Boost SQL Query Performance with Partitioning in SQL Server

Day 15/365 - SQL Tip: Mastering Conditional JOINs A Conditional JOIN is a powerful SQL technique where you add extra conditions directly inside the `ON` clause. Instead of simply matching rows using a key, you can control exactly which records should be joined. 📌 Basic Example SELECT   c.customer_name,   o.order_id,   o.order_status FROM customers c LEFT JOIN orders o   ON c.customer_id = o.customer_id   AND o.order_status = 'Completed'; In this query: * All customers are returned * Only completed orders are joined * Customers without completed orders still appear ❓Why This Matters Placing conditions in the `ON` clause preserves the behavior of an OUTER JOIN. If you move the condition to the `WHERE` clause, your `LEFT JOIN` can accidentally turn into an `INNER JOIN`. ❌ Risky Approach: The below query removes customers who have no completed orders. SELECT * FROM customers c LEFT JOIN orders o   ON c.customer_id = o.customer_id WHERE o.order_status = 'Completed'; ✅ Best Practice: Always place filtering conditions for the joined table inside the `ON` clause when working with `LEFT JOIN`. Where is this applicable in real-world scenarios? • Active customers only • Recent transactions • Date-range joins • Soft-delete handling • Category-specific matching Master this concept, and your SQL skills will level up instantly. #SQL #DataAnalytics #DataEngineering #LearnSQL #SQLTips #Database #Analytics #BusinessIntelligence #DataScience #ConditionalJoin

Last time, I talked about SQL Query order. Today, I will talk about the 𝗤𝘂𝗲𝗿𝘆 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗢𝗿𝗱𝗲𝗿. In SQL, queries are executed in a specific order, which can be quite different from the order in which the clauses are written. Here’s the logical order of SQL query execution: 1. FROM Specifies the tables from which to retrieve or manipulate data. 2. WHERE Filters rows based on specified conditions. Only rows that meet the conditions proceed to the next stage. 3. GROUP BY Groups rows into sets based on column(s) specified. Any aggregate functions (like SUM, COUNT, etc.) will now apply to each group. 4. HAVING Applies filters to groups created by GROUP BY. Only groups meeting these conditions move forward. 5. SELECT Determines which columns and expressions to return. Executes any functions or expressions listed in the SELECT clause. Deduplication of rows (DISTINCT) happens here if specified. 6. ORDER BY Sorts the result based on specified column(s) and sort direction (ASC or DESC). Does not impact the final rows selected, only the display order. 7. LIMIT Restricts the number of rows returned by the query. Useful for pagination or getting a specific subset of rows. Let's take an example with this simple SQL query: SELECT department, COUNT(employee_id) AS total_employees FROM employees WHERE status = 'active' GROUP BY department HAVING total_employees > 5 ORDER BY total_employees DESC LIMIT 10; This query would execute in the following order: 1. FROM employees 2. WHERE status = 'active' 3. GROUP BY department 4. HAVING total_employees > 5 5. SELECT department, COUNT(employee_id) AS total_employees 6. ORDER BY total_employees DESC 7. LIMIT 10 #TipsOnSQL #DataAnalysis

✅ *Basic SQL Commands Cheat Sheet* 🗃️ 🔹 *SELECT* — Select data from database 🔹 *FROM* — Specify table 🔹 *WHERE* — Filter query by condition 🔹 *AS* — Rename column or table (alias) 🔹 *JOIN* — Combine rows from 2+ tables 🔹 *AND* — Combine conditions (all must match) 🔹 *OR* — Combine conditions (any can match) 🔹 *LIMIT* — Limit number of rows returned 🔹 *IN* — Specify multiple values in WHERE 🔹 *CASE* — Conditional expressions in queries 🔹 *IS NULL* — Select rows with NULL values 🔹 *LIKE* — Search patterns in columns 🔹 *COMMIT* — Write transaction to DB 🔹 *ROLLBACK* — Undo transaction block 🔹 *ALTER TABLE* — Add/remove columns 🔹 *UPDATE* — Update data in table 🔹 *CREATE* — Create table, DB, indexes, views 🔹 *DELETE* — Delete rows from table 🔹 *INSERT* — Add single row to table 🔹 *DROP* — Delete table, DB, or index 🔹 *GROUP BY* — Group data into logical sets 🔹 *ORDER BY* — Sort result (use DESC for reverse) 🔹 *HAVING* — Filter groups like WHERE but for grouped data 🔹 *COUNT* — Count number of rows 🔹 *SUM* — Sum values in a column 🔹 *AVG* — Average value in a column 🔹 *MIN* — Minimum value in column 🔹 *MAX* — Maximum value in column

