SQL vs. Hadoop: Acid vs. Base

Summary

The script delves into the CAP theorem, a fundamental principle in distributed systems, introduced by Eric Brewer. It explains three core requirements: consistency, availability, and partition tolerance, noting that only two can be optimized simultaneously. The script contrasts traditional SQL Server's focus on consistency and availability with Hadoop's emphasis on partition tolerance and availability, sacrificing global consistency for scalability. It highlights the shift from ACID to BASE in big data technologies, where eventual consistency is prioritized over immediate consistency, reflecting a trade-off designed for handling massive datasets efficiently.

Takeaways

• 📚 The CAP Theorem, introduced by Eric Brewer, is foundational to understanding distributed systems like Hadoop. It states that only two of the three core requirements—Consistency, Availability, and Partition Tolerance—can be optimized at once.
• 🔄 Consistency in a distributed system means all operations succeed or fail together, as in transaction commit and rollback mechanisms.
• 🛡️ Availability refers to the system being operational and responsive to requests at all times, even in the event of node failures.
• 🌐 Partition Tolerance is the system's ability to continue functioning even if some parts of the network are disconnected.
• 🚫 Traditional SQL systems prioritize consistency and availability but are not tolerant of partitions being down, which differs from Hadoop's approach.
• 🔄 Hadoop and other big data technologies prioritize partition tolerance and availability, relaxing global consistency for scalability.
• 🔄 Eventual consistency is common in big data systems, where immediate consistency is not required, allowing for system scalability.
• 🔄 ACID (Atomicity, Consistency, Isolation, Durability) properties are central to relational databases, ensuring reliable processing of database transactions.
• 🔄 BASE (Basically Available, Soft state, Eventual consistency) is an alternative approach used in big data systems, favoring high availability and eventual consistency over strict ACID properties.
• 🔄 Two-phase commit is a method used in ACID systems to ensure consistency across distributed databases, introducing latency due to the need for all nodes to commit before proceeding.
• 🚫 Hadoop opts for BASE over ACID, forgoing two-phase commit in favor of higher availability and scalability, which may introduce some latency in achieving consistency.
• 🚫 The nature of data processed by big data technologies like Hadoop must be able to tolerate some imprecision to gain scalability benefits, making it unsuitable for applications requiring strict consistency, like bank account balances.

Q & A

What is the CAP theorem introduced by Eric Brewer?

The CAP theorem is a principle in distributed computing that states that of the three core requirements—consistency, availability, and partition tolerance—only two can be optimized at a time, with the third having to be relaxed or abandoned.

What does a consistent system mean in the context of the CAP theorem?

A consistent system, according to the CAP theorem, is one where the system operates fully or not at all. An example is transaction commit and rollback, where if all tables cannot be updated, all changes are reverted.

What does availability mean in the context of distributed systems?

Availability in distributed systems refers to the system being always ready to respond to requests. This implies that even if a node goes down, another node can immediately take over to ensure the system remains operational.

Can you explain the term 'partition tolerance' in the CAP theorem?

Partition tolerance means that the system can continue to run even if one of the partitions is down. In an MPP model, for instance, if one server handling a month's data is down, a highly partition-tolerant system would still operate and provide correct results.

How does the CAP theorem apply to the differences between SQL Server and Hadoop?

SQL Server emphasizes consistency and availability but is not tolerant of partitions being down, whereas Hadoop and other big data technologies prioritize partition tolerance and availability, relaxing global consistency.

What is the significance of the ACID concept in traditional relational databases like SQL Server?

The ACID concept ensures the reliability of database transactions. It stands for Atomicity, Consistency, Isolation, and Durability, meaning transactions are processed reliably and data remains consistent and intact.

What is the difference between ACID and BASE in terms of database transaction handling?

ACID ensures immediate consistency and uses mechanisms like two-phase commit, which can introduce latency. BASE, on the other hand, prioritizes availability and partition tolerance over immediate consistency, allowing for eventual consistency.

Why is the BASE approach suitable for big data systems like Hadoop?

The BASE approach is suitable for big data systems because it allows for greater scalability and availability. It is willing to forgo immediate consistency in exchange for the ability to handle thousands of partitions, some of which may be offline.

What are the implications of using Hadoop for applications that require strict transaction control?

Using Hadoop for applications that require strict transaction control, such as banking systems, may not be suitable because Hadoop follows the BASE approach, which does not guarantee immediate consistency and may not meet the ACID requirements necessary for such applications.

How should one's mindset be adjusted when working with Hadoop compared to traditional SQL databases?

When working with Hadoop, one must understand that it is designed for scalability and may not provide immediate consistency across nodes. This requires an adjustment in mindset from relying on ACID properties to accepting eventual consistency and the trade-offs involved.

What considerations should be made when choosing between using Hadoop and SQL Server for a particular application?

