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#𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 → 𝗖𝗿𝗮𝗰𝗸 𝗔𝗻𝘆 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗪𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
➜ 𝟭. 𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 → Combine data from multiple tables
🡆 INNER JOIN → Returns matching records from both tables
🡆 LEFT JOIN → Returns all records from the left table + matches from the right
🡆 RIGHT JOIN → Returns all records from the right table + matches from the left
🡆 FULL JOIN → Returns all records from both tables
➜ 𝟮. 𝗗𝗔𝗧𝗔 𝗙𝗜𝗟𝗧𝗘𝗥𝗜𝗡𝗚 → Retrieve specific records efficiently
🡆 WHERE → Filters rows before aggregation
🡆 HAVING → Filters after aggregation
🡆 BETWEEN, IN, LIKE → Advanced filtering
➜ 𝟯. 𝗔𝗚𝗚𝗥𝗘𝗚𝗔𝗧𝗜𝗢𝗡 & 𝗚𝗥𝗢𝗨𝗣𝗜𝗡𝗚 → Summarize data
🡆 GROUP BY → Groups data based on a column
🡆 COUNT, SUM, AVG, MIN, MAX → Perform calculations
➜ 𝟰. 𝗦𝗨𝗕𝗤𝗨𝗘𝗥𝗜𝗘𝗦 & 𝗖𝗧𝗘𝘀 → Break complex queries
🡆 Subquery → Nested query inside another query
🡆 Common Table Expressions (CTE) → Temporary named query
➜ 𝟱. 𝗜𝗡𝗗𝗘𝗫𝗜𝗡𝗚 & 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 → Speed up queries
🡆 Indexes → Improve search speed
🡆 EXPLAIN PLAN → Analyze query performance
➜ 𝟲. 𝗔𝗗𝗩𝗔𝗡𝗖𝗘𝗗 𝗦𝗤𝗟 𝗧𝗢𝗣𝗜𝗖𝗦
🡆 Window Functions → RANK, ROW_NUMBER, LEAD, LAG
🡆 Stored Procedures & Triggers → Automate tasks
🡆 Normalization & Denormalization → Optimize database design
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➜ 𝟭. 𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 → Combine data from multiple tables
🡆 INNER JOIN → Returns matching records from both tables
🡆 LEFT JOIN → Returns all records from the left table + matches from the right
🡆 RIGHT JOIN → Returns all records from the right table + matches from the left
🡆 FULL JOIN → Returns all records from both tables
➜ 𝟮. 𝗗𝗔𝗧𝗔 𝗙𝗜𝗟𝗧𝗘𝗥𝗜𝗡𝗚 → Retrieve specific records efficiently
🡆 WHERE → Filters rows before aggregation
🡆 HAVING → Filters after aggregation
🡆 BETWEEN, IN, LIKE → Advanced filtering
➜ 𝟯. 𝗔𝗚𝗚𝗥𝗘𝗚𝗔𝗧𝗜𝗢𝗡 & 𝗚𝗥𝗢𝗨𝗣𝗜𝗡𝗚 → Summarize data
🡆 GROUP BY → Groups data based on a column
🡆 COUNT, SUM, AVG, MIN, MAX → Perform calculations
➜ 𝟰. 𝗦𝗨𝗕𝗤𝗨𝗘𝗥𝗜𝗘𝗦 & 𝗖𝗧𝗘𝘀 → Break complex queries
🡆 Subquery → Nested query inside another query
🡆 Common Table Expressions (CTE) → Temporary named query
➜ 𝟱. 𝗜𝗡𝗗𝗘𝗫𝗜𝗡𝗚 & 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 → Speed up queries
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🡆 EXPLAIN PLAN → Analyze query performance
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🡆 Window Functions → RANK, ROW_NUMBER, LEAD, LAG
🡆 Stored Procedures & Triggers → Automate tasks
🡆 Normalization & Denormalization → Optimize database design
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Forwarded from ENG. Hussein Sheikho
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فرصة عمل
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Forwarded from Learn Python Hub
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𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 - 𝗙𝘂𝗹𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Let’s break them down with clarity:
𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises
---
𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Let’s break them down with clarity:
𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises
---
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SQL Query Optimaztion.pdf
897.9 KB
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This channels is for Programmers, Coders, Software Engineers.
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✔️ Mastering ACID properties for data reliability.
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