When to Choose Block, File, or Object Storage: Access Pattern Alignment

Access Pattern Is Everything
Storage choice depends on how your application reads and writes data. Random reads and writes of small chunks point to block storage. Sequential reads with occasional bulk writes point to object storage. Hierarchical organization with shared concurrent access points to file storage. Misaligning storage type with access pattern creates either performance problems or unnecessary complexity.
Consider a database storing user profiles. Each profile update modifies 1-10KB in the middle of a B tree. Block storage lets the database write exactly those sectors. Object storage would require rewriting the entire database file for each update. File storage would work but adds unnecessary POSIX overhead for a single process workload.
When Block Storage Wins
Use block storage when your application needs direct sector access with predictable sub millisecond latency. Databases write specific pages to specific offsets. Virtual machines need raw disk access for their guest filesystems. Workloads with 10,000+ IOPS (input output operations per second) requirements demand block storage because file and object overhead becomes the bottleneck.
Block storage typically costs more per gigabyte because it provisions dedicated performance. A database needing 16,000 IOPS pays for that capacity whether used or not. The price covers low latency guarantees, typically 1-2ms at the 99th percentile.
When File Storage Wins
Use file storage when multiple processes or servers need concurrent access to shared data with POSIX semantics. A rendering farm where 100 nodes read model assets and write output frames benefits from network attached file storage. The shared namespace and locking semantics coordinate access without application level distributed locking.
File storage also suits lift and shift migrations where applications expect filesystem APIs. Rewriting an application to use object storage APIs requires development effort. Mounting a network filesystem is transparent to the application.
When Object Storage Wins
Use object storage when you have large immutable blobs accessed primarily by key lookup. Images, videos, backups, logs, and data lake files fit perfectly. Write once, read many patterns align with object immutability. Costs drop to $0.02-0.03 per GB per month compared to $0.08-0.10 for block storage.
Object storage scales to exabytes because each object is independent. There is no shared metadata server bottleneck. The trade off is latency: first byte latency runs 50-100ms compared to sub millisecond for block storage.
⚠️ Key Trade-off: Block storage costs 3-5x more than object storage but delivers 100x lower latency. Choose based on whether your workload is latency sensitive or capacity sensitive.

💡 Key Takeaways

✓Access pattern determines storage choice: random small updates need block, immutable blobs need object, shared concurrent access needs file

✓Block storage delivers 10,000+ IOPS at 1-2ms latency but costs $0.08-0.10 per GB per month

✓Object storage scales to exabytes at $0.02-0.03 per GB but with 50-100ms first byte latency

✓File storage suits shared access workloads and lift and shift migrations requiring POSIX semantics

✓Misaligning storage with access pattern creates either performance bottlenecks or unnecessary cost

📌 Interview Tips

1Present a decision framework: ask about update frequency first. If data changes in place, eliminate object storage. If data is immutable, object storage likely wins on cost.

2When discussing cost, quantify the difference. A petabyte in block storage costs roughly $100,000 per month versus $20,000 in object storage. That 5x difference justifies architecting for immutability.

3If asked about hybrid approaches, explain how a typical system stores structured data in block backed databases while offloading blobs to object storage. The database holds a pointer to the object key.

← Back to Block vs Object vs File Storage Overview