Mastering the Raft Consensus Algorithm: A Comprehensive Tutorial in Distributed Systems

Summary

This engaging video introduces the Raft consensus algorithm, crucial for ensuring reliability and consistency in distributed systems, akin to a team of servers working in unison. Through an analogy of overworked interns, it underscores the inevitability of server failures and the catastrophic impact on businesses, proposing the deployment of multiple service instances as a remedy. Highlighting the challenge of keeping data synchronized across replicas, it distinguishes Raft for its simplicity and efficiency over other consensus algorithms like Paxos and Proof of Work. The video delves into Raft's mechanisms, including leader election and log replication, illustrating how it achieves consensus and maintains a strong consistency model, making it a favored choice for distributed systems and databases.

Takeaways

• 📊 Servers, like overworked interns, are prone to failure, which can be devastating for businesses, leading to lost customers and revenue.
• 🔧 Running multiple instances of a service across different regions can mitigate the impact of failures, ensuring zero interruption.
• 🛠️ Consensus algorithms are essential for maintaining sync and consistency across replicas in a distributed system, preventing data discrepancies.
• 💻 Raft is an understandable consensus algorithm that simplifies the coordination of nodes to achieve consensus, making it easier to implement than Paxos.
• 📖 Raft was introduced as a more approachable alternative to Paxos, aiming to solve consensus through single-leader election and log replication.
• 📚 State machine replication across nodes ensures that if they start with the same state and perform the same operations, they will maintain consistency.
• 📬 Raft employs two types of RPCs for in-cluster communication: RequestVotes for leader election and AppendEntries for log replication and as a heartbeat mechanism.
• ⏰ Raft's leader election process involves randomized election timeouts and requires a majority vote from followers, promoting fairness and reducing the risk of split votes.
• 📈 Log replication in Raft ensures all nodes agree on a sequence of values, with the leader committing entries only after a majority of followers have appended them.
• ⚡️ Raft supports linearizability, providing a strong consistency model, but scalability can be a challenge due to the single-leader bottleneck.

Q & A

What is a distributed system and what problem does it introduce related to consensus?

A distributed system is one where there are multiple nodes or replicas working together to provide a service. The problem this introduces related to consensus is making sure all the nodes stay in sync with the latest data and agree on the system state.

What is the paxos algorithm and what are some of its drawbacks?

The paxos algorithm is a consensus algorithm commonly used in distributed systems. It is used by systems like Apache Zookeeper and Google's Chubby lock service. Some drawbacks are that it has a reputation for being difficult to understand and implement.

How does Raft tackle the consensus problem differently than Paxos?

Raft uses leader election and log replication. A single leader node receives requests from clients and replicates log entries to follower nodes. Once a majority of followers have written the log entry, it can be committed.

What are the different states a node can take on in a Raft cluster?

The different states are: follower, candidate, and leader. All nodes start as followers. If a follower times out without hearing from a leader, it becomes a candidate and attempts to get elected leader.

What types of RPC messages does Raft use for communication?

Raft uses two types of RPC messages: RequestVote for electing a new leader and AppendEntries for the leader to replicate log entries and send heartbeats.

How does a Raft leader commit a new log entry?

The leader replicates the new log entry to followers using AppendEntries RPCs. Once a majority of followers have appended the entry, the leader commits it and applies it to its state machine.

What are some benefits of the Raft consensus algorithm?

Benefits include understandability, relative simplicity in implementation, and linearizability guarantees from having a single leader.

What are some drawbacks of Raft consensus?

A drawback is potential scalability issues from having all requests go through a single leader node. The leader also requires acknowledgement from a majority of followers for every operation.

What are some examples of systems that use Raft consensus?

Examples include CockroachDB, MongoDB, Consul, Nomad, and Vault by HashiCorp.

Where can I find open-source Raft implementations to use?

New Raft by eBay (C++) and Hashicorp's Raft (Go) are two open-source options linked in the video description.

