Principles of Distributed Consensus: Paxos and Raft Algorithms Explained

📚 Understanding Distributed Consensus

Distributed consensus is a fundamental problem in computer science that deals with agreeing on a single data value among a distributed network of processes or systems. Imagine multiple computers trying to decide on the next entry in a shared log. Consensus algorithms ensure that even if some of these computers fail or the network becomes unreliable, the remaining computers can still agree on a consistent and correct value.

📜 History and Background

The problem of consensus has been studied since the late 1970s, with early work focusing on fault-tolerant systems. Leslie Lamport introduced Paxos in 1989, although it wasn't widely understood or adopted until later. Raft, designed to be more understandable than Paxos, was introduced by Diego Ongaro and John Ousterhout in 2014.

🔑 Key Principles of Paxos

Paxos is a family of protocols for achieving consensus. The basic Paxos algorithm involves three types of agents: Proposers, Acceptors, and Learners.

• ⚙️ Proposer: Proposes a value to be accepted.
• ✅ Acceptor: Accepts a proposed value. A value is considered chosen when a majority of acceptors have accepted it.
• 🎓 Learner: Learns the chosen value.

The algorithm works in two phases:

1. Prepare Phase: A proposer sends a prepare request to a majority of acceptors, asking them to promise not to accept any proposals with a lower number than the one proposed.
2. Accept Phase: If the proposer receives promises from a majority of acceptors, it sends an accept request with the proposed value. Acceptors accept the value if they haven't promised to ignore it.

Mathematical Representation:

Let $v$ be the value proposed, and $n$ be the proposal number. The acceptor accepts the value $v$ associated with proposal number $n$ if it hasn't promised to ignore proposals with numbers higher than $n$.

💡 Key Principles of Raft

Raft achieves consensus through a leader election process. The system elects a leader, and all changes are proposed by the leader and replicated to the followers.

• 👑 Leader Election: Raft elects a leader who is responsible for proposing new log entries.
• 📝 Log Replication: The leader replicates its log to the followers.
• 🔒 Safety: Raft guarantees that if any server has committed a log entry, all other servers that eventually become available will also commit that entry.

Raft divides time into terms, each beginning with an election. If a follower doesn't hear from a leader within a certain timeout period, it starts an election.

🌐 Real-world Examples

• 🔑 Paxos: Used in Google's Chubby lock service and Spanner distributed database.
• 📦 Raft: Used in etcd (a distributed key-value store), Consul (a service mesh solution), and CockroachDB (a distributed SQL database).
• ☁️ Cloud Computing: Both Paxos and Raft are used extensively in cloud computing environments to ensure data consistency and fault tolerance.

✔️ Conclusion

Paxos and Raft are powerful algorithms for achieving distributed consensus. While Paxos is known for its theoretical elegance, Raft is designed for understandability and ease of implementation. Both play crucial roles in ensuring the reliability and consistency of distributed systems.

📚 Introduction to Distributed Consensus

Distributed consensus is a fundamental problem in distributed computing. It involves multiple nodes in a distributed system agreeing on a single decision, even when some nodes may fail or messages may be lost. Algorithms like Paxos and Raft are designed to solve this problem.

📜 History and Background

The problem of distributed consensus has been studied since the late 1970s. Paxos, developed by Leslie Lamport, was introduced in 1989 but remained difficult to understand and implement. Raft, created by Diego Ongaro and John Ousterhout in 2014, was designed to be more understandable than Paxos while providing similar functionality.

🔑 Key Principles of Paxos

• 🏛️ Proposers: Nodes that propose a value to be agreed upon.
• 🗳️ Acceptors: Nodes that vote on the proposed values. They maintain state and ensure consistency.
• 📜 Learners: Nodes that learn the agreed-upon value once a consensus is reached.
• 🔄 Quorum: A minimum number of acceptors that must agree on a value for it to be considered accepted. Typically, a quorum is a majority of acceptors.
• 🔢 Two-Phase Commit: Paxos operates in two phases: Prepare and Accept. The Prepare phase ensures that a proposer's value is not overridden by an earlier proposal. The Accept phase commits the chosen value.

