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15/06/2026

Everything will get better after this difficult time. πŸ₯°

02/06/2026

πŸš€ SQL Interview Series #10 β€” What are Aggregate Functions in SQL?

Aggregate Functions are used to perform calculations on a set of values and return a single result. They are essential for summarizing and analyzing data in SQL.

πŸ”Ή COUNT() β†’ Returns the number of rows
πŸ”Ή SUM() β†’ Returns the total sum of values
πŸ”Ή AVG() β†’ Returns the average value
πŸ”Ή MIN() β†’ Returns the smallest value
πŸ”Ή MAX() β†’ Returns the largest value

πŸ“Œ Example Use Cases:
βœ… Count total orders
βœ… Calculate total revenue
βœ… Find average sales
βœ… Get highest and lowest values
βœ… Generate business reports and analytics

πŸ’‘ Remember:
Aggregate Functions summarize data and return a single value. When combined with `GROUP BY`, they can calculate results for each group instead of the entire dataset.

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πŸš€ SQL Interview Series #10 β€” Aggregate Functions αž€αŸ’αž“αž»αž„ SQL αž‚αžΊαž‡αžΆαž’αŸ’αžœαžΈ?

Aggregate Functions αž‚αžΊαž‡αžΆαž˜αž»αžαž„αžΆαžšαžŠαŸ‚αž›αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž‚αžŽαž“αžΆαž›αžΎαžŸαŸ†αžŽαž»αŸ†αž‘αž·αž“αŸ’αž“αž“αŸαž™αž˜αž½αž™ αž αžΎαž™αž”αž„αŸ’αž αžΆαž‰αž›αž‘αŸ’αž’αž•αž›αžαŸ‚αž˜αž½αž™αŸ”

πŸ”Ή COUNT() β†’ αžšαžΆαž”αŸ‹αž…αŸ†αž“αž½αž“ Rows
πŸ”Ή SUM() β†’ αž”αžΌαž€αžŸαžšαž»αž”αžαž˜αŸ’αž›αŸƒαž‘αžΆαŸ†αž„αž’αžŸαŸ‹
πŸ”Ή AVG() β†’ αž‚αžŽαž“αžΆαžαž˜αŸ’αž›αŸƒαž˜αž’αŸ’αž™αž˜
πŸ”Ή MIN() β†’ αžšαž€αžαž˜αŸ’αž›αŸƒαžαžΌαž…αž”αŸ†αž•αž»αž
πŸ”Ή MAX() β†’ αžšαž€αžαž˜αŸ’αž›αŸƒαž’αŸ†αž”αŸ†αž•αž»αž

πŸ“Œ αž§αž‘αžΆαž αžšαžŽαŸαž€αžΆαžšαž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αŸ–
βœ… αžšαžΆαž”αŸ‹αž…αŸ†αž“αž½αž“ Order αžŸαžšαž»αž”
βœ… αž‚αžŽαž“αžΆαž”αŸ’αžšαžΆαž€αŸ‹αž…αŸ†αžŽαžΌαž›αžŸαžšαž»αž”
βœ… αž‚αžŽαž“αžΆαž€αžΆαžšαž›αž€αŸ‹αž‡αžΆαž˜αž’αŸ’αž™αž˜
βœ… αžšαž€αžαž˜αŸ’αž›αŸƒαžαŸ’αž–αžŸαŸ‹αž”αŸ†αž•αž»αž αž“αž·αž„αž‘αžΆαž”αž”αŸ†αž•αž»αž
βœ… αž”αž„αŸ’αž€αžΎαžαžšαž”αžΆαž™αž€αžΆαžšαžŽαŸ αž“αž·αž„αžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™

πŸ’‘ αž„αžΆαž™αž…αžΆαŸ†αŸ–
Aggregate Functions αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αžŸαž„αŸ’αžαŸαž”αž‘αž·αž“αŸ’αž“αž“αŸαž™ αž“αž·αž„αž”αž„αŸ’αž αžΆαž‰αž›αž‘αŸ’αž’αž•αž›αžαŸ‚αž˜αž½αž™αŸ” αž“αŸ…αž–αŸαž›αž”αŸ’αžšαžΎαž‡αžΆαž˜αž½αž™ `GROUP BY` αžœαžΆαž’αžΆαž…αž‚αžŽαž“αžΆαž›αž‘αŸ’αž’αž•αž›αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αŸ’αžšαž»αž˜αž“αžΈαž˜αž½αž™αŸ—αž”αžΆαž“αŸ”

31/05/2026

πŸš€ SQL Interview Series #9 β€” What is the purpose of the GROUP BY clause?

