Any concept.
clear in 60 seconds.

Cache eviction policies determine which data to remove when caches are full.

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

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.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

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.

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.