🔑 Key Principles of Raft

• 🧑‍⚖️ Leader Election: Raft elects a single leader who is responsible for making decisions. If the leader fails, a new leader is elected.
• ➕ Log Replication: The leader replicates its log of decisions to the other nodes (followers).
• 🛡️ Safety: Raft guarantees that if any node has committed a log entry, all other nodes that reach that entry will commit the same entry.
• ⏱️ Heartbeats: The leader sends periodic heartbeat messages to the followers to maintain its authority. If a follower doesn't receive a heartbeat within a certain timeout, it becomes a candidate and starts a new election.
• ↔️ Terms: Raft divides time into terms. Each term begins with an election. A node can only become a leader for a specific term.

💡Real-World Examples

• 💾 Distributed Databases: Both Paxos and Raft are used in distributed databases to ensure consistency of data across multiple nodes. Examples include Google's Spanner (Paxos) and etcd (Raft).
• 📦 Configuration Management: Raft is often used in configuration management systems to ensure that all nodes have the same configuration. Kubernetes uses etcd, which implements Raft, for storing cluster state.
• ⛓️ Blockchain: Consensus algorithms are crucial in blockchain technology. While many blockchains use Proof-of-Work or Proof-of-Stake, some private or consortium blockchains use variations of Paxos or Raft for faster and more deterministic consensus.

📝 Conclusion

Paxos and Raft are vital consensus algorithms for building reliable distributed systems. While Paxos is historically significant and widely used, Raft offers a more understandable alternative, making it a popular choice for many modern distributed applications. Understanding the core principles of these algorithms is essential for anyone working with distributed systems.

📚 Introduction to Distributed Consensus

Distributed consensus is the process of achieving agreement on a single data value among distributed processes or systems. This is crucial in distributed systems to ensure reliability, fault tolerance, and consistency, especially when components may fail or be unreliable.

📜 History and Background

The problem of distributed consensus has been studied since the late 1970s. Leslie Lamport introduced Paxos in 1989, though it was initially difficult to understand. Raft, developed more recently, aims to provide a more understandable alternative to Paxos.

🔑 Key Principles of Paxos

• 🗳️ Roles: Paxos involves three types of roles: Proposers, Acceptors, and Learners.
• 🔢 Propose: Proposers suggest a value to Acceptors.
• ✅ Accept: Acceptors may accept a proposed value.
• 🎓 Learn: Learners learn the chosen value once a majority of Acceptors have accepted it.
• 🛡️ Quorum: Paxos requires a quorum (majority) of Acceptors to agree on a value to ensure consistency.

🌊 Paxos Algorithm Simplified

Paxos operates in two phases:

1. Prepare Phase:
   • ⬆️ A proposer selects a proposal number $n$ higher than any number it has seen. It sends a prepare request with $n$ to a majority of acceptors.
   • 🤝 If an acceptor receives a prepare request with number $n$ greater than any prepare request it has already responded to, it promises to ignore all proposals with number less than $n$ and responds with the highest-numbered proposal (if any) that it has already accepted.
2. Accept Phase:
   • 💡 If the proposer receives responses from a majority of acceptors, it sends an accept request to those acceptors with the number $n$ and a value $v$. If any acceptor reported a value, then $v$ is the reported value; otherwise, $v$ is the value the proposer wants to propose.
   • 🛡️ If an acceptor receives an accept request with number $n$, it accepts the value unless it has already promised to consider only proposals with number greater than $n$.

⛵ Key Principles of Raft

• 👑 Leader Election: Raft elects a leader who is responsible for making decisions.
• 🪵 Log Replication: The leader replicates its log entries to the followers.
• 🔒 Safety: Raft ensures that if any server has committed a log entry, all other servers that commit will commit the same entry.

🗺️ Raft Algorithm Simplified

Raft divides time into terms, each beginning with an election. There are three states: Leader, Follower, and Candidate.

1. Leader Election:
   • ⏳ When followers don't hear from a leader, they become candidates and request votes.
   • ✅ The candidate that receives a majority of votes becomes the new leader.
2. Log Replication:
   • ➕ The leader takes all client commands and appends them to its log as new entries. It then replicates these entries to all followers.
   • 🔄 Followers accept log entries and acknowledge the leader.
3. Safety:
   • 🛡️ Raft guarantees that if a log entry has been committed on one server, it will eventually be committed on all other servers.

🌐 Real-world Examples

• 🗄️ Paxos: Used in Google's Chubby lock service and Spanner database.
• 🐘 Raft: Used in etcd (Kubernetes' backing store), Consul, and CockroachDB.
• ☁️ Distributed Databases: Both algorithms are fundamental in building reliable distributed databases.

