memory, the implementation sends this event to the other processes (via shared memory or as a message). Because of concurrency and failures, a process might receive events in any order. The implementation delivers an event, i.e., makes it visible to the process, only if all the events that causally precede it have themselves been delivered. This requires the implementation to maintain meta-data that represents the causal relationships between memory accesses. In brief, the implementation includes the following steps: (1) Maintain causal context meta-data at every process to summarise what updates causally precede the current state. (2) When a process updates memory, tag the update event with the causal context of that process, to summarise what updates causally precede this update. (3) A process that has received some update event may deliver it only if the event's tag causally precedes the causal context of the receiving process. (As a side effect of delivery, add the new event to the causal context of the receiving process.) Otherwise, the update was received too early, and must remain buffered until event matches the context. In the meantime, the implementation either passively waits to receive the missing events, or actively fetches them from their source. This approach enables availability under partition. There are two common representations for the causal context meta-data. One is to maintain an explicit dependency graph of the causal dependence relation. Because such a graph can grow arbitrarily large, an event is often tagged with only its immediate predecessors; determining its transitive predecessors requires a distributed graph traversal. The other is to maintain a vector clock, with one entry per process (or group of processes), counting the number of events generated by the process or group. This representation has a fixed size, and the ordering of events can be inferred by a simple comparison of the vectors. To precisely determine which events are dependent and which are concurrent in a fully peer-to-peer system, the size of the metadata is at least proportional to the number of active writers. However, a precise determination of concurrency is generally overkill. Causal consistency requires only that causally-dependent events be delivered in order; it does not matter if two concurrent events end up being ordered. Therefore, the size can be decreased arbitrarily by using safe approximation techniques. In the limit, a single scalar (a Lamport clock) suffices, at the cost of removing any concurrency. The size of