6 SQL Query Fixes to Boost Performance

Day 4: Speaking SQL — Structure, Constraints, and the Query Lifecycle Today marked a significant shift as I transitioned from understanding the "Why" of SQL to exploring the "How." I focused on the commands that build and manipulate data, revealing the intricacies of SQL beyond just SELECT *. Key Takeaways from Today: - The 5 SQL Categories: SQL encompasses more than one language. It includes DDL (Data Definition Language) for building structures, DML (Data Manipulation Language) for moving data, DQL (Data Query Language) for retrieving information, and DCL/TCL (Data Control Language/Transaction Control Language) for security and safety. - Constraints are King: I revisited the importance of PRIMARY KEYS, FOREIGN KEYS, and CHECK constraints, which serve as the strict enforcers of data integrity, preventing "dirty data" from entering the system. - NULL is not 0: A critical distinction in SQL. NULL represents an unknown or absence of value, while 0 is a numeric value, and '' is an empty string. Understanding these differences is essential for maintaining data integrity. - The Query Lifecycle: I explored what happens when executing a query. The database undergoes three stages: Parse (Can I read this?), Plan (What's the fastest way?), and Execute (Carrying out the command). Additionally, I delved into Indexing—the "Table of Contents" that enables efficient data retrieval without scanning every row. #SQL #Day4 #Database #Backend #SoftwareEngineering #DataIntegrity #SystemDesign #FullStack I’ve summarized these core foundations in my Day 4 Medium post:

🚨 Database Series #23: T-SQL Introduction Think SQL is just SELECT, INSERT, UPDATE, DELETE? That’s surface-level. Real power in Microsoft SQL Server comes from T-SQL — where your database starts behaving like a programming language. 🔍 Core Concept T-SQL extends SQL with: ✔ Variables → store temporary values ✔ Control Flow → decision-making logic ✔ Error Handling → controlled failure This is where queries become logic-driven execution units. 💻 Code Sample Variables + Control Flow DECLARE @UserCount INT SELECT @UserCount = COUNT(*) FROM Users IF @UserCount > 100 BEGIN PRINT 'High user volume' END ELSE BEGIN PRINT 'Normal load' END WHILE Loop (Controlled Iteration) DECLARE @Counter INT = 1 WHILE @Counter <= 3 BEGIN PRINT 'Processing...' SET @Counter = @Counter + 1 END Error Handling (TRY / CATCH) BEGIN TRY INSERT INTO Users(Name) VALUES (NULL) END TRY BEGIN CATCH PRINT 'Error occurred: ' + ERROR_MESSAGE() END CATCH 🧩 Visual Diagram T-SQL Execution Flow │ ├── Variables → store state │ ├── Control Flow │ ├── IF → decision │ └── WHILE → repetition │ └── TRY/CATCH → error control ⚠️ Common Mistake Treating T-SQL like a full programming language Examples: ❌ Complex loops for data processing ❌ Row-by-row operations (RBAR) ❌ Business logic inside SQL 🚨 Problem: SQL Server is optimized for set-based operations, not procedural loops. 🧠 Practical Takeaway Use T-SQL for: ✔ Conditional logic close to data ✔ Lightweight automation ✔ Controlled error handling Avoid: ❌ Over-engineering logic in the database Golden rule: ➡️ Let SQL handle data, not replace your application layer. 💬 Let’s Discuss Do you use T-SQL mainly for: A) Simple queries B) Stored procedures with logic C) Heavy business logic Where do you draw the line? 🔜 Next in the series: Index Optimization — How to Make Queries Fly ⚡