Outlines

00:00

🌐 Introduction to Raft: The Consensus Algorithm for Distributed Systems

This segment introduces the Raft algorithm within the context of building reliable web servers using Go. It begins with an analogy comparing backend infrastructure to overworked interns, highlighting the inevitability of system failures and their impact on online businesses. The discussion then shifts to the concept of distributed systems as a solution to mitigate service disruptions, emphasizing the need for consensus among multiple instances of a service to maintain synchronization and consistency. It introduces various consensus algorithms, focusing on Raft as an understandable and implementable option compared to others like Proof of Work and Paxos, setting the stage for a deeper exploration of Raft's mechanics.

05:00

🔧 Mechanics of Raft: Achieving Consensus in Distributed Systems

This part delves into the specifics of the Raft algorithm, explaining how it ensures consistency across a distributed system through the replication of state machines and log entries. It outlines the role of servers in the system, including endpoints for key-value store interactions. The process of state machine replication, vital for synchronizing replicas, is detailed alongside the election of a leader responsible for directing the flow of commands. The dynamics of Raft's single leader election, follower roles, and the communication protocols used for in-cluster communication, including the election process and log entry replication, are thoroughly explained. This section aims to clarify how Raft facilitates consensus and log replication, emphasizing the importance of a majority agreement among nodes for the system's reliability and consistency.

10:00

🔄 Log Replication and Commitment Process in Raft

The final section focuses on the log replication and commitment process within the Raft algorithm, crucial for performing operations across the distributed system. It explains how a leader replicates a new entry across followers' logs and the conditions required for an operation to be committed and applied to the state machine. This detailed examination includes the consistency checks performed by followers, the requirement for a majority to write the new entry to their logs, and the process by which the leader commits an entry and notifies followers. The benefits of having a single leader, such as guaranteeing linearizability, are discussed along with potential scalability challenges. The segment concludes by mentioning the implementation of Raft in various technologies and inviting viewers to explore further resources, including the original paper and practical implementations in Go and C++.

Keywords

💡distributed system

A distributed system is a group of nodes or computers that work together as a single system. The video describes a distributed key-value store spread across multiple servers. Distributed systems help improve reliability by eliminating single points of failure.

💡consensus

Consensus refers to getting all nodes or replicas in a distributed system to agree on a value or state. This is critical to keep the nodes synchronized. The video focuses on the Raft consensus algorithm for achieving consensus.

💡state machine replication

State machine replication involves replicating a state machine (program) across multiple nodes and feeding the nodes the same sequence of commands so their states stay synchronized.

💡leader election

Raft uses single leader election where one node acts as the leader. Only the leader can receive client requests and replicate entries to follower nodes' logs.

💡log replication

With log replication, nodes agree on an ordered log of operations instead of a single value. New entries are appended to the nodes' logs and replicated across the cluster.

💡heartbeat

The leader sends periodic heartbeat messages to followers so they know it is still alive. If no heartbeat is received within the timeout period, a follower becomes a candidate and starts a new election.

💡linearizability

Having a single leader provides linearizability, the strongest consistency model, by establishing a total order on operations. However, it can become a scalability bottleneck.

💡term

A term acts as a logical time period in Raft. It increments whenever an election happens. Using terms and comparing logs helps resolve conflicts during elections.

💡commit

Once an entry has been replicated to a majority of follower logs, the leader commits the entry. This applies it to the state machine, updating the replicated state.

💡rpcs

Raft uses remote procedure calls (rpcs) for communication between nodes. Key rpcs include RequestVote for leader election and AppendEntries for log replication.

Highlights

Introduction to the raft algorithm in the context of reliable web servers with Go.

Analogy of backend infrastructure's susceptibility to failure with overworked interns.

The critical impact of server downtime on online businesses, leading to lost customers and revenue.

Mitigating service failure impact by running multiple instances across different regions.

Challenges of maintaining consensus in a distributed system of nodes.

The necessity of consensus algorithms for synchronizing replicas in a distributed system.

Overview of consensus algorithms like Proof of Work and its application in blockchain.

Introduction of the Paxos algorithm and its complexity in implementation.

Introduction to Raft as an easier alternative to Paxos for achieving consensus.

Detailed explanation of Raft's approach through single leader election and log replication.

State machine replication across nodes to ensure synchronized states.

Process of electing a leader among nodes and the role of a leader in Raft.

Mechanism of log replication in Raft to ensure consistency across replicas.

Benefits of Raft's single leader model for linearizability and potential scalability issues.

