LLD principles in python

Here’s a clean and well-structured note on SOLID principles in Low-Level Design (LLD) with Python examples:

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SOLID Principles in Python (LLD Notes)

SOLID is an acronym for five design principles that help make software more maintainable, scalable, and understandable. It’s especially useful in object-oriented design.

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🔹 S – Single Responsibility Principle (SRP)

Definition: A class should have only one reason to change.

Bad Example:

class Report:
    def __init__(self, data):
        self.data = data

    def generate_report(self):
        # logic to generate report
        pass

    def save_to_file(self, filename):
        # logic to save report to file
        pass

📛 Problem: This class does both report generation and file handling.

Good Example:

class Report:
    def __init__(self, data):
        self.data = data

    def generate_report(self):
        return f"Report Data: {self.data}"

class FileSaver:
    def save_to_file(self, report_data, filename):
        with open(filename, 'w') as f:
            f.write(report_data)

✅ Now, each class has only one responsibility.

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🔹 O – Open/Closed Principle (OCP)

Definition: Software entities should be open for extension but closed for modification.

Bad Example:

class Discount:
    def apply(self, customer_type):
        if customer_type == "regular":
            return 0.1
        elif customer_type == "vip":
            return 0.2

📛 Adding new types requires modifying the class.

Good Example:

class Discount:
    def apply(self):
        raise NotImplementedError

class RegularDiscount(Discount):
    def apply(self):
        return 0.1

class VIPDiscount(Discount):
    def apply(self):
        return 0.2

def get_discount(discount: Discount):
    return discount.apply()

✅ You can extend without modifying existing code.

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🔹 L – Liskov Substitution Principle (LSP)

Definition: Subtypes must be substitutable for their base types. if a function works with a base class, it should work seamlessly with any subclass without requiring modifications

Bad Example:

class Bird:
    def fly(self):
        pass

class Ostrich(Bird):
    def fly(self):
        raise Exception("Can't fly")

📛 Ostrich violates LSP because it can’t behave like a Bird.

Good Example:

class Bird:
    pass

class FlyingBird(Bird):
    def fly(self):
        print("Flying")

class Ostrich(Bird):
    def run(self):
        print("Running")

✅ Only the right subclasses inherit specific behavior.

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🔹 I – Interface Segregation Principle (ISP)

Definition: No client should be forced to depend on methods it does not use.

Bad Example:

class Machine:
    def print(self): pass
    def scan(self): pass
    def fax(self): pass

class OldPrinter(Machine):
    def print(self): pass
    def scan(self): raise NotImplementedError
    def fax(self): raise NotImplementedError

📛 Forced to implement unnecessary methods.

Good Example:

class Printer:
    def print(self): pass

class Scanner:
    def scan(self): pass

class Fax:
    def fax(self): pass

class MultiFunctionPrinter(Printer, Scanner, Fax):
    def print(self): pass
    def scan(self): pass
    def fax(self): pass

✅ Clients implement only what they need.

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🔹 D – Dependency Inversion Principle (DIP)

Definition: Depend on abstractions, not on concretions. It states that high-level modules should not depend on low-level modules, and both should depend on abstractions (interfaces or abstract classes).

Bad Example:

class MySQLDatabase:
    def connect(self):
        pass

class App:
    def __init__(self):
        self.db = MySQLDatabase()  # tightly coupled

Good Example:

class Database:
    def connect(self):
        pass

class MySQLDatabase(Database):
    def connect(self):
        print("Connecting to MySQL")

class App:
    def __init__(self, db: Database):
        self.db = db

    def start(self):
        self.db.connect()

✅ Easily testable and replaceable (e.g., use SQLite, mock DBs).

