Object Storage

Overview

Object Storage is a storage architecture that manages data as objects — each object contains the data itself, metadata, and a unique identifier. Unlike file systems (hierarchical) or block storage (fixed-size blocks), object storage provides a flat namespace with virtually unlimited scalability, making it ideal for storing unstructured data: images, videos, documents, backups, logs, and data lake content. In Vetora's simulator, the Object Storage component models upload/download latency, throughput, and the interaction between object storage and CDN edge caches.

The object storage access pattern is fundamentally different from databases. Objects are accessed by key (a unique identifier like a path) and are typically written once, read many times, and rarely updated in place. Instead of updating an object, you write a new version and optionally delete the old one. This immutable write pattern enables powerful optimizations: objects can be replicated across availability zones without conflict resolution, cached aggressively at CDN edges, and tiered to cheaper storage classes as they age.

Multipart upload is essential for large objects. Instead of uploading a 5GB video in a single HTTP request (which would fail on any network interruption), multipart upload splits the object into parts (5MB–5GB each), uploads them in parallel or sequentially, and assembles them server-side. If a part fails, only that part needs to be retried. This approach provides reliable uploads over unreliable networks and improves throughput through parallelism. Vetora models multipart upload behavior and shows how part size and parallelism affect total upload time.

Lifecycle policies automate data management across storage tiers. A typical policy might keep objects in standard storage for 30 days (fast, expensive), transition to infrequent access storage at 30 days (cheaper, retrieval fee), archive to glacier/cold storage at 90 days (cheapest, slow retrieval), and delete at 365 days. These policies reduce storage costs by 50–80% compared to keeping all data in standard storage. Vetora models storage costs across tiers to help you optimize your data lifecycle strategy.

Versioning provides a safety net against accidental deletion and data corruption. When versioning is enabled, every overwrite or delete creates a new version rather than destroying the original. You can restore any previous version at any time. Combined with Object Lock (write-once-read-many), versioning provides compliance-grade data protection for financial records, medical imaging, and regulatory archives. The cost trade-off is that versioned objects consume more storage, as all versions are retained until explicitly deleted.

When to Use

Key Parameters in Vetora

Real-World Examples

Frequently Asked Questions

Related Components