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R03 · Storage & Query Optimization

Delta-Chain Topology Optimization

SkeinDB proposes delta-chained MVCC but doesn't specify the optimal chain structure. Linear delta chains degrade read performance as chain length grows. Alternative topologies — tree-structured deltas, skip-list shortcuts — could provide superior read-write tradeoffs. The interaction between chain topology and LSM compaction creates a rich optimization space not yet formally studied.

Research Proposal — Mapped to backlog in docs/RESEARCH_BACKLOG.md

🔬 What's Novel

🔧 Technical Approach

Phase 1 — Formal Model

Build a cost model for delta chain operations: version reconstruction cost vs. chain length/topology, write cost for appending new deltas, and space overhead for each topology variant.

Phase 2 — Topology Design

Evaluate three topologies: (a) linear chains with periodic full snapshots, (b) skip-list chains with geometric snapshot spacing for O(log n) reconstruction, (c) tree-structured chains with branching at version forks.

Phase 3 — Compaction Integration

Design compaction policies that consider delta chain state: consolidate long chains, restructure topology by adding skip pointers, and prune unreachable versions.

Phase 4 — Adaptive Selection

Per-key topology selection based on access patterns. Keys with frequent historical reads get skip-list chains; keys accessed only at latest version use simple linear chains.

🧪 Hypotheses

H1

Skip-list-style delta chains provide O(log n) version reconstruction with O(1) write amplification overhead.

H2

Workload-aware chain topology selection outperforms any single static topology across mixed workloads.

H3

Compaction can restructure delta chains opportunistically, amortizing reorganization cost into existing I/O.

🔗 SkeinDB Integration

ValueID Store
MVCC Engine
LSM / Compaction
SkeinQL RPC
Delta Values

📚 Key References