elasticsearch

Elasticsearch mapping design requires defining explicit field types and using index aliases for safe reindexing. You can manage index lifecycles by creating daily rollover policies to automate index transitions and maintain cluster health.

Elasticsearch shard sizing should be between 10-50 GB per shard for optimal performance. Continuous cluster health monitoring is required to diagnose allocation issues, tune performance, and ensure efficient search and analytics operations.

Elasticsearch query DSL enables crafting complex queries and building powerful data aggregations. You use the query DSL to filter and analyze documents, supporting use cases like real-time dashboarding for user activity logs.

Elasticsearch supports building search experiences, log analytics pipelines, and time-series data platforms. It enables fast, real-time dashboarding and reporting on user behavior by indexing large volumes of user activity logs.

Elasticsearch requires explicit mapping design and understanding of field types to prevent data type conflicts and optimize search performance. Explicit mappings ensure structured data ingestion for accurate query DSL operations and efficient aggregations.

The best way to reindex Elasticsearch data without downtime is using index aliases. You point the alias to a new index with updated mappings, reindex the data, and switch the alias, ensuring continuous search availability.