What is CAP Theorem?

Summary

In this video, Jamil Spain from IBM explores the CAP Theorem, a fundamental concept in distributed systems and cloud-native design. The theorem, introduced by Eric Brewer, posits that a distributed system can only guarantee two out of three desirable properties: consistency, availability, and partition tolerance. Spain uses MongoDB and Apache Cassandra as examples to illustrate how different databases prioritize these properties, emphasizing the trade-offs involved in system design. He also extends the discussion to microservices architecture, suggesting that the CAP principles can guide decisions about service responsibilities and resilience.

Takeaways

• 🍰 The CAP Theorem is a fundamental concept in distributed systems, illustrating the trade-offs between consistency, availability, and partition tolerance.
• 👤 The theorem was developed by Eric Brewer during his Ph.D. at MIT in the early 2000s, focusing on cloud native design and distributed architectures.
• 🔤 The acronym CAP stands for Consistency, Availability, and Partition Tolerance, which are the three key aspects of distributed systems design.
• 🔄 Consistency ensures all clients get the same data at the same time, reflecting a synchronized state across the system.
• 🚀 Availability means that every request receives a response, without guarantee that it contains the most recent version of the information.
• 🔄 Partition Tolerance refers to the system's ability to continue operating when some nodes have lost communication with others.
• 🚫 According to the CAP Theorem, it's impossible for a distributed system to simultaneously achieve all three properties; at most, two can be fully realized.
• 📚 The choice between the three depends on the specific requirements and priorities of the system being designed.
• 🌐 MongoDB is highlighted as an example of a database that prioritizes Consistency and Partition Tolerance, using a primary-secondary replication model.
• 🌐 Apache Cassandra is presented as an example of an AP (Availability and Partition Tolerance) system, where all nodes are equal and synchronization is eventual.
• 🤔 The CAP Theorem challenges designers to make deliberate decisions about trade-offs in their distributed systems, considering the importance of each property.
• 🛠 The principles of the CAP Theorem can also be applied to microservices architecture, guiding decisions on how to balance consistency, availability, and partition tolerance in different service components.

Q & A

What is the CAP Theorem?

The CAP Theorem, also known as Brewer's Theorem, states that in a distributed system, it is impossible for a database to simultaneously provide more than two out of the following three guarantees: Consistency, Availability, and Partition Tolerance.

Who developed the CAP Theorem?

Eric Brewer developed the CAP Theorem while he was getting his Ph.D. at MIT in the early 2000s.

What does the acronym 'CAP' stand for in the context of the CAP Theorem?

In the CAP Theorem, 'C' stands for Consistency, 'A' for Availability, and 'P' for Partition Tolerance.

What does Consistency mean in the CAP Theorem?

Consistency in the CAP Theorem means that all clients will get the same data at the same time, ensuring that the data is synchronized across the system.

What does Availability mean in the CAP Theorem?

Availability in the CAP Theorem refers to the system's ability to serve read and write requests at all times, even in the event of a node failure.

What is Partition Tolerance in the CAP Theorem?

Partition Tolerance in the CAP Theorem is the system's ability to continue operating even when nodes lose communication with each other, ensuring that the system can recover and re-synchronize.

Why can only two out of the three aspects of the CAP Theorem be achieved at any given time?

The reason only two out of three can be achieved is due to the inherent trade-offs in distributed systems. Prioritizing one aspect often comes at the expense of the other two, as they are mutually exclusive in certain scenarios.

Can you provide an example of a database that focuses on Consistency and Partition Tolerance?

MongoDB is an example of a database that focuses on Consistency and Partition Tolerance, with a primary node handling all writes and secondary nodes replicating from the primary to ensure data synchronization.

How does MongoDB handle node failures to ensure Partition Tolerance?

In the event of a primary node failure in MongoDB, an election process occurs where one of the secondary nodes becomes the new primary, ensuring the system continues to operate and maintain data consistency.

What is an example of a distributed system that focuses on Availability and Partition Tolerance?