The `GROUP BY` clause is used to organize rows with the same values into groups, allowing aggregate functions such as `COUNT()`, `SUM()`, and `AVG()` to calculate results for each group instead of the entire dataset.

πŸ“Œ Why use GROUP BY?
βœ… Group similar records together
βœ… Generate summaries per category
βœ… Work with aggregate functions
βœ… Create meaningful reports and analytics

Example:
Without `GROUP BY`, `SUM(amount)` returns the total sales of all customers.

With `GROUP BY customer_id`, `SUM(amount)` returns the total sales for each customer individually.

πŸ’‘ Think of it this way:
`GROUP BY` = Divide data into groups
Aggregate Functions = Calculate results for each group

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πŸš€ SQL Interview Series #9 β€” GROUP BY Clause αž˜αžΆαž“αž‚αŸ„αž›αž”αŸ†αžŽαž„αž’αŸ’αžœαžΈ?

`GROUP BY` αžαŸ’αžšαžΌαžœαž”αžΆαž“αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αžŠαžΆαž€αŸ‹αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαŸ‚αž›αž˜αžΆαž“αžαž˜αŸ’αž›αŸƒαžŠαžΌαž…αž‚αŸ’αž“αžΆαž…αžΌαž›αž‡αžΆαž€αŸ’αžšαž»αž˜ αžŠαžΎαž˜αŸ’αž”αžΈαž±αŸ’αž™ Aggregate Functions αžŠαžΌαž…αž‡αžΆ `COUNT()`, `SUM()` αž“αž·αž„ `AVG()` αž’αžΆαž…αž‚αžŽαž“αžΆαž›αž‘αŸ’αž’αž•αž›αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αŸ’αžšαž»αž˜αž“αžΈαž˜αž½αž™αŸ— αž‡αŸ†αž“αž½αžŸαž±αŸ’αž™αž‚αžŽαž“αžΆαž›αžΎαž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž˜αžΌαž›αŸ”

πŸ“Œ αž αŸαžαž»αž’αŸ’αžœαžΈαžαŸ’αžšαžΌαžœαž”αŸ’αžšαžΎ GROUP BY?
βœ… αžŠαžΆαž€αŸ‹αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŸαŸ’αžšαžŠαŸ€αž„αž‚αŸ’αž“αžΆαž‡αžΆαž€αŸ’αžšαž»αž˜
βœ… αž”αž„αŸ’αž€αžΎαžαžšαž”αžΆαž™αž€αžΆαžšαžŽαŸαžŸαž„αŸ’αžαŸαž”αžαžΆαž˜αž”αŸ’αžšαž—αŸαž‘
βœ… αž”αŸ’αžšαžΎαž‡αžΆαž˜αž½αž™ Aggregate Functions
βœ… αž‡αž½αž™αžœαž·αž—αžΆαž‚αž‘αž·αž“αŸ’αž“αž“αŸαž™αž”αžΆαž“αž€αžΆαž“αŸ‹αžαŸ‚αž„αžΆαž™αžŸαŸ’αžšαž½αž›

αž§αž‘αžΆαž αžšαžŽαŸαŸ–
αž”αžΎαž˜αž·αž“αž”αŸ’αžšαžΎ `GROUP BY` αž‘αŸ `SUM(amount)` αž“αžΉαž„αž”αžΌαž€αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αŸ”

αž”αžΎαž”αŸ’αžšαžΎ `GROUP BY customer_id` αžœαžΆαž“αžΉαž„αž”αž„αŸ’αž αžΆαž‰αž…αŸ†αž“αž½αž“αžŸαžšαž»αž”αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž’αžαž·αžαž·αž‡αž“αž˜αŸ’αž“αžΆαž€αŸ‹αŸ—αžŠαŸ„αž™αž‘αŸ‚αž€αž–αžΈαž‚αŸ’αž“αžΆαŸ”

