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

Choosing the right storage type requires matching storage semantics to your application's access patterns, consistency requirements, and scale targets. The wrong choice creates performance bottlenecks or cost explosions.

Choose block storage when you need high IOPS random access with partial updates and strict write ordering. Transactional databases like PostgreSQL or MySQL require frequent 4 to 16 KiB random writes to B-Tree pages and Write Ahead Log (WAL) segments. These workloads demand 50,000 to 200,000 IOPS with sub millisecond latencies and must control flush ordering through filesystem barriers and FSYNC semantics. Amazon EBS or Azure Ultra Disk provide this with up to 256,000 IOPS per volume. Block storage costs $0.07 to $0.10 per GB month but enables the low latency critical for query performance.

Choose file storage when multiple clients need shared POSIX semantics including directories, atomic rename, and file locking. Machine learning feature stores serving hundreds of training nodes, CI/CD systems sharing build artifacts across dozens of runners, or media rendering farms all benefit from familiar filesystem tooling and collaborative access. Amazon EFS or Google Filestore High Scale deliver 10+ GB/s aggregate throughput and handle metadata operations like stat and rename atomically. The trade off is higher cost at $0.20 to $0.35 per GB month and 10 to 20 ms latencies for metadata heavy workloads. Hot directories with millions of files can create metadata bottlenecks requiring sharding strategies.

Choose object storage for massive scale unstructured data where durability and cost matter more than per request latency. Data lakes, log archives, backup targets, media libraries, and machine learning training datasets all fit this pattern. Amazon S3 handles large sequential transfers efficiently through multipart uploads and parallel downloads, enabling multi Gbps throughput from a single client. At $0.02 to $0.03 per GB month with 99.999999999% durability across AZs, object storage is the default for petabyte scale. Dropbox Magic Pocket uses erasure coding across data centers to store exabytes at even lower cost. The limitation is whole object granularity with no partial updates: modifying 1 KB in a 1 GB object requires rewriting the entire object.

💡 Key Takeaways

•Block storage wins for databases and VM disks needing 50,000+ IOPS at sub millisecond latency with 4 to 16 KiB random writes and strict flush ordering through FSYNC semantics

•File storage fits shared POSIX workflows like ML feature stores or CI/CD artifact sharing where hundreds of clients need atomic rename, directory operations, and file locking despite 10 to 20 ms metadata latencies

•Object storage is default for petabyte scale unstructured data like logs, backups, and media where $0.02 per GB month cost and 11 nines durability outweigh 10 to 50 ms latencies and lack of partial updates

•Small object inefficiency: billions of tiny objects under 100 KB create index overhead and slow performance; bundle into larger segments or use packing layers like Meta Haystack which consolidates images into log structured files

•Network and per request costs dominate at scale: object storage egress fees and per GET/PUT charges can exceed storage costs for high traffic workloads; front with CDN or regional caching

📌 Examples

PostgreSQL on Amazon EBS: 150,000 IOPS with io2 volumes for random B-Tree page updates, separate WAL volume to isolate write amplification, p99 query latency under 5 ms

Airbnb ML feature store on Amazon EFS: 200+ EC2 training instances reading shared Parquet files with POSIX locking, 15 GB/s aggregate throughput, atomic feature updates via rename

Dropbox Magic Pocket: exabyte scale object storage using 10+4 erasure coding across racks, achieving lower cost than triple replication while maintaining high durability for user file backups

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