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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.

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

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

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

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

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.

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

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

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.

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

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

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

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

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.

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

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

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

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

DNS helps microservices find each other, but it has limitations that may require a service registry.

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

Kafka uses state stores to remember previous events in stateful stream processing.

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

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

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

Database indexing speeds up data retrieval by allowing quick lookups.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Synchronous communication waits for a response, while asynchronous continues without waiting.

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