🚀 **Understanding VIEW in SQL Server** A **VIEW** in SQL Server is a **virtual table** created from a `SELECT` query. It does not usually store data itself — it displays data from one or more tables whenever you query it. Think of it as a **saved query** that you can use like a table. --- 🔹 **Why Use a VIEW?** ✅ Simplify complex JOIN queries ✅ Reuse business logic ✅ Improve security by exposing selected columns only ✅ Make application queries cleaner ✅ Easier maintenance --- 🔹 **Basic Syntax** ```sql CREATE VIEW vw_EmployeeList AS SELECT Id, Name, Department FROM Employees; ``` Now use it like this: ```sql SELECT * FROM vw_EmployeeList; ``` --- 🔹 **Example with JOIN** ```sql CREATE VIEW vw_CustomerOrders AS SELECT    c.Name,   o.OrderId,   o.Amount FROM Customers c JOIN Orders o  ON c.CustomerId = o.CustomerId; ``` Then simply: ```sql SELECT * FROM vw_CustomerOrders; ``` --- 🔹 **Real Benefit** Instead of repeating a long query in many places, create it once as a VIEW and reuse it everywhere. --- 🔹 **Important Notes** ⚠️ A normal VIEW does **not automatically improve performance** ⚠️ It is mainly for organization, reusability, and security ⚠️ Avoid using too many nested views --- 🔹 **When to Use It** ✔ Reports ✔ Repeated joins ✔ Shared business logic ✔ Cleaner backend queries ✔ Restrict direct table access --- 💡 **Simple Summary** A VIEW is a **virtual table based on a SQL query**. It helps developers write cleaner and more maintainable SQL code. #SQLServer #Database #TSQL #BackendDevelopment #SoftwareEngineering #Programming #DataEngineering #SQLTips

Most SQL mistakes happen because people think queries run in the same order they are written. If you write SQL and still wonder why WHERE cannot use an alias from SELECT, this is often the reason. A useful way to picture SQL is a warehouse. On paper, the query is written like this: SELECT ... FROM ... WHERE ... GROUP BY ... HAVING ... ORDER BY But the logical processing order is different: FROM → get the data WHERE → filter rows GROUP BY → build groups HAVING → filter groups SELECT → shape the final result ORDER BY → sort it This is where a lot of confusion starts: aliases not working in WHERE, aggregation mistakes, and queries that behave differently than expected. If you only read SQL top to bottom, you see syntax. If you understand the logical processing order, you understand what the query is really doing. That is when SQL starts making much more sense. I made a visual of this idea using a warehouse analogy: SQL is written in one order for humans, but logically processed in another order by the database. Have you noticed that understanding execution order makes SQL much easier?

🚀 SQL Query Optimization — Write Fast, Not Just Working Queries Many queries work… But not all queries perform well in production 😬 --- 🔹 Common Mistakes ❌ Using SELECT * ❌ Missing indexes ❌ Using functions on columns in WHERE ❌ Not filtering early ❌ Ignoring execution plan --- 🔹 Optimization Tips ✔️ Select only required columns ✔️ Use proper indexes ✔️ Use WHERE efficiently ✔️ Avoid unnecessary joins ✔️ Use pagination (OFFSET / FETCH) ✔️ Analyze execution plan --- 🔹 Example ❌ Slow Query SELECT * FROM Users WHERE YEAR(CreatedDate) = 2024; ✅ Optimized Query SELECT Id, Name FROM Users WHERE CreatedDate >= '2024-01-01' AND CreatedDate < '2025-01-01'; 👉 Index can be used properly now 🚀 --- 🔹 Reality Check A slow query in development = 🔥 Big problem in production --- 🔹 Pro Tip 👉 Always ask: “Can my query use an index?” --- 💡 I’m focusing more on writing efficient queries, not just correct ones. What’s your go-to SQL optimization trick? 👇 --- #sql #database #backenddeveloper #optimization #softwareengineering #developers #dotnet

Most SQL queries don’t fail because of logic. They fail because of performance. I remember working on a project where a query was written perfectly — correct logic, clean structure, and returning the expected results… But it was still slow. That’s when it clicked for me: Even a “correct” query can be inefficient. Working with large datasets, I’ve seen this a lot — queries that return the right result but take way too long to run. The difference between an average SQL developer and a strong one? 👉 It’s not syntax 👉 It’s not writing complex queries 👉 It’s how you think about data A few things I’ve learned along the way: • Complex queries don’t always mean better performance • Small changes (like indexing, better joins, filtering early) can make a big difference • Execution plans show what’s really happening behind the scenes — which joins or operations are slowing things down • SQL works best when you think in sets, not step-by-step logic In one case, optimizing queries helped reduce execution time by around 40% and improved overall system performance. Still learning every day, but one thing is clear: Good SQL is not just about getting the result — it’s about getting it efficiently. Simple example: ❌ SELECT * FROM Orders ✅ SELECT PolicyID, PersonID, PolicyStartDate FROM PolicyDetails Just selecting what you need can already make things faster. Curious — how do you usually approach query optimization? #SQL #DataEngineering #PerformanceTuning #ETL #Databases

