Low-Level Design(LLD) Interview Questions - System Design

Low-Level Design (LLD) is a crucial phase in software development that involves turning high-level architectural designs into implementable modules. In this post, we will look into the top 20 commonly asked interview questions on the low-level design

1. What is the purpose of Low-Level System Design in software development?

Low-Level System Design (LLD) serves as a detailed blueprint that bridges high-level architecture with actual implementation-ready modules. It defines the internal logic of components, class structures, APIs, data flow, and interactions at a granular level. The purpose is to:

• Ensure the system is scalable, maintainable, and extensible.
• Translate abstract architectural ideas into clear class diagrams, sequence diagrams, and detailed logic.
• Minimize ambiguity before coding starts, reducing design flaws later in development.
• Provide developers with a roadmap for implementation while keeping the design consistent with overall architectural goals.

2. How does database indexing optimize query performance?

Database indexing improves query performance by creating auxiliary data structures that allow faster lookups compared to scanning entire tables. Common indexing techniques include B-Trees, Hash Indexes, and Bitmap Indexes. Benefits:

• Speeds up SELECT queries by allowing quick access to rows based on key values.
• Reduces disk I/O, since the database doesn’t need to scan all rows.
• Supports efficient range queries, joins, and filtering.

Note: Trade-off: Indexes require additional storage and can slow down write operations (INSERT/UPDATE/DELETE), so careful index design is essential.

3. What are the essential considerations in designing a schema for a relational database?

Schema design in LLD must balance performance, integrity, and scalability. Key considerations include:

• Normalization: Eliminate redundancy and anomalies while balancing performance (avoid over-normalization).
• Constraints: Use primary keys, foreign keys, unique, and check constraints for data consistency.
• Indexes: Create indexes on frequently queried columns for faster lookups.
• Relationships: Properly define one-to-one, one-to-many, and many-to-many relationships.
• Data types: Choose appropriate types (e.g., INT vs BIGINT, VARCHAR length) to save space and optimize queries.
• Scalability: Consider sharding, partitioning, and denormalization when handling large datasets.
• Security: Apply access controls and encryption where required.

4. Why is concurrency control important in multi-threaded systems?

Concurrency control ensures data integrity and system correctness when multiple threads access shared resources simultaneously. Without it, race conditions, deadlocks, or inconsistent states may occur. In LLD, concurrency is managed using:

• Locks & Mutexes: Ensure exclusive access to critical sections.
• Semaphores: Control access to resources with limited availability.
• Atomic operations: Guarantee indivisible updates to shared variables.
• Read-Write locks: Optimize scenarios with multiple readers but few writers.

Note: By applying concurrency control, systems remain predictable, safe, and efficient under parallel execution.

5. What are UML Behavioral Diagrams?

UML Behavioral Diagrams capture the dynamic aspects of a system, modeling how objects interact and change state in response to events. They focus on what the system does over time, not its static structure. Types include:

• Use Case Diagrams: Represent interactions between actors and the system.
• Sequence Diagrams: Show object interactions in a time sequence.
• Activity Diagrams: Model workflows or business processes.
• State Machine Diagrams: Show object states and transitions.
• Communication Diagrams: Highlight interactions between objects via messages.

Note: These diagrams are crucial in LLD for visualizing control flow, system logic, and runtime behavior.

6. How do you model a sequence diagram for a user login process in UML?

A sequence diagram shows how objects interact in a time-ordered sequence. For a user login process:

Actors/Objects: User, Login Controller, Authentication Service, Database.

Flow:

• User enters credentials -> sent to Login Controller.
• Login Controller forwards them to Authentication Service.
• Authentication Service queries Database to validate credentials.
• Database responds with user record / failure.
• Authentication Service returns success/failure to Login Controller.
• Login Controller sends response back to the User.

Diagram notation: Objects are shown as lifelines, messages as horizontal arrows, and activation bars show method execution.

Note: The diagram visually explains how the system ensures authentication via sequential message passing.

7. How would you model the behavior of a system using a state diagram in UML?

A state diagram models how an object/system transitions between different states in response to events.

Notation:

• States -> rounded rectangles.
• Transitions -> arrows labeled with triggering events.
• Initial state -> filled black circle.
• Final state -> double circle.

Example (Payment System): States: Pending -> Processing -> Completed / Failed.

Transitions:

• "Payment Approved" -> Processing -> Completed.
• "Payment Failed" -> Processing -> Failed.

Usage: Helps in modeling lifecycle of entities such as sessions, orders, transactions.

8. What factors influence the choice of appropriate data structures in Low-Level System Design?

The choice of data structures depends on:

• Access Patterns: Read-heavy (e.g., caching with hash maps) vs write-heavy (linked lists, queues).
• Time Complexity Needs: Fast lookups (hash tables), ordered access (balanced trees, heaps).
• Space Complexity: Memory constraints may require compact structures (arrays, bitsets).
• Concurrency Requirements: Thread-safe structures (concurrent queues, lock-free lists).
• Scalability & Fault Tolerance: Distributed hash tables for large-scale systems.
• Use Case: e.g., Graphs for social networks, tries for autocomplete, queues for message systems.