Day 30 of mastering SQL 📘Views in SQL 🔍 What is a View? A View in SQL is a virtual table created from a query. It does not store data itself — it shows data from one or more tables. 👉 Think of it like a saved query that you can reuse anytime. 🧠 Why Use Views? ✔ Simplifies complex queries ✔ Enhances security (hide sensitive columns) ✔ Reusability (no need to write same query again) ✔ Cleaner and organized code 🧾 Syntax CREATE VIEW view_name AS SELECT column1, column2 FROM table_name WHERE condition; 💡 Example CREATE VIEW high_salary_employees AS SELECT name, salary FROM employees WHERE salary > 50000; 👉 Now you can use: SELECT * FROM high_salary_employees; 🔄 Update View CREATE OR REPLACE VIEW view_name AS SELECT ... ❌ Drop View DROP VIEW view_name; ⚠️ Important Points View does not store data Always shows latest data from table Some views are not updatable (depends on query) #SQL #Database #techskills

L38 (29) inner join: where your data overlaps. an `inner join` is the most common type of join in sql. it acts as a strict filter, returning *only* the rows that have matching values in both tables based on your join condition. if a row exists in table a but has no match in table b, it gets dropped from the result set. here is how you pull matching records from a `customer` table and an `orders` table: > the syntax: select columns  from table1  inner join table2  on table1.column = table2.column; > the real-world query: select customer.id, customer.name, orders.ordername from customer inner join orders on customer.id = orders.id; tip: the hidden default! did you know that `inner join` is the default join type in sql? if you are reading someone else's code and they simply typed `join` instead of `inner join`, the database engine is automatically executing an inner join under the hood. however, explicitly writing `inner join` is considered best practice for code readability! #DBMS #SQL #Databases

🚀 **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

🧠 SQL Execution Plan — The Secret Behind Fast Queries Writing a SQL query is easy. Writing a fast SQL query is what makes the real difference in interviews and production systems 👇 Whenever a query is slow, the first thing every developer should check is the Execution Plan. 🔷 What is an Execution Plan? An Execution Plan shows how SQL Server decides to execute your query. 👉 It tells you: • Which table SQL Server accesses first • What type of joins are being used • Whether it is performing a Scan or a Seek • Which operation is taking the highest cost • Where the query is spending most of its time 💡 In simple words: it is the roadmap SQL Server follows to fetch your data. 🔷 Why is it Important? Two queries may return the same result, but one may take: ✅ 1 second ❌ 30 seconds The Execution Plan helps you understand why. It helps in: • Query optimization • Finding performance bottlenecks • Reducing logical reads • Improving production performance Without checking the execution plan, optimization becomes guesswork. 🔷 Types of Execution Plans ✅ Estimated Execution Plan → Shows what SQL Server plans to do before execution Shortcut: Ctrl + L ✅ Actual Execution Plan → Shows what SQL Server actually did after execution Shortcut: Ctrl + M 💡 Actual Execution Plan is more useful for performance tuning. 🔷 Common Operators You Should Know 🔸 Table Scan → Reads the entire table ❌ Slow for large tables 🔸 Index Scan → Scans many rows from an index ⚠️ Better than Table Scan 🔸 Index Seek → Directly jumps to required rows ✅ Fast and efficient 🔸 Key Lookup → Fetches extra columns from the main table ⚠️ Too many can slow performance 🔸 Nested Loop / Hash Match / Merge Join → Join strategies chosen by SQL Server 🔷 Interview Question Q: How do you identify why a query is slow? 👉 I first check the Actual Execution Plan, look for scans, key lookups, and expensive joins, then optimize the query accordingly. This shows practical knowledge, not just theory. 💡 Final Thought Anyone can write SQL queries. But understanding the Execution Plan is what makes you a better developer🚀 Stay tuned for my next post on how to use indexes according to the Execution Plan in SQL Server😊 #sqlserver #sql #executionplan #database #performanceoptimization #backenddeveloper #interviewprep #sqldeveloper #queryoptimization #dotnetdeveloper