L38 (21) sql operators: the engine behind the `where` clause. operators allow you to perform logic, math, and pattern matching to filter your data perfectly. here is the ultimate cheat sheet: > 1. arithmetic operators perform math right inside your queries. (+, -, *, /, %) select * from employees where age + 1 = 60; > 2. comparison operators use these to set exact conditions. (=, <>, !=, >, <, >=, <=) select * from employees where age > 20; > 3. logical operators chain multiple conditions together to get highly specific results. `and` — both conditions must be true. `or` — either condition can be true. `not` — reverses the condition entirely. select * from employees where age > 20 and department = 'it'; > 4. the `in` & `not in` operators cleaner than writing multiple `or` conditions. matches against a list. select * from employees where department in ('it', 'hr'); > 5. pattern matching with `like` used with wildcards to find specific string patterns. `%` = matches zero or more characters. `_` = matches exactly one single character. select * from employees where name like 'a%'; -- starts with 'a' select * from employees where name like '_a%'; -- second letter is 'a' > 6. ranges with `between` grabs data within an inclusive range. select * from employees where salary between 1200 and 1500; > 7. null checks with `is null` tip: you can NEVER use `= null` or `!= null` in sql. `null` represents an unknown value, so it cannot equal anything mathematically. you must use `is null` or `is not null`. select * from employees where department is not null; > 8. bitwise operators operate at the binary level. `&` (bitwise and), `|` (bitwise or). rarely used in typical queries, sometimes used for flags or low-level logic. operators are the backbone of every `where` clause you'll ever write. master these, and you master data filtering. #DBMS #SQL #Databases

SQL Day 31: Learned Stored Procedures Ever rewritten the same query 10 times for 10 different customers? There's a better way. A stored procedure is a precompiled SQL code that can be saved and reused. If you have an SQL query that you write over and over again, save it as a stored procedure, and then just call it to execute it. A stored procedure can also have parameters, so it can act based on the parameter value(s) that is passed. Say you run a small shop. Every day, you check orders for a specific customer. Instead of writing this every time: SELECT * FROM orders WHERE customer_id = 5; You create a stored procedure once: CREATE PROCEDURE GetCustomerOrders @CustomerID INT AS BEGIN  SELECT * FROM orders WHERE customer_id = @CustomerID; END; Then you just call it with ANY customer: EXEC GetCustomerOrders @CustomerID = 5; EXEC GetCustomerOrders @CustomerID = 12; EXEC GetCustomerOrders @CustomerID = 27; Same logic. Different values. Zero rewrite. Why this matters beyond SQL: Learning SQL isn't just about writing queries. It's about: ✅ Spotting repetition ✅ Building reusable solutions ✅ Explaining them clearly #SQL #Dataanalytics#LearningInPublic #Women inTech #ProblemSolving

Most people use SQL every day — but do you know the 12 rules that define a TRUE relational database? 📋 CODD'S 12 RULES — SIMPLIFIED ━━━━━━━━━━━━━━━━━━━━ ✅ Rule 1 — Information Rule All data is stored in tables (rows & columns). Period. ✅ Rule 2 — Guaranteed Access Reach any value using: Table Name + Primary Key + Column Name ✅ Rule 3 — NULL Handling NULL ≠ zero or blank. It means UNKNOWN. Treat it consistently. ✅ Rule 4 — Active Online Catalog DB metadata (schema, tables) is stored AS data — queryable like any table. ✅ Rule 5 — Powerful Language One language (SQL) covers DDL + DML + transactions. No exceptions. ✅ Rule 6 — View Update Rule Views must be updatable wherever logically possible. ✅ Rule 7 — Relational Operations INSERT / UPDATE / DELETE + set operations (UNION, INTERSECT) all supported. ✅ Rule 8 — Physical Independence Change your storage engine or file structure? Your app shouldn't care. ✅ Rule 9 — Logical Independence Add or rename columns? Existing user views stay unaffected. ✅ Rule 10 — Integrity Independence Constraints belong in the DATABASE — not scattered across your application code. ✅ Rule 11 — Distribution Independence Data can live across multiple servers — users see one unified system. ✅ Rule 12 — Non-Subversion Rule No backdoor bypass of integrity rules. Low-level access cannot override constraints.