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Database indexing speeds up data retrieval by allowing quick lookups.

Composite indexes speed up database queries that filter or sort by multiple columns.

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

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

B-Trees help databases find data quickly without scanning every row.

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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Filters check conditions without affecting relevance, while queries do.

API pagination helps manage large datasets by breaking them into smaller parts.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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.

An inverted index helps search engines quickly find documents containing specific terms.

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Cache stampede occurs when many requests hit the database due to a cache miss.

Cache penetration occurs when requests for non-existent data overload the database.

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