RAFT Consensus Explained

In this article, we delve into the realm of consensus algorithms, essential components in distributed systems for achieving consensus among a network of nodes. Consensus algorithms are crucial for ensuring that each node in a distributed network agrees on a single data value.

Distributed Consensus Algorithms

These algorithms must fulfil three core properties:

1. Agreement: Every correct process must agree on the same value.
2. Validity: Any value agreed upon must have been proposed by one of the processes.
3. Termination: Eventually, every correct process must decide on a value.

An additional foundational concept in this field is the CAP Theorem, which posits that a distributed system can only simultaneously satisfy two out of the following three properties: Consistency, Availability, and Partition Tolerance. This theorem is critical in understanding the trade-offs involved in designing and selecting consensus algorithms.

Consensus algorithms must handle various types of failures. These include:

• Network Failures: Disruptions in network communication.
• Partition Failures: Splits in the network that prevent communication between subsets of nodes.
• Recovery Failures: Challenges in restoring a node's state after a crash.
• Byzantine Failures: Failures where nodes may behave maliciously. The term originates from the Byzantine Generals Problem described in a seminal 1980s computer science paper. This problem illustrates a scenario where generals, encircling a city, must coordinate an attack simultaneously but face challenges due to potentially disloyal generals and unreliable messengers.

RAFT

Among various consensus algorithms, this discussion focuses on the Raft algorithm. Raft stands for "Reliable, Replicated, Redundant, And Fault-Tolerant". Unlike some algorithms that use synchronised clocks, Raft is based on randomised timers and a concept called "terms". Here’s how it works:

• Terms: Each Raft term begins with an election to choose a leader. Terms are sequential; they start at zero and increment with each new term. Each term begins with a leader election phase, typically a brief period relative to the term's duration.
• Node States: Nodes in a Raft cluster can be in one of three states: follower, candidate, or leader.Followers are passive elements that respond to requests from leaders and candidates.Candidates are nodes that initiate an election due to a timeout or leader failure.Leaders manage all client interactions and log replication across followers.

Election Process:

• If a follower does not receive communication from the leader within a random timeout period, it assumes there is no active leader and transitions to a candidate state.
• The candidate votes for itself and issues a request for votes from other followers.
• Upon receiving a vote request, a follower increments its term number, votes for the candidate if it hasn’t already voted in the current term, and resets its election timer.
• A candidate that receives a majority of votes becomes the leader.

Leader Operations:

• The leader regularly sends heartbeat messages to keep other nodes from triggering an election. These messages also act as keep-alives for the followers.
• If the leader fails, and followers don’t receive heartbeats within a timeout period, they become candidates and start a new election.

Data Commitment:

• Leaders accept client requests and replicate the entry to the follower logs.
• Once an entry has been replicated on a majority of the nodes, the leader commits the entry and informs the followers.
• Only after a majority of followers have acknowledged the commit is the entry applied to the state machine.

This model ensures that as long as the majority of the nodes are operational and can communicate, the system remains robust and consistent, capable of tolerating failures from a minority of nodes. Raft’s design makes it easier to understand and implement correctly compared to other consensus algorithms, providing a reliable foundation for building distributed systems.

Sample Implementation

This Python script simulates a very basic version of Raft with leader election and heartbeats. It lacks full log replication and does not handle network partitions or persistent storage of logs. However, it serves as a good starting point for understanding the basic mechanics of the Raft protocol.

# Sample Implementation of RAFT
# Farshid Ashouri 

import random
import threading
import time


class Node(threading.Thread):
    def __init__(self, node_id, cluster):
        super().__init__()
        self.node_id = node_id
        self.cluster = cluster
        self.state = "follower"
        self.term = 0
        self.voted_for = None
        self.votes_received = 0
        self.leader_id = None

    def run(self):
        while True:
            if self.state == "follower":
                self.follow()
            elif self.state == "candidate":
                self.start_election()
            elif self.state == "leader":
                self.lead()

    def follow(self):
        timeout = random.uniform(1, 2)
        start_time = time.time()
        while time.time() - start_time < timeout:
            if self.cluster.messages:
                message = self.cluster.get_message(self.node_id)
                if message and message["term"] >= self.term:
                    if message["type"] == "heartbeat":
                        start_time = time.time()
                        self.term = message["term"]
                        self.leader_id = message["leader_id"]
                    elif message["type"] == "vote_request" and (
                        self.voted_for is None
                        or self.voted_for == message["candidate_id"]
                    ):
                        self.cluster.send_message(
                            {
                                "type": "vote_response",
                                "term": self.term,
                                "vote_granted": True,
                                "candidate_id": message["candidate_id"],
                            }
                        )
                        self.voted_for = message["candidate_id"]
        self.state = "candidate"

