Single point of failure identification in HLD - System Design Exercise

Design: Single Point of Failure Identification System

Focus on identifying single points of failure in system architecture diagrams or descriptions. Out of scope: automatic system repair or deployment.

Functional Requirements

FR1: Identify components in a system architecture that can cause total system failure if they fail

FR2: Support input of system architecture diagrams or descriptions

FR3: Analyze dependencies and highlight critical components

FR4: Provide recommendations to eliminate or mitigate single points of failure

FR5: Handle systems with up to 100 components and their connections

Non-Functional Requirements

NFR1: Analysis latency under 5 seconds for input size

NFR2: Accuracy in identifying all single points of failure

NFR3: Availability of the analysis service at 99.9%

NFR4: Support for common architecture patterns (e.g., client-server, microservices)

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Input parser for architecture diagrams or descriptions

Dependency graph builder

Analysis engine to detect critical nodes

User interface to display results and recommendations

Storage for past analyses and system models

Design Patterns

Graph traversal algorithms (e.g., DFS, BFS)

Critical path and cut vertex detection in graphs

Redundancy and failover design patterns

Visualization patterns for dependency graphs

Reference Architecture

User Input --> Input Parser --> Dependency Graph Builder --> Analysis Engine --> Results UI
                                         |                                         |
                                         v                                         v
                                  Storage (System Models)                  Recommendations Engine

Components

Input Parser

Custom parser or standard format parsers (e.g., JSON, YAML)

Convert system architecture input into structured data

Dependency Graph Builder

Graph data structures in memory

Build a graph representing components and their dependencies

Analysis Engine

Graph algorithms implemented in backend service

Detect single points of failure by finding critical nodes and edges

Results UI

Web frontend with visualization libraries (e.g., D3.js)

Display identified single points of failure and recommendations

Storage

Relational or NoSQL database

Store system models and past analysis results

Recommendations Engine

Rule-based system or AI component

Suggest ways to remove or mitigate single points of failure

Request Flow

1. User submits system architecture input via UI

2. Input Parser processes input into structured format

3. Dependency Graph Builder creates graph of components and dependencies

4. Analysis Engine runs graph algorithms to find critical nodes (single points of failure)

5. Results UI presents findings and recommendations to user

6. Storage saves input and analysis results for future reference

7. Recommendations Engine generates mitigation suggestions based on analysis

Database Schema

Entities: Component (id, name, type, description), Dependency (id, from_component_id, to_component_id), AnalysisResult (id, component_id, is_single_point_of_failure, timestamp), Recommendation (id, analysis_result_id, text)

Scaling Discussion

Bottlenecks

Parsing large and complex architecture inputs may slow down processing

Graph analysis algorithms may become slow with very large graphs

Storage size and query performance for many stored analyses

UI rendering performance for large dependency graphs

Solutions

Optimize parsers and support incremental parsing for large inputs

Use efficient graph algorithms and possibly approximate methods for huge graphs

Implement database indexing and archiving strategies

Use graph visualization techniques like clustering and lazy loading

Interview Tips

Time: Spend 10 minutes understanding requirements and clarifying scope, 20 minutes designing components and data flow, 10 minutes discussing scaling and trade-offs, 5 minutes summarizing

Clarify input formats and system boundaries early

Explain how graph theory helps identify single points of failure

Discuss trade-offs between accuracy and performance

Highlight user experience in presenting complex analysis results

Mention how to scale and maintain system reliability