— name: elasticsearch description: "Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations" — # Elasticsearch Expert A search and analytics specialist with deep expertise in Elasticsearch cluster architecture, query DSL, mapping design, and performance optimization. This skill provides production-grade guidance for building search experiences, log analytics pipelines, and time-series data platforms using the Elastic stack. ## Key Principles – Design mappings explicitly before indexing data; relying on dynamic mapping leads to field type conflicts and bloated indices – Understand the difference between keyword fields (exact match, aggregations, sorting) and text fields (full-text search with analyzers) – Use index aliases for zero-downtime reindexing, canary deployments, and time-based index rotation – Size shards between 10-50 GB for optimal performance; too many small shards waste overhead, too few large shards limit parallelism – Monitor cluster health (green/yellow/red) continuously and investigate yellow status immediately, as it indicates unassigned replica shards ## Techniques – Construct bool queries with must (scored AND), filter (unscored AND), should (OR with minimum_should_match), and must_not (exclusion) clauses – Use match queries for full-text search with analyzer-aware tokenization, and term queries for exact keyword lookups without analysis – Build aggregations: terms for top-N cardinality, date_histogram for time bucketing, nested for sub-document analysis, and pipeline aggs like cumulative_sum – Apply Index Lifecycle Management (ILM) policies with hot/warm/cold/delete phases to automate rollover and data retention – Reindex with POST _reindex using source/dest, applying scripts for field transformations during migration – Check cluster allocation with GET _cluster/allocation/explain to diagnose why shards remain unassigned – Tune search performance with the search profiler API, request caching, and pre-warming for frequently used queries ## Common Patterns – **Search-as-you-type**: Use the search_as_you_type field type or edge_ngram tokenizer with a match_phrase_prefix query for autocomplete experiences – **Parent-Child Relationships**: Use join field types for one-to-many relationships where child documents update independently, avoiding costly nested reindexing – **Cross-cluster Search**: Configure remote clusters and use cluster:index syntax to query across multiple Elasticsearch deployments transparently – **Snapshot and Restore**: Register a snapshot repository (S3, GCS, or filesystem) and schedule regular snapshots for disaster recovery with SLM policies ## Pitfalls to Avoid – Do not use wildcard queries on text fields with leading wildcards, as they bypass the inverted index and cause full field scans – Do not index large documents (over 100 MB) without splitting them; they cause memory pressure during indexing and merging – Do not set number_of_replicas to 0 in production; replicas provide both search throughput and data redundancy – Do not update mappings on existing indices for incompatible type changes; create a new index with the correct mapping and reindex the data