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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.

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

DNS resolution translates a URL into an IP address for web access.

Microservices can communicate using five key patterns based on their needs.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

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

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

Elasticsearch ILM automates data management through defined lifecycle phases.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

B-Trees help databases find data quickly without scanning every row.

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

A reverse proxy routes client requests to different backend services efficiently.