Full-text search with Elasticsearch: indexing, querying, and optimization

Full-text search is one of the most requested features in modern applications. Elasticsearch provides powerful, scalable full-text search that handles typos, synonyms, ranking, and aggregation. But a poorly configured Elasticsearch cluster can be slow, unstable, and expensive.

Indexing is the process of making your data searchable. Define an index mapping that specifies the fields, their data types, and how they should be analyzed. The analyzer determines how text is tokenized, filtered, and normalized. Use the standard analyzer for general text.

Query DSL is Elasticsearch's powerful query language. The match query is your default for full-text search. Use term queries for exact matches on keyword fields. Use bool queries to combine multiple conditions. Use function_score to boost results based on recency or popularity.

Performance optimization starts with index design. Use appropriate shard counts too few shards limits parallelism, too many creates overhead. A good rule of thumb is 20-40 GB per shard. Use refresh intervals of 30 seconds for write-heavy workloads.

Aggregations in Elasticsearch provide analytics capabilities. Use terms aggregations for faceted search and counting. Use date_histogram for time-series analysis. Use significant_terms to find unusual patterns in your data.

Scale your cluster as your data grows. Start with 3 nodes for production. Add nodes when disk usage exceeds 70% or when query latency increases. Use index lifecycle management to automatically roll over indices and delete old data.

Elasticsearch is not a primary database. It's a search engine layered over your primary data store. Design your architecture so your primary database handles CRUD operations and Elasticsearch handles search.

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Rizwan Saleem | https://rizwansaleem.co