What is Raft Consensus Algorithm?

Raft Consensus Algorithm

The Raft Consensus Algorithm is a distributed consensus algorithm designed to ensure fault tolerance and consistency in distributed systems, particularly in scenarios where multiple nodes need to agree on a common state. It was introduced by Diego Ongaro and John Ousterhout in 2014 and has since become widely adopted due to its simplicity and understandability.

Raft operates by electing a leader among a group of nodes, known as a Raft cluster. The leader is responsible for managing the state of the system and coordinating updates. The algorithm guarantees that if a majority of the nodes agree on a value, that value becomes committed and is applied to the system's state.

What is consensus?

Consensus represents a foundational challenge within fault-tolerant distributed systems, requiring multiple servers to converge on shared values. Once consensus is reached on a value, it becomes immutable. Traditional consensus algorithms advance with the availability of a majority of servers; for example, a cluster comprising 5 servers can maintain operation even in the face of 2 failures. However, further failures beyond this threshold impede progress, ensuring that erroneous results are never returned.

The concept of consensus typically emerges within the framework of replicated state machines, a fundamental approach to constructing fault-tolerant systems. Each server hosts a state machine alongside a log. The state machine represents the critical component requiring fault tolerance, such as a hash table. From a client perspective, interactions with the system appear as though they're interacting with a singular, reliable state machine, even if some servers in the cluster experience failures. Each state machine processes commands from its respective log. For instance, in the case of a hash table, log commands could include operations like "set x to 3". A consensus algorithm facilitates agreement among servers on the commands present in their logs. This algorithm ensures that if one state machine applies "set x to 3" as the nth command, no other state machine will ever execute a different nth command. Consequently, each state machine processes an identical series of commands, producing consistent results and arriving at identical states.

A consensus protocol tolerating failures must have the following features.

• Validity : If a process decides(read/write) a value, then it must have been proposed by some other correct process
• Agreement : Every correct process must agree on the same value
• Termination : Every correct process must terminate after a finite number of steps.
• Integrity : If all correct processes decide on the same value, then any process has the said value.

Key features of the Raft algorithm

1. Leader Election: Raft uses a leader-based approach, where one node is elected as the leader among the cluster. Leader election ensures that only one node at a time is responsible for coordinating updates and managing the state of the system.

2. Term-Based: Raft operates in terms, with each term representing a period during which a single leader is elected. Terms prevent the system from having multiple leaders concurrently and ensure orderly transitions of leadership.

3. Log Replication: Raft nodes maintain a log of state changes, and the leader is responsible for replicating its log entries to other nodes in the cluster. This ensures that all nodes eventually converge to the same state.

4. Safety Properties: Raft guarantees safety properties such as leader completeness, which ensures that if a log entry is committed in one term, it will be committed in all future terms, and state machine safety, which ensures that nodes agree on the order of log entries and apply them in the same order.

5. Leader Failure Handling: If the leader fails or becomes unreachable, Raft initiates a new leader election to select a new leader. This ensures system availability even in the presence of node failures.

Now, there can be two types of systems assuming only one client:

Single server system

The client interacts with a system having only one server with no backup. There is no problem in achieving consensus in such a system.

Multiple server system

The client interacts with a system having multiple servers. Such systems can be of two types.

• Symmetric: Any of the multiple servers can respond to the client and all the other servers are supposed to sync up with the server that responded to the client’s request, and
• Asymmetric: Only the elected leader server can respond to the client. All other servers then sync up with the leader server.

Such a system in which all the servers replicate(or maintain) similar data(shared state) across time can for now be referred to as, replicated state machine.

We shall now define some terms used to refer individual servers in a distributed system.

• Leader: Only the server elected as leader can interact with the client. All other servers sync up themselves with the leader. At any point of time, there can be at most one leader(possibly 0, which we shall explain later)
• Follower: Follower servers sync up their copy of data with that of the leader’s after every regular time intervals. When the leader server goes down(due to any reason), one of the followers can contest an election and become the leader.
• Candidate: At the time of contesting an election to choose the leader server, the servers can ask other servers for votes. Hence, they are called candidates when they have requested votes. Initially, all servers are in the Candidate state.

So, the above system can now be labelled as in the following snap.

How Does the Raft Algorithm Work?

The Raft algorithm operates in two main stages: leader election and log replication.

Leader Election

In the leader election stage, the nodes in the system elect a leader by running a voting process. During this process, each node will request votes from the other nodes, and the node that receives the majority of votes will be elected as the leader.

If the current leader fails or becomes unavailable, the other nodes will start a new election process to elect a new leader. This ensures that the system always has a leader and can continue to process requests and replicate data.

Log Replication

Once a leader has been elected, the Raft algorithm enters the log replication stage. In this stage, the leader is responsible for receiving and processing client requests and replicating the changes to the other nodes in the system.

To ensure that all nodes have a consistent view of the data, the leader maintains a log of all changes made. When a client requests, the leader will append the request to its log and replicate the log entry to the other nodes in the system. The other nodes will then apply the log entry to their local copies of the data.

If the leader fails or becomes unavailable, the other nodes will start a new election process to elect a new leader. The new leader will continue the log replication process from where the previous leader left off.

