Demystifying Distributed Consistency: CAP Theorem and Raft Algorithm Explained
1. Everyday Analogy: “Agreement” in Team Collaboration
Imagine a group of friends planning a trip but living in different cities. Messages have delays and some may lose connection, so opinions may not be unified. The consistency challenge in distributed systems is similar: how to get multiple machines to “agree” even under unreliable networks is crucial.
2. Consistency Models and the CAP Theorem
1. Overview of Consistency Models
| Model | Description | Everyday Analogy |
|---|---|---|
| Strong Consistency | All nodes see the latest data instantly | Everyone receives the updated plan simultaneously |
| Eventual Consistency | Data eventually syncs but may be temporarily inconsistent | Some get the plan earlier, others later |
| Weak Consistency | No guarantee of synchronization; states may diverge for long periods | Everyone has different travel plans |
2. CAP Theorem Trade-offs
CAP theorem states that a distributed system cannot simultaneously guarantee Consistency (C), Availability (A), and Partition tolerance (P); only two can be achieved at the same time.
CAP Theorem Diagram:
Consistency (C)
/ \
/ \
Availability (A) — Partition tolerance (P)
| Trade-off | Representative Systems | Use Cases |
|---|---|---|
| CA | Single-node databases | Stable network, no partitions |
| CP | ZooKeeper | Systems needing strong consistency |
| AP | Dynamo, Cassandra | Highly available, eventually consistent systems |
3. Replica Mechanisms and Data Consistency
Replication improves reliability and performance, but keeping replicas consistent is challenging. Common replication methods:
- Primary-Backup: Primary handles writes; backups asynchronously sync
- Multi-Master: Multiple writable nodes, complex conflict resolution
Consistency guarantees rely on consensus algorithms to synchronize logs and state.
4. Core Consistency Protocol: Raft Algorithm Explained
Raft is known for simplicity and divides nodes into three roles:
Raft Roles:
Leader Followers Candidate
↑ ↑ ↑
| ←——— Election Process ———→ |
1. Leader Election
- All nodes start as Followers
- After election timeout, a node becomes Candidate and requests votes
- Gains majority votes to become Leader
2. Log Replication
- Leader receives client commands and appends them to its log
- Concurrently replicates logs to Followers
- Commits logs after majority acknowledgement, updates state machine
3. Safety and Fault Tolerance
- Ensures log consistency and prevents split-brain
- Uses term numbers to prevent outdated leaders from committing logs
- Handles network partitions and node failures
// Raft log append pseudocode example
func (rf *Raft) AppendEntries(args *AppendEntriesArgs, reply *AppendEntriesReply) {
rf.mu.Lock()
defer rf.mu.Unlock()
if args.Term < rf.currentTerm {
reply.Success = false
return
}
rf.log = append(rf.log, args.Entries...)
reply.Success = true
}
5. Practical Tips for Observation and Debugging
- Observe leader election and heartbeat via logs
- Test fault tolerance by simulating network partitions
- Use Go debugging tools like
Delveto trace state changes
6. Terminology Mapping Table
| Everyday Term | Technical Term | Explanation |
|---|---|---|
| Meeting Host | Leader | Coordinates log replication and state updates |
| Attendees | Follower | Receives leader commands and stays in sync |
| Candidate | Candidate | Initiates election to become leader |
| Voting | Vote | Mechanism for electing leader |
7. Thought Chain and Exercises
- How does the CAP theorem guide real-world system design?
- How does Raft prevent split-brain scenarios?
- Implement a simplified Raft supporting election and log replication.
8. Conclusion: Protect Distributed Data Consistency with Raft
Distributed consistency is the cornerstone for stable system operation. The CAP theorem helps understand design trade-offs, and the Raft algorithm provides a clear and practical implementation path. Mastering these is a key step toward becoming a distributed systems expert.