The choice between Hadoop and SQL Server should be based on the application's requirements. If immediate consistency and transaction control are critical, SQL Server may be more appropriate. If scalability and handling large volumes of data with eventual consistency are priorities, Hadoop could be the better choice.

Outlines

00:00

📚 Introduction to Hadoop and the CAP Theorem

This paragraph introduces the fundamental concepts of Hadoop's operation through the lens of Eric Brewer's CAP theorem, introduced around 2000. The CAP theorem is a principle of distributed computing that posits only two out of three core requirements—consistency, availability, and partition tolerance—can be optimized simultaneously. The paragraph explains these terms in the context of transaction systems, highlighting the trade-offs inherent in distributed systems. It contrasts traditional SQL Server's emphasis on consistency with Hadoop's focus on partition tolerance and availability, suggesting a shift in mindset from immediate consistency (ACID properties) to eventual consistency (BASE properties) for scalability in big data technologies.

05:02

🛠️ The Application of CAP Theorem in SQL Server vs. Hadoop

This paragraph delves into the practical application of the CAP theorem by comparing SQL Server and Hadoop. It emphasizes the importance of consistency in SQL Server environments, where transactions are atomic with commit and rollback mechanisms. In contrast, Hadoop and other big data technologies prioritize partition tolerance and availability, relaxing global consistency to achieve scalability. The paragraph also discusses the implications for transaction control, explaining that Hadoop operates on the BASE model, which allows for eventual consistency rather than immediate consistency. It concludes by noting the importance of choosing the right technology based on business requirements, as the two systems are designed for different needs and cannot be interchanged without consideration of their foundational principles.

Keywords

💡CAP Theorem

The CAP Theorem, introduced by Eric Brewer in 2000, is a concept in distributed systems that states that out of Consistency, Availability, and Partition Tolerance, only two can be simultaneously optimized. In the context of the video, it is used to explain why Hadoop and similar big data technologies prioritize Partition Tolerance and Availability over Consistency, differing from traditional SQL databases.

💡Consistency

In distributed systems, Consistency refers to the property where all nodes see the same data at the same time. The video describes it with the example of a transaction commit and rollback, where if all parts of a transaction cannot be updated, then none of the changes are applied. This is a key requirement in SQL Server but is often relaxed in big data systems like Hadoop.

💡Availability

Availability is the measure of a system's readiness to accept and process requests. The video mentions that in SQL systems, if a node fails, another node takes over, ensuring high availability. In contrast, Hadoop and big data technologies are designed to remain available even if some nodes are down, which is a core aspect of their design.

💡Partition Tolerance

Partition Tolerance is the system's ability to continue operating despite arbitrary partitioning due to network failures. The video explains that in an MPP (Massively Parallel Processing) model, a system with high partition tolerance can still function even if one of the partitions is down, which is a fundamental aspect of Hadoop's architecture.

💡SQL Server

SQL Server is a relational database management system that emphasizes consistency and availability but is not tolerant of partitions being down. The video contrasts SQL Server with Hadoop to highlight the differences in their approach to the CAP Theorem, with SQL Server focusing on consistency and Hadoop on partition tolerance and availability.

💡Hadoop

Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is highlighted in the video as an example of a big data technology that emphasizes partition tolerance and availability, and relaxes global consistency, in line with the CAP Theorem.

💡ACID Properties

ACID stands for Atomicity, Consistency, Isolation, and Durability, which are properties that guarantee reliable processing of database transactions. The video explains that traditional SQL databases follow ACID properties, ensuring the integrity of transactions, while big data systems like Hadoop often move away from strict ACID compliance.

💡BASE

BASE stands for Basically Available, Soft state, Eventual consistency, and is an acronym that contrasts with ACID. The video describes BASE as the approach taken by Hadoop and other big data technologies, where they prioritize availability and eventual consistency over immediate consistency, allowing for greater scalability and flexibility.

💡Two-Phase Commit

Two-Phase Commit is a type of transaction protocol used in distributed databases to ensure atomicity. The video mentions it in the context of ACID properties, where it is used to achieve immediate consistency across distributed nodes, but it introduces latency and can impact scalability, which is why Hadoop and similar systems often forgo it.

💡Scalability

Scalability refers to the ability of a system to handle a growing amount of work or to expand resources as needed. The video discusses how Hadoop's design, which prioritizes partition tolerance and availability, allows for greater scalability compared to traditional SQL databases that strictly follow ACID properties.

💡Eventual Consistency

Eventual Consistency is a property of a distributed system where the system guarantees that, if no new updates are made to the system, the system will eventually reach a consistent state. The video contrasts this with immediate consistency, explaining that big data systems like Hadoop may not have immediate consistency but will achieve it over time.

Highlights

Introduction to Eric Brewer's CAP theorem and its relevance to distributed processing systems.

CAP theorem states that only two of the three core requirements - consistency, availability, and partition tolerance - can be optimized at once.