Apache Cassandra is an example of a distributed system that focuses on Availability and Partition Tolerance, allowing all nodes to independently serve read and write requests and synchronize data eventually.

How does the CAP Theorem apply to microservices architecture?

The CAP Theorem can be applied to microservices architecture by considering the trade-offs between Consistency, Availability, and Partition Tolerance when designing individual components. For example, a web frontend might prioritize Availability to ensure user requests are always served, while the backend might focus on Consistency to maintain accurate data state.

Outlines

00:00

📚 Introduction to CAP Theorem

In this introductory paragraph, Jamil Spain, a Developer Advocate with IBM, sets the stage for a discussion on the CAP Theorem. The theorem, developed by Eric Brewer during his Ph.D. at MIT in the early 2000s, is a fundamental concept in cloud native design and distributed architectures. It is particularly relevant to how databases are designed for distribution. The acronym CAP stands for Consistency, Availability, and Partition tolerance, each representing a core aspect of distributed systems. The speaker emphasizes that typically, only two of these three aspects can be achieved simultaneously, echoing the cliché 'you can't have your cake and eat it too,' which highlights the inherent trade-offs in system design.

05:02

🔄 Exploring the CAP Theorem's Implications in Databases

This paragraph delves deeper into the CAP Theorem's practical implications, especially in the context of database systems. Jamil Spain uses MongoDB as an example to illustrate the trade-offs between consistency and partition tolerance. MongoDB operates with a primary node and multiple secondary nodes, ensuring data consistency through replication from the primary node. In the event of a primary node failure, MongoDB employs an election process to promote a secondary node to primary status, thus maintaining partition tolerance. However, during this transition, the system may temporarily sacrifice availability for consistency and partition tolerance. The speaker also touches on the intersection of the CAP principles and how they pair up in different scenarios, emphasizing the importance of choosing the right database based on the specific requirements of the distributed system architecture.

Keywords

💡CAP Theorem

The CAP Theorem is a concept in distributed systems that states a system can only simultaneously achieve two out of the following three guarantees: Consistency, Availability, and Partition Tolerance. The theorem is central to the video's theme as it discusses how different databases prioritize these aspects. For example, the video mentions that MongoDB focuses on Consistency and Partition Tolerance, while Apache Cassandra emphasizes Availability and Partition Tolerance.

💡Consistency

Consistency in the context of the CAP Theorem means that all clients see the same data at the same time. It is one of the three core aspects that a distributed system can prioritize. The video explains that in MongoDB, data is written to a primary node and replicated to secondary nodes, ensuring consistency across the system.

💡Availability

Availability refers to the guarantee that every request receives a response, without guarantee that it contains the most recent version of the information. The video uses Apache Cassandra as an example of a system that prioritizes availability, as it allows nodes to continue operating and serving data even if some are down or out of sync.

💡Partition Tolerance

Partition tolerance is the system's ability to continue operating despite arbitrary partitioning due to network failures. The video discusses how MongoDB handles partition by having a brief election process when a primary node goes down, ensuring the system can recover and maintain operation.

💡Distributed Architectures

Distributed architectures are systems where components are spread across multiple locations and communicate over a network. The video script discusses how the CAP Theorem applies to the design of distributed databases, such as MongoDB and Apache Cassandra, and the trade-offs they make in terms of consistency, availability, and partition tolerance.

💡Eric Brewer

Eric Brewer is the person credited with formulating the CAP Theorem during his Ph.D. at MIT. His work is foundational to the video's discussion, as it provides the theoretical framework for understanding the trade-offs in distributed system design.

💡IBM

IBM is mentioned in the video as the company of the speaker, Jamil Spain, who is a Developer Advocate. This sets the context for the video as coming from a perspective of someone who advocates for the use of IBM's technologies and solutions in software development.

💡MongoDB

MongoDB is a type of NoSQL database that is highlighted in the video as an example of a system that prioritizes Consistency and Partition Tolerance over Availability. The script explains how MongoDB's design with a primary and secondary node structure supports its consistency model.

