Consistency vs Availability: The Eternal Struggle in Distributed Databases

Imagine millions of customers trying to book last-minute deals on a hotel or flight during one of the biggest sale events of the year, and while some customers can book, others see failures while making their bookings. This inconsistency results in frustrated customers and logistical nightmares. This typical scenario highlights a fundamental challenge in distributed systems and databases: how do you balance consistency and availability? This article aims to highlight the nuances of this balancing act along with the complexities and trade-offs that are in play.

CAP Theorem, Consistency, and Availability

To understand the nuances better, it’s important to understand the CAP theorem. As there are several other articles on the internet explaining this, we will refrain from going into details. However, to highlight what the CAP theorem is per Eric Brewer (who formulated the CAP theorem), a system can achieve only two of the three guarantees: Consistency, Availability, and Partition Tolerance. In simple words, during a network partition (when communication between nodes is disrupted), a system must choose between being consistent (all the nodes showing the same data) versus all the nodes being available (all the requests will receive a response). 

Consistency in distributed databases means that each read receives the most recent write. This ensures that the data is accurate and reliable, especially if it’s a system that is being built for financial transactions where even a slight discrepancy can lead to a major issue. However, as highlighted by the CAP theorem, strong consistency results in increased latency and complexity. A perfect example of a database that prioritizes high consistency is Google Spanner, as it covers scenarios that require high data integrity. Google Spanner achieves this with the help of an innovative API called TrueTime API, which provides globally synchronized timestamps and bounded uncertainty. It achieves globally consistent timestamps with the help of GPS and atomic clocks for time synchronizations across different availability zones and data centers. It also offers synchronous replication and strong consensus protocols to achieve high consistency.

Availability, as we already know, ensures that the system continues to operate even if certain nodes fail. This is crucial for system reliability and for high-traffic applications. Prioritizing this again, as mentioned earlier, will result in eventual consistency problems, where different nodes show different data. Cassandra and DynamoDB (DDB) exemplify this approach, helping you handle massive and distributed workloads efficiently.

Trade-Off: Real-World Applications

It’s crucial to understand the implications of either consistency or availability based on the needs of your application. Financial institutions or organizations that handle payment data often need to prioritize consistency to ensure transactional accuracy, whereas social media applications or organizations that cater to continuous engagement might want to tilt toward availability.

A consistency-first system might leverage a globally synchronized clock to ensure strong consistency across data centers regardless of high latency, whereas an availability-first system might leverage Amazon’s DDB’s tunable consistency levels to allow developers to pick between high and low consistency based on their requirements.

It’s equally important to understand what consistency models can be used to make design decisions:

• Strong consistency: Guarantees that all nodes see the same data at one time
• Eventual consistency: Ensures that all nodes will eventually converge at the same state; This works well where immediate consistency is not crucial.
• Other models: Models like causal consistency (which maintains the order of operations), read-your-writes (which ensures users see every update), and session consistency (maintaining state in the single session)

Strategies for Achieving the Right Balance

Hybrid Approaches

Many systems are designed to adopt a hybrid model to use tunable consistency levels. For example, Amazon’s DDB allows users to select consistency levels based on their needs. Applications can specify using a parameter in DDB —  ConsistentRead —  in the read requests; thereby, offering more flexibility.

Context-Driven Decisions

The choice of designing the systems towards consistency and availability must be based on the requirements of your application. Prioritize strong consistency if you need to ensure accurate and reliable transactions. Otherwise, prioritize availability of your application if it requires high user engagement and high transaction volumes. 

CRDTs and Advanced Consensus Algorithms

There are emerging technologies like Conflict-Free Replicated Data Types and advanced consensus algorithms that offer promising solutions that can mitigate this tradeoff between consistency and availability. CRDTs allow for concurrent updates that can be made without conflicts, thereby achieving both availability and consistency and becoming an ideal choice for applications like live document editing or distributed file systems.

Raft and Multi-Paxos are two consensus algorithms that enhance fault tolerance and consistency in distributed systems. These algorithms make sure that all nodes agree on the same value even in case of network failures or node partitions. Google Spanner, mentioned earlier, leverages a combination of Multi-Paxos and TrueTime (globally synchronized clock) to provide strong consistency and data integrity across different geographical regions.

Conclusion

Balancing the battle for consistency and availability requires a good understanding of your application needs and its trade-offs. By leveraging the correct strategy of using a hybrid approach, context-driven decision, or using an emerging technology, you can optimize your system to meet performance, scalability, and data integrity requirements. At the end of the day, whether you pick between strong and eventual consistency in DDB or leverage an advanced consensus algorithm, the goal is to design a reliable and robust system that meets customer needs and experience.