Solved Hard SQL Problem with Double JOIN

Day 18/100 of #100DaysOfCode 💻 Short query. Clear logic. Clean result. 🎯 Customer Placing the Largest Number of Orders:- Find the customer who placed the most orders from the Orders table. GROUP BY to count orders per customer → ORDER BY COUNT DESC to rank them → LIMIT 1 to grab the top one. Done. ✅ 💡 The real insight here - LIMIT 1 is one of those small but powerful tools. Instead of writing a subquery or CTE to find the max, just sort and slice. Cleaner and faster. Sometimes the best query is the shortest one. 🧩 Not every day is a Hard problem grind, some days you just stay consistent, show up, and keep the streak alive. That matters too. 😄 #SQL #100DaysOfCode #LearningInPublic #DataAnalytics #Consistency #DevJourney #LeetCode

39 problems solved. No shortcuts, no cramming. Just one problem a day — sometimes two if the first one clicks fast. Linked lists, backtracking, SQL queries, greedy scans. Each one a little uncomfortable at first, then suddenly obvious once it lands. That's kind of the whole point. Dashboard → https://lnkd.in/eWjTD6_P Repo → https://lnkd.in/ePM5inyX #LeetCode #CodingJourney #100DaysOfCode #BuildInPublic #SoftwareEngineering #ConsistencyOverMotivation #ProblemSolving

Solving the SQL 50 challenge on LeetCode has been an exciting and confidence-boosting journey. Each problem pushed me to think deeper, optimize queries, and strengthen my understanding of SQL concepts. From basic queries to advanced topics like joins, aggregations, subqueries, and window functions — this journey was truly enriching 💡 📘 What’s in my notes/document? A structured study guide covering: All 50 problems with solutions Clear explanations for each query Key SQL concepts like SELECT, JOINs, Aggregations, Window Functions, and more Practical patterns and best practices for real-world scenarios Once you get a solid grip on fundamentals, problems labeled medium or even hard start feeling like easy. It’s all about consistency and practice. #SQL #LeetCode #DataEngineering #LearningJourney #sql50

🚀 Day 10/30 of My LeetCode Journey (SQL Focus) Double digits! Staying consistent and sharpening my SQL skills every day 📊🔥 🔹 **SQL Problem of the Day** 👉 *Employee Bonus* Given `Employee` and `Bonus` tables, write a query to report the name and bonus of employees who: • Have a bonus less than 1000 • OR did not receive any bonus 💡 *Key Concept:* LEFT JOIN + handling NULL values with conditional filtering. Understanding joins and NULL handling is making SQL much more intuitive now 💡 Day 10 done ✅ #LeetCode #30DaysChallenge #SQL #CodingJourney #Consistency #ProblemSolving #DataAnalytics #Learning

🚀 High Performance SQL Query on LeetCode! Just solved “Customer Placing the Largest Number of Orders” and this one felt extra satisfying 😄 📊 Result: ✔️ Accepted ✅ (19/19 test cases passed) ✔️ Runtime: 427 ms ⚡ ✔️ Beats ~80% of submissions 🚀 💡 Problem Insight: The task was to find the customer who placed the maximum number of orders — a great use case of GROUP BY + aggregation + subquery. 🧠 My Approach: Counted orders per customer Compared it with the maximum order count using a subquery Returned the customer with the highest count SELECT customer_number FROM Orders GROUP BY customer_number HAVING COUNT(order_number) = ( SELECT MAX(order_count) FROM ( SELECT COUNT(order_number) AS order_count FROM Orders GROUP BY customer_number ) AS temp ); 🔥 Key Takeaway: Combining aggregation + nested queries helps solve ranking-type problems efficiently. Consistency is paying off — step by step improving both logic and performance 💪 #SQL #LeetCode #CodingJourney #Database #Learning #Tech #PlacementPreparation #Consistency

