In this lesson, you will learn how distributed consensus algorithms enable multiple database nodes to agree on a single, shared state despite network partitions or node failures.
The Problem of Distributed State
When a database is spread across multiple nodes, ensuring that every node processes the same series of writes in the same order is notoriously difficult. If one node experiences a network delay or a crash, the remaining nodes must decide whether to continue processing transactions to maintain high availability or pause to ensure strict consistency. Protocols like Paxos and Raft solve this by transforming the system into a replicated state machine.
How Raft Simplifies Consensus
Raft achieves consensus by electing a "Leader" node. All client writes must flow through this leader. The process follows three main steps:
- Log Replication: The leader receives a command, appends it to its local write-ahead log, and sends it to all followers.
- Quorum Acknowledgement: The leader waits for a majority of nodes (a quorum) to acknowledge that they have successfully appended the entry to their own local logs.
- Commitment: Once the majority confirms, the leader marks the entry as "committed" and applies it to its state machine.
Because the leader only proceeds once a majority has persisted the data, even if a minority of nodes fail, the system remains consistent. The state machine on every node will eventually execute the same sequence of logs:
-- Conceptual sequence on each node:
-- 1. Append (Log Entry ID: 101, Data: "UPDATE accounts SET balance = balance - 50")
-- 2. Wait for Majority (Quorum check)
-- 3. Commit (Apply to local database engine)
Ensuring Durability
These algorithms rely heavily on the Write-Ahead Logging (WAL) mechanics we discussed previously. By forcing nodes to persist log entries to stable storage before responding to the leader, the database ensures that if a node crashes and reboots, it can reconcile its state by replaying logs from the last known consensus point. Paxos operates on similar principles of "proposers" and "acceptors" but uses a more complex multi-round voting mechanism, whereas Raft is optimized for understandability and sequential consistency.
Key Takeaway: Distributed consensus algorithms ensure high availability and data integrity by requiring a majority of nodes to agree on the order and persistence of log entries before any state change is finalized.