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

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

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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

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

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

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

FAANG engineers use high-level design principles to create scalable systems.

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.