SQL is a performance language, not just a database language

🚀 Day 7 of MySQL Journey Today’s focus: Core SQL Concepts (Before LIKE Operator) 🔹 Execution Order → FROM → WHERE → SELECT 🔹 Comparison Operators → > < = != 🔹 Logical Operators → AND | OR | NOT 🔹 Arithmetic Operations → Real-time calculations (Discounts 💸) 🔹 BETWEEN & IN → Handling ranges & multiple values 🔹 DISTINCT → Removing duplicates 🔹 IS NULL / IS NOT NULL → Handling missing data 🔹 SELECT Basics & Aliasing 💡 Practiced writing queries using real-time product tables 💡 Understood how SQL actually executes behind the scenes Consistency matters 💯 Day 7 done — getting stronger step by step. #MySQL #SQL #Database #LearningJourney #Consistency #BackendDevelopment #FullStackJava

Stop treats 'NULL' like it's just 'nothing'! Knowing the difference between NULL, an Empty String (''), and a Blank Space (' ') isn't just about syntax—it’s critical for data integrity and accurate reporting. I've combined these concepts into one comprehensive 'Nullability Masterclass' infographic to show you exactly how they differ across: 1) Data Type & Representation 2) Storage & Performance 3) The best functions for handling them (like COALESCE and NULLIF) This cheat sheet will save you hours of debugging and help you write more resilient queries. Save this post for later, and let me know your favorite SQL tip for handling missing values below! #SQL #DataAnalytics #DataScience #PostgreSQL #DataEngineering #MySQL #DatabaseManagement

https://lnkd.in/e5k8zFQA I’ve just published a new episode in my SQL learning series, focusing on Temporary Tables and how they compare with Common Table Expressions (CTEs). As SQL queries become more complex, understanding when to use the right tool becomes essential. Temporary tables allow us to store and reuse intermediate results, while CTEs help structure queries more clearly. In this lesson, I break down the differences and explain when each approach is most effective. This is a key concept for writing efficient and scalable SQL in real-world applications. The full video is now live on my channel, The Data Boy. #SQL #MySQL #DataAnalytics #AdvancedSQL #LearningSQL #TheDataBoy

Not every SQL session looks clean or planned. Sometimes it’s just random queries, trying things out, breaking stuff, fixing it, and moving on. It might look messy from the outside… but that’s where real learning happens. Consistency in SQL > perfectly structured practice. Just showing up, typing queries, and staying curious. #SQL #Consistency #LearningJourney #BuildInPublic #MySQL

💡 While working on converting XML-based procedures to JSON, I came across an interesting behavior that many developers overlook 👇 👉 SQL Server is NOT fully case-sensitive by default But… 👉 JSON parsing inside SQL Server IS case-sensitive 🚨A real issue that i faced on today. -- SQL OPENJSON(@json, '$.Achievement') // JSON { "achievement": [...] } 👉 Result: No data returned (no error!) Reason? Achievement ≠ achievement Because -- JSON is case-sensitive JSON_VALUE(@json, '$.Achievement ') ❌ NULL JSON_VALUE(@json, '$.achievement') ✅ Works #SQLServer #JSON #BackendDevelopment #Database #Learning #Debugging #TechTips

Why NOT EXISTS is faster than LEFT JOIN (in many cases) Both queries solve the same problem: Find records that don’t have a match But internally, they behave very differently. LEFT JOIN: • Joins entire tables • Creates intermediate result • Then filters NULLs NOT EXISTS: • Checks row by row • Stops at first match • Avoids unnecessary scans LEFT JOIN = “process everything, then filter” NOT EXISTS = “check and skip early” That’s why NOT EXISTS often performs better on large datasets. But remember: Modern SQL optimizers can rewrite both — always check execution plans. Small query change. Big performance difference. #SQL #SQLTips #AdvancedSQL #QueryOptimization #Database #DataEngineering #Analytics #SQLServer #PostgreSQL #CodingTips

Completed LeetCode's SQL 50 — 50 problems covering joins, aggregations, subqueries, and window functions. One unexpected takeaway: MySQL silently accepts incomplete GROUP BY clauses and picks arbitrary row values, while stricter dialects like PostgreSQL would throw an error outright. The kind of thing that causes subtle bugs in production."

🚨 Most developers learn SELECT in 10 minutes and think they're done. Here's what they miss 👇 📌 Post 2 of my SQL Series — SELECT Mastery You write this: ``` SELECT → FROM → WHERE → ORDER BY → LIMIT ``` But the database actually runs THIS: ``` FROM → JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT ``` You're telling the DB what you want. The DB decides HOW to get it. This is called the Logical Query Processing Order — and most devs have no idea it exists. 💡 Why does this matter? ✅ It explains why you CAN'T use a SELECT alias in a WHERE clause ✅ It explains why HAVING filters AFTER GROUP BY ✅ It explains why ORDER BY can reference your alias — it runs LAST ✅ It helps you write faster, cleaner, bug-free queries 🛠 Works on PostgreSQL, MySQL & Aurora DB. Real queries. Zero fluff. ━━━━━━━━━━━━━━━━━━━━━━ 👇 3 things I need from you: 1️⃣ Drop "SELECT" in the comments if this blew your mind 2️⃣ REPOST — someone on your feed needs to see this today 3️⃣ FOLLOW me so you don't miss Post 3 🔔 Quick poll 👇 Did YOU know about the query execution order before this? 🟢 YES — drop "I knew it!" 🔴 NO — drop "Mind blown 🤯" Let's see how many of us actually knew this! 👇 #SQL #SQLSeries #SELECTMastery #PostgreSQL #MySQL #AuroraDB #Database #BackendDevelopment #SoftwareEngineering #DataEngineering #LearnSQL

MariaDB micro-blog: CTEs = Cleaner, Smarter Queries CTEs (Common Table Expressions) let you create a temporary result set inside your query, making complex logic easier to read and reuse. Think of it as a “named subquery” you can reference like a table. Example (Top active users instead of countries): WITH top_users AS ( SELECT user_id, COUNT(*) AS orders_count FROM orders WHERE order_date >= NOW() - INTERVAL 30 DAY GROUP BY user_id ORDER BY orders_count DESC LIMIT 10 ) SELECT u.name, t.orders_count FROM users u JOIN top_users t USING (user_id); Why this matters: • Breaks complex queries into readable steps • Reuse the same result multiple times • Easier debugging and maintenance CTEs don’t store data, they exist only during query execution, acting like temporary, in-memory result sets. Cleaner queries = fewer mistakes and faster optimization. #MariaDB #SQL #DatabasePerformance #DBA #DatabaseSpa #MySQL