πŸ’‘ αž„αžΆαž™αž…αžΆαŸ†αŸ–
`GROUP BY` = αž”αŸ‚αž„αž…αŸ‚αž€αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‡αžΆαž€αŸ’αžšαž»αž˜
Aggregate Functions = αž‚αžŽαž“αžΆαž›αž‘αŸ’αž’αž•αž›αž€αŸ’αž“αž»αž„αž€αŸ’αžšαž»αž˜αž“αžΈαž˜αž½αž™αŸ—

31/05/2026

πŸš€ SQL Interview Series #7 β€” Main Types of SQL Commands

SQL is more than just querying data. It provides different categories of commands to define, manipulate, secure, and manage database transactions.

πŸ”Ή DDL (Data Definition Language)
Used to define and modify database structures.
Commands: CREATE, ALTER, DROP, TRUNCATE

πŸ”Ή DML (Data Manipulation Language)
Used to retrieve and manipulate data.
Commands: SELECT, INSERT, UPDATE, DELETE

πŸ”Ή DCL (Data Control Language)
Used to control user permissions and access.
Commands: GRANT, REVOKE

πŸ”Ή TCL (Transaction Control Language)
Used to manage transactions and maintain data integrity.
Commands: COMMIT, ROLLBACK, SAVEPOINT

πŸ“Œ Quick Summary:
βœ… DDL β†’ Structure
βœ… DML β†’ Data
βœ… DCL β†’ Security
βœ… TCL β†’ Transactions

Understanding these command categories is a fundamental skill for every Backend Developer, Database Administrator, and Software Engineer.

πŸš€ SQL Interview Series #7 β€” αž”αŸ’αžšαž—αŸαž‘αžŸαŸ†αžαžΆαž“αŸ‹αŸ—αž“αŸƒ SQL Commands

SQL αž˜αž·αž“αž˜αŸ‚αž“αž˜αžΆαž“αžαŸ’αžšαžΉαž˜αžαŸ‚αž€αžΆαžšαžŸαŸ’αžœαŸ‚αž„αžšαž€αž‘αž·αž“αŸ’αž“αž“αŸαž™ (Query) αž”αŸ‰αž»αžŽαŸ’αžŽαŸ„αŸ‡αž‘αŸαŸ” αžœαžΆαžαŸ’αžšαžΌαžœαž”αžΆαž“αž”αŸ‚αž„αž…αŸ‚αž€αž‡αžΆαž”αŸ’αžšαž—αŸαž‘ Command αž•αŸ’αžŸαŸαž„αŸ—αž‚αŸ’αž“αžΆαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž‚αŸ’αžšαž”αŸ‹αž‚αŸ’αžšαž„ Database αž‘αžΆαŸ†αž„αž˜αžΌαž›αŸ”

πŸ”Ή DDL (Data Definition Language)
αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž”αž„αŸ’αž€αžΎαž αž“αž·αž„αž€αŸ‚αž”αŸ’αžšαŸ‚αžšαž…αž“αžΆαžŸαž˜αŸ’αž–αŸαž“αŸ’αž’ DatabaseαŸ”
Commands: CREATE, ALTER, DROP, TRUNCATE

πŸ”Ή DML (Data Manipulation Language)
αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αžŸαŸ’αžœαŸ‚αž„αžšαž€ αž”αž“αŸ’αžαŸ‚αž˜ αž€αŸ‚αž”αŸ’αžšαŸ‚ αž“αž·αž„αž›αž»αž”αž‘αž·αž“αŸ’αž“αž“αŸαž™αŸ”
Commands: SELECT, INSERT, UPDATE, DELETE

πŸ”Ή DCL (Data Control Language)
αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž‚αŸ’αžšαž”αŸ‹αž‚αŸ’αžšαž„αžŸαž·αž‘αŸ’αž’αž· αž“αž·αž„αž€αžΆαžšαž…αžΌαž›αž”αŸ’αžšαžΎ DatabaseαŸ”
Commands: GRANT, REVOKE