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✅ Summary Table

Principle	Purpose	Python Tip
SRP	One reason to change	Separate concerns into separate classes
OCP	Extend, don’t modify	Use inheritance or composition
LSP	Subtypes must behave correctly	Avoid breaking base class contracts
ISP	Avoid fat interfaces	Split interfaces into smaller ones
DIP	Depend on abstractions	Use dependency injection

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Absolutely! Here’s a concise yet practical note on the DRY (Don’t Repeat Yourself) principle for your Low-Level Design (LLD) notes, with Python examples:

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DRY Principle in Python (LLD Notes)

🔹 What is DRY?

DRY stands for Don’t Repeat Yourself.

It emphasizes:

🛑 Avoiding code duplication by ensuring that every piece of knowledge or logic is represented once and only once in a system.

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✅ Why DRY Matters

• Improves maintainability – fix a bug once instead of multiple places.

• Reduces errors – duplicate logic can easily get out of sync.

• Promotes clean, modular design.

• Helps with code reuse and testability.

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❌ Common Violations of DRY

1.

Copy-pasting code blocks

# Repeated logic for calculating discounts
def discount_customer1(price):
    return price - (price * 0.1)

def discount_customer2(price):
    return price - (price * 0.1)

2.

Repeated business rules

if user.age >= 18:
    print("Eligible")

# elsewhere
if customer.age >= 18:
    print("Eligible")

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✅ Applying DRY Correctly

✅ Use Functions

def apply_discount(price, rate):
    return price - (price * rate)

discount_customer1 = apply_discount(100, 0.1)
discount_customer2 = apply_discount(200, 0.1)

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✅ Use Constants for Shared Values

MIN_AGE = 18

def is_eligible(user):
    return user.age >= MIN_AGE

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✅ Use Inheritance / Composition

class Animal:
    def speak(self):
        print("Generic sound")

class Dog(Animal):
    def speak(self):
        print("Bark")

class Cat(Animal):
    def speak(self):
        print("Meow")

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✅ Use Loops Instead of Repetitive Code

# Bad
print("Hello Alice")
print("Hello Bob")
print("Hello Charlie")

# Good
names = ["Alice", "Bob", "Charlie"]
for name in names:
    print(f"Hello {name}")

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🧠 DRY vs. Abstraction Abuse

⚠️ Be careful not to over-abstract or extract logic prematurely. DRY is not about blindly removing duplication—it’s about meaningful abstraction.

Bad abstraction leads to:

• Hard-to-read code

• Tightly coupled logic

• Early optimization mistakes

Use DRY with common sense and clarity in mind.

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🔁 DRY in Practice (Best Practices)

Practice	DRY-Friendly Approach
Logging	Use a common logger utility
Validation rules	Centralize with reusable validators
Reusable data structures	Create helper classes or modules
Repeated SQL queries	Parameterize and reuse in one place
Repeated error handling	Use decorators or context managers

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🧩 DRY + SOLID = Clean Design

DRY is complementary to SOLID:

• SRP avoids repetition of concerns.

• OCP enables reuse through extensions.

• DIP enables reuse via abstractions.

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Glad you’re finding these helpful! Here’s a clean and developer-friendly note on the YAGNI (You Aren’t Gonna Need It) principle for your LLD design notes, with real-world examples in Python.

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YAGNI Principle in Python (LLD Notes)

🔹 What is YAGNI?

YAGNI = You Aren’t Gonna Need It

It means:

Do not implement a feature or functionality until it is actually required.

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✅ Why YAGNI Matters

• Saves development time

• Keeps code lean and focused

• Reduces complexity

• Prevents overengineering

• Encourages iterative development

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❌ Violating YAGNI (Anti-patterns)

❌ Building features “just in case”

class ReportGenerator:
    def generate_pdf(self):
        pass

    def generate_excel(self):  # Nobody needs this yet
        pass

    def generate_html(self):  # Not in the scope, but added “just in case”
        pass

📛 Problem: You added HTML and Excel report generation before they were actually needed.

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❌ Over-designing for future scenarios

class Animal:
    def fly(self):  # Assuming all animals might fly in the future
        pass

📛 Problem: Not all animals fly. Don’t add methods based on assumptions.

