Immutability for safety in LLD - System Design Exercise

Design: Immutability for Safety in Software Systems

Focus on designing immutability principles and data flow in a software system to ensure safety and concurrency. Out of scope are specific UI designs or database engine internals.

Functional Requirements

FR1: Ensure data objects cannot be changed after creation to prevent accidental or malicious modification.

FR2: Support concurrent access to shared data without locks or race conditions.

FR3: Allow safe sharing of data across different parts of the system or threads.

FR4: Provide mechanisms to create new versions of data when updates are needed without altering original data.

FR5: Maintain system performance with immutable data structures.

Non-Functional Requirements

NFR1: System should handle up to 10,000 concurrent users accessing shared data.

NFR2: Latency for read operations should be under 100ms p99.

NFR3: Availability target of 99.9% uptime.

NFR4: Memory usage should be optimized to avoid excessive copying of data.

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Immutable data structures (e.g., persistent collections)

Versioning or copy-on-write mechanisms

Concurrency control without locks

Garbage collection or memory management for old versions

APIs for creating and accessing immutable data

Design Patterns

Persistent data structures

Copy-on-write

Event sourcing

Functional programming principles

Snapshot isolation

Reference Architecture

Client
  |
  v
Immutable Data API Layer
  |
  v
Immutable Data Store (Persistent Data Structures)
  |
  v
Version Manager & Garbage Collector

Components

Immutable Data API Layer

Custom library or service layer

Provides interfaces to create, read, and update data immutably by returning new versions without modifying originals.

Immutable Data Store

Persistent data structures (e.g., trees, tries)

Stores data in immutable form allowing sharing of unchanged parts between versions to save memory.

Version Manager

Service or module

Tracks different versions of data objects and manages access to correct versions.

Garbage Collector

Background process

Cleans up old versions of data no longer referenced to free memory.

Request Flow

1. Client requests data read via Immutable Data API Layer.

2. API Layer fetches requested version from Immutable Data Store.

3. Client requests data update via API Layer.

4. API Layer creates a new immutable version of data using persistent data structures.

5. New version is stored in Immutable Data Store and registered with Version Manager.

6. Old versions remain unchanged and accessible until no longer needed.

7. Garbage Collector periodically removes unreferenced old versions.

Database Schema

Entities: - DataObject: id (PK), content (immutable), version_number, parent_version_id (nullable) Relationships: - Each DataObject version links to its parent version (if any) forming a version chain. - Multiple versions can coexist; only one is active or latest per logical data entity.

Scaling Discussion

Bottlenecks

Memory usage grows with many versions due to data copying.

Version Manager can become a bottleneck managing many versions.

Garbage collection pauses can affect latency.

Read latency may increase if many versions exist.

Solutions

Use structural sharing in persistent data structures to minimize copying.

Shard Version Manager by data partitions to distribute load.

Implement incremental or concurrent garbage collection to reduce pauses.

Cache frequently accessed versions to reduce read latency.

Interview Tips

Time: Spend 10 minutes understanding immutability benefits and challenges, 15 minutes designing components and data flow, 10 minutes discussing scaling and trade-offs, 10 minutes for questions and clarifications.

Explain why immutability improves safety and concurrency.

Describe how persistent data structures enable efficient immutable data.

Discuss versioning and garbage collection to manage data lifecycle.

Highlight trade-offs between immutability and performance.

Show awareness of scaling challenges and practical solutions.