HLD vs LLD - Learning Module

Loading content...

0/273

How HLD and LLD Complement Each Other

Two Halves of One Whole

High-Level Design and Low-Level Design are not opposing approaches—they are complementary halves of a unified design process. Like the relationship between a city's master plan and its building blueprints, HLD and LLD work together to create systems that are both architecturally sound and well-implemented.

The most effective engineers understand this synergy. They don't see HLD and LLD as separate activities performed by different people at different times. Instead, they see a continuous design conversation where architectural decisions inform implementation and implementation realities reshape architecture.

What You Will Learn

By the end of this page, you will understand how HLD and LLD inform each other, how to bridge the gap between architecture and implementation, and how to develop the integrated thinking that characterizes world-class system designers.

The Bidirectional Relationship

HLD and LLD influence each other in both directions. This bidirectional flow is essential to understand.

HLD → LLD: Architecture Constrains Implementation

High-level architectural decisions establish the constraints within which low-level design operates:

Service boundaries determine what code lives where. If the User Service and Order Service are separate, you can't have a class that directly accesses both databases.
Technology choices constrain implementation options. Choosing a NoSQL database means your data modeling patterns differ from SQL.
Communication patterns affect how code is structured. Synchronous APIs lead to different class designs than event-driven messaging.
Scalability requirements influence algorithm choices. If HLD demands horizontal scaling, LLD must avoid in-memory state or provide synchronization.
Reliability requirements affect error handling patterns. If HLD specifies 99.99% availability, LLD must include circuit breakers, retries, and graceful degradation.

LLD → HLD: Implementation Informs Architecture

Conversely, low-level design discoveries often reshape high-level decisions:

Infeasible algorithms may force architectural changes. If the required computation can't meet latency SLAs, you might need to precompute or cache at the architecture level.
Library limitations may change technology choices. If a library doesn't support a critical feature, you might need to reconsider the technology stack.
Complex domain logic may suggest different service boundaries. If two 'separate' services share too much logic, perhaps they should be merged.
Performance reality may differ from estimates. LLD spikes that reveal unexpected bottlenecks may force architectural redesign.
Implementation patterns may generalize. A pattern that works well in one component might become an architectural standard.

Design Is a Conversation

Think of HLD and LLD as ongoing dialogue, not sequential phases. The architecture proposes; the implementation disposes—or validates. Good designers maintain this conversation throughout the project lifecycle, not just at the beginning.

Traceability: Connecting the Levels

Traceability means you can trace how high-level architectural decisions manifest in low-level implementation and vice versa. This connection is what makes the two levels a coherent whole.

From HLD to LLD:

HLD Decision	LLD Manifestation
User Service handles authentication	`AuthenticationController`, `TokenService`, `UserRepository` classes
Use message queue for async processing	`EventPublisher` interface, `KafkaEventPublisher` implementation
Cache user profiles for low latency	`ProfileCache` class wrapping Redis client
API Gateway handles rate limiting	Middleware classes implementing rate limit logic
Event-driven architecture	Event classes, handlers, subscription management

From LLD to HLD:

LLD Observation	HLD Implication
`OrderProcessor` class needs user data	Order Service depends on User Service; API needed
Complex transaction logic spans classes	Consider if these should be in same service
`CacheManager` is duplicated across services	Extract as shared library or dedicated cache service
`PaymentValidator` has external API calls	Payment Service needs network timeout handling in architecture
Unit tests require complex mocking	Perhaps service boundaries are wrong

Documentation Enables Traceability

Traceability isn't automatic. It requires documentation that connects levels—architecture decision records (ADRs) that reference affected components, class-level comments that reference architectural requirements, and diagrams that zoom between levels consistently.

The Feedback Loop: Learning from Implementation

The most valuable aspect of HLD-LLD integration is the feedback loop. Implementation experience feeds back into architectural evolution.

Types of Feedback:

Feedback That Improves Architecture

•Performance data — Actual latencies and throughputs reveal whether architectural assumptions hold. If Service A → B → C takes 500ms when the assumption was 50ms, architecture must change.
•Failure patterns — Real failures expose architectural weaknesses. A cascade of failures across services suggests missing circuit breakers or too-tight coupling.
•Development velocity — If adding features consistently requires cross-service changes, boundaries may be wrong. Architecture should enable independent work.
•Operational burden — If a service requires constant manual intervention, its design may be too complex or its failure modes poorly handled.
•Team friction — If teams repeatedly step on each other's code, service boundaries may not align with team boundaries.