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✅ Following YAGNI (Good Practices)

✅ Build for today’s needs

class ReportGenerator:
    def generate_pdf(self):
        # Implement only what the client currently requires
        pass

✅ Simple and to the point.

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✅ Add functionality

only when it’s needed

Use Agile practices:

Build → Test → Get Feedback → Then Expand

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📌 YAGNI vs Planning Ahead

Myth (Overengineering)	Reality (YAGNI)
“We might need this…”	“We’ll add it when we need it”
“Let’s future-proof”	“Let’s be agile, not psychic”

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🛠️ YAGNI in Python – Use Cases

✅ Avoid writing unnecessary abstraction layers

Bad:

class ServiceLayer:
    def __init__(self, repository):
        self.repository = repository

Good:

# Just use the repository directly if there's no added value yet.

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✅ Avoid writing generic base classes too early

Bad:

class BaseUserService:
    pass

class AdminService(BaseUserService):
    pass

class GuestService(BaseUserService):
    pass

Good:

# Just create what is required now. Add inheritance later if needed.
class AdminService:
    pass

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✅ When to Break YAGNI

YAGNI is not about being lazy. You can break it when:

• The cost of adding the feature later is much higher

• You’re writing a framework or shared libraries

• You’re implementing security or compliance features

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✅ Summary

Principle	Summary
YAGNI	Don’t build it until you actually need it
Main Benefit	Keeps your code simple, clean, and focused
Risk of Violation	Leads to bloat, complexity, and waste

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KISS Principle in Python (LLD Notes)

🔹 What is KISS?

KISS = Keep It Simple, Stupid

Definition:

Design and write code in the simplest way possible. Avoid unnecessary complexity.

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✅ Why KISS Matters

• Easier to read and understand

• Fewer bugs and easier testing

• Easier to maintain and extend

• Helps collaborators grasp your code quickly

✨ “Simple is better than complex.” – Zen of Python

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❌ Violating KISS (Anti-patterns)

❌ Over-engineering a simple task

def calculate_total(cart):
    total = 0
    for item in cart:
        if isinstance(item, dict):
            for k, v in item.items():
                if k == "price":
                    total += v

📛 Over-complicated loop for summing prices.

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✅ Applying KISS (Good Practice)

✅ Write clean and direct code

def calculate_total(cart):
    return sum(item["price"] for item in cart)

✅ Much simpler and easier to read.

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✅ Use Python’s built-in features

# Bad
names = ["alice", "bob", "charlie"]
capitalized = []
for name in names:
    capitalized.append(name.capitalize())

# Good (KISS)
capitalized = [name.capitalize() for name in names]

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🧠 KISS in Design Decisions

Problem Area	KISS Solution
Overuse of patterns	Use them only when necessary
Complex UIs	Start with a minimal viable interface
Deep inheritance	Prefer composition over inheritance
Nested logic	Break into smaller functions

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✅ Break Big Problems into Small Pieces

Instead of writing a 100-line function:

def process_user_data():
    # lots of logic

Break it down:

def fetch_data(): pass
def validate_data(): pass
def transform_data(): pass
def save_data(): pass

def process_user_data():
    fetch_data()
    validate_data()
    transform_data()
    save_data()

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🧩 KISS + Other Principles

• KISS + DRY: Keep it simple AND avoid repetition.

• KISS + YAGNI: Keep it simple by not adding what’s unnecessary.

• KISS + SRP: Simpler when a class/function does just one thing.

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🛠️ KISS Checklist

• Is my code as short as possible?

• Is it readable by someone new to the codebase?

• Am I avoiding clever “hacks”?

• Can I explain this logic in 30 seconds?

• Can I remove any abstraction, parameter, or step?

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✅ Summary

Principle	Summary
KISS	Keep it simple. Avoid unnecessary complexity
Main Benefit	Easier maintenance, fewer bugs, cleaner logic
Rule of Thumb	If it’s hard to understand, it’s probably too complex

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