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A technique to prevent concurrent access to a shared resource in a distributed environment. It prevents conflicts and ensures serialized access to resources.

States that it is impossible for a distributed data store to simultaneously guarantee Consistency, Availability, and Partition Tolerance. It helps system designers to understand trade-offs while designing distributed systems.

Communication protocols used to spread information in a distributed system, akin to human gossip. They are robust and scalable but do not guarantee immediate consistency.

A distributed algorithm used to achieve consensus among nodes in a distributed system to ensure that either all nodes commit to a transaction or none do. It guarantees atomicity but can be a performance bottleneck and is not fault-tolerant of coordinator failure.

Transactions that span multiple nodes in a distributed system. They must ensure ACID properties (Atomicity, Consistency, Isolation, Durability) across all involved nodes.

Operations that can be applied multiple times without changing the result beyond the initial application. Idempotency is crucial for reliability in a distributed system, especially in the face of network and system failures.

An architectural pattern where state changes are logged as a sequence of events. This allows distributed systems to rebuild state by replaying the event log, facilitates event replay for debugging, and enables complex event stream analysis.

A method for capturing temporal ordering of events in a distributed system. Logical clocks do not measure physical time but an arbitrary sequence that provides a partial ordering of events.

Describe the set of rules for the visibility of updates in a distributed system to achieve consistency. They are critical in designing distributed databases and cache consistency protocols.

A mechanism for capturing causality in a distributed system without relying on synchronized physical clocks. They are used to track the partial ordering of events and to detect concurrency.

A consensus algorithm that works in a network with unreliable nodes. Paxos ensures that a single value is chosen by a set of proposers even in the event of message losses or failures, but it can be complex to implement.