Reza Bashiri’s Post

Most developers know indexes make queries faster. But if you don't understand the tradeoffs, you'll either index too much and slow your database down or too little and kill your read performance. Here's what's actually happening 👇 When you query a database with no index, it scans every single row in the table. That's fine at 1,000 rows. But at 10 million rows? It's a disaster!!!. An index lets the database jump straight to the data it needs:- like a book index that takes you to the exact page instead of making you read the whole textbook. Under the hood, most databases use a B-tree structure. Instead of checking millions of rows, the database makes roughly 30 decisions and arrives at the answer. That's the difference between a slow app and a fast one. Indexes cost you on writes. Every INSERT, UPDATE, or DELETE forces the database to update the index too, not just the table. The more indexes you have, the more overhead every write carries. So the strategy is simple: - Index columns you filter and search on frequently - Prioritise columns with lots of unique values; IDs, emails, timestamps - Avoid indexing boolean or low-variety columns; they rarely help - Go easy on tables that get written to constantly Indexing is a deliberate decision, not a default setting. Get it right, and your queries fly. Get it wrong, and that performance debt compounds fast at scale. _________________________________________ What's the worst index-related bug you've ever seen? Drop it in the comments 👇 #Database #DatabaseIndexing #SQL #SoftwareEngineering #BackendDevelopment #TechTips #DataEngineering #Programming #SystemDesign #Engineering

Something I wanted to share — A small change that made a big performance impact While working with database queries, I noticed some APIs were taking longer than expected. The issue? Missing indexes. After adding proper indexes on frequently queried columns: * Query time reduced significantly * API response became faster What I learned: Performance issues are not always about code sometimes it’s about how data is accessed. Indexes act like shortcuts for the database. But they should be used wisely, because: * Too many indexes increase storage * Can slow down write operations Small optimization, big impact. #SQL #Database #Performance #BackendEngineering

Most developers know indexes make queries faster. But if you don't understand the tradeoffs, you'll either index too much and slow your database down or too little and kill your read performance. Here's what's actually happening 👇 When you query a database with no index, it scans every single row in the table. That's fine at 1,000 rows. But at 10 million rows? It's a disaster!!!. An index lets the database jump straight to the data it needs:- like a book index that takes you to the exact page instead of making you read the whole textbook. Under the hood, most databases use a B-tree structure. Instead of checking millions of rows, the database makes roughly 30 decisions and arrives at the answer. That's the difference between a slow app and a fast one. Indexes cost you on writes. Every INSERT, UPDATE, or DELETE forces the database to update the index too, not just the table. The more indexes you have, the more overhead every write carries. So the strategy is simple: - Index columns you filter and search on frequently - Prioritise columns with lots of unique values; IDs, emails, timestamps - Avoid indexing boolean or low-variety columns; they rarely help - Go easy on tables that get written to constantly Indexing is a deliberate decision, not a default setting. Get it right, and your queries fly. Get it wrong, and that performance debt compounds fast at scale. _________________________________________ What's the worst index-related bug you've ever seen? Drop it in the comments 👇 Follow FiloTech Analytics #Database #DatabaseIndexing #SQL #SoftwareEngineering #BackendDevelopment #TechTips #DataEngineering #Programming #SystemDesign #Engineering

Stop blaming the Server! 🛑 Optimization starts with your SQL Queries. Developing a large-scale application is one thing, but making sure it performs well under heavy data load is the real challenge. After 10 years in the industry, I’ve seen many developers jump to upgrade the hardware when a system slows down, but the solution often lies in the code. Here are 3 quick SQL optimization tips that can save you hours of debugging and server costs: 1. *Avoid "SELECT ": It’s tempting, but fetching unnecessary columns increases I/O overhead. Always specify the columns you need. 2. Indexing is Key (But don't overdo it): Proper indexing on WHERE and JOIN columns can speed up queries by 100x. However, too many indexes can slow down your INSERT and UPDATE operations. Balance is everything. 3. Use EXISTS instead of IN for subqueries: In many cases, EXISTS performs better as it stops the scan as soon as it finds a match, whereas IN might process the entire subquery first. As a Senior Developer, I believe that writing code is easy, but writing optimized code is an art. How do you handle performance bottlenecks in your legacy systems? Let's discuss in the comments! 👇 #SQLServer #DatabaseOptimization #DotNetDeveloper #PerformanceTuning #SoftwareEngineering #CodingTips #TechCommunity #SuratTech

One of the most underrated skills in working with databases is writing clean and efficient queries. It’s not just about getting the correct result… It’s about how you get it. I’ve seen cases where a query works perfectly, but causes performance issues because it’s not optimized. Small improvements like: Avoiding unnecessary joins Using proper indexes Filtering data early Can make a huge difference. 👉 A good query gives results. 👉 A great query gives results efficiently. #SQL #Database #Performance #Backend #Engineering