Healthy Feedback Cycle:

HLD (Architecture)
     │
     ▼
  LLD (Design & Implement)
     │
     ▼
  Production (Operate & Observe)
     │
     ▼
  Insights (Performance, failures, velocity)
     │
     ▼
  Revisit HLD (Evolve architecture)
     │
     ▼
  [Cycle continues]

This cycle should be continuous, not a one-time waterfall. The architecture evolves based on real-world learnings.

Ignoring the Feedback

Systems that never revisit architecture based on implementation experience become increasingly divorced from reality. 'Technical debt' often means the gap between the documented architecture and how the system actually works has grown too large. Close the loop.

Bridging Artifacts: Documents That Connect

Certain documentation artifacts serve as bridges between HLD and LLD, making the connection explicit and maintainable.

Key Bridging Artifacts

•Architecture Decision Records (ADRs) — Document architectural decisions with context, options considered, and decision rationale. Link to affected components. Example: 'ADR-007: Event-driven communication between Order and Inventory services.'
•API Contracts (OpenAPI, gRPC) — The interface between HLD's 'services communicate' and LLD's 'here are the methods.' Contracts must satisfy architectural requirements while being implementable.
•Domain Model — The conceptual model that both architecture and implementation share. Entities like 'User,' 'Order,' 'Product' appear in both HLD diagrams and LLD class hierarchies.
•Context Maps (DDD) — Show how bounded contexts (HLD) relate and how translation happens between them (LLD concern). Bridges domain thinking across levels.
•Non-Functional Requirements (NFRs) — Requirements like latency, throughput, availability flow from HLD down to LLD implementation choices. Each LLD component should trace to NFRs it helps satisfy.
•Component Diagrams — The transitional diagrams between container-level HLD and class-level LLD. Show modules within a service without going to individual classes.

Living Documentation

Bridging artifacts are only valuable if they're maintained. Stale documentation is worse than none—it misleads. Invest in keeping these documents current, or automate their generation from code/configuration where possible.

Case Study: E-Commerce Checkout

Let's trace how HLD and LLD work together through a concrete example: designing the checkout flow for an e-commerce system.

HLD Phase:

Requirement: Users can purchase items in their cart. The system must handle payment, reduce inventory, create an order record, and send confirmation.

Architectural decisions:

Service decomposition: Four services—Cart Service, Order Service, Payment Service, Inventory Service
Communication: Synchronous for user-facing flow, async for notifications
Data ownership: Each service owns its data (no shared databases)
Failure handling: Saga pattern for distributed transaction

Result: A container diagram showing four services, a message queue for events, and data flow arrows.

LLD Phase (Order Service focus):

Classes:

OrderController — HTTP endpoint for checkout
CheckoutOrchestrator — Coordinates the saga
OrderFactory — Creates Order domain objects
OrderRepository — Persists orders
PaymentClient — HTTP client for Payment Service
InventoryClient — HTTP client for Inventory Service
CheckoutSagaState — Tracks saga progress
CompensationHandler — Handles rollback scenarios

Interfaces:

PaymentProcessor — Abstraction over payment service
InventoryReserver — Abstraction over inventory service

Key patterns:

Saga pattern for distributed transaction
Repository pattern for data access
Client abstraction for external services

Feedback from LLD to HLD:

During implementation, the team discovered:

Inventory checks need to be fast — Realized that checking inventory during checkout adds latency. Architectural change: add inventory cache with eventual consistency.
Saga compensation is complex — The compensation logic for failed payments while inventory is reserved is error-prone. Architectural consideration: explore two-phase commit for critical transactions, or simplify saga states.
Payment retries need idiocy key — Current API contract doesn't support idempotency. Architectural change: add idempotency key to all write operations.
Order confirmation notification is slow — Synchronous email sending adds 2 seconds. Already addressed by architecture (async via queue), but needed LLD to implement properly.

