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

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

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

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

Filters check conditions without affecting relevance, while queries do.

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

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

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

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

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

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

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

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

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

Cache invalidation is about knowing when to refresh cached data.

A CDN speeds up web applications by caching content closer to users.

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

Sharding splits databases into smaller parts to improve performance and scalability.

Distributed caching spreads data across multiple servers for better performance and reliability.