Dynamic Content-Oriented Indexing and Replication for High-Performance Storage and Analysis of Big Data in the IPFS Network

Abstract

This paper presents an architecture for dynamic, content-oriented indexing and adaptive replication that enables high-performance storage and analysis of big data on IPFS. We first outline key gaps of vanilla IPFS for analytics – no global content search, non-guaranteed persistence without coordinated pinning, static replication, and highly variable retrieval latency – and address them with two components: (1) a two-tier distributed index (per-attribute/keyword inverted lists as IPFS objects plus a lightweight catalog that maps search keys to index CIDs via DHT/IPNS or CRDT-based dissemination); and (2) an adaptive replication service that aggregates access telemetry and adjusts replica counts and placement using hysteresis thresholds and topology-aware selection. The contribution is a theoretical proposal and architectural blueprint; no prototype or experimental results are reported here. We discuss integration with analytical engines through two paths: a pragmatic FUSE mount that exposes IPFS content as a local filesystem to Spark/Flink, and prospective native connectors that parallelize block reads over the IPFS API. For tabular datasets, dataset metadata (schema, partitioning, file CIDs) is maintained in IPFS to support versioning and reproducibility. A plan for comparative evaluation versus HDFS, Ceph, and S3 (e.g., TPC-DS and subsets of Common Crawl) is outlined. Expected benefits are faster content discovery, higher throughput under skew and multi-tenant load, and improved resilience, with modest index/coordination overheads. The approach combines the openness of a decentralized P2P substrate with the manageability required by enterprise-scale analytics.