Benefits of Using the Raft Algorithm

There are several benefits to using the Raft algorithm for achieving consensus in distributed systems:

1. Simplicity: The Raft algorithm is relatively simple compared to other distributed systems consensus algorithms. This makes it easier to understand and implement, which can be especially useful for developers who are new to the field.
2. Safety: The Raft algorithm ensures that the system remains consistent by allowing only a single leader to make changes to the system at any given time. This helps to prevent conflicts and data corruption.
3. Liveness: The Raft algorithm guarantees that the system will continue progressing, even if some nodes fail or become unavailable. This is achieved through leader election and log replication, which help to ensure that the system can recover from failures and continue operating.
4. Flexibility: The Raft algorithm is highly flexible and can be customized to fit the needs of a wide range of distributed systems. This includes adjusting the election timeout, the number of votes required to become a leader, and other vital parameters.
5. Wide adoption: The Raft algorithm has gained widespread adoption in distributed systems, with many popular projects, such as etcd and CockroachDB, using it as their primary consensus algorithm. This helps to ensure that the Raft algorithm has been thoroughly tested and is robust in practice.

Raft Alternatives

• Paxos - Variants: multi-paxos, cheap paxos, fast paxos, generalised paxos
• Practical Byzantine Fault Tolerance algorithm (PBFT)
• Proof-of-Stake algorithm (PoS)
• Delegated Proof-of-Stake algorithm (DPoS)

Implementing the Raft Algorithm

At a high level, the Raft algorithm can be implemented using the following steps.

1. Initialize the system with a set of nodes and a shared log.
2. Periodically elect a leader from among the nodes.
3. The leader can accept and replicate new log entries to the other nodes.
4. Ensure that all nodes have a consistent view of the log by sending and receiving heartbeats.

Initializing the System

To initialize the system, we first need to set up a set of nodes participating in the Raft algorithm. Each node will have its state machine responsible for handling requests and replicating log entries.

In addition to the nodes, we will also need to set up a shared log to store the system's state. The log will consist of a series of entries, each representing a change to the system state.

Leader Election

In the leader election phase, the Raft algorithm uses a voting process to select a leader node from among the participating nodes in the distributed system. This process begins when a node starts up or when the current leader node becomes unavailable.

Each node in the system maintains a current term number, increasing every time a new leader is elected. When a node starts up, or a leader becomes unavailable, it initiates a new election by incrementing its term number and sending out RequestVote RPCs to the other nodes in the system.

The RequestVote RPC includes the node’s term number, its current log index and term, and the identity of the candidate node. The other nodes in the system will then vote for the candidate if the following conditions are met.

• The candidate’s term number is greater than or equal to the node’s current term number.
• The candidate’s log is up-to-date, containing all of the log entries from the node’s current log up to a certain index.

If the node votes for the candidate, it will also update its current term number to match its term number.

Once the candidate has received votes from most of the nodes in the system, it becomes the leader and begins the log replication phase.

Log Replication

To ensure consistency and reliability, the Raft algorithm uses a replicated log to store updates to the shared state of the distributed system. Each node in the system maintains its copy of the log, which is updated through log replication.

Log replication works by sending log entries from the leader node to the other nodes in the system. Each log entry consists of a command that is applied to the shared state of the system, along with a term number that identifies the leader that issued the command.

The leader node is responsible for receiving new log entries from clients, appending them to its log, and replicating them to the other nodes in the system. The other nodes in the system are responsible for storing the replicated log entries and applying them to their copies of the log.

Commit

Once a log entry has been replicated to a majority of the nodes in the system, it is considered committed and can be applied to the shared state of the system. This ensures that any updates to the shared state are consistent across all nodes in the system and that the state can be recovered in the event of a node failure.

The Raft algorithm uses a commit index to track the progress of log replication and ensure that updates are applied in the correct order. The commit index is incremented each time a log entry is committed, and the shared state is updated to reflect the changes made by the log entry.

The Raft algorithm also includes mechanisms for handling log inconsistencies and conflicts, such as using a quorum of nodes to vote on the correct log entry in the event of a disagreement. This ensures that the shared state of the system remains consistent and reliable, even in the face of node failures or network partitions.

Limitations of Raft Algorithm

• Raft is strictly single Leader protocol. Too much traffic can choke the system. Some variants of Paxos algorithm exist that address this bottleneck.
• There are a lot of assumptions considered to be acting, like non-occurrence of Byzantine failures, which sort of reduces the real life applicability.
• Raft is a more specialized approach towards a subset of problems which arise in achieving consensus.
• Cheap-paxos(a variant of Paxos), can work even when there is only one node functioning in the server cluster. To generalise, K+1 replicated servers can tolerate shutting down of/ fault in K servers.

Summary

In summary, the Raft algorithm stands out as a robust solution for attaining consensus in distributed systems. Engineered to prioritize simplicity in comprehension and implementation, it presents numerous advantages compared to alternative consensus algorithms, such as accelerated convergence and heightened fault tolerance. Although its adoption within blockchain technology remains limited at present, its potential impact on the landscape of distributed systems and blockchains looms large. With the expanding utilization of distributed systems, the significance of the Raft algorithm is poised to ascend further.