Hello! For the last day of the week, let’s explore an essential concept in databases: consensus.
One fundamental problem in the context of distributed systems is the need to achieve consensus. In essence, consensus is about getting a set of processes to agree on something in a fault-tolerant way.
To be specific to the domain of databases, it means that a set of nodes—potentially distributed across different locations—have to agree on a value or a decision, even in the presence of failures or network partitions.
Why is consensus important? In a distributed database with multiple nodes, achieving strong consistency requires all nodes to agree on the same value for a given key, regardless of which node is queried. Consensus ensures that each node reflects the same data, guaranteeing consistent responses across the system.
Consider two conflicting queries: one reaches node 1 to set foo=42, while another reaches node 2 at the same time to set foo=200. Without consensus, these nodes might return different values. Consensus ensures all nodes agree on one value: either foo=42 or foo=200.
Consensus is used on many occasions in the context of databases, including:
Leader election: Ensures all nodes agree on a single leader.
Data replication: Guarantees that all nodes have the same state of data, avoiding discrepancies between replicas.
Data recovery: Ensures that during recovery from node failures, all nodes agree on the correct state of the database.
Distributed query execution: For complex queries that require interacting with multiple nodes, consensus ensures that all nodes agree on the execution plan and the results.
Consensus algorithms must satisfy the following properties:
Termination: The consensus process is not stuck forever.
Integrity: Once a decision is reached, no node will change its mind later.
Agreement: All the nodes agree on the same outcome.
As we discussed in Safety and Liveness, consensus algorithms should respect both safety and liveness properties. For example, multiple nodes reaching a consensus to agree on a given value must satisfy:
Safety: Ensures that the nodes won’t agree on conflicting values, e.g., some nodes agreeing on one value and others on another value.
Liveness: Ensures that the nodes eventually find an agreement, even if some node fails or there are network issues.
Also, let’s mention two widely-used consensus algorithms1:
Paxos: A foundational consensus algorithm used in systems like Apache Zookeeper.
Raft: An algorithm designed to be easier to implement and used in systems like etcd and CockroachDB.
In conclusion, consensus ensures that distributed nodes agree on a single state or value, which is essential for enforcing strong consistency models.
We will explore these two algorithms in more detail in future issues.