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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.

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.

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

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

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