πŸ”Ή TCL (Transaction Control Language)
αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž‚αŸ’αžšαž”αŸ‹αž‚αŸ’αžšαž„ Transaction αž“αž·αž„αžšαž€αŸ’αžŸαžΆαž‘αž»αž€αž—αžΆαž–αžαŸ’αžšαžΉαž˜αžαŸ’αžšαžΌαžœαž“αŸƒαž‘αž·αž“αŸ’αž“αž“αŸαž™αŸ”
Commands: COMMIT, ROLLBACK, SAVEPOINT

πŸ“Œ αž„αžΆαž™αž…αžΆαŸ†αŸ–
βœ… DDL β†’ Structure (αžšαž…αž“αžΆαžŸαž˜αŸ’αž–αŸαž“αŸ’αž’)
βœ… DML β†’ Data (αž‘αž·αž“αŸ’αž“αž“αŸαž™)
βœ… DCL β†’ Security (αžŸαž·αž‘αŸ’αž’αž·αž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹)
βœ… TCL β†’ Transactions (αž”αŸ’αžšαžαž·αž”αžαŸ’αžαž·αž€αžΆαžš)

αž€αžΆαžšαž™αž›αŸ‹αžŠαžΉαž„αž–αžΈ SQL Command Types αž‚αžΊαž‡αžΆαž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“αž‚αŸ’αžšαžΉαŸ‡αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Backend Developer, Database Administrator αž“αž·αž„ Software Engineer αž‚αŸ’αžšαž”αŸ‹αžšαžΌαž”αŸ”

29/05/2026

πŸš€ SQL Interview Series #6 β€” Understanding SQL Joins

SQL Joins are one of the most important concepts in relational databases. They allow you to combine data from multiple tables using a related column, helping you answer real-world business questions.

πŸ”Ή INNER JOIN
Returns only records that exist in both tables.

πŸ”Ή LEFT JOIN
Returns all records from the left table and matching records from the right table. Unmatched rows return NULL values.

πŸ”Ή RIGHT JOIN
Returns all records from the right table and matching records from the left table. Unmatched rows return NULL values.

πŸ”Ή FULL OUTER JOIN
Returns all records from both tables. Missing matches are filled with NULL values.

πŸ“Œ Quick Summary:
βœ… INNER JOIN = Matching data only
βœ… LEFT JOIN = All left + matching right
βœ… RIGHT JOIN = All right + matching left
βœ… FULL JOIN = Everything from both tables

Mastering joins is essential for backend development, reporting, analytics, and database design.
πŸš€ SQL Interview Series #6 β€” αžŸαŸ’αžœαŸ‚αž„αž™αž›αŸ‹αž’αŸ†αž–αžΈ SQL Joins

SQL Join αž‚αžΊαž‡αžΆαž‚αž“αŸ’αž›αžΉαŸ‡αžŸαŸ†αžαžΆαž“αŸ‹αž˜αž½αž™αž“αŸ…αž€αŸ’αž“αž»αž„ Relational Database αžŠαŸ‚αž›αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž±αŸ’αž™αž™αžΎαž„αž—αŸ’αž‡αžΆαž”αŸ‹αž‘αž·αž“αŸ’αž“αž“αŸαž™αž–αžΈαžαžΆαžšαžΆαž„αž…αŸ’αžšαžΎαž“αž‡αžΆαž˜αž½αž™αž‚αŸ’αž“αžΆαžαžΆαž˜αžšαž™αŸˆ Key ឬ Column αžŠαŸ‚αž›αž‘αžΆαž€αŸ‹αž‘αž„αž‚αŸ’αž“αžΆαŸ”

πŸ”Ή INNER JOIN
αž‘αžΆαž‰αž™αž€αžαŸ‚αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαŸ‚αž›αž˜αžΆαž“αž“αŸ…αž€αŸ’αž“αž»αž„αžαžΆαžšαžΆαž„αž‘αžΆαŸ†αž„αž–αžΈαžšαŸ”

