Reza Bashiri’s Post

🚀 Day 8/45 – Backend Engineering (Database Optimization) Today I revisited one of the most impactful things I worked on: Improving query performance by 30%. 💡 What I learned: 🔹 Problem: Slow API responses Inefficient queries scanning large datasets 🔹 Fixes that worked: ✅ Indexing frequently queried columns 👉 Reduced full table scans ✅ Optimizing joins 👉 Avoided unnecessary data fetching ✅ Selecting only required fields 👉 Reduced data transfer 🔹 Key insight: Even well-written APIs become slow if the database layer is not optimized. 🛠 Practical: Analyzed query execution and optimized SQL using indexing and better query design. 📌 Real-world impact: Faster API responses Reduced database load Better scalability under traffic 🔥 Takeaway: Backend performance is not just about code — it’s about how efficiently you interact with the database. Currently building a production-ready backend system — sharing real backend lessons daily. https://lnkd.in/gJqEuQQs #Java #SpringBoot #Database #MySQL #BackendDevelopment #Performance

As a backend developer, I realized that writing APIs is only half the job — understanding SQL is equally important. Here are some SQL fundamentals I recently revised: ✔ Joins (INNER, LEFT, RIGHT, FULL) → Combining data from multiple tables ✔ Indexing → Improves query performance ✔ Normalization → Reduces data redundancy What stood out to me is how indexing can drastically improve query speed when working with large datasets. It’s easy to overlook these basics, but they make a huge difference in real-world applications. Have you worked with SQL optimization in your projects? #SQL #BackendDevelopment #Database #FullStack #WebDevelopment

I spent two hours optimizing a slow query. Indexes. Rewrites. Stack Overflow rabbit holes. Then a senior engineer looked over my shoulder and typed two words. EXPLAIN ANALYZE. And just like that — the database told us exactly what it was doing. Every step. Every scan. Every place it was working too hard. I had been guessing. The answer was sitting in the query planner the whole time. That moment changed how I debug slow queries forever. I stopped assuming. Started asking the database itself. Because here's the thing nobody tells you early on — your database has opinions about your queries. It has a plan. A cost estimate. A reason it's slow. And it will tell you all of it. If you just know to ask. I don't touch a slow query without it now. Feel like I was handed a cheat code two years too late. What's the tool or command that made you feel like you'd been doing things the hard way all along? #SoftwareEngineering #BackendDevelopment #Databases #SQL #EngineeringLife #TechLessons #CodeLife

A senior engineer fixed our 9-second query with just 3 words query. `CREATE INDEX idx` The page went from 9,000ms to 12ms. I watched it happen. I didn't fully understand why until much later, and that gap cost me a lot of debugging hours I shouldn't have spent. Database indexing is one of those topics every developer uses but almost nobody explains properly. Most tutorials tell you what to type. Almost none explain what's actually happening inside the database when you do. So I prepare a multi-page free guide that breaks it down simply, visuals, real SQL, production patterns. Here's what's inside: 🌳 How a B-Tree index actually works (with diagrams) 📊 5 index types and when to use each one 🚧 4 common ways your index gets silently ignored 📋 The leftmost prefix rule for composite indexes ⚖️ The real cost of indexes on write-heavy systems 🔬 How to read EXPLAIN ANALYZE output like a senior dev 🏗 3 real-world indexing patterns (fintech, JSONB, soft deletes) 📋 A cheatsheet you'll reference for years Every code example runs on PostgreSQL. Most also work on MySQL. Whether you're junior or mid-level, this guide will change how you think about query performance. Download it below. 👇 ♻️ Repost if someone on your team would benefit from this. #BackendEngineering #PostgreSQL #SystemDesign #SoftwareEngineering #DatabasePerformance

"Clean Architecture" sounds impressive. But I've seen codebases that claim it and are still a mess. Here's what it actually means in practice 👇 It's NOT about folder structure. It's about DEPENDENCY DIRECTION. In GoFoody (.NET 8 project I built), we had: Domain (entities, business rules)   ↑ Application (use cases, interfaces)   ↑ Infrastructure (DB, external APIs)   ↑ API (controllers, DTOs) The rule: inner layers know NOTHING about outer layers. Domain doesn't import Entity Framework. Application doesn't know if you're using PostgreSQL or MongoDB. Why it matters: → Swap PostgreSQL for MySQL → change 1 file, not 40 → Test business logic without spinning up a database → New team member understands the system by reading Domain first The mistake I made early on: organizing by FILE TYPE (models/, controllers/, services/). It looks clean. It's not. Unrelated code ends up next to each other. Organize by FEATURE (billing/, auth/, orders/). Then enforce the dependency ring. #DotNet #CleanArchitecture #Backend #SoftwareDesign #CSharp

