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

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

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.

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.

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

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

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

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

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

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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

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

Sharding splits databases into smaller parts to improve performance and scalability.

Choosing between a shared database and a database per service affects system design significantly.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

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

Load shedding helps systems reject excess requests to maintain performance during high demand.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Message queues allow services to communicate asynchronously, improving scalability and reliability.