πŸ”Ή LEFT JOIN
αž‘αžΆαž‰αž™αž€αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαžšαžΆαž„αžαžΆαž„αž†αŸ’αžœαŸαž„ αž“αž·αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαŸ‚αž›αž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„αž–αžΈαžαžΆαžšαžΆαž„αžαžΆαž„αžŸαŸ’αžαžΆαŸ†αŸ” αž”αŸ’αžšαžŸαž·αž“αž”αžΎαž˜αž·αž“αž˜αžΆαž“αž€αžΆαžšαž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„ αž“αžΉαž„αž”αž„αŸ’αž αžΆαž‰αž‡αžΆ NULLαŸ”

πŸ”Ή RIGHT JOIN
αž‘αžΆαž‰αž™αž€αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαžšαžΆαž„αžαžΆαž„αžŸαŸ’αžαžΆαŸ† αž“αž·αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαŸ‚αž›αž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„αž–αžΈαžαžΆαžšαžΆαž„αžαžΆαž„αž†αŸ’αžœαŸαž„αŸ” αž”αŸ’αžšαžŸαž·αž“αž”αžΎαž˜αž·αž“αž˜αžΆαž“αž€αžΆαžšαž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„ αž“αžΉαž„αž”αž„αŸ’αž αžΆαž‰αž‡αžΆ NULLαŸ”

πŸ”Ή FULL OUTER JOIN
αž‘αžΆαž‰αž™αž€αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαžšαžΆαž„αž‘αžΆαŸ†αž„αž–αžΈαžš αž αžΎαž™αž€αž“αŸ’αž›αŸ‚αž„αžŠαŸ‚αž›αž˜αž·αž“αž˜αžΆαž“αž€αžΆαžšαž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„ αž“αžΉαž„αž”αž„αŸ’αž αžΆαž‰αž‡αžΆ NULLαŸ”

πŸ“Œ αžŸαž„αŸ’αžαŸαž”αžαŸ’αž›αžΈαŸ—
βœ… INNER JOIN = αž‘αž·αž“αŸ’αž“αž“αŸαž™αžŠαŸ‚αž›αž•αŸ’αž‚αžΌαž•αŸ’αž‚αž„αž‚αŸ’αž“αžΆ
βœ… LEFT JOIN = αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαž„αž†αŸ’αžœαŸαž„
βœ… RIGHT JOIN = αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαž„αžŸαŸ’αžαžΆαŸ†
βœ… FULL JOIN = αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‘αžΆαŸ†αž„αž’αžŸαŸ‹αž–αžΈαžαžΆαžšαžΆαž„αž‘αžΆαŸ†αž„αž–αžΈαžš

αž€αžΆαžšαž™αž›αŸ‹αžŠαžΉαž„αž’αŸ†αž–αžΈ Join αž‚αžΊαž‡αžΆαž‡αŸ†αž“αžΆαž‰αž…αžΆαŸ†αž”αžΆαž…αŸ‹αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Backend Developer, Data Analyst αž“αž·αž„ Software Engineer αž‚αŸ’αžšαž”αŸ‹αžšαžΌαž”αŸ”

29/05/2026

SQL Interview Series #5 β€” WHERE vs HAVING

Understanding the difference between WHERE and HAVING is essential for writing efficient SQL queries.

βœ… WHERE

Filters rows before grouping and aggregation.
Cannot be used with aggregate functions like COUNT(), SUM(), or AVG().
Helps reduce the dataset before processing.

βœ… HAVING

Filters grouped results after GROUP BY.
Designed for conditions involving aggregate functions.
Useful when you need to filter summarized data.

