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Network partitions occur when servers are operational but can't communicate, affecting system performance.

Leader election helps distributed systems select one coordinator from many nodes.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

Message queues allow services to communicate asynchronously, improving scalability and reliability.

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

Vector clocks help track the order and concurrency of events in distributed systems.

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

A timeout indicates a deadline was exceeded, not necessarily a failure.

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

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Graceful degradation allows apps to function partially during service failures.

Availability is about access; reliability is about correct performance.

Request-Response asks for a result, while Events notify that something has happened.

Database replication enhances data availability and read performance but has challenges like replication lag.

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

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.