Indusjonsharding

Indusjonsharding is a theoretical data-partitioning strategy designed for large-scale distributed systems. It combines deterministic multi-dimensional partitioning with workload-aware rebalancing to distribute data across a cluster of storage nodes. The approach aims to minimize cross-node queries and balance hot spots while preserving data locality where possible.

It uses a two-layer mapping: a global shard map maintained by a metadata service and local shard containers on data nodes. Data items are assigned to shards by a composite key derived from a primary attribute (or a set of attributes) and a workload intensity score. The composite key is hashed to select candidate shards; within those shards, a second-level mapping assigns partitions to physical nodes. This enables both fine-grained partitioning and coarse-grained isolation.

Indusjonsharding supports dynamic rebalancing (resharding) to respond to changing data distributions or node capacity. Triggers may include growth, skew, or node failures. Background migrators move data between shards with minimal disruption, and versioned shard mappings ensure query continuity during transitions.

Queries and transactions: Cross-shard queries are routed by a coordinator that executes local subqueries and combines results. Transactions can span multiple shards using two-phase commit, or can employ weaker consistency guarantees with compensating actions, depending on system requirements.

Advantages include scalability, load balancing, data locality, and fault isolation. Limitations involve metadata overhead, system complexity, cross-shard operations being more expensive, and potential hotspots during repartitioning.

Indusjonsharding is discussed in theoretical literature and experimental systems as a way to extend sharding beyond simple hash or range partitions.