πŸ“Œ Remember:

WHERE = Row-level filtering
HAVING = Group-level filtering

Mastering when to use each clause will help you write cleaner, faster, and more scalable SQL queries.
SQL Interview Series #5 β€” αž—αžΆαž–αžαž»αžŸαž‚αŸ’αž“αžΆαžšαžœαžΆαž„ WHERE αž“αž·αž„ HAVING

αž€αžΆαžšαž™αž›αŸ‹αžŠαžΉαž„αž–αžΈ WHERE αž“αž·αž„ HAVING αž‚αžΊαž‡αžΆαž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“αžŸαŸ†αžαžΆαž“αŸ‹αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αžΆαžšαžŸαžšαžŸαŸαžš SQL Query αž±αŸ’αž™αž˜αžΆαž“αž”αŸ’αžšαžŸαž·αž‘αŸ’αž’αž—αžΆαž–αŸ”

βœ… WHERE

αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Filter αž‘αž·αž“αŸ’αž“αž“αŸαž™αž‡αžΆαžšαŸ€αž„αžšαžΆαž›αŸ‹ Row αž˜αž»αž“αž–αŸαž›αž’αŸ’αžœαžΎ Grouping ឬ AggregationαŸ”
αž˜αž·αž“αž’αžΆαž…αž”αŸ’αžšαžΎ Aggregate Functions αžŠαžΌαž…αž‡αžΆ COUNT() , SUM() ឬ AVG() αž”αžΆαž“αž‘αŸαŸ”
αž‡αž½αž™αž€αžΆαžαŸ‹αž”αž“αŸ’αžαž™αž‘αž·αž“αŸ’αž“αž“αŸαž™αž˜αž»αž“αž–αŸαž› Database αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαŸ”

βœ… HAVING

αž”αŸ’αžšαžΎαžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Filter αž›αž‘αŸ’αž’αž•αž›αž”αž“αŸ’αž‘αžΆαž”αŸ‹αž–αžΈ GROUP BYαŸ”
αž’αžΆαž…αž”αŸ’αžšαžΎαž‡αžΆαž˜αž½αž™ Aggregate Functions αž”αžΆαž“αŸ”
αžŸαžΆαž€αžŸαž˜αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž€αžΆαžšαžαŸ’αžšαž½αžαž–αž·αž“αž·αžαŸ’αž™αž›αž‘αŸ’αž’αž•αž›αžŠαŸ‚αž›αž”αžΆαž“αžŸαž„αŸ’αžαŸαž”αžšαž½αž…αŸ”

πŸ“Œ αž„αžΆαž™αž…αžΆαŸ†αŸ–

WHERE = Filter តអម Row
HAVING = Filter តអម Group

αž€αžΆαžšαž‡αŸ’αžšαžΎαžŸαž”αŸ’αžšαžΎ Clause αžαŸ’αžšαžΉαž˜αžαŸ’αžšαžΌαžœ αž“αžΉαž„αž’αŸ’αžœαžΎαž±αŸ’αž™ Query αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αž˜αžΆαž“αž—αžΆαž–αžŸαŸ’αž’αžΆαž αž›αžΏαž“ αž“αž·αž„αž„αžΆαž™αžαŸ‚αž‘αžΆαŸ†αž“αŸ…αž–αŸαž› Project αž€αžΆαž“αŸ‹αžαŸ‚αž’αŸ†αŸ”

29/05/2026

πŸš€ NestJS Multiple Middleware

NestJS allows multiple middleware to run sequentially using the `apply()` method.

```ts
consumer
.apply(cors(), helmet(), logger)
.forRoutes(CatsController);
```

βœ… `cors()` runs first
βœ… `helmet()` runs next
βœ… `logger` runs after that
βœ… Finally, the request reaches the controller

Middleware executes in the same order it is provided inside `apply()`.

Always remember:
If middleware doesn’t send a response, call `next()` β€” otherwise the request will hang.

πŸš€ Multiple Middleware αž“αŸ…αž€αŸ’αž“αž»αž„ NestJS

NestJS αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž²αŸ’αž™ middleware αž…αŸ’αžšαžΎαž“αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαžαžΆαž˜αž›αŸ†αžŠαžΆαž”αŸ‹αžŠαŸ„αž™αž”αŸ’αžšαžΎ `apply()` methodαŸ”

```ts
consumer
.apply(cors(), helmet(), logger)
.forRoutes(CatsController);
```

βœ… `cors()` αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαž˜αž»αž“αž‚αŸ
βœ… `helmet()` αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαž”αž“αŸ’αž‘αžΆαž”αŸ‹
βœ… `logger` αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαž”αž“αŸ’αž‘αžΆαž”αŸ‹αž‘αŸ€αž
βœ… αž…αž»αž„αž€αŸ’αžšαŸ„αž™ request αž‘αŸ…αžŠαž›αŸ‹ controller

