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Cache invalidation is about knowing when to refresh cached data.

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

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

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

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

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

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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

Read-after-write consistency ensures users see their latest updates immediately.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Graceful degradation allows apps to function partially during service failures.

Connection pooling reuses database connections but can cause issues if misconfigured.

Elasticsearch ILM automates data management through defined lifecycle phases.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

You can update Elasticsearch indices without downtime using alias switching.

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.