noc19-cs33 Lec 15 Consistency Solutions

Summary

The video explores various consistency models in distributed systems, contrasting eventual and strong consistency. It explains how eventual consistency, exemplified by Cassandra, offers faster reads and writes but lacks immediate accuracy. In contrast, strong consistency guarantees synchronized visibility of operations across clients. The discussion also highlights intermediate models like per-key sequential consistency, CRDTs, red-blue consistency, and causal consistency, each balancing performance and reliability differently. The content emphasizes the importance of selecting the appropriate model based on application needs, addressing the CAP theorem's trade-offs between consistency, availability, and partition tolerance, ultimately underscoring modern developments like NewSQL systems.

Takeaways

• 😀 Eventual consistency, supported by systems like Cassandra, allows faster reads and writes, ensuring replicas converge over time.
• 😀 Strong consistency provides immediate visibility of operations across clients but is difficult to achieve in distributed systems.
• 😀 Per-key sequential consistency offers a global order for operations on a per-key basis, enhancing performance compared to eventual consistency.
• 😀 CRDTs (Commutative Replicated Data Types) allow commutative operations that yield the same result regardless of their order.
• 😀 Red-blue consistency divides operations into commutative (blue) and ordered (red), improving performance while maintaining consistency.
• 😀 Causal consistency respects partial order based on information flow, ensuring related writes and reads are appropriately sequenced.
• 😀 Linearizability is a strong consistency model where each operation is visible to all clients instantaneously.
• 😀 Sequential consistency ensures that execution results resemble some sequential order across clients, maintaining a specified order.
• 😀 NewSQL systems combine NoSQL features with ACID properties to meet modern application demands.
• 😀 The CAP theorem highlights the trade-offs in distributed system design, stating that one can only choose two out of consistency, availability, and partition tolerance.

Q & A

What are the two extremes of consistency models mentioned in the transcript?

The two extremes of consistency models mentioned are eventual consistency and strong consistency.

What is eventual consistency and how does it function in systems like Cassandra?

Eventual consistency ensures that once all writes to a key cease, all replicas will eventually converge to the same value, allowing for faster read and write operations.

Why might a system choose a weak consistency model?

A system may choose a weak consistency model to achieve higher availability and partition tolerance while still providing some level of consistency.

What is the per-key sequential consistency model?

Per-key sequential consistency guarantees that all operations on a key are seen in a global order, providing better performance than eventual consistency.

How do CRDTs (Commutative Replicated Data Types) operate?

CRDTs allow for operations that can be applied in any order, such as increment operations, ensuring consistent outcomes without requiring coordination between nodes.

What distinguishes red-blue consistency from other models?

Red-blue consistency classifies operations into commutative (blue) and non-commutative (red) operations, allowing blue operations to be executed in any order while red operations must maintain a specific order.

What does causal consistency ensure in a distributed system?

Causal consistency ensures that reads respect the causal relationships between writes, meaning a read will return values based on prior writes that are causally related.

What is linearizability in terms of strong consistency?

Linearizability is a strong consistency model where each operation appears to take effect instantaneously across the entire system, similar to operations on a single-node storage system.

How does sequential consistency differ from linearizability?

Sequential consistency allows for operations to be executed in a specified sequential order, but does not require them to appear instantaneously, unlike linearizability.

What are the main characteristics of BASE properties in NoSQL systems?

BASE properties in NoSQL systems stand for Basically Available, Soft state, and Eventually consistent, emphasizing availability and performance over strict consistency.

What does the CAP theorem state regarding distributed systems?

The CAP theorem states that a distributed system cannot guarantee consistency, availability, and partition tolerance simultaneously; it must prioritize two of these properties.

What is the significance of the ACID properties in traditional RDBMS?

The ACID properties (Atomicity, Consistency, Isolation, Durability) ensure strong consistency and reliability in traditional relational database management systems.