9. When designing a database schema, what are the benefits of normalization?

Normalization provides several benefits:

• Eliminates redundancy -> avoids storing the same data in multiple places.
• Improves storage efficiency -> less disk space usage.
• Enhances data integrity -> reduces anomalies in insert, update, delete operations.
• Clearer relationships -> ensures tables represent a single concept or entity.
• Easier maintenance -> schema changes are simpler with minimal side effects.

Note: However, over-normalization may increase join complexity, so often balanced with denormalization for performance.

10. How do you design an efficient logging and monitoring system for a complex application?

An effective logging & monitoring system requires both structured logging and real-time observability:

Logging

• Define log format (JSON/structured text).
• Set log levels (DEBUG, INFO, WARN, ERROR, FATAL).
• Ensure centralized log collection (e.g., ELK Stack, Splunk, Fluentd).
• Add correlation IDs to trace requests across microservices.

Monitoring

• Collect system metrics (CPU, memory, request latency).
• Use time-series databases (Prometheus, InfluxDB) for metric storage.
• Implement dashboards (Grafana, Kibana).

Alerting & Anomaly Detection

• Configure alerts for threshold breaches (e.g., high error rate, DB latency).
• Use ML-based anomaly detection for proactive monitoring.

Best Practices

• Avoid excessive logging (affects performance).
• Separate application logs, security logs, audit logs.
• Ensure compliance with data privacy/security standards.

11. What are Design Patterns?

Design patterns are general, reusable solutions to recurring problems in software design. They are not code, but rather templates or guidelines that can be applied in different contexts. They improve software by:

• Promoting reusability and best practices.
• Reducing design errors by applying proven solutions.
• Making systems more scalable, maintainable, and extensible.

Examples:

• Singleton: Ensure only one instance of a class exists (e.g., database connection pool).
• Observer: One-to-many dependency for event-driven systems (e.g., UI listeners).
• Factory: Delegates object creation logic, avoiding direct instantiation.

12. What are Design Patterns? Explain their importance in software development.

Design patterns are standardized, reusable approaches to solving common design challenges in object-oriented systems. Importance in Software Development:

• Consistency: Developers follow a common language and structure.
• Maintainability: Easier to understand, modify, and extend code.
• Flexibility: Patterns decouple components, enabling system evolution.
• Faster Development: Avoid reinventing the wheel by applying known solutions.
• Scalability: Help design systems that adapt to growth in users and data.

In short: Design patterns capture collective software engineering wisdom in structured solutio

13. Can you explain the Singleton Design Pattern and its use cases?

The Singleton Pattern ensures:

1. A class has only one instance.
2. It provides a global access point to that instance.

Implementation (in Java-like pseudocode):

class Singleton {
    private static Singleton instance;
    private Singleton() {} // private constructor
    public static Singleton getInstance() {
        if (instance == null) {
            instance = new Singleton();
        }
        return instance;
    }
}

Use Cases:

• Database connection pools (only one shared instance).
• Configuration managers (centralized global config).
• Logging services (consistent, global logging mechanism).

Note: Overuse can introduce global state -> harder to test and maintain.

14. What is the Observer Design Pattern? How would you implement it in a real-world scenario?

The Observer Pattern establishes a one-to-many dependency between objects. When the subject (publisher) changes state, all its observers (subscribers) are notified automatically.

Key Components:

• Subject: Maintains list of observers & notifies them.
• Observers: Subscribe and react to changes.

Real-world Scenarios:

• GUI frameworks: Button (subject) notifies listeners (observers) on click.
• Messaging systems: Publisher sends updates -> multiple subscribers receive them.
• Stock trading apps: Stock price change (subject) -> all trader dashboards update (observers).

Pseudocode Example:

interface Observer {
    void update(String msg);
}
class Subject {
    List<Observer> observers = new ArrayList<>();
    void addObserver(Observer o) { observers.add(o); }
    void notifyAll(String msg) {
        for (Observer o : observers) o.update(msg);
    }
}

15. Describe the Factory Design Pattern and when you would use it.

The Factory Pattern provides an interface for creating objects, but defers the instantiation logic to subclasses or a centralized factory method.

Benefits:

• Encapsulates object creation logic -> client code doesn’t need to know the concrete class.
• Promotes loose coupling between client and implementation.
• Simplifies maintenance when new object types are added.

When to Use:

• When the type of object isn’t known until runtime.
• When working with a family of related objects.
• To centralize complex creation logic.