Usage of Raft in technologies like HashiCorp's Consul, Nomad, Vault, MongoDB, and CockroachDB.

Recommendations for production-ready implementations of Raft and further resources.

Transcripts

welcome back to our Channel where we

bring you practical courses from

seasoned Engineers today we're diving

into the world of the raft algorithm a

consensus algorithm that we've detailed

in our course reliable web servers with

go now let's imagine your back-end

infrastructure as a group of overworked

interns they're susceptible to failure

whether it's from too much coffee or not

enough sleep likewise servers are also

susceptible to failure whether it's from

a coding bug or disc failure these

failures can be as unpredictable as they

are

inevitable this is especially critical

if you're only running one instance of

your service when a failure happens and

your service goes down not only will it

take some time for you to be alerted

about this failure but it will also take

even more time to address and resolve

this failure every minute of downtime is

devastating to any online business it

will experience lost customers lost

revenue or severe damage to its brand

reputation one way that we can mitigate

the impact of a failure is to spin up

more instances of our service for

example if we run multiple instances of

our service across many different

regions around the world even if one

fails and goes offline there will still

be others running and keeping the

service afloat with zero Interruption

with all of these instances or nodes

working together to keep the service

afloat what we have created is a

distributed system as with any

distributed system we start coming

across a common problem consensus given

that all of these nodes are copies of

each other how do we get all of the

replicas to stay in sync and consistent

so that regardless of any failures all

of the replicas are up to dat with the

latest data for example let's say you

register a username for a new account

underneath the hood a database processes

a transaction that creates a new record

for your account suppose the data for

this database exists on several replicas

and your username only ends up on one of

these replicas if other clients are

reading data from any of the remaining

replicas then the username will

mistakenly be reported as available for

registration we need to make sure that

all of the replicas agree that the

username has already been taken we can

solve this problem with a consensus

algorithm a consensus algorithm

coordinates all of the nodes within a

distributed system to come to an

agreement or achieve consensus on a

value there are many consensus

algorithms for example there's proof of

work which involves node solving

difficult mathematical puzzles to

validate transactions and record them on

the blockchain you may already know this

as mining the solution serves as proof

that miners expended significant

computing power to validate a

transaction proof of work is the

underlying mechanism for consensus in

popular decentralized networks like

Bitcoin and ethereum the paxos algorithm

a legendary computer science algorithm

that was first introduced as a

theoretical solution to distributed

consensus in a 1989 paper titled the

part-time Parliament by computer

scientist Leslie Lamport the paxos

algorithm is one of most commonly used

consensus algorithms in distributed

systems it is used by Apache zookeeper

and Google's chubby distributed lock

service however given the paxos

algorithm's reputation for being

difficult to understand and Implement

today I want to talk about an

alternative consensus algorithm that's

designed to be much easier to understand

and

Implement raft raft was first proposed

in a 2014 paper titled in search of an

understandable consensus algorithm by

Diego angaro and John ousterhout Raph

tackles the problem of consensus through

single leader election and L replication

to understand how they solve consensus

in a distributed system let's imagine a

distributed key value server that runs

on a cluster of three nodes each node or

replica hosts a state

machine

log and raap

protocol in the context of distributed

systems a state machine is a fancy term

for a program that's replicated for our

scenario the state machine that's hosted

on each replica is a server that

features endpoints for interacting with

the key Value Store such as a get

endpoint for retrieving a value from the

store by a key a post endpoint for

setting a key with a value in the store

a delete end point for deleting a key

and its corresponding

value if we replicate This Server onto

each of these nodes then as long as they

all begin with the same state and

perform the same operations in the same

order then they will all end up with the

same state this process of replicating

State machines across nodes and sending

these nodes the same sequence of

commands is commonly known as state

machine replication anytime a replica

receives a command such as setting a new

key with a value in the store the

replica appends and saves the command as

a new entry in its log these commands

get fed to the replica's state machine

as input every replica's log must always

contain the same exact sequence of

commands for the replicas to remain

synchronized so the question now is who

sends commands to these replicas that's

where single leer election comes in each

replica in the cluster can take on any

of the following states but can only

take on just one state at any given time

follower candidate

leader all replicas start out on the

follower State any replica that's a

follower can only accepts commands when

no leader is present or is unresponsive

