Block storage vs object storage vs file storage in HLD - Scaling Approaches Compared

Scalability Analysis - Block storage vs object storage vs file storage

Growth Table: Block, Object, and File Storage

Scale	Block Storage	Object Storage	File Storage
100 users	Simple SAN or local disks; low latency; easy management	Basic object store; metadata overhead minimal; good for backups	Single NAS device; shared file access; simple permissions
10,000 users	Multiple SAN devices; need for volume management; IOPS start to matter	Distributed object store; metadata grows; eventual consistency considered	Clustered NAS; file locking and concurrency challenges appear
1,000,000 users	Storage area network scaling limits; expensive hardware; complex management	Highly scalable object storage clusters; metadata indexing and search critical	Distributed file systems (e.g., Lustre, GlusterFS); performance bottlenecks in metadata servers
100,000,000 users	Rare at this scale; cost and complexity very high; mostly replaced by object storage	Massive object storage with multi-region replication; strong metadata services; optimized for cloud	Very large distributed file systems; metadata and locking become major bottlenecks; complex caching needed

First Bottleneck

At small to medium scale, block storage bottlenecks appear in IOPS and volume management as the number of users and data grow.

For file storage, metadata servers and file locking cause bottlenecks as concurrency increases.

Object storage first bottleneck is metadata service scalability and network bandwidth due to large object counts and replication.

Scaling Solutions

• Block Storage: Use faster SSDs, add more volumes, implement volume striping, and scale horizontally with storage arrays.
• File Storage: Deploy distributed file systems with metadata server clustering, caching, and partitioning to reduce locking contention.
• Object Storage: Scale metadata services horizontally, use consistent hashing for data distribution, implement multi-region replication, and leverage CDNs for content delivery.

Cost Analysis (Back-of-Envelope)

• At 1M users, assuming 10 requests/user/day, total requests = 10M/day ≈ 115 QPS.
• Block storage IOPS needed can reach tens of thousands; SSDs or NVMe required.
• Object storage metadata operations can reach hundreds of thousands QPS; requires distributed metadata services.
• File storage metadata servers may need to handle thousands of concurrent locks and operations.
• Network bandwidth: For 1PB data with 10% daily churn, need ~9 Gbps sustained bandwidth for replication and access.

Interview Tip

Start by defining each storage type simply. Then discuss typical use cases and scaling challenges. Identify bottlenecks by scale and propose targeted solutions. Use real numbers to show understanding of limits and costs.

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 database before considering sharding or hardware upgrades.

Key Result

Object storage scales best for massive data and users due to its distributed metadata and replication design; block storage is limited by hardware IOPS and volume management; file storage faces metadata and locking bottlenecks at scale.