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The ability of a system to handle a growing amount of work by adding resources to the system. Example: Horizontal scaling in cloud computing.

It is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: Consistency, Availability, and Partition Tolerance. Example: NoSQL databases often sacrifice consistency for availability and partition tolerance.

Stateful services maintain state between requests, whereas stateless services do not. Example: HTTP is stateless, but Web applications often implement sessions to make it stateful.

A system in which components located on networked computers communicate and coordinate their actions by passing messages. Example: The World Wide Web.

A type of database partitioning that separates very large databases into smaller, faster, more easily managed parts called data shards. Example: MongoDB uses sharding to spread data across a cluster of machines.

All nodes see the same data at the same time. Example: A distributed database using strong consistency models.

The ability to continue operating in the event of the failure of some of its components. Example: Redundant servers in a web service.

A fault presenting different symptoms to different observers. A byzantine fault tolerance system can operate correctly even when components are faulty. Example: Blockchain systems.

An algorithm for generating a partial ordering of events in a distributed system and detecting causality violations. Example: Used in DynamoDB to resolve conflicts.

The system continues to operate despite arbitrary partitioning due to network failures. Example: A globally distributed database resilient to network splits.

The system is always up and operational. Example: A high-availability cluster database.

A database transaction that includes one or more network hosts. Example: A financial transaction that spans multiple bank databases.

A consensus algorithm for managing a replicated log that is designed to be more understandable than Paxos. Example: etcd, a distributed key-value store uses Raft for consensus.

Mechanisms that ensure that database transactions are performed concurrently without violating the consistency of the database. Example: Locks and timestamps in database systems.

Distributing workloads across multiple computing resources. Example: Using a round-robin algorithm to distribute incoming web traffic among servers.

A consistency model used in distributed systems, where the system guarantees that, given enough time without new updates, all accesses will return the last updated value. Example: DNS system updates.

A distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or roll back the transaction. Example: Database systems that ensure transaction atomicity across multiple nodes.