Definition of consistency in the context of distributed systems: a system operates fully or not at all.

Example of consistency in transaction commit and rollback processes.

Definition of availability: the system is always available to respond to requests.

Illustration of availability in SQL systems with cluster configurations for redundancy.

Definition of partition tolerance: the system continues to run even if one of the partitions is down.

Explanation of how Hadoop and big data technologies emphasize partition tolerance and availability over global consistency.

Difference between SQL Server and Hadoop in terms of consistency and partition tolerance.

Discussion on how traditional commit rollback is not a part of most big data systems.

Introduction of the BASE concept in big data systems, contrasting with the ACID properties of relational databases.

Explanation of the trade-off between immediate consistency and latency in ACID systems.

Hadoop's willingness to forgo two-phase commit for greater availability and scalability.

The necessity for data nature to tolerate imprecision in order to achieve scalability in big data systems.

Inappropriateness of BASE for applications requiring strict consistency, such as bank account balances.

Emphasis on the fundamental differences in business requirements between SQL Server and Hadoop, and their respective applications.

The importance of using Hadoop where it is most effective, considering its strengths and limitations.

Transcripts

as we start to look at how Hadoop

actually works it's really important to

understand why it works that way a good

explanation of that can be found in Eric

Brewer's cap theorem this is a theorem

of distributive processing that was

introduced around year 2000 the cap

theorem looks at the core requirements

of a distributed processing system and

it suggests that of these three core

requirements only two of them can be

optimized at once while the Third has to

be relaxed or abandoned the three

requirements are consistency

availability and partition tolerance and

let's talk about what those terms really

mean if a system is consistent it means

it operates fully or not at all and a

good example of this that we're probably

all familiar with is transaction commit

roll back so as we are going to update a

system and we update three different

tables if we can't update all the tables

then all the changes come back so if

we're trying to update an order detail

table and an order header that has a

total if we can't update the details

then we can't update the header either

that would be a consistent

system availability is what it says it

is the system is always available so in

a SQL system that's S&P we might have a

cluster so that if a node goes down the

other cluster node immediately takes

over and answers requests the third is

partition tolerance and by partition we

really mean that if we are in that MPP

model and in the previous lesson we said

maybe a different server handles every

month of the year partition tolerance

means that the system can still continue

to run even if one of those partitions

is down a system that was highly

partition tolerance would say that it's

okay if the system still runs we can

still basically get the right answers

even if a partition is down so if we

apply the cap theorem to looking at the

differences between SQL Server say and

Hado we get this kind of a view that in

SQL Server consistency is absolutely

important we have to have consistency we

have Commit roll back we have

distributed transactions and so on we

have to get that and we need

availability obviously SQL isn't

particularly tolerant about partitions

being down if we were to distribute our

months over 12 different servers and one

of the servers was down we would in a

relational SQL Server environment we

would consider the database to be

offline because it's not all there

Hadoop as well as most other big data

Technologies emphasize partition

tolerance and availability and relax

Global consistency so they're

fundamentally different and that means

that in most Big Data Systems that

traditional commit roll back really

isn't a part of the system so you might

have eventual consistency but you don't

have to have consistency immediately and

all the time in the SQL Server model

we're used to the acid concept where we

can rely on the consistency of data we

can rely on the Integrity of

transactions and so on we have to adjust

our mindset a little bit with Hadoop

knowing that what is it's trying to do

is have thousands of partitions and if

it wasn't tolerant of one of those

partitions being offline then the whole

system wouldn't work it would become

increasingly slow difficult to manage

and and so on but what this leads to is

that Hadoop and most Big Data

Technologies don't follow the same acid

Concepts that most relational databases

follow instead they follow something

that's kind of known as Bas it's

basically a ailable meaning not all your

partition is always going to be there

but eventually there will be consistency

and it's probably review for you but in

the in the acid idea we can have

distributed databases across the laot of

nodes but they have to be consistent

before subsequent queries can be

released to query them and then we'll

very often have two-phase Commit for

this so we will get immediate

consistency but we also have some

latency involved with that we have to

wait for distributed nodes to commit

their Data before the entire trans

action can be committed and so on so

there's a little bit of a give on

scalability

there Hadoop follows the base concept

where it's willing to forgo two-phase

commit it's willing to have a little bit

less consistency across nodes in

exchange for that it gets even more

availability gets even more scalability

and all this is well and good and it's

fine but we do have to kind of keep in

mind that the nature of the data needs

to be able to tolerate some of this

imprecision in order to get that

scalability so if we do have something

like say bank account balances being

updated Bas isn't a really good model

for that we really need assd for that so

as we go through the technology and look

at it this is always good to keep in

mind that these two technologies were

designed with fundamentally different

business requirements and at some level

we can't really mix those requirements

together so we we can use Hadoop but we

need to use it where it really works

well and where it applies and not use it

where we need some of the acid kind of

requirements around transaction

control