🏗️ System DesignID:
distributed-inverted-indexDistributed Inverted Index & Skip-List
分布式倒排索引与跳表求交
🎯Core Definition
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.
💡Use Cases
Web search engines (Google, Bing), distributed search clusters (Elasticsearch, OpenSearch, Tantivy), and e-commerce product catalogs.
⚡Key Problems Solved
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.
🎯5 High-Frequency Exam Points
1
Compare Document Partitioning vs Term Partitioning in distributed search across network fan-out and query throughput?
2
Derive the time complexity of Skip-List-accelerated Posting List intersection and optimal skip interval sizing?
3
How do WAND and Block-Max WAND algorithms use upper-bound term scores to prune posting traversals for accelerated Top-K search?
4
Explain integer posting list compression algorithms (VByte, FoR, SIMD-BP128) in reducing RAM footprint and accelerating decompression?
5
Explain the LSM-Tree-style real-time ingestion architecture (Memory Buffers + Immutable Segments + Async Merge) in search engines?
📖 In-depth Guide:📄 search-and-ad-system-design →
Updated 2026-08-14🎯
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