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Global Load Balancing helps route users to the nearest and healthiest server region.

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.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

Jitter helps spread out client retries to avoid traffic spikes.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

Availability Zones help prevent downtime by isolating failures across multiple locations.

Partial failure means some services fail while others keep running, impacting system reliability.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

GeoDNS routes users to the nearest server based on their location for better performance.

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

Graceful degradation allows apps to function partially during service failures.