💡Apache Cassandra

Apache Cassandra is another NoSQL database mentioned in the video, which is used to illustrate a system that prioritizes Availability and Partition Tolerance. The video describes how Cassandra's design allows for continuous operation and eventual synchronization of data across nodes.

💡Microservices

Microservices is an architectural style that structures an application as a collection of loosely coupled services. The video suggests thinking about the CAP Theorem in the context of microservices, considering how different services might prioritize different aspects of the theorem based on their responsibilities.

💡Single Responsibility Principle (SRP)

The Single Responsibility Principle is a principle in software development that states that a class or module should have only one reason to change. The video connects this principle to the design of microservices, emphasizing that each service should have one job, which relates to how the CAP Theorem might be applied to individual components of a system.

Highlights

CAP Theorem was developed by Eric Brewer during his Ph.D. at MIT in the 2000s.

CAP Theorem is relevant to cloud native design and distributed architectures.

The acronym CAP stands for Consistency, Availability, and Partition tolerance.

Consistency ensures all clients get the same data at the same time.

Availability means data is always replicated across all nodes.

Partition tolerance is about recovery and reconnection when nodes are out of sync.

Only two out of the three CAP properties can be achieved at any given time.

The choice between CAP properties depends on the specific needs of the system.

MongoDB focuses on consistency and partition tolerance, with a primary-secondary node design.

In MongoDB, data is always written to the primary node and replicated to secondary nodes.

MongoDB ensures consistency by writing in one place and reading from the same source.

In case of a primary node failure in MongoDB, a brief election process occurs for a new primary node.

MongoDB's partition tolerance is handled by the election process and reconnection of nodes.

Apache Cassandra is an example of a system focusing on availability and partition tolerance (AP).

Cassandra has no primary server, with all nodes being independent and always available.

Cassandra achieves eventual consistency through synchronization among nodes.

Cassandra handles partition tolerance by synchronizing nodes when they come back online.

The CAP Theorem can be applied to microservices architecture, considering front-end, back-end, and middle-tier components.

Single Responsibility Principle (SRP) in microservices aligns with CAP principles for architectural decisions.

Web front-end services might prioritize availability, while back-end services might focus on consistency.

Transcripts

Have you ever heard the cliché

you can't have your cake and eat it,

too?

Well, that's a cliché that's really

reduced down that there's always

some side of sacrifice involved

in any situation.

Now we're not here to talk about

life scenarios

here, but it kind of relates to our

topic today on CAP

Theorem.

Hello, my name is Jamil Spain,

Developer Advocate with IBM.

Now, this theorem really has, we

have to give our credit due.

It came from a person called Eric

Brewer, who developed this

while he was getting his Ph.D.

at MIT.

Roughly in the 2000s.

And the topic of conversation here

was cloud native design,

distributed architectures.

And this principle really relates

down to how databases

are designed to be distributed in

nature.

So now that we've defined

where the background comes from,

let's really break down the acronyms

now of AP Cap

and the C stands

for consistency.

Which really deduces down

that all the clients need to

be able to get the same data

at the same time.

All right. All the data is

consistent there.

The next the a availability.

All right. As data is written, is

it actually always replicated

across to all the nodes that

are there?

And the last is.

The P is for partition.

And we're going to add that on

really partition tolerance.

OK.

And so that really is if, let's say,

one or more of the notes come

out of communication out of sync.

How well do they recover from that?

Do they have a procedure for

balancing those out and getting

reconnected? And what happens after

that occurs?

So now we have all three that are

here, CAP.

Let's talk about how is represented

and how it's actually talked about

how you'll find it.

So let's put these down.

You'll often see them pictured

this way.

CAP with three circles

and really you you see these

acronyms that come from.

What are you going to achieve?

Now I said, we want to relate this

back to. You can't have your cake

and eat it, too.

Well, that's the situation here.

You really can only have two out of

the three at any given time.

So in a lot of you, a distributed

architecture is your decisions

you make on which

database to use.

You know, it really depends about

what's most important here.

