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Elasticsearch fuzzy search allows finding results even with typos in queries.

Elasticsearch uses analyzers and synonyms to improve search accuracy by connecting similar words.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

You can update Elasticsearch indices without downtime using alias switching.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

Elasticsearch index templates automatically apply settings to new indices based on patterns.

Elasticsearch aliases allow zero-downtime switching between indices for applications.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

Elasticsearch ILM automates data management through defined lifecycle phases.

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

Filters check conditions without affecting relevance, while queries do.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.