🚀 Day 26 – 30 Days SQL LeetCode Challenge Today’s problem was simple but highlights an important real-world scenario 👀 📌 Today's Problem: Article Views I (LeetCode #1148) 🧠 Problem Statement: Find all authors who viewed at least one of their own articles. 💡 Key SQL Concepts Used: • DISTINCT • Filtering with conditions • Comparing columns within the same row 📚 What I Practiced Today: ✔ Comparing values within the same table ✔ Removing duplicates using DISTINCT ✔ Writing clean filtering queries 🔥 This pattern is useful in: • Detecting self-actions (self-purchases, self-views) • Fraud detection • User behavior analysis 🔗 GitHub Repository: https://lnkd.in/e8aV37dA #SQL #LeetCode #DataAnalytics #30DaysOfSQL #LearningInPublic

🚀 Day 34/50 – #LeetCode Challenge Today’s problem: Product Price at a Given Date 📊 This one really tested my understanding of handling time-based data in SQL. 💡 Key Learning: When dealing with historical data, it's important to focus on the latest valid record before a given date — not just any record. 🔍 Approach I used: Consider all products Find the most recent price change on or before the target date (2019-08-16) If no change exists → use default price = 10 Used concepts like MAX(date), GROUP BY, and JOIN 🧠 What I improved today: Writing cleaner SQL queries Handling edge cases (no previous data) Thinking in terms of real-world data scenarios 🔥 Consistency is building confidence step by step. #Day34 #LeetCode #SQL #DataStructures #CodingJourney #Consistency #PlacementPreparation

SQL Progress: Logic & CASE Statements! Today I solved another Medium challenge on LeetCode. This problem was a great lesson in how to calculate percentages and rates directly in SQL. What I learned today: 1. AVG with CASE WHEN: I learned that I can use AVG(CASE WHEN condition THEN 1.0 ELSE 0.0 END) to calculate a rate. It’s a very clear way. 2. Handling NULLs in Rates: By using a LEFT JOIN between the Signups and Confirmations tables, I ensured that users with no actions are still included, and the AVG function automatically treats them as 0 if they don't meet the "confirmed" criteria. 3. Precision with ROUND: Used ROUND(..., 2) to make sure the final confirmation rate is clean and meets the required format(0.00). I would love to learn from your experience: is ther another methods cleaner? قليل مستمر خير من كثير منقطع #SQL #DataEngineering #PostgreSQL #LeetCode #100DaysOfCode #DataAnalytics #ProblemSolving

✅ Solved a SQL problem on LeetCode — Day 48 of my SQL Journey 💪 Not all active users are valuable… Some look busy 👀 Today’s problem was about identifying zombie sessions — users who scroll endlessly but never click, never buy, never engage. Active on paper. Dead in reality. The approach: • Calculated session duration using MIN() and MAX() timestamps • Counted scrolls and clicks with conditional aggregation • Computed click-to-scroll ratio inside HAVING • Filtered sessions: duration > 30 min, scrolls ≥ 5, ratio < 0.20, zero purchases What I practised: • Conditional aggregation using CASE WHEN • Session-level grouping • Ratio logic inside HAVING • Translating behaviour into SQL filters What stood out — Scrolling is passive. Clicking is intent. Buying is a commitment. A session full of scrolls but no clicks It's just a ghost. That’s where the real insight lies. SQL doesn’t just count events. It reads the story behind them. Consistent learning, one query at a time 🚀 #SQL #LeetCode #DataAnalytics #LearningInPublic #SQLPractice

✅ Solved a SQL problem on LeetCode — Day 39 of my SQL Journey 💪 Some customers order anytime… But some follow patterns you can actually measure 📊 Today’s problem: identify “Golden Hour Customers” — people who consistently order during peak hours and leave high ratings. I used a single-pass aggregation query to: • Count total orders per customer • Flag orders placed during peak hours ⏰ • Calculate peak-hour percentage • Compute average rating while handling NULLs • Filter customers across all conditions at once What I practised: • CASE WHEN inside aggregations • Applying multiple conditions with HAVING • Time-based filtering using HOUR() • Combining behavioural and quality metrics in one query What stood out — Good customers aren’t just active… they’re consistent in when and how they engage. That’s where the real insight lies. SQL isn’t just about breaking problems into steps. Sometimes it’s about bringing everything together — cleanly. Consistent learning, one query at a time 🚀 #SQL #LeetCode #SQLPractice #DataAnalytics #LearningInPublic