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

Filters check conditions without affecting relevance, while queries do.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

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

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

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

Elasticsearch documents aren't searchable immediately due to the indexing process.

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

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

You can update Elasticsearch indices without downtime using alias switching.

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

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

A document in Elasticsearch isn't searchable right after it's saved due to the refresh process.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

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

Elasticsearch ILM automates data management through defined lifecycle phases.

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

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

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

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

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

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