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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.

BM25 and field boosting help Elasticsearch rank search results based on relevance.

Elasticsearch rescoring improves search result rankings using a two-stage process.

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

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

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Elasticsearch's function_score combines text relevance with business signals for better rankings.

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

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

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.

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

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

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