Good architecture pays for itself. I just finished integrating 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 into my Mini Message Board project, moving away from temporary local storage to a persistent database. The most satisfying part? It took almost no time at all. Because I had strictly followed the 𝗠𝗩𝗖 (𝗠𝗼𝗱𝗲𝗹-𝗩𝗶𝗲𝘄-𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿) pattern from the start, I didn't have to touch a single line of code in my views or my main application logic. I only had to update the controllers. 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗽𝗿𝗶𝗻𝘁: • 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Created a custom script to initialize my tables and seed the data, making the deployment process repeatable. • 𝗠𝘂𝗹𝘁𝗶-𝗣𝗮𝗮𝗦 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: I hosted the database on a different service than the backend. Managing those connections and environment variables was a great lesson in distributed systems. • 𝗨𝗫 𝘄𝗶𝘁𝗵 𝗘𝗝𝗦: Since I’m using server-side rendering, I used Express Validator to handle "sticky" form data. If a user makes a mistake, the form doesn't clear, it stays populated with their previous input alongside a helpful error message. It’s one thing to build an app that works. It’s another to build one that is easy to upgrade. Now that I've moved to SQL, I’m seeing exactly why relational databases are the industry standard for data integrity. #PostgreSQL #NodeJS #WebDevelopment #Database #TheOdinProject #BackendEngineer

Most developers focus on writing better code. But the real performance killer? A poorly optimized database. 👇 I see this mistake constantly in production systems — developers optimize their code but completely ignore the database layer. Here are the 4 Database Optimization techniques that separate average developers from great ones: 1️⃣ Indexing Without proper indexes, your DB scans every single row on every query. Result: Speed up data retrieval by up to 100x. 2️⃣ Normalization Storing duplicate data seems fine at first — until it causes bugs, inconsistencies, and bloated storage. Result: Reduce data redundancy, keep your DB clean. 3️⃣ Query Optimization A single poorly written SQL query can bring a production server to its knees. Result: Write efficient SQL queries, save server resources. 4️⃣ Partitioning When your tables hit millions of rows, performance degrades fast. Result: Split large tables for better performance and scalability. Master these 4 — and you'll build faster apps, ace backend interviews, and write production-grade code. Sharing this quick animated breakdown for the dev community 🎥 🌐 Visit our website: www.developersstreet.com 📞 +91 9412892908 #BackendDevelopment #DatabaseOptimization #SQL #SoftwareEngineering #WebDevelopment #Programming #DevelopersStreet #TechTips #CareerGrowth #IndianDeveloper

Behind every fast dashboard or report, there is an optimized database. From indexing to query optimization, small improvements in SQL design can lead to significant performance gains. As a Data Analyst, focusing on efficient data processing is key to delivering reliable insights📊 #SQL #DataAnalyst #Database #Optimization

JSONB seemed like the right call. Flexible fields, fast to ship, no upfront schema wrangling. Looked fine - until it wasn't. At 100k users, a new feature needed to query across those flexible fields. Performance collapsed. What felt like flexibility had become a shapeless blob Postgres couldn't reason about efficiently. The real problem wasn't performance. It was that 10+ services had grown dependencies on that schema. Migrating meant coordinating across all of them simultaneously - backfill scripts, rollout risk, every consumer touched at once. Not a weekend fix. 𝐂𝐨𝐝𝐞 𝐢𝐬 𝐬𝐨𝐟𝐭. 𝐒𝐜𝐡𝐞𝐦𝐚 𝐢𝐬 𝐠𝐞𝐨𝐥𝐨𝐠𝐲. I see engineers treat database design like code architecture - iterative, refactorable, fixable later. That mental model works for services. It fails for schemas the moment other systems start depending on your data shape. Code refactors are local. 𝐒𝐜𝐡𝐞𝐦𝐚 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐚𝐫𝐞 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐞𝐯𝐞𝐧𝐭𝐬. Every JSONB field, every nullable column added for convenience is a commitment made on behalf of every future service that touches that table. Treat it like a public API contract, not an implementation detail. The overhead of thinking harder upfront is real. So is the migration you'll avoid at scale. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐬𝐜𝐡𝐞𝐦𝐚 𝐝𝐞𝐬𝐢𝐠𝐧 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮'𝐫𝐞 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞𝐝 𝐭𝐨 𝐬𝐡𝐢𝐩 𝐟𝐚𝐬𝐭? #SoftwareEngineering #DatabaseDesign #BackendDevelopment #SystemDesign

I added indexes that made queries slower. Been trying to really understand why. Most backend devs are told: "slow query? add an index." Sometimes it helps. Sometimes it does nothing. Sometimes the DB actually gets slower. Here's what's starting to click for me → 1. An index is a sorted shortcut. A separate, ordered copy of columns so the DB can jump to the row instead of scanning the whole table. 2. Column order matters in composite indexes. An index on (user_id, created_at) helps queries filtering by user_id alone, or user_id + created_at together. it does NOT help a query filtering only by created_at. Leftmost prefix rule. 3. Indexes slow down writes. A table with 5 indexes = 6 writes per insert. On write-heavy tables, this quietly becomes your bottleneck. 4. The optimizer can ignore your index. Wrap a column in a function (LOWER(email)), and the index is useless, unless it's a functional index. 5. EXPLAIN is your friend. If you see "Seq Scan" on a big table, your index isn't working. Still a lot I'm learning here. If you've worked with indexes, I'd love to hear what concept took a while to click for you, or anything I might have oversimplified above. Always happy to learn from others. #BackendEngineering #Databases #SoftwareEngineering