Middleware αž“αžΉαž„αžŠαŸ†αžŽαžΎαžšαž€αžΆαžšαžαžΆαž˜αž›αŸ†αžŠαžΆαž”αŸ‹αžŠαŸ‚αž›αž”αžΆαž“αžŠαžΆαž€αŸ‹αž“αŸ…αž€αŸ’αž“αž»αž„ `apply()`αŸ”

αžŸαžΌαž˜αž…αž„αž…αžΆαŸ†αžαžΆαŸ–
αž”αžΎ middleware αž˜αž·αž“αž”αžΆαž“ send response αž‘αŸ αžαŸ’αžšαžΌαžœαž αŸ… `next()` αž”αžΎαž˜αž·αž“αžŠαžΌαž…αŸ’αž“αŸ„αŸ‡αž‘αŸ request αž“αžΉαž„αž‡αžΆαž”αŸ‹ (hanging request)αŸ”

28/05/2026

πŸš€ NestJS Middleware & Dependency Injection

NestJS middleware fully supports Dependency Injection (DI).

Just like controllers and providers, middleware can inject services and dependencies from the same module through the constructor.

βœ… Cleaner architecture
βœ… Reusable business logic
βœ… Better scalability
βœ… Easy service access inside middleware

πŸš€ NestJS Middleware αž“αž·αž„ Dependency Injection

NestJS middleware αž‚αžΆαŸ†αž‘αŸ’αžš Dependency Injection (DI) αž–αŸαž‰αž›αŸαž‰αŸ”

αžŠαžΌαž…αž‡αžΆ controller αž“αž·αž„ provider αžŠαŸ‚αžš middleware αž’αžΆαž… inject service ឬ dependency αž–αžΈ module αžŠαžΌαž…αž‚αŸ’αž“αžΆαž”αžΆαž“αžαžΆαž˜αžšαž™αŸˆ constructorαŸ”

βœ… αžšαŸ€αž”αž…αŸ† code αž”αžΆαž“αžŸαŸ’αž’αžΆαž
βœ… Reusable business logic
βœ… αž„αžΆαž™αžŸαŸ’αžšαž½αž› scale project
βœ… αž’αžΆαž…αž”αŸ’αžšαžΎ service αž“αŸ…αž€αŸ’αž“αž»αž„ middleware αž”αžΆαž“

27/05/2026

πŸš€ Express.js Middleware Basics

Middleware is the core of Express.js request handling.

βœ… Execute code
βœ… Modify request & response
βœ… End the request-response cycle
βœ… Pass control using next()

Always remember:
If you don’t send a response, call next() β€” otherwise the request will hang.

πŸš€ αž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“αž‚αŸ’αžšαžΉαŸ‡ Express.js Middleware

Middleware αž‚αžΊαž‡αžΆαžŸαŸ’αž“αžΌαž›αž€αŸ’αž“αž»αž„αž€αžΆαžšαž‚αŸ’αžšαž”αŸ‹αž‚αŸ’αžšαž„ request αž“αŸ…αž€αŸ’αž“αž»αž„ Express.jsαŸ”

βœ… αžŠαŸ†αžŽαžΎαžšαž€αžΆαžš code
βœ… αž€αŸ‚αž”αŸ’αžšαŸ‚ request αž“αž·αž„ response
βœ… αž”αž‰αŸ’αž…αž”αŸ‹ request-response cycle
βœ… αž”αž“αŸ’αžαž‘αŸ… middleware αž”αž“αŸ’αž‘αžΆαž”αŸ‹αžŠαŸ„αž™αž”αŸ’αžšαžΎ next()

αžŸαžΌαž˜αž…αž„αž…αžΆαŸ†αžαžΆαŸ–
αž”αžΎ middleware αž˜αž·αž“αž”αžΆαž“ send response αž‘αŸ αžαŸ’αžšαžΌαžœαž αŸ… next() αž”αžΎαž˜αž·αž“αžŠαžΌαž…αŸ’αž“αŸ„αŸ‡αž‘αŸ request αž“αžΉαž„αž‡αžΆαž”αŸ‹ (hanging request)αŸ”

26/05/2026

πŸš€ What are ERD Styles?

