Any concept.
clear in 60 seconds.

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

Jitter helps spread out client retries to avoid traffic spikes.

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful degradation allows apps to function partially during service failures.

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

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Failover ensures systems remain operational by managing leader changes during failures.

Crash failures stop a service, while network failures disrupt communication.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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

Consistent hashing helps distribute data across servers with minimal movement.

The Token Bucket algorithm helps manage how many requests a server can handle.

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

Retry storms occur when too many retries overload a failing service.

Timeouts in distributed systems indicate suspicion, not confirmed failure.