💡 Conclusion

Paxos and Raft are essential algorithms for achieving distributed consensus, each with its own strengths. Paxos is a foundational algorithm known for its robustness, while Raft offers a more understandable approach. Both play critical roles in ensuring the reliability and consistency of modern distributed systems.

📚 Introduction to Distributed Consensus

Distributed consensus is a fundamental problem in distributed computing, where multiple servers must agree on a single decision. This is crucial for maintaining consistency and reliability in systems where components can fail. Algorithms like Paxos and Raft are designed to solve this problem, ensuring that even if some servers crash or become unavailable, the remaining servers can still reach an agreement.

📜 History and Background

The problem of distributed consensus has been studied since the late 1970s. Paxos, introduced by Leslie Lamport, was a groundbreaking solution but was initially difficult to understand and implement. Raft, developed more recently, aims to provide a more understandable alternative while maintaining similar fault-tolerance properties.

• 📅 Paxos: Proposed by Leslie Lamport in 1989, formally published in 1998.
• 🚢 Raft: Developed by Diego Ongaro and John Ousterhout at Stanford University and presented in 2014.

🔑 Key Principles of Paxos

Paxos achieves consensus through multiple rounds of proposing and accepting values. It involves three types of agents: Proposers, Acceptors, and Learners. The algorithm ensures that once a value is chosen, all processes eventually learn about it, even if some processes fail.

• 🗳️ Proposer: Initiates the consensus process by proposing a value.
• ✅ Acceptor: Votes on proposed values and ensures only one value is chosen.
• 🎓 Learner: Learns the chosen value once a quorum of acceptors has agreed.
• 🔢 Algorithm Overview: Paxos operates in two phases: Prepare and Accept. The Prepare phase ensures that the proposer has the authority to propose a value, and the Accept phase ensures that a majority of acceptors agree on the proposed value.
• ➗ Quorum: Any majority of acceptors. Ensures that any two quorums have at least one member in common, preventing conflicting decisions.

🔑 Key Principles of Raft

Raft simplifies consensus by electing a leader who is responsible for log replication and decision-making. The algorithm divides time into terms, and each term has a leader. If a leader fails, a new leader is elected.

• 👑 Leader Election: Raft elects a leader to manage log replication.
• 🪵 Log Replication: The leader replicates its log entries to followers.
• 🛡️ Safety: Raft guarantees that if a log entry is committed on one server, it will eventually be committed on all other servers.
• ⏱️ Terms: Raft divides time into terms, each beginning with an election.
• 💔 Leader Failure: If a leader fails, followers initiate a new election.

⚙️ Real-world Examples

Both Paxos and Raft are used in various distributed systems to ensure data consistency and fault tolerance.

• 💾 Paxos: Used in Google's Chubby lock service and Spanner distributed database.
• 📦 Raft: Used in etcd (Kubernetes' backing store), Consul (service mesh), and CockroachDB (distributed SQL database).

🧮 Mathematical Representation

Paxos can be mathematically represented through its consensus protocol. Let $v$ be the value proposed, and let $n$ be the proposal number. The algorithm ensures that if a value $v$ is chosen with proposal number $n$, then any subsequent proposal must have a number greater than $n$ or propose the same value $v$.

Raft ensures consensus through log replication. If $L_i[k]$ represents the $k$-th log entry on server $i$, then Raft guarantees that if $L_i[k]$ is committed, then for all servers $j$, $L_j[k] = L_i[k]$ eventually.

🧪 Practical Differences and Trade-offs

While both algorithms solve the consensus problem, they have different trade-offs.

• 🧠 Complexity: Raft is generally considered easier to understand and implement than Paxos.
• ⚡ Performance: Paxos can achieve higher throughput in certain scenarios, but Raft's leader-based approach simplifies coordination.
• 🛠️ Implementation: Raft is designed with implementability in mind, making it a popular choice for new distributed systems.

🔑 Key Differences Summary

💡 Conclusion

Paxos and Raft are essential algorithms for achieving distributed consensus. Paxos offers a robust but complex solution, while Raft provides a more understandable and implementable alternative. Understanding these algorithms is crucial for building reliable and consistent distributed systems.

