Distributed Inverted Index & Skip-List

A Distributed Inverted Index is the foundational data structure and distributed partitioning architecture supporting sub-10ms keyword search across billions of documents; mapping Terms to Posting Lists (ordered arrays of Document IDs, Term Frequencies, Positions, and Payloads), indices are distributed across clusters via Document Partitioning or Term Partitioning; when evaluating multi-term queries (`Term A AND Term B`), the engine utilizes Skip Lists or SIMD-accelerated instructions to jump across posting IDs for rapid intersection alongside Block-Max WAND (Weak AND) dynamic score pruning.

Web search engines (Google, Bing), distributed search clusters (Elasticsearch, OpenSearch, Tantivy), and e-commerce product catalogs.

Scanning billions of documents naively takes minutes; inverted indices turn full-text matching into constant-time hash lookups and integer list intersections, scaling throughput to tens of thousands of queries per second per node.

Compare Document Partitioning vs Term Partitioning in distributed search across network fan-out and query throughput?

Derive the time complexity of Skip-List-accelerated Posting List intersection and optimal skip interval sizing?

How do WAND and Block-Max WAND algorithms use upper-bound term scores to prune posting traversals for accelerated Top-K search?

Explain integer posting list compression algorithms (VByte, FoR, SIMD-BP128) in reducing RAM footprint and accelerating decompression?

Explain the LSM-Tree-style real-time ingestion architecture (Memory Buffers + Immutable Segments + Async Merge) in search engines?