Geo-Dispersed Erasure Coding
An erasure coding scheme that distributes data fragments and parity blocks across geographically separated sites, providing durability and data locality at lower storage overhead than full replication.
Summary
An erasure coding scheme that distributes data fragments and parity blocks across geographically separated sites, providing durability and data locality at lower storage overhead than full replication.
Geo-dispersed erasure coding extends the durability model of object storage beyond a single data center. Instead of replicating full copies to each site (3x overhead), data is erasure-coded across sites (typically 1.2-1.5x overhead) while maintaining the ability to reconstruct from any subset of sites.
- Geo-dispersed erasure coding increases read latency. Reconstruction requires fetching fragments from multiple geographic sites, adding network round-trip time to every read.
- Failure domain is now geographic. If too many sites are unreachable simultaneously (beyond the erasure code's tolerance), data becomes temporarily unavailable — unlike multi-copy replication where any single copy suffices.
solvesRebuild Window Risk — erasure coding across sites reduces single-site vulnerabilityconstrained_byRepair Bandwidth Saturation — cross-site repair consumes WAN bandwidthscoped_toObject Storage, Geo / Edge Object Storage
Definition
An erasure coding scheme that distributes data fragments across multiple geographic sites, so that any configurable subset of sites can reconstruct the full object. Provides both durability and data locality across regions.
Traditional replication (3 copies across 3 AZs) is expensive. Geo-dispersed erasure coding achieves equivalent or better durability at lower storage overhead (typically 1.2-1.5x vs. 3x for replication) while keeping data fragments close to multiple compute locations.
Multi-region durable storage with low overhead, cross-site data availability, disaster-resilient object storage.
Connections 5
Outbound 5
constrained_by2Resources 3
MinIO erasure coding documentation covering data protection, healing, and the trade-offs between parity and storage efficiency.
Ceph community analysis of erasure coding overhead comparing storage efficiency and rebuild costs across coding profiles.
AWS S3 durability model documentation explaining how S3 achieves 11 nines of durability through cross-AZ erasure coding.