📚 Introduction to Distributed Consensus

Distributed consensus is a fundamental problem in distributed computing. It involves multiple servers agreeing on a single value, even when some servers might fail or be unreliable. Algorithms like Paxos and Raft provide solutions to this problem, ensuring consistency and reliability in distributed systems.

📜 History and Background

The problem of distributed consensus gained prominence with the rise of distributed databases and systems. Paxos, introduced by Leslie Lamport in 1989 (though published in 1998), was one of the earliest and most influential solutions. However, Paxos is notoriously difficult to understand and implement. Raft, created by Diego Ongaro and John Ousterhout at Stanford in 2014, was designed as a more understandable alternative to Paxos.

🔑 Key Principles of Paxos

• 🗳️ Proposers: Nodes that propose a value to be agreed upon.
• 🔬 Acceptors: Nodes that vote on the proposed values.
• 👓 Learners: Nodes that learn the agreed-upon value once a consensus is reached.
• 🧮 Rounds: Paxos operates in rounds, with each round having a unique ID.
• 🛡️ Quorum: A majority of acceptors must agree on a value for it to be chosen.

🔑 Key Principles of Raft

• 👑 Leader Election: Raft elects a leader who is responsible for making decisions.
• ➕ Log Replication: The leader replicates its log of decisions to the followers.
• 🔄 Safety: Raft ensures that if a log entry is committed on one server, it is eventually committed on all servers.
• ⏱️ Timeouts: Raft uses timeouts to detect leader failures and trigger new elections.
• 🔑 Terms: Raft divides time into terms, each starting with a leader election.

🧮 Paxos Algorithm Explained

Paxos ensures consensus through multiple rounds of proposing and accepting values. Here's a simplified overview:

1. A proposer sends a prepare request to a quorum of acceptors.
2. If the acceptors haven't already accepted a value for that round, they promise to ignore any future proposals with lower round numbers.
3. The proposer then sends an accept request with a value.
4. If a quorum of acceptors accept the value, it is considered chosen.

➕ Raft Algorithm Explained

Raft simplifies consensus by electing a leader that manages log replication. Here's a simplified overview:

1. Leader Election: Servers start as followers and can become candidates if they don't hear from the leader.
2. Log Replication: The leader appends new entries to its log and replicates them to the followers.
3. Commitment: Once a majority of followers have replicated the log entry, the leader commits it, and informs the followers.

💡 Real-world Examples

• 💾 Databases: Distributed databases like CockroachDB use consensus algorithms to ensure data consistency across multiple nodes.
• 📦 Configuration Management: Systems like etcd and Consul use Raft to manage distributed configuration data.
• 🔑 Locking Services: Apache ZooKeeper uses a Paxos-like protocol for providing distributed locking and coordination.
• ☁️ Cloud Computing: Cloud platforms use consensus algorithms for various tasks, such as leader election and resource management.

🔑 Formulae and Concepts

• 🧮 Quorum Size: The minimum number of acceptors or followers required to agree on a value. For example, if there are $N$ servers, the quorum size is typically $\frac{N}{2} + 1$.
• ⏱️ Timeout Values: In Raft, appropriate timeout values are crucial for timely leader election and fault detection.
• ➕ Log Consistency: Raft ensures log consistency by requiring followers to have the same log entries as the leader.

🧪 Advantages and Disadvantages

• ✅ Paxos Advantages: Well-established, proven algorithm.
• ❌ Paxos Disadvantages: Difficult to understand and implement.
• ✅ Raft Advantages: Easier to understand and implement, strong leader-based approach.
• ❌ Raft Disadvantages: Performance can be affected by leader failures.

🌍 Conclusion

Paxos and Raft are crucial algorithms for achieving distributed consensus in various systems. While Paxos is a foundational algorithm, Raft provides a more understandable and practical alternative. Understanding these algorithms is essential for anyone working with distributed systems, ensuring data consistency and reliability.

📚 Understanding Distributed Consensus

Distributed consensus is a fundamental problem in computer science that involves multiple servers agreeing on a single value or state, even when some servers might fail or the network might experience disruptions. Algorithms like Paxos and Raft are designed to solve this problem and ensure the reliability and consistency of distributed systems.