Example (Shape Factory):

interface Shape { void draw(); }
class Circle implements Shape { public void draw() {...} }
class Square implements Shape { public void draw() {...} }

class ShapeFactory {
    public Shape getShape(String type) {
        if (type.equals("Circle")) return new Circle();
        if (type.equals("Square")) return new Square();
        return null;
    }
}

Used in: GUI libraries, dependency injection, parsing frameworks.

16. What is the Strategy Design Pattern?

The Strategy Pattern is a behavioral design pattern that enables selecting an algorithm’s behavior at runtime. Instead of hardcoding multiple algorithms inside a class, the Strategy pattern encapsulates each algorithm in its own class and allows the system to switch between them dynamically.

Structure:

• Context -> The class that uses a strategy.
• Strategy Interface -> Defines a common algorithm interface.
• Concrete Strategies -> Different implementations of the algorithm.

Use Cases:

• Payment processing (e.g., Credit Card, UPI, PayPal strategies).
• Sorting algorithms (switch between QuickSort, MergeSort, HeapSort).
• Compression algorithms (ZIP, RAR, GZIP).

Benefit: Promotes flexibility, reduces conditional logic (if-else chains), and supports the Open/Closed Principle (new strategies can be added without modifying existing code).

17. How would you design a logging mechanism for troubleshooting and performance analysis in a distributed system?

Designing an effective logging mechanism in a distributed environment requires centralization, traceability, and observability:

Log Structure & Levels

• Use structured logging (e.g., JSON) for machine readability.
• Define levels: DEBUG, INFO, WARN, ERROR, FATAL.
• Include contextual metadata (request ID, service name, timestamp).

Centralized Logging: Aggregate logs from multiple nodes into a centralized system (e.g., ELK Stack, Splunk, or Fluentd).

Correlation & Traceability: Use correlation IDs or distributed tracing (e.g., Jaeger, Zipkin, OpenTelemetry) to follow requests across microservices.

Performance Monitoring

• Capture latency metrics, slow queries, and error rates.
• Enable anomaly detection & alerts for unusual log patterns.

Goal: Ensure that developers can troubleshoot quickly and analyze performance bottlenecks in a large-scale distributed system.

18. Describe the factors influencing the choice of appropriate algorithms in the design of a sorting system for large datasets.

When designing a sorting system for large datasets, key factors include:

Data Size & Distribution

• Small datasets -> In-memory algorithms (QuickSort, MergeSort).
• Huge datasets -> External sorting (e.g., External Merge Sort for disk-based data).

Time Complexity Requirements

• If performance is critical -> O(n log n) algorithms like MergeSort.
• If partially sorted -> Insertion Sort may be efficient.

Memory Constraints

• Limited memory -> In-place algorithms (HeapSort, QuickSort).
• Large memory available -> MergeSort (stable but requires extra space).

Parallelization Needs: For distributed systems -> Parallel MergeSort, MapReduce-based sorting (Hadoop, Spark).

Stability: If duplicate handling/order matters -> Stable algorithms (MergeSort, TimSort).

Example: Google’s Bigtable & Hadoop use external merge sort because datasets exceed RAM

19. In Low-Level System Design, how do you handle versioning and backward compatibility in evolving software systems?

Versioning ensures that new changes do not break existing clients. In LLD, best practices include:

API Versioning

• Use versioned endpoints (/api/v1/users, /api/v2/users).
• Deprecate old versions gradually with warnings.

Database Schema Evolution

• Use migration tools (Liquibase, Flyway) for controlled schema updates.
• Keep backward compatibility in DB changes (e.g., avoid dropping columns immediately).

Feature Toggles: Introduce new features gradually, controlled by flags.

Backward-Compatible Protocols: Support older clients while adding new fields (e.g., Protobuf optional fields).

Testing: Regression tests to ensure existing functionality works.

Goal: Smooth system evolution without breaking legacy clients.

20. How would you design a secure authentication and authorization system in a distributed application?

A secure system requires strong authentication (who you are) and authorization (what you can access):

Authentication

• Token-based Authentication (OAuth 2.0, JWTs).
• Password Security -> Hashing with bcrypt/argon2, salting.
• Multi-Factor Authentication (MFA) -> SMS, email, TOTP apps.
• SSO (Single Sign-On) -> Centralized authentication across services.

Authorization

• RBAC (Role-Based Access Control) -> Roles like admin, user, guest.
• ABAC (Attribute-Based Access Control) -> Context-based rules (e.g., location, time).
• Fine-grained permissions -> At resource level (row/field security).

Distributed Security Considerations

• Use Identity Providers (IdP) like Keycloak, Auth0, Okta.
• Secure token exchange across microservices (validate JWTs).
• Apply TLS/SSL for data-in-transit encryption.

Auditing & Monitoring

• Maintain logs of login attempts, access denials.
• Detect anomalies (e.g., unusual login locations).

Example: A microservice app using OAuth 2.0 + JWT + RBAC ensures that each request carries a valid token, which is verified before granting access.