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Learning Raft by Building It

· 3 min read

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The best way I’ve found to learn distributed systems is to build them.

When I first started exploring the consensus problem, I quickly realized how abstract it felt. In theory it’s “just agreeing on a value.” In practice? Machines crash, networks drop packets, two nodes both think they’re in charge… and suddenly your “cluster” isn’t clustered at all.

That’s why I chose to implement Raft in Go—not because the world needs another Raft implementation, but because building it forces you to understand what really happens when theory meets reality.

Why Raft?

Consensus is about making sure multiple replicas of a system agree on the same sequence of operations, even in the face of failures. Raft was designed to be:

  • Understandable → clear roles (leader, follower, candidate) and clean rules.
  • Practical → widely used in production systems like etcd (Kubernetes datastore), Consul, CockroachDB, and TiKV.
  • Reliable → once something is committed, it’s committed everywhere.

In short: Raft makes a messy cluster behave like a single reliable system.

In practice, Raft is used for:

  • Metadata/configuration stores (etcd, Consul).
  • Databases with replication (CockroachDB, TiKV).
  • Coordination services where strong consistency matters.

How Raft Works (at a High Level)

Leader Election

  • Nodes start as followers.
  • If they don’t hear from a leader, they hold an election.
  • Majority vote decides the leader; ties resolve via randomized timeouts.

Log Replication

  • Clients send commands (e.g. SET key value) to the leader.
  • The leader appends the command to its log and replicates it to followers.
  • Once a majority acknowledge, the entry is committed and applied everywhere.

Safety

  • One leader per term.
  • Logs with the same index and term are identical.
  • Committed entries are never rolled back.

What I Actually Built

To keep things simple, I built a toy Raft-backed key/value store:

  • Three nodes running in one process.
  • Elections and heartbeats managed with timers.
  • Commands flow through leader election, replication, and commitment.
  • Snapshots and write-ahead logs for persistence, so nodes can restart without forgetting their state.

It’s not production-ready—no network partitions, no cluster reconfiguration—but it demonstrates the core Raft mechanics clearly.

Why Build Instead of Just Read?

Because building makes the abstractions real.

  • Timeouts stop being abstract numbers—you feel them when your leader disappears.
  • Watching logs replicate across nodes shows you how consistency emerges.
  • Crashing a node mid-commit teaches more than any paper footnote ever could.

That hands-on experience is what makes distributed systems less mysterious and more approachable.

What’s Next

This post was about the concepts—why Raft matters and what problems it solves.

In another post, I’ll walk through the actual Go implementation of the toy KV store: how elections are coded, how log replication is wired, and where the tricky edge cases live.

Takeaway

Consensus is a hard problem, but Raft makes it accessible. By implementing even a small version, you can see how leader election, log replication, and safety guarantees keep a cluster consistent.

And once you’ve seen Raft in action, you’ll recognize it as the beating heart of many of today’s distributed systems.

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