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Requirements speak in the language of users: "The system should allow customers to place orders." Code speaks in the language of machines: memory addresses, function calls, object instantiations. Between these two worlds lies a chasm—and Low-Level Design is the bridge.
The translation from requirements to code is perhaps the most error-prone step in software development. Misunderstandings here propagate through the entire system. Features get built that nobody asked for. Edge cases get missed. Integration fails because developers interpreted the same requirement differently.
By the end of this page, you will understand how LLD serves as a systematic translation layer—converting business requirements into technical specifications that developers can implement consistently and correctly.
Consider a typical requirement:
"Users should be able to reset their passwords via email."
This single sentence carries hidden complexity:
Even 'simple' requirements hide dozens of decisions. If developers make these decisions independently, inconsistencies emerge. LLD is where these decisions are made—explicitly, deliberately, and consistently.
The bridging function of LLD:
LLD takes ambiguous requirements and produces unambiguous specifications. It answers every question that a developer might have, not by predicting all questions, but by providing a structural framework where the answers can be derived.
The first step in bridging is entity identification—finding the key nouns in requirements that become classes or objects in design.
Technique: Noun Extraction
Read through requirements and highlight significant nouns. These often become entities in your design.
REQUIREMENT:"A customer can place an order containing multiple products. Each order must have a shipping address and payment method. The system should track order status from placement to delivery." NOUN EXTRACTION:- Customer → Entity: Customer class- Order → Entity: Order class - Products → Entity: Product class- Shipping Address → Value Object or Entity: Address class- Payment Method → Entity: PaymentMethod class- Order Status → Enum or State: OrderStatus RELATIONSHIPS IDENTIFIED:- Customer HAS-MANY Orders- Order HAS-MANY Products (through OrderItem)- Order HAS-ONE ShippingAddress- Order HAS-ONE PaymentMethod- Order HAS-ONE OrderStatus (with state transitions)Distinguishing entity types:
Not every noun becomes an entity. LLD requires distinguishing:
| Question | Entity | Value Object | Enum |
|---|---|---|---|
| Does it need a unique identifier? | Yes | No | No |
| Can two with the same attributes be different? | Yes (different IDs) | No (they're equal) | N/A |
| Does it change over time? | Often | Rarely (usually immutable) | Never |
| Is the set of possible values fixed? | No | No | Yes |
| Example | Customer, Order | Address, Money | Status, Type |
While nouns become entities, verbs become methods. The actions described in requirements translate to operations that entities can perform.
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REQUIREMENT:"Customers can add products to their cart, update quantities, and proceed to checkout. The system calculates shipping costs based on destination and applies available discounts." VERB EXTRACTION → METHODS:- "add products" → Cart.addItem(product, quantity)- "update quantities" → Cart.updateItemQuantity(productId, quantity)- "proceed to checkout" → CheckoutService.initiateCheckout(cart)- "calculates shipping" → ShippingCalculator.calculate(destination, items)- "applies discounts" → DiscountEngine.apply(cart, customerProfile) METHOD SIGNATURES REFINED:interface Cart { addItem(product: Product, quantity: number): void; updateItemQuantity(productId: string, quantity: number): void; removeItem(productId: string): void; getTotal(): Money; getItems(): ReadonlyArray<CartItem>;} interface CheckoutService { initiateCheckout(cart: Cart): CheckoutSession; completeCheckout(session: CheckoutSession, payment: PaymentDetails): Order; cancelCheckout(sessionId: string): void;}Good method design follows principles: methods should do one thing, names should be descriptive, parameter lists should be small, and return types should be meaningful. LLD is where these decisions are made explicitly.