How Do You Optimize a Query with Multiple Joins, Filters, and Pagination? 🤔 If you think pagination will optimize DB performance, then bro you are 𝘄𝗿𝗼𝗻𝗴 — because pagination is only applied at the end of the filtration and joins. That does not really optimize the query. It only optimizes the DB network bandwidth, nothing else. 📡 In this case, we need to carefully analyze the query and database structure. 🧠 If we join all tables first and then apply filters and pagination, let’s see what the database actually does.   • First, it joins the tables and creates a temporary table in memory. 🗂️   • Then it applies filters. 🔎   • And at the end, it applies pagination. 📄 All the memory and CPU utilization happens during joins and filters. ⚙️ If there is a GROUP BY clause, it will require even more processing power. 📊 If the dataset is too large, our DB processor can 𝗲𝗮𝘀𝗶𝗹𝘆 𝘀𝗽𝗶𝗸𝗲. 📈 Now the question is: 𝗵𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗶𝘁? 🤔 If our DB design and query allow step-by-step filtering, like:   • First apply filters on the user table   • Then apply filters on another table   • And so on... Then we can reduce the join data stage by stage by using the 𝗪𝗜𝗧𝗛 clause, which is known as a 𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 (𝗖𝗧𝗘). 🧩 CTEs help reduce the temporary table size during joins, and our filters can be applied faster. ⚡ Then the next stage joins only with the already filtered data. Finally, we can apply pagination. 📄 If a join is only required for data viewing and not for filtering, we can also apply that 𝗷𝗼𝗶𝗻 𝗮𝗳𝘁𝗲𝗿 𝗽𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻, when the dataset is already very small. This also helps optimize our query. 🚀 I have been using the WITH clause (CTE) in many of my large queries, and it has helped me a lot in improving query performance. 💡 #realMoneyLearnings #Databases #SQL #MySQL #DatabasePerformance #QueryOptimization #BackendEngineering #SoftwareEngineering #SystemDesign #TechLearning #LearningInPublic

🚨 Database Pagination Done Wrong Most developers start with: 👉 OFFSET + LIMIT And it works… until it doesn’t. 💥 The problem? By page 10, 50, or 100… your database is doing more and more work just to skip rows. OFFSET doesn’t “jump” — it scans and discards. 📉 Real impact: - Slow queries on large datasets - High database load - Terrible user experience In one of my projects (10M+ rows table), pagination queries reached: ⏱️ ~3 seconds per request That’s not scalable. ⚡ The fix? Keyset Pagination (Seek Method) Instead of: ❌ OFFSET 10000 LIMIT 20 We use: ✅ WHERE id > last_seen_id LIMIT 20 🔥 Results after switching: - Query time dropped from 3s → 15ms - Consistent performance (no matter the page) - Massive reduction in DB load 🧠 Why it works: Keyset pagination uses indexed columns to “seek” directly to the next set of rows — no scanning, no skipping. ⚠️ Trade-offs: - No random page jumps (you move forward/backward) - Requires stable sorting (usually by indexed column like ID or timestamp) 💡 Lesson: If your dataset is growing and you're still using OFFSET… You’re building a performance problem — not a feature. #BackendEngineering #Databases #Performance #SystemDesign #Scalability #SQL

🚀 Database Indexing (Part 1): The Foundation of Fast Queries Before scaling systems with partitioning or distributed caching, the first step is Database Indexing. If your queries are slow, you’re likely missing the right indexes. 🔹 What is Database Indexing? Database Indexing is a technique used to improve query performance by creating a structure that allows faster data lookup. 👉 Like a book index — jump directly to the data instead of scanning everything. 🔹 How It Works Without Index ❌ ➡ Full Table Scan (O(n)) With Index ✅ ➡ Faster Lookup (O(log n)) 🔹 Types of Indexes 1️⃣ B-Tree Index (Most Common) Default index in most databases Supports: Equality (=) Range (>, <, BETWEEN) Sorting 2️⃣ Hash Index Best for exact match (=) Very fast lookup 👉 Limitation: ❌ No range queries ❌ No sorting 3️⃣ Composite Index Multiple columns Example: (user_id, created_at) 👉 Follows left-to-right rule 4️⃣ Unique Index Ensures no duplicate values Example: email, username 5️⃣ Full-Text Index Used for search functionality Example: product search, keyword search 🔹 Benefits ✅ Faster query execution ✅ Efficient searching ✅ Reduced full table scans ✅ Better performance for large datasets 💬 In Part 2, I’ll cover real-world problems, trade-offs, and best practices. #Database #BackendDevelopment #Java #SQL #Performance #Optimization

💥 SQL Query slow? It’s NOT always the query 👇 We often think: 👉 “Query slow hai → optimize query” But in real projects… it’s not always that simple 😤 🔍 The Reality: Sometimes your query is perfectly fine… But still takes seconds or even minutes ⏳ ✔️ Concurrency problems 👉 Multiple users hitting the same data at the same time ✔️ Deadlocks 👉 Queries waiting on each other ✔️ Heavy transactions 👉 Long-running operations slowing everything down ✅ What I Learned: Performance tuning is NOT just about: ❌ Query optimization It’s about: ✔️ Understanding the entire system behavior ⚡ Pro Tip: Before optimizing query → check: 👉 Execution plan 👉 Active sessions 👉 Locks & waits 💬 Have you ever faced a slow query where the issue wasn’t the query itself? Let’s discuss 👇 🔖 Save this post—this mindset shift is important! #sql #database #performance #developer #coding #tricks