Requirements

Functional Requirements

• Store and retrieve objects (up to 5 TB per object)
• Support bucket-based organization
• RESTful API: PUT, GET, DELETE objects
• Versioning and lifecycle management
• Access control lists (ACLs) and bucket policies
• Multipart upload for large objects
• Event notifications on object changes

Non-Functional Requirements

• Durability: 99.999999999% (11 nines)
• Availability: 99.99% for standard tier
• Scale: 10 trillion objects, 100 PB per region
• Latency: First byte < 100ms for GET
• Throughput: 5,500 GET requests/second per prefix

Back-of-the-Envelope Estimation

API Design

PUT /{bucket}/{key}
Content-Type: image/jpeg
Body: <binary data>
Response: { "etag": "d41d8cd98f00b204e9800998ecf8427e" }

GET /{bucket}/{key}
Response: <binary data>
Headers: Content-Length, ETag, Last-Modified

DELETE /{bucket}/{key}
Response: 204 No Content

POST /{bucket}/{key}?uploads
Response: { "upload_id": "upload_123" }

PUT /{bucket}/{key}?uploadId=upload_123&partNumber=1
Response: { "etag": "part_etag" }

POST /{bucket}/{key}?uploadId=upload_123
Body: { "parts": [...] }

High-Level Architecture

Detailed Design

Data Model

// Object Metadata
{
  bucket: "images",
  key: "photos/2026/06/photo1.jpg",
  size: 2048576,           // 2 MB
  content_type: "image/jpeg",
  etag: "d41d8cd98f00b204",
  created_at: "2026-06-20T10:00:00Z",
  storage_class: "STANDARD",
  version_id: "v1",
  checksum: "sha256:abc123...",
  parts: [                 // For multipart uploads
    { part_num: 1, offset: 0, size: 5242880, etag: "..." },
    { part_num: 2, offset: 5242880, size: 5242880, etag: "..." }
  ]
}

Data Chunking and Placement

Large objects are split into chunks for efficient storage and replication:

Replication Strategy

Metadata Architecture

The metadata service is the control plane:

Multipart Upload

For large objects, multipart upload enables:

• Parallel uploads of chunks
• Resumable uploads on failure
• Upload of objects larger than 5 GB

Practice Exercises

1. Design: How would you implement object versioning? What are the storage implications of keeping all versions vs. lifecycle policies?

2. Durability: If you use 3x replication across 3 data centers, calculate the probability of data loss given a 0.1% annual disk failure rate per node.

3. Scale: Design a system to handle 5,500 GET requests per second per prefix. How would you distribute load across storage nodes?

4. Optimization: How would you implement a CDN cache invalidation system for objects stored in S3? Design for both instant and eventual consistency.

What to Learn Next