📜 History and Background

The problem of distributed consensus gained prominence with the rise of distributed systems and the need for fault-tolerant solutions. Paxos, introduced by Leslie Lamport in 1989, was one of the earliest and most influential algorithms for achieving consensus. However, its complexity made it difficult to understand and implement. Raft, developed more recently, aims to provide a more understandable alternative to Paxos while maintaining similar properties.

🔑 Key Principles of Paxos

• 🏛️ Roles: Paxos defines three roles: Proposers, Acceptors, and Learners. Nodes can take on one or more of these roles.
• 🔢 Propose: A proposer suggests a value to be agreed upon.
• ✅ Accept: Acceptors vote on the proposed values.
• 📢 Learn: Learners are informed about the accepted value.
• 🛡️ Quorum: Paxos relies on quorums (majorities) to ensure that a value is agreed upon, even if some nodes fail.
• ⏳ Two-Phase Commit: Paxos typically involves two phases: prepare and accept. In the prepare phase, proposers find out the highest proposal number seen by acceptors. In the accept phase, proposers send an accept request with the chosen value.
• 🧮 Numbering: Proposals are ordered using unique numbers to prevent conflicts.

🔑 Key Principles of Raft

• 🧑‍⚖️ Leader Election: Raft elects a leader who is responsible for coordinating consensus.
• 🪵 Log Replication: The leader replicates its log of commands to the followers.
• 🗳️ Voting: Followers vote for a candidate to become the leader.
• 🔄 State Machine Consistency: Raft ensures that all servers agree on the same sequence of state transitions.
• ⏱️ Timeouts: Raft uses timeouts to detect leader failures and trigger new elections.
• 🚧 Term Numbers: Raft divides time into terms, each starting with an election. Term numbers are used to detect outdated information.
• 📌 Commitment: Entries are considered committed once a majority of servers have stored them.

🌍 Real-World Examples

• 💾 Distributed Databases: Paxos and Raft are used in distributed databases like Google's Spanner and etcd to ensure data consistency and fault tolerance.
• 📦 Configuration Management: Systems like Kubernetes use Raft to manage cluster configuration and ensure that all nodes have the same view of the system.
• 🏦 Financial Transactions: Distributed consensus algorithms are crucial in financial systems for ensuring that transactions are processed reliably and consistently across multiple servers.
• 🔑 Blockchain Technology: Some blockchain implementations utilize consensus mechanisms related to Paxos or Raft for validating transactions and maintaining the integrity of the distributed ledger.

💡 Conclusion

Paxos and Raft are essential algorithms for achieving distributed consensus in fault-tolerant systems. While Paxos is historically significant and widely used, Raft offers a more understandable approach. Understanding the principles behind these algorithms is crucial for building reliable and scalable distributed applications.

📚 What is Distributed Consensus?

Distributed consensus is the process by which a group of computers (or nodes) in a distributed system come to an agreement on a single value or state, even when some of the nodes may fail or be unreliable. It's like a team of people trying to decide on a restaurant, but some of them might be lying about their preferences or unavailable. The goal is to reach a decision that everyone agrees on, despite these challenges.

📜 History and Background

The problem of distributed consensus has been studied for decades in computer science. Some key milestones:

• 🕰️ 1980s: Early research into fault-tolerant systems.
• ✍️ 1990s: Leslie Lamport introduces Paxos, a family of protocols for achieving consensus.
• ⛵ 2014: Diego Ongaro and John Ousterhout publish the Raft consensus algorithm, designed to be more understandable than Paxos.

🔑 Key Principles of Paxos

Paxos is a family of protocols, not a single algorithm. The most well-known is Basic Paxos, which involves three types of agents:

• 🙋 Proposer: Attempts to propose a value.
• ⚖️ Acceptor: Accepts or rejects proposed values.
• 📢 Learner: Learns the chosen value.

Basic Paxos operates in two phases:

• 1️⃣ Prepare Phase: A proposer sends a prepare request to a majority of acceptors with a proposal number ($n$). If an acceptor receives a prepare request with a number higher than any it has seen, it promises to ignore any future prepare requests with lower numbers and responds with the highest accepted value (if any) and its associated proposal number.
• 2️⃣ Accept Phase: If the proposer receives responses from a majority of acceptors, it sends an accept request to those acceptors with the proposal number ($n$) and a value. If any acceptor receives an accept request with a number equal to the highest prepare request it has seen, it accepts the value.

