Read-heavy vs write-heavy systems in HLD - Scaling Approaches Compared

Scalability Analysis - Read-heavy vs write-heavy systems

Growth Table: Read-heavy vs Write-heavy Systems

Users	Read-heavy System	Write-heavy System
100 users	Single server handles reads easily; DB writes low volume	Single server handles writes; DB writes manageable
10,000 users	Read cache (Redis/Memcached) added; DB handles writes well	DB write load increases; write latency may rise; caching less effective
1,000,000 users	Multiple read replicas; heavy caching; DB write master may bottleneck	Write DB bottleneck; need sharding or partitioning; caching limited
100,000,000 users	Global CDN for reads; geo-distributed read replicas; write DB sharded and scaled horizontally	Complex write sharding; eventual consistency; write queues and batching; high infrastructure cost

First Bottleneck

In read-heavy systems, the first bottleneck is usually the database's ability to serve many read queries simultaneously. Without caching or read replicas, the DB CPU and disk I/O get overwhelmed.

In write-heavy systems, the bottleneck is the database's write throughput. Writes are slower and require disk persistence, so the DB write master or primary node becomes the choke point.

Scaling Solutions

• Read-heavy: Use caching layers (Redis, Memcached) to serve frequent reads quickly.
• Deploy read replicas to distribute read load across multiple DB instances.
• Use CDNs to cache static content closer to users globally.
• Write-heavy: Implement database sharding or partitioning to split write load across multiple DB nodes.
• Use write queues or batching to smooth spikes in write traffic.
• Consider eventual consistency models to reduce write latency.
• Scale application servers horizontally to handle increased write requests.

Back-of-Envelope Cost Analysis

Assuming 1 million users with 10 requests per second each:

• Read-heavy: 10 million reads/sec, 1 million writes/sec.
• One DB instance can handle ~10,000 QPS; need ~1000 read replicas for reads.
• Caching can reduce DB reads by 90%, lowering replicas to ~100.
• Write DB handles 1 million writes/sec; requires sharding into ~100 shards (10k writes/shard).
• Network bandwidth: 1 Gbps = 125 MB/s; high traffic needs multiple network interfaces or data centers.

Interview Tip

Start by clarifying if the system is read-heavy or write-heavy. Then discuss bottlenecks specific to that pattern. Propose targeted solutions like caching and read replicas for read-heavy, or sharding and write queues for write-heavy. Always mention trade-offs like consistency and cost.

Self Check

Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Add read replicas and implement caching to reduce load on the primary DB. For write-heavy growth, consider sharding or write queues to distribute write load.

Key Result

Read-heavy systems scale well with caching and read replicas, while write-heavy systems require sharding and write optimization to handle growth.