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

Cache invalidation is about knowing when to refresh cached data.

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

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.