Functional requirements tell you what the system does. Non-functional requirements tell you how well it does it—performance, security, reliability, maintainability. These translate into LLD through structural decisions:
| NFR Category | Example Requirement | LLD Translation |
|---|---|---|
| Performance | Response time < 200ms | Caching strategy, async processing, efficient data structures |
| Scalability | Handle 10K concurrent users | Stateless services, horizontal scaling patterns, load balancing interfaces |
| Security | Protect against injection attacks | Input validation classes, authorization interfaces, secure data handling |
| Reliability | 99.9% uptime | Retry mechanisms, circuit breaker patterns, fallback strategies |
| Maintainability | Easy to modify and extend | SOLID principles, design patterns, clear separation of concerns |
| Testability | Unit testable components | Dependency injection, interface-based design, mock-friendly architecture |
Example: Security requirement translation
Requirement: "User passwords must be securely stored and never logged."
LLD decisions:
Password value object that wraps the raw passwordPassword class has no toString() that reveals the valuePasswordHasher interface for hashing with pluggable algorithmsUserRepository stores only hashed passwords, never plaintextPassword fields automatically1234567891011121314151617181920212223242526272829303132333435363738394041424344
// Password value object prevents accidental exposureclass Password { private readonly value: string; constructor(plaintext: string) { this.value = plaintext; } // Deliberately omit getValue() - force hashing instead // Never expose in logs or serialization toString(): string { return "[REDACTED]"; } toJSON(): string { return "[REDACTED]"; } // The only way to use the password hash(hasher: IPasswordHasher): HashedPassword { return hasher.hash(this.value); } verify(hasher: IPasswordHasher, hash: HashedPassword): boolean { return hasher.verify(this.value, hash); }} // Interface allows algorithm substitutioninterface IPasswordHasher { hash(password: string): HashedPassword; verify(password: string, hash: HashedPassword): boolean;} // Concrete implementation can be swappedclass BCryptHasher implements IPasswordHasher { hash(password: string): HashedPassword { // BCrypt implementation } verify(password: string, hash: HashedPassword): boolean { // BCrypt verification }}LLD fits into a systematic pipeline where information flows from abstract to concrete. Understanding this pipeline clarifies LLD's role:
Good LLD maintains traceability—you can trace each class and method back to a requirement, and each requirement forward to its implementation. This traceability enables impact analysis: when requirements change, you know exactly what code is affected.
Requirements rarely document edge cases completely, yet edge cases cause most bugs in production. LLD is where edge cases are identified and designed for explicitly.
Systematic edge case discovery:
For each operation, ask:
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// Without edge case considerationclass NaiveOrderService { placeOrder(customerId: string, items: Product[]): Order { // Just creates the order - what could go wrong? return new Order(customerId, items); }} // With deliberate edge case designclass RobustOrderService { placeOrder(customerId: string, items: Product[]): OrderResult { // Edge case: no customer const customer = this.customerRepository.find(customerId); if (!customer) { return OrderResult.failure("Customer not found"); } // Edge case: empty order if (items.length === 0) { return OrderResult.failure("Cannot place empty order"); } // Edge case: customer account suspended if (customer.isSuspended) { return OrderResult.failure("Account suspended"); } // Edge case: items no longer available const unavailable = items.filter(item => !item.isAvailable()); if (unavailable.length > 0) { return OrderResult.failure("Some items unavailable", unavailable); } // Edge case: exceeds order limit if (items.length > this.config.maxItemsPerOrder) { return OrderResult.failure( `Maximum ${this.config.maxItemsPerOrder} items per order` ); } // Happy path const order = this.createOrder(customer, items); return OrderResult.success(order); }} // Result type makes edge cases explicit in the APItype OrderResult = | { success: true; order: Order } | { success: false; error: string; details?: any };Using result types (like Either, Result, or discriminated unions) instead of exceptions makes edge cases part of the API contract. Callers can't ignore them—they must handle both success and failure paths explicitly.
How do you know if your LLD correctly translates the requirements? Validation techniques ensure the bridge is sound:
Let's consolidate what we've learned about LLD's bridging function:
What's next:
We've seen how LLD bridges requirements and code. The next page dives into the scope of LLD—exactly what elements it covers: classes, interfaces, methods, and interactions. We'll explore each in depth to build a complete picture of what LLD encompasses.
You now understand LLD's critical role as a translation layer. It converts business needs into technical specifications—systematically, explicitly, and traceably. Next, we'll explore the full scope of what LLD covers.