Submits stock update requests
Cloud / Knowledge library
Raft Consensus
Replicates a log through elected leaders, terms, and majority agreement
Why learn Raft Consensus?
It structures consensus for understandability and implementation
The problem it solves
It keeps replicated state machines consistent through leader failures
Where it came from
Raft Consensus is a distributed systems concept that replicates a log through elected leaders, terms, and majority agreement. Its guarantees and tradeoffs depend on the concrete implementation and operating context.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Raft Consensus and distinguish it from a neighboring concept.
- Choose a realistic task where Raft Consensus is appropriate.
- Identify a failure mode caused by misusing Raft Consensus.
- Interpret a small design or result involving Raft Consensus.
- Explain one tradeoff or limitation of Raft Consensus.
Documentation and sources
- Designing Data-Intensive Applications (dataintensive.net)
Connect the ideas
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Real World Example
Synchronized Global Inventory Ledger
A multinational logistics firm managing real-time stock levels across distributed regional warehouses.
The business problem
Inconsistent inventory counts caused by network partitions, leading to overselling items that are already allocated to other regions.
How this topic solves it
Implementing the Raft consensus algorithm to ensure all regional nodes agree on a single, linearizable sequence of inventory updates, preventing conflicting state changes.
- Elect a single leader node through a majority vote to coordinate all incoming inventory write requests.
- The leader receives an update, appends it to its local log, and replicates the entry to follower nodes.
- Follower nodes acknowledge receipt of the log entry, confirming persistence in their local storage.
- Once a majority of nodes acknowledge the entry, the leader commits the change and notifies followers to apply it to their state machines.
- If a leader fails, followers initiate a new election based on term numbers to restore service.
This diagram shows how a leader node coordinates state changes across a cluster to ensure consistency.
Manages log and coordinates quorum
Replicates log entries
Replicates log entries
- Inventory API → proposes update → Leader Node
- Leader Node → replicates log → Follower A
- Leader Node → replicates log → Follower B
- Follower A → acknowledges → Leader Node
- Follower B → acknowledges → Leader Node
Expected business outcome
A unified, reliable source of truth for inventory levels that remains consistent even if individual nodes or network segments experience temporary failures.
Limitations and checks
The primary limitation is latency; because every write requires a round-trip to a majority of nodes, the system performance is constrained by the slowest link in the majority quorum. You can check this by monitoring the time elapsed between a client request and the commit confirmation.