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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.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

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.

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

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

Stateless servers are easier to scale because they don't store user session data locally.

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

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

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

Jitter helps spread out client retries to avoid traffic spikes.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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.

API pagination helps manage large datasets by breaking them into smaller parts.

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.

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

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

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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

FAANG engineers use high-level design principles to create scalable systems.

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

Elasticsearch documents aren't searchable immediately due to the indexing process.

Replicas keep your system available, while snapshots allow for data recovery.

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.