System Design Article

Search Indexing at Scale (Elasticsearch)

Difficulty: Hard

Search at scale is two systems in one: an indexing pipeline that ingests, transforms, and stores documents into an inverted index (and increasingly a vector index), and a query path that distributes searches across shards, scores results, and merges them under tight latency budgets. Elasticsearch and OpenSearch are the dominant production engines, and almost every large product runs one. This lesson covers the architecture: how Lucene segments and inverted indexes work, how Elasticsearch shards and replicates them, the tokenization and analyzer pipeline that determines what 'matches' mean, the query coordinator -> shard fan-out -> merge flow, hybrid search (lexical + vector), reindexing strategies, and the operational realities (hot shards, mapping explosions, garbage collection pauses, write amplification). The goal is to leave you able to design and operate search for any catalog from a million to billions of documents.