SQL vs. Hadoop: Acid vs. Base
Summary
TLDRThe 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
📚 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.
🛠️ 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.
Mindmap
Keywords
💡CAP Theorem
💡Consistency
💡Availability
💡Partition Tolerance
💡SQL Server
💡Hadoop
💡ACID Properties
💡BASE
💡Two-Phase Commit
💡Scalability
💡Eventual Consistency
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
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