the followers must elect a new leader a

leader is responsible for receiving

requests from a client and sending

commands to followers only the leader

can receive requests from a client in

case the client tries to send a request

to a follower we place a load balancer

in front of the cluster to redirect this

request to the leader this ensures all

requests go to the leader each follower

sets an election time out which is a

specific time interval within which the

follower must hear back from a leader

raft randomizes the election timeout for

each follower but typically the election

timeout Falls within the range of 150

milliseconds to 300 milliseconds the

moment a follower reaches its election

time out and it does not hear back from

a leader the follower becomes a

candidate initiates an election for a

new leader and votes for itself to

request votes from other followers the

candidate sends a request votes message

to them and waits for them to reply back

with their

votes request votes is one of two types

of remote procedure calls or rpcs that's

employed by the raft protocol for

in-cluster

communication the message includes

information about the total number of

entries in a candidate's log and the

term of the latest entry a term is a

counter value that represents an

arbitrary time period during the

lifetime of a raft cluster each replica

starts with a term of zero and each

replica maintains its own term the term

increments anytime an election begins an

election can begin for any reason

whether it's brought about after a

leader goes offline or if the network

experiences enough latency that a

follower reaches its election time out

despite a leader still being

alive followers will not vote for the

candidate if there are any

inconsistencies in the candidate's log

if the candidate receives the majority

of votes from followers then the

candidate becomes the new leader if the

candidate fails to be elected then the

candidate reverts back to being a

follower once a leader has been elected

the leader emits append entries messages

to followers within the raft cluster

append entries is the other type of

remote procedure calls or rpcs that's

employed by the raft protocol for

in-cluster communication and it serves

as both a heartbeat mechanism and tells

followers to replicate new log

entries a heartbeat timeout determines

how often these messages are sent to

followers so that they know the leader

is still alive now let's take everything

that we've learned so far and see what

happens when the leader of our raft

cluster for our distributed key value

server receives a request from a client

to set a key with a value in the store

upon receiving a request from a client

the leader appends the set operation as

a new entry in its log note that

appending a new entry in the log does

not actually actually perform the

operation we would need to commit the

entry for the operation to be performed

for this to happen we need a majority of

the followers to have this operation

appended to their logs and so to

replicate this entry across all of the

replicas the leader sends append entries

messages to the followers in its cluster

upon receiving an append entries message

a follower performs a consistency check

to verify that its log is identical to

the leaders after passing the

consistency check the follower appends

this set operation as a new entry in

their logs once a majority of followers

have written this new entry to their

logs the leader commits the entry and

applies it to its state machine

essentially we now have a replica in our

cluster that has updated their key value

store with the new key and value then

the leader sends append entries messages

to the followers in its cluster but this

time to notify followers that the entry

has been committed and that they too

should commit the

entry and with that the cluster has now

come to consensus about the state of the

distributed key value server this is

known as log replication with log

replication replicas agree on a sequence

of values not on a single value as with

the paxos algorithm a benefit of having

a single leader is that it guarantees

linearizability the strongest

consistency model but at the same time

scalability can be an issue having every

client request go through a single

leader can become a bottleneck

especially when the leader requires

acknowledgement from a majority of

followers for every single operation

nevertheless the raft algorithm is used

by a number of Hashi corpse Technologies

such as console Nomad and Vault and and

buy popular databases like mongod DB and

Cockroach DB in fact cockroach DB has a

blog post on scaling raft which I've

Linked In the description of this video

below if you are looking for a

production ready implementation of the

raft algorithm for own projects then

have a look at new raft a C++

implementation of the raft algorithm by

eBay and raft a goang implementation of

the raft algorithm

by Hashi Corp both of them are linked in

the description of this video below if

you would like to learn more about the

raft algorithm then you can read the

original in search of an understandable

consensus algorithm paper which I've

Linked In the description of this video

please comment below on other topics

that you would like us to cover in

future videos and don't forget to like

And subscribe to our channel for more

exciting content if you would like to

learn how to build your very own

distributed key value server with go and

the raft consensus algorithm then check

out our course reliable web servers with

go which I've Linked In the description

of this video below bye