A simplified visualization of Paxos:

🔑 Key Principles of Raft

Raft is designed to be more understandable than Paxos. It breaks the consensus problem into smaller, more manageable subproblems:

• 🗳️ Leader Election: Choosing a leader node.
• 📝 Log Replication: Replicating log entries across nodes.
• 🔒 Safety: Ensuring that all nodes agree on the same log entries.

Raft operates with three types of nodes:

• 👑 Leader: Handles all client requests and manages log replication.
• 🧑‍💼 Follower: Passively receives log entries from the leader and votes in leader elections.
• candidate: Used during the leader election process.

Key aspects of Raft include:

• ➕ Term: Raft divides time into terms, each beginning with an election.
• 🪵 Log: An ordered sequence of entries, each containing a state command.
• 🔄 Replication: The leader replicates its log to the followers, and if followers crash or become unavailable, they catch up when they rejoin the cluster.

🌐 Real-World Examples

• 🗄️ etcd: A distributed key-value store used in Kubernetes, relying on Raft for consensus.
• 🐘 ZooKeeper: A centralized service for maintaining configuration information, naming, providing distributed synchronization, and group services, using a Paxos-like protocol.
• 💾 CockroachDB: A distributed SQL database that uses a variant of Raft called Multi-Paxos.

💡 Conclusion

Paxos and Raft are crucial algorithms for achieving distributed consensus in various systems. While Paxos is historically significant and widely implemented, Raft offers a more understandable alternative, making it increasingly popular in modern distributed systems. Understanding these algorithms is essential for anyone working with distributed systems, ensuring data consistency and reliability across multiple nodes.

📚 Introduction to Distributed Consensus

Distributed consensus is the process of achieving agreement on a single data value among distributed systems or processes. It's crucial in scenarios where reliability and fault tolerance are paramount, such as in distributed databases, cloud computing, and blockchain technologies. Algorithms like Paxos and Raft are designed to solve this problem, ensuring that even if some nodes fail or messages are lost, the system can still reach a consistent decision.

📜 History and Background

The problem of distributed consensus has been studied for decades. Leslie Lamport introduced Paxos in 1989, although it was initially difficult to understand and implement. Raft, proposed by Diego Ongaro and John Ousterhout in 2014, was designed to be more understandable than Paxos while providing similar fault-tolerance guarantees.

• 🏛️ Paxos: Developed by Leslie Lamport, known for its complexity but foundational in distributed systems.
• 🚢 Raft: Created by Ongaro and Ousterhout, designed for understandability and ease of implementation.

🔑 Key Principles of Paxos

Paxos achieves consensus through multiple rounds of proposing and accepting values. It involves three types of agents: Proposers, Acceptors, and Learners. The algorithm ensures that once a value is chosen, all processes agree on that value, even if some processes fail.

• ⚙️ Proposers: Initiate the consensus process by proposing a value.
• ✅ Acceptors: Vote on proposed values; a value is accepted if a majority of acceptors vote for it.
• 🎓 Learners: Learn the chosen value once a consensus is reached.
• 🔢 Quorum: Requires a majority of acceptors to agree to ensure consistency.

🔑 Key Principles of Raft

Raft achieves consensus through a leader election process. One server is elected as the leader, and it is responsible for replicating log entries to the other servers (followers). If the leader fails, a new leader is elected.

• 👑 Leader Election: A server is elected leader for a term.
• 🪵 Log Replication: The leader replicates log entries to followers.
• 🔒 Safety: Raft guarantees that if a log entry is committed, it will remain committed.
• 💔 Fault Tolerance: Raft can tolerate the failure of some servers.

🛠️ Real-world Examples

• 💾 Distributed Databases: Both Paxos and Raft are used in distributed databases to ensure data consistency across multiple nodes. Examples include Google's Spanner (Paxos-inspired) and etcd (Raft).
• ☁️ Cloud Computing: Used in cloud platforms for managing configurations and coordinating services.
• ⛓️ Blockchain: Some blockchain technologies use consensus algorithms similar to Paxos or Raft to validate transactions.

🧪 Paxos vs. Raft: A Comparison

💡 Conclusion

Paxos and Raft are fundamental algorithms for achieving distributed consensus. While Paxos is a foundational algorithm known for its complexity, Raft offers a more understandable and implementable solution. Both algorithms play crucial roles in ensuring reliability and fault tolerance in various distributed systems.