These LLD insights led to three architectural refinements before the second iteration. This is the synergy in action.

Team Collaboration Across Levels

How teams collaborate affects how well HLD and LLD integrate. Several models exist:

Team Collaboration Models
Model	Description	Pros	Cons
Centralized Architecture	Dedicated architects define HLD; teams implement LLD	Consistent vision; clear authority	Bottleneck; disconnected from reality
Collaborative Design	Architects and developers design together	Informed decisions; shared ownership	More meetings; harder to coordinate
Federated Architecture	Each team owns HLD for their domain; architects set standards	Autonomy with guardrails; scales well	Potential inconsistency; requires mature teams
Embedded Architects	Architects work within teams, rotate periodically	Deep knowledge; strong connection	Expensive; limited reach

Effective Patterns:

Design reviews that span levels — Review both the architectural proposal and the proposed class structure. Catch misalignments early.
Architecture office hours — Time for developers to consult with architects on how to fit LLD into HLD constraints.
Rotating design leads — Different team members lead design at different levels, building capability across the team.
Implementation spikes before architecture sign-off — Validate architectural assumptions with LLD exploration before committing.
Blameless postmortems — When failures occur, trace from production back through LLD to HLD. Everyone learns.

Conway's Law

Organizations design systems that mirror their communication structures (Conway's Law). If HLD and LLD are done by disconnected groups with poor communication, the resulting system will have misaligned levels. Tightly couple the people who do HLD and LLD.

Developing Integrated Design Thinking

The ultimate goal is to develop integrated design thinking—the ability to fluidly move between levels, understanding how each influences the other.

Habits of Integrated Designers

•Always ask 'What does this mean at the other level?' — When making an HLD decision, immediately consider LLD implications. When implementing LLD, validate it fits the architecture.
•Trace requirements through both levels — For any feature, trace from user need → architectural components → classes → methods. Gaps indicate design problems.
•Maintain mental models at both levels — Keep the architectural diagram in your head while writing code. Keep implementation realities in mind while discussing architecture.
•Question mismatches — If your class structure doesn't match the architectural components, something is wrong. Investigate.
•Communicate at the right level — With architects, speak architecture. With developers, speak implementation. With stakeholders, translate appropriately.
•Refactor across levels — When you refactor classes, consider if the architecture should change. When architecture evolves, propagate changes to LLD.

Exercises to Develop This Skill:

Reverse engineering: Take a codebase you know. Draw the HLD it implies. Does it match documentation? Does it match what developers think?
Implementation planning: Take an HLD diagram. Plan the classes and interfaces for one component. Does the architecture provide enough guidance, or are there gaps?
Failure tracing: For a production incident, trace from symptom → code → component → architecture. What level was the root cause at?
Design at both levels: For any feature, produce both an architectural sketch and a class diagram before implementing. Reference both during code review.

Summary: The Unity of Design

We've explored how HLD and LLD work together. Let's consolidate the key insights:

Key Takeaways

•HLD and LLD are bidirectional — Architecture constrains implementation; implementation informs architecture. Both directions are essential.
•Traceability connects the levels — You should be able to trace how any HLD decision manifests in LLD and vice versa.
•The feedback loop enables evolution — Production experience feeds back through LLD to HLD, enabling continuous architectural improvement.
•Bridging artifacts make the connection explicit — ADRs, API contracts, domain models—these documents connect thinking at both levels.
•Team collaboration must span levels — Design reviews, architecture office hours, and integrated teams ensure alignment.
•Integrated thinking is the goal — The best designers fluidly move between levels, understanding how each influences the other.

Module Conclusion:

In this module, we've distinguished High-Level Design from Low-Level Design, understood what each encompasses, learned when to focus on each, and seen how they complement each other.

This foundation is crucial because the rest of this curriculum will operate at both levels. When we discuss caching strategies, we'll consider both architectural placement (HLD) and implementation patterns (LLD). When we design databases, we'll think about service data ownership (HLD) and schema design (LLD).

With this conceptual framework in place, you're ready to dive into the specifics of system design.

Module Complete

You've completed Module 2: HLD vs LLD — Understanding the Difference. You now understand both design levels, when to apply each, and how they work together synergistically. This integrated perspective will serve you throughout your system design journey.