In this lesson, you will learn how Paxos and Raft allow distributed nodes to agree on the exact order of transactions within a shared log, ensuring your database remains consistent even when components fail.
The Problem of Distributed Ordering
When you have multiple database nodes, you face a dilemma: how do you ensure that all nodes process transactions in the exact same sequence? If Node A records a "Debit" followed by a "Credit," but Node B records them in reverse, your data will become corrupt. We need a way to reach a "consensus" on what the next entry in the transaction log should be.
How Raft Simplifies the Log
Raft is a consensus algorithm that elects a single Leader. Imagine a classroom where only one student (the Leader) is allowed to write on the whiteboard (the Log). All other nodes (Followers) must follow the Leader's instructions.
- Append Requests: When a new transaction arrives, the Leader appends it to its local log.
- Replication: The Leader sends this log entry to all Followers.
- Quorum: Once a majority of nodes acknowledge they have saved the entry, the transaction is considered "committed."
If the Leader crashes, the remaining nodes hold an election to pick a new one, ensuring that no transaction is lost as long as a majority of nodes are still alive.
A Simplified View of Log Agreement
Think of the log as a series of indexed slots. Each node wants to ensure that its entry at index 5 matches everyone else's.
-- Conceptual log replication state
Node A: [1:Txn_A, 2:Txn_B, 3:Txn_C] (Committed)
Node B: [1:Txn_A, 2:Txn_B, 3:Txn_C] (Committed)
Node C: [1:Txn_A, 2:Txn_B] (Waiting for Heartbeat)
In this setup, the Leader (Node A) will continue to send AppendEntries requests. If Node C is behind, the Leader forces Node C to match the Leader's log state until they are perfectly synchronized. This guarantees that your transaction log is not just a collection of events, but a single, immutable, and consistent source of truth across your entire cluster.
Key Takeaway: Paxos and Raft provide a robust mechanism for distributed nodes to reach consensus on the sequence of transaction logs, ensuring that all database copies remain identical even during network partitions or node failures.