But let's talk about how these pair

up. So if I take the intersection

of these, this would be

a C.A.

C.P.

and A.P.

OK. And I'm going to outline

these as we go from here.

So when it comes down to

it, let's take a

database like MongoDB.

All right. This is going to be

focused on the consistency

and partition partition tolerance

there. Why?

From a consistency?

Well, we know from the design of

Mongo is that it has a

primary node,

and there are also secondary node.

All the rights go to the primary

node and all

the secondary nodes as they

as you add multiple ones.

They all replicate directly from

the masters logs of transactions

and everything is there.

So you get the consistency from

there that that

data will always be in sync because

you always writing in one place and

is always reading reads always come

from that one source of

action there.

So in the event

we have the consistency that that

checks box that piece there.

Now, from a petition, let's say what

happens in the event that one of the

notes goes down, your master goes

down, your

primary node goes down, then

one. There's a brief moment

where an election

process has to happen.

One of the secondary nodes becomes

the primary node, and then

you know that other primary comes

back up. It becomes then a secondary

node. So in that brief time

that that procedure is handled,

the partition balancing there.

But in that time that the primary

goes down, it's not going to allow

any reads to me rights to occur

to the situation there.

So you have a moment where there's

going to be all reads that are

available, right?

And so that's really how a lot

of these kind of peer up to match

based upon what you need.

What's most important for you is,

you know, having that recovery model

in place and being

able to always guarantee that

consistency in the event

that you may have some availability

outage there as well.

So that's great for that, C.P..

Now let's deal with the other cross

section of that, which is AP

now here, let's take a use case

of a

distributed system like Apache

Cassandra.

And I wanted to be able to show the

opposite variance

here with Apache Cassandra.

The difference is for Mongo

is that there is really no primary

server, so all

of the nodes are

kind of

all the notes

are kind of independent as they

go. So we're going to always have

that availability.

All right. They're going to always

be available to serve out,

rewrite data.

All right.

And in the event

of a partition.

So with that process of

so you always have the availability

since there are always, always

running now from a petition

perspective, they

do something called eventually

they all have to synchronize with

each other. And so because they are

all kind of distributed,

they're always all can read and

write to each of those.

They have some period where they're

all thinking.

So you won't have always instant

consistency there that you will get

with Mongo DB, but

at least they have a facility

set in place to be able to

synchronize with each other

as, let's say, one of them goes, one

of the nodes goes down.

They have a procedure when it comes

back up. Of course, it has a job of

kind of catching back up to date

with the others there.

So that kind of solves the

AP for that.

And so generally, you'll see

on the web, think about when you

look at distributed databases,

what do they offer here?

All right.

And pick two of these that you want

to achieve.

They may not ever be a situation

where you have all three available

here. Before we end this talk,

I do want to talk a little bit about

let's take this a step further.

As technologies, we have to

challenge ourselves as well to

think through a lot of the decisions

we make.

And for me, I've thought about

that. We could also apply

this to microservices and

how your you make decisions about

how you architect your particular

components, whether their front end,

back end or in the middle part

as well. So think about decisions

like from a web front end

availability may be a concern and

that situation, you may not be able

to pair two of these

necessarily, but at least

have in mind the piece that he

wants to play.

Now we know with most microservices

they achieve these single

responsibility principle

that you really have one and only

job that you're supposed to allocate

or do in your architecture.

So from a web front end, I may

have multiple replicas

to meet the availability because

that's most important for that.

I want everyone to always

as you request a web

page or front end, I want you

any client to always be able to get

responses there as well.

And so then I would take.

Move on to the distributed tier

where I then hit the back end

to make sure that that suffices,

that it can always maybe deliver

that data as well.

So just kind of think about it that

way, how

your how these cap principles

meet the

responsibility.

We do SRP single responsibility

principle here.

And this just touched the iceberg

here, but I definitely hope this

was useful in understanding the

background of CAP theorem and how

it can apply for you and your

architectures.

Thank you.

If you have any questions, please

drop us a line below.

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