ERD styles are different ways to visually represent data relationships in a database. Each style uses its own symbols and notation to show entities, attributes, and connections.

πŸ’‘ Common ERD styles:

Chen Style β†’ Uses shapes like rectangles, ovals, diamonds πŸ”·
Crow’s Foot β†’ Uses table format with β€œmany” fork notation 🐾
Bachman Style β†’ Uses arrows to show relationships ➑️
IDEF1X β†’ Advanced model with detailed structure βš™οΈ
Barker Style β†’ Similar to Crow’s Foot with optional lines πŸ“Š

πŸ“Š Simple idea:
Different styles, same goalβ€”clearly show how data is connected in a system.

πŸ”₯ Choose the style that fits your project, tools, and team.



πŸš€ ERD Styles αž‡αžΆαž’αŸ’αžœαžΈ?

ERD Styles αž‚αžΊαž‡αžΆαžœαž·αž’αžΈαž•αŸ’αžŸαŸαž„αŸ—αžŸαž˜αŸ’αžšαžΆαž”αŸ‹αž”αž„αŸ’αž αžΆαž‰αž‘αŸ†αž“αžΆαž€αŸ‹αž‘αŸ†αž“αž„αž‘αž·αž“αŸ’αž“αž“αŸαž™αž€αŸ’αž“αž»αž„ database αžŠαŸ„αž™αž”αŸ’αžšαžΎαžŸαž‰αŸ’αž‰αžΆ αž“αž·αž„αžšαž…αž“αžΆαž”αŸαž‘αŸ’αž˜αžαž»αžŸαŸ—αž‚αŸ’αž“αžΆαŸ”

πŸ’‘ αž”αŸ’αžšαž—αŸαž‘ ERD StylesαŸ–

Chen Style β†’ αž”αŸ’αžšαžΎαžšαžΆαž„ rectangle, oval αž“αž·αž„ diamond πŸ”·
Crow’s Foot β†’ αž”αŸ’αžšαžΎαžαžΆαžšαžΆαž„ αž“αž·αž„αžŸαž‰αŸ’αž‰αžΆ β€œmany” 🐾
Bachman Style β†’ αž”αŸ’αžšαžΎαž–αŸ’αžšαž½αž‰αž”αž„αŸ’αž αžΆαž‰αž‘αŸ†αž“αžΆαž€αŸ‹αž‘αŸ†αž“αž„ ➑️
IDEF1X β†’ αžšαž…αž“αžΆαžŸαž˜αŸ’αž–αŸαž“αŸ’αž’αž›αž˜αŸ’αž’αž·αž αž“αž·αž„αž€αž˜αŸ’αžšαž·αžαžαŸ’αž–αžŸαŸ‹ βš™οΈ
Barker Style β†’ αžŠαžΌαž… Crow’s Foot αž˜αžΆαž“αž”αž“αŸ’αž‘αžΆαžαŸ‹ dashed πŸ“Š

πŸ“Š αž‚αŸ†αž“αž·αžαžŸαžΆαž˜αž‰αŸ’αž‰αŸ–
Style αžαž»αžŸαŸ—αž‚αŸ’αž“αžΆ αž”αŸ‰αž»αž“αŸ’αžαŸ‚αž˜αžΆαž“αž‚αŸ„αž›αž”αŸ†αžŽαž„αžŠαžΌαž…αž‚αŸ’αž“αžΆβ€”αž”αž„αŸ’αž αžΆαž‰αž‘αž·αž“αŸ’αž“αž“αŸαž™αž²αŸ’αž™αž„αžΆαž™αž™αž›αŸ‹αŸ”

πŸ”₯ αž‡αŸ’αžšαžΎαžŸαžšαžΎαžŸ style αžŠαŸ‚αž›αžŸαžΆαž€αžŸαž˜αž“αžΉαž„ project αž“αž·αž„αž€αŸ’αžšαž»αž˜αž€αžΆαžšαž„αžΆαžšαŸ”

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