Causal consistency is a consistency model used in distributed systems where causal relationships between operations must be preserved. In MongoDB, it ensures that a write operation always propagates to all nodes in the system before any subsequent read operation can retrieve the modified data.
In more detail, when an application writes to MongoDB, it specifies a ‘write concern‘ that determines how many nodes must acknowledge the write before it is considered committed. If a write concern has a ‘majority‘ setting, it means that the write must be acknowledged by a majority of the nodes in the cluster, which guarantees that the write has propagated to all nodes in the system. Once the write is successful, the client will receive a special acknowledgement known as a ‘write concern acknowledgement‘.
When a client executes a read operation, it specifies a ‘read concern‘ that determines which visibility guarantees MongoDB will provide. A ‘majority‘ read concern ensures that a read operation will always see the most recently committed data from a majority of the nodes in the system, which ensures that all prior writes are visible. These guarantees are provided by a logical clock system that tracks the order of operations and their dependencies.
The implications of causal consistency for read and write operations are that:
1. Write operations can be slower because they must propagate to a majority of the nodes before they are considered committed. However, this ensures that subsequent read operations will always see the most recently committed data.
2. Read operations can be optimized if the client knows that the data being read was previously written by the same client. In this case, the client can specify a ‘snapshot read concern‘ that will guarantee that the read operation only sees the writes made by that client. This avoids the need to propagate the write to a majority of nodes, which can be faster.
3. In multi-document transactions, MongoDB will ensure that all operations in the transaction are causally consistent, which means that they will be executed in a way that preserves their causal relationships.
Overall, causal consistency is a useful consistency model for distributed systems that require strong guarantees about the causality of operations. However, it comes at the cost of slower write operations, which may not be a good match for systems that require high write throughput.