SeaweedFS vs MinIO: High-Concurrency Distributed S3 Object Storage at Multi-Petabyte Scale

SeaweedFS and MinIO are both S3-compatible object storage systems capable of handling high concurrency at multi-petabyte scales, but they are designed to address different use cases. MinIO is an erasure-coded, monolithic object store optimized for low-latency GET/PUT operations with consistent performance across a large number of aggregates.

SeaweedFS, on the other hand, is a blob store based on Facebook's Haystack paper, offering O(1) volume-ID lookups, optional cloud storage tiering, and a POSIX layer (FUSE + filer) on top of its storage backend.

Benchmark tests were conducted on a 9-node NVMe cluster with Samsung PM9A3 drives and Mellanox ConnectX-5 network interfaces, as well as on AWS spot-instance clusters for TCO modeling. The key metrics compared were GET p99 latency (4KB and 1MB), PUT p99 latency (1MB with erasure-coded 8+4 configuration), aggregate GET throughput, and small-file metadata operations.

MinIO demonstrated lower GET p99 latency for both 4KB and 1MB requests, as well as higher aggregate GET throughput. SeaweedFS, however, had better small-file metadata performance, with ~55k ops/s compared to MinIO's ~190k ops/s, due to its lower overhead for each file. SeaweedFS also required less RAM overhead at idle, with only 180 MB versus MinIO's 1.2 GB for its drives.

SeaweedFS has a more extensive S3 API coverage, with ~85% supported functions versus MinIO's 99% AWS-conformance-certified API. SeaweedFS also offers native cold-tier read support via remote mount, but MinIO provides S3 Gateway as a first-class feature for remote tiers. SeaweedFS provides a 5-year support SLA through a paid subscription, while MinIO offers a more community-driven approach with optional paid commercial support.

In summary, MinIO is preferred for moderate object counts with heavy, latency-sensitive S3 API traffic and requires minimal operational expertise. SeaweedFS is more suitable for billions of small-to-medium files, POSIX access requirements, and massive metadata economy, but demands a more skilled operational team to manage a multi-component distributed system.