Storage access patterns in HLD - System Design Exercise

Design: Storage Access Patterns Design

Design focuses on common storage access patterns and their architectural implications. Does not cover specific database engine internals or hardware-level optimizations.

Functional Requirements

FR1: Support efficient data retrieval for different use cases

FR2: Handle both read-heavy and write-heavy workloads

FR3: Provide low latency access to frequently used data

FR4: Ensure data consistency and durability

FR5: Support batch and real-time data processing

Non-Functional Requirements

NFR1: System should handle up to 100,000 concurrent requests

NFR2: Average read latency should be under 50ms

NFR3: Availability target of 99.9% uptime

NFR4: Data size can grow to multiple terabytes

NFR5: Support horizontal scaling

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Cache layer (e.g., Redis, Memcached)

Primary database (SQL or NoSQL)

Message queues for asynchronous writes

Batch processing systems

API gateway or service layer

Design Patterns

Cache-aside pattern

Read-through and write-through caching

Eventual consistency with asynchronous writes

CQRS (Command Query Responsibility Segregation)

Sharding and partitioning

Bulk loading and batch processing

Reference Architecture

          +-------------+          +-------------+          +-------------+
          |   Clients   | <------> | API Gateway | <------> | Cache Layer |
          +-------------+          +-------------+          +-------------+
                                         |                        |
                                         |                        v
                                         |                 +-------------+
                                         |                 | Primary DB  |
                                         |                 +-------------+
                                         |
                                         v
                                  +----------------+
                                  | Message Queue  |
                                  +----------------+
                                         |
                                         v
                                  +----------------+
                                  | Batch Process  |
                                  +----------------+

Components

Clients

Any client application

Initiate data requests and receive responses

API Gateway

REST/gRPC API server

Handle client requests, route to cache or database

Cache Layer

Redis or Memcached

Store frequently accessed data for low latency reads

Primary DB

Relational (PostgreSQL) or NoSQL (MongoDB)

Persistent storage of data with consistency guarantees

Message Queue

Kafka or RabbitMQ

Buffer write operations for asynchronous processing

Batch Process

Apache Spark or custom batch jobs

Process large volumes of data asynchronously

Request Flow

1. Client sends a read request to API Gateway.

2. API Gateway checks Cache Layer for data.

3. If data is in cache (cache hit), return data to client immediately.

4. If data is not in cache (cache miss), API Gateway queries Primary DB.

5. Primary DB returns data to API Gateway, which updates Cache Layer and responds to client.

6. For write requests, API Gateway writes data to Primary DB and publishes an event to Message Queue.

7. Batch Process consumes events from Message Queue for asynchronous processing or analytics.

Database Schema

Entities depend on application domain but generally include: - Data Entity: stores main data records with unique IDs - Cache Metadata: tracks cache keys and expiration - Event Log: stores write events for asynchronous processing Relationships: - One-to-many between Data Entity and Event Log (each data change produces events) - Cache is a key-value store mapping keys to data snapshots

Scaling Discussion

Bottlenecks

Cache layer can become a bottleneck if not scaled properly

Primary database may face high write or read load

Message queue throughput limits asynchronous processing

Batch processing jobs may take longer with growing data size

Solutions

Use distributed cache clusters with sharding and replication

Scale database vertically or horizontally with read replicas and partitioning

Use high-throughput message queue systems and partition topics

Optimize batch jobs with incremental processing and parallelism

Interview Tips

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

Explain different storage access patterns and when to use them

Discuss trade-offs between latency, consistency, and scalability

Highlight importance of caching and asynchronous processing

Describe how components interact and data flows through the system

Address scaling challenges and practical solutions