Elasticsearch & OpenSearch

Databases

Indexes, isolation, storage engines, shard and partition keys, and zero-downtime migrations you can ship without a maintenance window.

• Elasticsearch & OpenSearch

  6 studies
  1. 1.Elasticsearch & OpenSearch — Inverted Indexes, Relevance & OpsInterview hub: inverted indexes, BM25, shards, ILM, and Elasticsearch versus OpenSearch versus Solr. Use a search cluster for full-text, filters, and aggregations, and keep multi-row transactions in an OLTP store.
  2. 2.Inverted Index, Analyzers, Tokenization & MappingsAn inverted index maps each term to a posting list. Analyzers turn raw strings into those terms, and mappings decide which analyzer and field type apply. A wrong mapping silently drops recall or explodes the cluster.
  3. 3.Query DSL, Relevance Scoring (TF-IDF/BM25) & Filters vs QueriesQuery DSL composes bool, match, term, and range clauses. Relevance defaults to BM25. Filters answer yes or no and cache well. Queries compute the score that ranks hits.
  4. 4.Sharding, Replicas, Routing & Cluster HealthAn index is split into primary shards with zero or more replicas. Writes route by a key, searches scatter and gather, and cluster health is green, yellow, or red based on whether those shards are allocated.
  5. 5.Indexing Pipelines, Bulk, ILM & SnapshotsProduction search lives or dies on the ingest path: bulk requests, refresh versus flush, ILM or ISM rollover, and snapshots to a repository that is usually object storage.
  6. 6.Ecosystem — OpenSearch vs Elasticsearch vs Solr (and when not to use a search engine)Elasticsearch, OpenSearch, and Solr all sit on Lucene. They differ in API, license, and operations. The senior answer includes when a search engine is the wrong tool.