    def start_election(self):
        self.term += 1
        self.voted_for = self.node_id
        self.votes_received = 1
        self.cluster.broadcast(
            {
                "type": "vote_request",
                "term": self.term,
                "candidate_id": self.node_id,
            }
        )
        start_time = time.time()
        timeout = random.uniform(1, 2)
        while time.time() - start_time < timeout:
            message = self.cluster.get_message(self.node_id)
            if (
                message
                and message["type"] == "vote_response"
                and message["term"] == self.term
            ):
                self.votes_received += 1
            if self.votes_received > len(self.cluster.nodes) // 2:
                self.state = "leader"
                return
        self.state = "follower"

    def lead(self):
        while self.state == "leader":
            self.cluster.broadcast(
                {
                    "type": "heartbeat",
                    "term": self.term,
                    "leader_id": self.node_id,
                }
            )
            time.sleep(0.5)  # heartbeat interval


class Cluster:
    def __init__(self):
        self.nodes = [Node(i, self) for i in range(5)]
        self.messages = []

    def broadcast(self, message):
        for node in self.nodes:
            self.messages.append((node.node_id, message))

    def get_message(self, node_id):
        for i in range(len(self.messages)):
            if self.messages[i][0] == node_id:
                return self.messages[i][1]
        return None

    def send_message(self, message):
        self.messages.append((message["candidate_id"], message))

    def run(self):
        for node in self.nodes:
            node.start()


if __name__ == "__main__":
    cluster = Cluster()
    cluster.run()
        

Explanation

• Node Class: Each node runs as a thread and can be in one of three states: follower, candidate, or leader. Depending on its state, it executes different functions (follow, start_election, lead).
• Cluster Class: Manages nodes and simulates sending and receiving messages among them.

Test Your Knowledge

Here's a set of 10 questions and answers designed to assess understanding of the core concepts of the Raft consensus algorithm. Mastery of these questions suggests a strong grasp of how Raft works, its components, and its operational dynamics.

1. What is the main goal of the Raft consensus algorithm?

- Answer: The main goal of the Raft consensus algorithm is to ensure that all nodes in a distributed system agree on a single source of truth, despite failures. Raft achieves this through a leader election process, log replication, and a commitment approach.

2. How does Raft ensure that cluster nodes agree on the same data?

- Answer: Raft uses a leader-based approach where only the elected leader manages log entries. The leader takes client requests, appends them to its log, and replicates these entries across the follower nodes. Only when a majority of nodes have stored a log entry does the leader commit it to the state machine.

3. Describe the role of terms in Raft.

- Answer: In Raft, a term is a logical time period that represents a continuous period during which a leader remains in charge. Terms are incremented with each new leader election. Each node's current view of the term helps manage updates to the distributed log and coordinate leader elections.

4. What are the three states a node can be in a Raft cluster?

- Answer: A node in a Raft cluster can be in one of three states: follower, candidate, or leader. Followers passively participate by responding to requests from leaders and candidates. Candidates are potential leaders that initiate elections if they suspect there is no active leader. Leaders handle all client interactions and log replication.

5. Explain the leader election process in Raft.

- Answer: When a follower node times out without receiving a heartbeat from the leader, it transitions to the candidate state and starts an election. It increments its term, votes for itself, and requests votes from other nodes. If the candidate receives a majority of votes from the cluster, it becomes the new leader.

6. What happens during a leader failure in Raft?

- Answer: If the current leader fails or becomes disconnected, the follower nodes will eventually timeout due to not receiving heartbeats. These nodes will then transition to candidate state, initiate a new election process, and elect a new leader.

7. How does Raft handle log replication?

- Answer: The leader appends new log entries locally and then sends these entries to follower nodes. Followers append these entries to their logs. Once the leader has received acknowledgment from a majority of the followers that they have replicated the log entry, the entry is committed on the leader and then, the leader informs the followers to commit the entry as well.

8. What is a split vote and how does Raft resolve it?

- Answer: A split vote occurs when no single candidate wins a majority of votes in an election due to an even number of nodes or simultaneous candidate promotions. Raft resolves this by using randomized election timeouts, ensuring that split votes are resolved by nodes timing out at different intervals and starting new elections.

9. What are the conditions under which a follower grants its vote to a candidate in an election?

- Answer: A follower grants its vote to a candidate if the follower has not yet voted in the current term or if it recognises the candidate as having a log that is at least as up-to-date as its own.

10. Why is the concept of a committed log entry important in Raft?

- Answer: A committed log entry in Raft ensures data consistency and reliability across the cluster. Once a log entry is committed, it means a majority of the cluster has agreed on the log and the entry can be applied to the state machines. This prevents data loss and ensures that even if some nodes fail, the system's state remains consistent and recoverable.

Hope it helps.