end of day 3 week 1
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# Exo 1
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number = input("please enter an number :")
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if int(number) % 2:
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if the user name double in size
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the time of execution will double
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so the time of execution will be x 2
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T(m, n) = m x n
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2
week 1/day3/exo1.sql
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2
week 1/day3/exo1.sql
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-- exo 1
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SELECT * FROM users WHERE length(username) > 5
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3
week 1/day3/exo2.sql
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3
week 1/day3/exo2.sql
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-- exo 2
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SELECT username FROM users WHERE username ~ '^[A-Za-z]+$';
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10
week 1/day3/exo3.sql
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week 1/day3/exo3.sql
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-- exo 3
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SELECT username,
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CASE
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WHEN length(username) <= 20
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AND username ~ '^[A-Za-z]+$'
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THEN 'Accepted'
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ELSE 'Refused'
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END AS status
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FROM users;
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3
week 1/day3/exo4.sql
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3
week 1/day3/exo4.sql
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SELECT Count(*) FROM users
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WHERE length(username) <= 20
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AND username ~ '^[A-Za-z]+$';
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17
week 1/day3/exo5.md
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17
week 1/day3/exo5.md
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Answer in plain text:
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If the users table doubles in size, what happens to execution time?
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Write the runtime formula using:
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n = number of rows
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m = average username length
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if the number of user double a size
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the it mutiple by two the number of execution
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$$
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T(2n,m) = 2 \times n \times m
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$$
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Growth is linear
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14
week 1/day3/init_db.sql
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week 1/day3/init_db.sql
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CREATE TABLE users(
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id SERIAL PRIMARY KEY,
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username TEXT
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);
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INSERT INTO users (username) VALUES
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('admin'),
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('root1'),
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('John_Doe'),
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('Alice'),
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('Bob42'),
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('charlie'),
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('eve99'),
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('Mallory');
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168
week 1/day3/readme.md
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168
week 1/day3/readme.md
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# 📘 Week 1 · Day 3 — SQL Exercises
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This document describes **all exercises for Week 1 Day 3**, focused on **SQL conditionals, filtering, and algorithmic thinking** using **PostgreSQL**.
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The goal of today’s exercises is to translate the control-flow logic you used in **C (Day 1)** and **Python (Day 2)** into **declarative SQL queries**, while keeping the same performance and security mindset.
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---
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## 🎯 Learning Objectives
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By completing today’s exercises, you practice:
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* Replacing loops with SQL filtering (`WHERE`)
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* Using conditions to select and classify data
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* Understanding how databases execute algorithms internally
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* Reasoning about runtime complexity in a data-driven context
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* Applying validation logic at the database layer (security best practice)
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---
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## 🛠 Database Setup (Reference)
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All exercises assume the following PostgreSQL table:
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inti_db.sql
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```sql
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CREATE TABLE users (
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id SERIAL PRIMARY KEY,
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username TEXT
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);
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```
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Sample data:
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```sql
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INSERT INTO users (username) VALUES
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('admin'),
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('root1'),
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('John_Doe'),
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('Alice'),
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('Bob42'),
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('charlie'),
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('eve99'),
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('Mallory');
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```
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---
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## 🧪 Exercises
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### Exercise 1 — Length Filtering
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**Task:**
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Select all usernames that are **longer than 5 characters**.
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**Concepts practiced:**
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* `WHERE` clause
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* String length evaluation
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---
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### Exercise 2 — Letters-Only Usernames
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**Task:**
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Select usernames that:
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* Contain **only letters (A–Z, a–z)**
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* Contain **no digits**
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* Contain **no underscores or symbols**
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**Concepts practiced:**
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* Regular expressions in PostgreSQL
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* Character validation at the database level
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* Replacing character-by-character loops with pattern matching
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---
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### Exercise 3 — Accepted vs Refused Classification
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**Task:**
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Return a result set containing:
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* `username`
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* `status` column with values:
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* `Accepted`
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* `Refused`
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**Rules:**
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* Username length ≤ 20
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* Username contains letters only
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**Concepts practiced:**
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* `CASE WHEN` expressions
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* Conditional logic inside SQL queries
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* Translating `if / else` into declarative form
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---
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### Exercise 4 — Count Valid Usernames
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**Task:**
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Return **only the number** of usernames that are valid according to the same rules:
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* Length ≤ 20
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* Letters only
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**Concepts practiced:**
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* Aggregate functions (`COUNT`)
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* Combining filtering and aggregation
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* Thinking about algorithm cost without explicit loops
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---
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### Exercise 5 — Algorithm Analysis (Written)
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**Task:**
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Answer the following questions:
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1. If the `users` table **doubles in size**, what happens to the execution time?
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2. Write the runtime formula using:
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* `n` = number of rows
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* `m` = average username length
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**Expected reasoning:**
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* SQL engines still scan rows internally
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* Regex checks still evaluate characters
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**Expected formula:**
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```
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T(n, m) = n × m
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```
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Explain this **in plain language**, from a sysadmin or security perspective.
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---
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## 🔐 Security Perspective
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These exercises demonstrate why **validating data in SQL** is powerful:
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* Reduces reliance on frontend-only checks
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* Prevents bad data from entering critical systems
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* Improves consistency across applications
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* Aligns with real-world authentication, logging, and SIEM pipelines
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---
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## ✅ Completion Criteria
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Day 3 is considered complete when:
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* All four SQL queries execute correctly
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* The algorithm explanation clearly explains scaling behavior
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* The student can explain how SQL replaces procedural loops
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---
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**Next Step:** Week 1 Day 4 — deeper control flow and scripting (Bash or Rust, depending on track).
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