An old name for “operation-based CRDT”. The term “operation-based” is now preferred.
Two events are concurrent if they happened without knowledge of each other
(i.e. the node on which event A occurred had not heard of event B at the time
of A, and vice versa).
The key correctness property of CRDTs. Convergence requires the following: for
any two replicas, if they have processed the same set of updates, then those
replicas must be in the same state (regardless of the order in which they
processed those updates).
Convergent Replicated Data Type (CvRDT):
An old name for “state-based CRDT”. The term “state-based” is now preferred.
A variant of a state-based CRDT in which replicas don’t always send their
entire state, but rather send state differences (deltas). See
Delta State Replicated Data Types by
Paulo Sérgio Almeida, Ali Shoker, and Carlos Baquero.
In operation-based CRDTs, an effector is a function that updates the state of
a replica by applying an operation.
In operation-based CRDTs, a generator is a function that processes a data
modification action taken by a user, and produces an operation that can be
sent over the network and applied by an effector.
Last write wins (LWW):
A timestamp is attached to each update, and updates with higher timestamps
always overwrite values written by updates with lower timestamps. However, if
a replica has already applied an update with a higher timestamp, and it
subsequently receives an update with a lower timestamp, the latter update is
ignored. This approach is one way of ensuring convergence.
Observed-remove set (ORSet):
Another name for add-wins set. “Add-wins” is now preferred.
A CRDT in which each modification of the replicated data is encoded as an
operation, and operations are sent over the network. The system must ensure
that operations are not lost (by resending if necessary) and that each
operation is applied once (by ignoring duplicates). Operation-based CRDTs
ensure that any two concurrent operations commute; that is, replicas can apply
those operations in either order, and the outcome is the same.
An approach to replication in which a replica can process updates locally,
without waiting for communication with other replicas. This means that
replicas can become temporarily inconsistent, but CRDTs ensure that replicas
nevertheless converge towards a consistent state (see Strong Eventual
Consistency). See Optimistic Replication
by Yasushi Saito and Marc Shapiro.
maintaining a copy of some data on multiple computing devices (maybe servers,
maybe end-user devices). Those copies are called replicas.
A CRDT in which replicas synchronise by sending each other their entire state
over the network; when one replica receives such a state from another replica,
it uses a merge function to combine the two states. This merge function is
defined in such a way that it is commutative (i.e. merge(a,b) = merge(b,a)),
associative (i.e. merge(a,merge(b,c)) = merge(merge(a,b),c)), and idempotent
Strong Eventual Consistency (SEC):
A formal consistency model for CRDTs. It requires convergence (see above) and
eventual delivery (if one replica has processed an update, every other replica
that has not failed will also eventually process it). See
Conflict-Free Replicated Data Types
by Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski.
A special object used in some CRDTs to indicate that a value is absent (e.g.
because it has been deleted). Tombstones may increase the memory consumption
of a CRDT, as they continue to exist even if the corresponding data has been
deleted at the application level. There are algorithms for garbage-collecting
tombstones and freeing their memory.