⚡ Performance Impact of SQL JOINs – What Every Developer Should Know SQL JOINs are powerful—but if used incorrectly, they can seriously impact your query performance. Let’s break it down in a simple way 👇 ------------------------------------------------------ 🔍 Why JOIN Performance Matters When you use JOINs, the database engine has to: • Scan multiple tables • Match rows based on conditions • Return combined results 👉 The larger the data, the heavier the operation. 🔹 INNER JOIN (Faster in Most Cases) Why? Only returns matching records → less data to process ✅ Efficient when: • Both tables are properly indexed • You only need matched data 💡 Tip: Always index the JOIN columns 🔹 LEFT JOIN (Heavier than INNER JOIN) Why? Returns ALL rows from left table + matching rows ⚠️ Can slow down when: • Left table is large • Many unmatched rows exist 💡 Use only when you truly need all records from the main table 🔹 RIGHT JOIN (Similar to LEFT JOIN) Same performance behavior as LEFT JOIN, just reversed. ⚠️ Often avoided in practice 👉 Developers prefer rewriting it as LEFT JOIN for clarity 🚨 Common Performance Mistakes 🔸 Joining without indexes 🔸 Joining large tables unnecessarily 🔸 Using SELECT * instead of specific columns 🔸 Missing proper WHERE conditions 🟢 Best Practices for Better Performance 🔸 Index your JOIN columns 🔸Filter data early using WHERE 🔸Avoid unnecessary JOINs 🔸Use INNER JOIN when possible 🔸Limit returned columns 📌 Real Impact Poorly optimized JOINs can: • Slow down your application • Increase server load • Cause timeouts in large systems 💡 Tip: Always check your query using EXPLAIN to understand how the database executes your JOIN. 📣 Question for You: Have you ever faced slow queries because of JOINs? How did you optimize them? #SQL #DatabaseOptimization #Performance #WebDevelopment #DataEngineering #LearningSQL

Small SQL changes that made a noticeable difference Over time, I’ve noticed that performance issues are not always about complex tuning. Sometimes, small changes in how we write SQL make a big impact. Here are a few simple ones I’ve come across 👇 🔹 1️⃣ Avoid functions on indexed columns WHERE TO_CHAR(order_date,'YYYY-MM-DD') = '2024-01-01' 👉 Prevents index usage ✔ Better: WHERE order_date = DATE '2024-01-01' 🔹 2️⃣ NVL can affect performance WHERE NVL(status,'X') = 'A' 👉 Index may not be used ✔ Better: WHERE status = 'A' OR status IS NULL 🔹 3️⃣ Avoid SELECT * SELECT * FROM orders WHERE status = 'COMPLETE'; 👉 Fetches unnecessary data ✔ Better: SELECT order_id, order_date, amount FROM orders WHERE status = 'COMPLETE'; 🔹 4️⃣ NOT IN vs NOT EXISTS WHERE emp_id NOT IN (SELECT emp_id FROM terminated_employees) 👉 Fails if NULL exists ✔ Better: WHERE NOT EXISTS ( SELECT 1 FROM terminated_employees t WHERE t.emp_id = e.emp_id ) 💡 What I’ve learned Many performance improvements come from writing SQL in a way the optimizer can understand better — not just adding hints or indexes. Have you seen similar small changes make a difference? #OracleSQL #SQLTuning #Performance #DatabaseDevelopment #PLSQL

SQL Server Notes by AB | Note #12 | Histogram In Action | Original Draft Date: 19 Jan, 2022 | Re-posted on 27 Apr, 2026 | #SQLServerWithAmitBansal   In one of my previous notes, I had talked about the histogram, which is one of the most critical things in the stats object. Histogram, as the name suggests, is a bucketing technique of how the column data is distributed - the highest value of a bucket, which defines a boundary, how many rows are between two boundary values, how many rows are equal to a specific boundary value, how many unique values are there between a range and, how many rows are there on average per distinct value. All of this helps the optimizer make the right estimates. Here is a sample histogram on TotalDue column of SalesOrderHeader table (not putting down all the columns for brevity): RANGE_HI_KEY  RANGE_ROWS   EQ_ROWS =============  ============   ========= 26.2769         40              142 30.1444         17              202 . . (more rows) Now, let's we write a query: SELECT * FROM Sales.SalesOrderHeader WHERE TotalDue = 30.1444 The above query will return 202 rows. If check the cardinality estimation from the execution plan, you will observe the Estimated Number of Rows = 202 and the Actual Number of Rows=202. This is perfect and the best-case scenario for the optimizer where the estimate and the actual matched 100%. How did this happen? Well, this is called Histogram Step Hit. The predicate value mentioned in the query (WHERE condition) has a step representation in the histogram and the optimizer does a perfect estimation with EQ_ROWS (the number of rows equal to the step value), which is 202. The above is just a quick explanation of the ways how the optimizer leverages the histogram.   Demo URL: https://lnkd.in/g5-ef5gq. Want to read more SQL notes like this one? Here: https://bit.ly/ABSQLNotes. Looking for deep-dive content on SQL Server Performance Tuning? Get lifetime access to master class recordings. Check this: https://lnkd.in/d-JrAG78