Cache-aside pattern in HLD - Scalability & System Analysis

Scalability Analysis - Cache-aside pattern

Growth Table: Cache-aside Pattern Scaling

Users / Traffic	Cache Size	Cache Hit Rate	Database Load	Latency	Notes
100 users	Small (MBs)	Low to Medium	Low	Low	Cache warm-up ongoing, DB handles most reads
10,000 users	Medium (GBs)	Medium to High	Moderate	Moderate	Cache hit rate improves, DB load increases
1,000,000 users	Large (10s GBs)	High	Moderate to High	Low to Moderate	Cache critical to reduce DB load, eviction policies needed
100,000,000 users	Very Large (100s GBs+)	Very High	High	Low	Distributed cache clusters, sharding, and advanced eviction

First Bottleneck

The database becomes the first bottleneck as user traffic grows. Initially, the cache is cold and misses cause many DB reads. As traffic increases, the DB handles more queries, risking overload. Without an effective cache hit rate, DB CPU and I/O saturate first.

Scaling Solutions

• Cache Warm-up: Preload popular data to improve hit rate early.
• Horizontal Scaling: Add more cache nodes (distributed cache) to handle larger data and traffic.
• Eviction Policies: Use LRU or LFU to keep cache fresh and relevant.
• Database Read Replicas: Offload read queries to replicas to reduce primary DB load.
• Sharding: Partition data across multiple DB instances to scale writes and reads.
• Connection Pooling: Efficiently manage DB connections to avoid overload.
• Monitoring & Alerts: Track cache hit rates and DB load to react proactively.

Back-of-Envelope Cost Analysis

Assuming 1M users with 10 requests per second each:

• Total requests: 10 million QPS
• Cache hit rate: 80% -> 2 million DB queries per second
• Single DB instance handles ~10,000 QPS -> Need ~200 DB replicas
• Cache memory: 100 GB+ distributed across nodes
• Network bandwidth: 10M QPS * 1 KB/request ≈ 10 GB/s (distributed)

Scaling cache and DB horizontally is essential to handle this load.

Interview Tip

Start by explaining the cache-aside pattern basics: cache misses trigger DB reads, then data is cached. Discuss how cache hit rate affects DB load. Identify the DB as the first bottleneck at scale. Propose solutions like cache warm-up, horizontal scaling, and read replicas. Use numbers to justify your approach.

Self Check Question

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Implement or improve the cache-aside pattern to increase cache hit rate and reduce DB queries. This reduces DB load before scaling DB horizontally.

Key Result

The database is the first bottleneck as traffic grows; improving cache hit rate and scaling cache horizontally are key to reducing DB load and maintaining low latency.