
@posts = Post.all # <-- 1 query to fetch all posts
end

And in the view:

<% @posts.each do |post| %>
<div>
<h2><%= post.title %></h2>
<p>By <%= post.user.name %></p> <!-- <-- Uh‑oh, a query per post! -->
</div>
<% end %>

If we had 100 posts, that turned into 101 queries (one for the posts, then one per post to fetch the author). On a modest server it was fine, but under load it turned our app into a sluggish beast. I felt like Frodo staring at the mouth of Mount Doom—overwhelmed, but knowing I had to find a way past it.

The Revelation (The Insight)


The “aha!” moment came when I realized the ORM wasn’t lazy‑loading out of spite; it was just doing what we told it: fetch the posts first, then reach out for each association when asked. The fix? Tell the ORM to bring the related data along for the ride—eager loading.

But eager loading is only one piece of the optimization puzzle. Once we stopped the N+1 bleed, we started noticing other performance leaks: missing indexes, unoptimized joins, and queries that scanned entire tables when a simple WHERE clause would do.

Think of it like training in the Jedi Temple: you first learn to block a stray blaster bolt (eager loading), then you practice deflecting a barrage (indexing, query planning), and finally you learn to anticipate the opponent’s move (caching, pagination).

Wielding the Power (Code & Examples)

1. The N+1 Trap – Before


# Django view (simplified)
def post_list(request):
posts = Post.objects.all() # SELECT * FROM posts;
return render(request, "posts.html", {"posts": posts})

Template:

{% for post in posts %}
<li>{{ post.title }} by {{ post.author.name }}</li>
{% endfor %}

Each {{ post.author.name }} triggers a separate query to fetch the author. With 200 posts, that’s 201 round‑trips to the DB.

2. Eager Loading – After


def post_list(request):
# One query that pulls posts AND their authors via a JOIN
posts = Post.objects.select_related('author').all()
return render(request, "posts.html", {"posts": posts})

select_related tells Django to follow the foreign key to author and bring those columns in the same SQL statement. The result? One query, no matter how many posts we have.

3. The Index Trap – Before


We had a filter endpoint that let users search posts by published_at range:

SELECT * FROM posts WHERE published_at BETWEEN $1 AND $2;

No index on published_at meant a full table scan every time. On a table with a million rows, that was costly.

4. Adding the Index – After


CREATE INDEX idx_posts_published_at ON posts(published_at);

Now the planner can jump straight to the relevant rows, turning a seconds‑long scan into a millisecond‑range lookup.

5. Beyond Eager Loading – Prefetch for Collections


Sometimes you need the inverse: a list of authors with all their recent posts. select_related won’t cut it because it’s a many side.

def author_list(request):
# Fetch authors, then prefetch their posts in a second query
authors = Author.objects.prefetch_related(
Prefetch('post_set', queryset=Post.order_by('-published_at')[:5])
).all()
return render(request, "authors.html", {"authors": authors})

Two queries total: one for authors, one for the recent posts per author. No N+1, and we still get the data we need for the UI.

6. Query Planning – Spot the Hidden Cost


Even with eager loading, a poorly written WHERE clause can still hurt. I once saw:

SELECT * FROM orders WHERE created_at::date = '2024-09-01';

Casting the column prevented the index on created_at from being used. The fix?

SELECT * FROM orders
WHERE created_at >= '2024-09-01' AND created_at < '2024-09-02';

Now the index is happy, and the query runs in a blink.

Why This New Power Matters


After applying these patterns, our dashboard dropped from six seconds to under 300ms. The product team could finally ship those fancy filters without users abandoning the page. More importantly, the whole team started thinking about data access as a first‑class concern, not an afterthought.

  • Scalability: With eager loading and proper indexes, each additional user adds linear, not exponential, load.
  • Developer velocity: No more midnight firefighting over mysteriously slow endpoints; we have a repeatable checklist (eager load? index? avoid casting?).
  • User experience: Snappy responses translate to higher engagement, better conversion, and happier stakeholders.

It felt like finally mastering the Force—when you stop reacting to every disturbance and start anticipating the flow, everything becomes smoother.

Your Turn: The Challenge


I’ve shared the spells that worked for me, but every codebase has its own quirks. Here’s a quest for you:

  1. Find one endpoint in your app that loads a list of records with an associated field (like a user’s profile picture or a product’s category).
  2. Run a query log (Django’s connection.queries, Rails’ ActiveSupport::Notifications, or your ORM’s equivalent) and count how many DB hits occur per request.
  3. Apply eager loading (select_related / include / JOIN FETCH) wherever you see a per‑item lookup.
  4. Check your indexes on any columns used in WHERE, ORDER BY, or JOIN clauses. Add missing ones and verify the planner uses them (EXPLAIN ANALYZE).
  5. Measure the before/after response time and share the numbers—bonus points if you drop the query count from N+1 to a flat 2!

If you get stuck, drop a comment below; I love hearing about the dragons you’re slaying and the new tricks you discover. May your queries be swift and your indexes ever‑green!

Happy optimizing, fellow developer! 🚀#webdev #react #node #frontend #software #coding #development #engineering #inclusive #community
Slaying the N+1 Query Dragon: A Jedi’s Guide to Database Optimization

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