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.