20 CAP Theorem Interview Questions and Answers

In computer science, the CAP theorem states that it is impossible for a distributed system to simultaneously provide more than two of the following three guarantees:

– Consistency: Every read receives the most recent write or an error – Availability: Every request receives a response, without guarantee that it will be the most recent write – Partition tolerance: The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes

CAP Theorem Interview Questions and Answers

Here are 20 commonly asked CAP Theorem interview questions and answers to prepare you for your interview:

1. What is the CAP theorem?

The CAP theorem is a way of thinking about the tradeoffs between different types of distributed systems. The theorem states that it is impossible for a distributed system to simultaneously provide all three of the following guarantees:

– Consistency: All nodes in the system see the same data at the same time.
– Availability: Every node in the system can be reached and will respond in a timely manner.
– Partition tolerance: The system can continue operating even if some nodes are unavailable.

The CAP theorem is often used to help designers make decisions about which type of system to build. For example, a system that is designed for high availability may sacrifice consistency, while a system that is designed for strong consistency may sacrifice availability.

2. Can you explain what consistency, availability, and partition tolerance mean in context of the CAP theorem?

Consistency means that all nodes in the system see the same data at the same time. Availability means that every request receives a response – even if that response is an error. Partition tolerance means that the system continues to operate even if some nodes are unavailable.

3. Why does it not make sense to talk about a distributed system that provides all three properties: Consistency, Availability, and Partition Tolerance?

The CAP theorem is often used to help explain the trade-offs that need to be made when designing a distributed system. It states that it is impossible for a distributed system to provide all three of the following properties:

– Consistency: All nodes in the system see the same data at the same time.
– Availability: All nodes in the system are always available to respond to requests.
– Partition Tolerance: The system can continue to operate even if some nodes are unavailable.

The reason that it does not make sense to talk about a distributed system that provides all three properties is because it is simply not possible. You have to choose two out of the three, and the choice you make will depend on the specific needs of your system.

4. If a database were to provide two of the three properties (Consistency, Availability, and Partition Tolerance), which would be most important for your use case?

The most important property would depend on the use case. For example, if the database were being used for a e-commerce site, then Availability would be the most important property, as the site would need to be available to users at all times. However, if the database were being used for a medical application, then Consistency would be the most important property, as the data needs to be accurate and up-to-date.

5. How can we improve scalability by using data partitions?

Data partitions can improve scalability by allowing data to be spread across multiple servers. This way, if one server becomes overloaded, the others can pick up the slack. Additionally, data partitions can help to improve performance by allowing data to be accessed in parallel.

6. Are there any systems that offer all 3 properties?

There are some systems that come close to offering all three properties, but there is no system that offers all three properties perfectly. For example, a system might offer high availability and partition tolerance, but sacrifice consistency in order to do so.

7. Is it possible to have a distributed system with high availability while still providing strong consistency guarantees?

No, it is not possible. The CAP theorem states that it is impossible for a distributed system to provide all three of the following guarantees:

– Consistency: All nodes in the system see the same data at the same time.
– Availability: All nodes in the system can be reached and data can be read or written at any time.
– Partition tolerance: The system can continue to operate even if some nodes are unavailable.

One of the three must be sacrificed in order for the other two to be possible.

8. Do you think it’s better to have a system that has eventual or weak consistency? Explain why?

I think it’s better to have a system that has eventual consistency. Eventual consistency means that the system will eventually converge on a consistent state, even if it doesn’t start out in a consistent state. This is opposed to strong consistency, which requires that the system always start in a consistent state.

The reason I prefer eventual consistency is that it’s more flexible. In a system with eventual consistency, you can make changes to the data without having to first coordinate with all of the other users of the system. This can be helpful when you need to make changes quickly, or when there are a lot of users who could potentially be impacted by a change.

9. In a multi-master replication scenario, do you think it’s better to go for strong or eventual consistency?

In a multi-master replication scenario, it is better to go for eventual consistency. The reason for this is that in a multi-master replication scenario, there are multiple copies of the data that are being updated independently. If you were to go for strong consistency, then this would mean that all of the copies of the data would have to be updated in a coordinated fashion, which would be very difficult to do.

10. When building a strongly consistent system, how should you deal with network partitions?

When building a strongly consistent system, you should design your system in such a way that it can tolerate network partitions. This means that your system should be able to continue functioning even if some of the nodes in the system are unavailable. One way to do this is to use a quorum-based approach, where a certain number of nodes must be available in order for the system to continue functioning.

11. Suppose you are building an application like Twitter where users want to view new tweets as soon as they get posted. Would this fall under the category of an eventually consistent or strongly consistent system?

In this case, the system would need to be eventually consistent. This is because it is not possible to guarantee that every user will see every new tweet as soon as it gets posted. However, it is possible to guarantee that every user will eventually see every new tweet.

12. How would you design a messaging app that uses a microservice architecture?

There are a few different ways to design a messaging app using a microservice architecture. One way would be to have a separate microservice for each type of message (text, audio, video, etc.). Another way would be to have a microservice for each chat room or conversation. Yet another way would be to have a microservice for each user. Each of these microservices would then need to communicate with each other in order to deliver messages.

13. Which option would you choose when designing an ordering service for Amazon – Eventual or Strongly Consistent? Explain why?

Eventual consistency would be the best option for an ordering service for Amazon. Eventual consistency means that the system will eventually converge on a consistent state, even if it is not always immediately consistent. Strongly consistent means that the system will always be immediately consistent, but this is not always possible or practical. Eventual consistency is a good compromise between the two options.

14. What are some examples of databases that support ACID transactions?

Some examples of databases that support ACID transactions are MySQL, Oracle, and Microsoft SQL Server.

15. What is BASE? What does it stand for?

BASE is an acronym for “Basic Availability, Soft state, Eventual consistency”. It is a model for distributed systems that was proposed by Eric Brewer in his 2001 paper “CAP Twelve Years Later: How the “Rules” Have Changed”. The BASE model relaxes the requirements of the CAP theorem in order to allow for more scalable and available systems. In particular, it allows for systems that are only eventually consistent, as opposed to always consistent.

16. How does BASE achieve availability?

BASE (Basically Available, Soft state, Eventual consistency) is a model for distributed data systems that relaxes the requirements of ACID (Atomicity, Consistency, Isolation, Durability) in order to achieve availability. In the BASE model, data is allowed to be in an inconsistent state as long as the system is available to process requests. Over time, the data will eventually converge to a consistent state.

17. What happens when multiple updates happen simultaneously on different nodes in a BASE system?

In a BASE system, when multiple updates happen simultaneously on different nodes, the system will eventually converge on a single, consistent state. However, it is possible for some of the updates to be lost in the process.

18. What are some situations where you should avoid using a NoSQL database?

The CAP theorem states that it is impossible for a distributed database to simultaneously provide all three of the following guarantees:

– Consistency: All nodes in the database see the same data at the same time.
– Availability: Every request for data receives a response.
– Partition tolerance: The database can continue operating even if some nodes are unavailable.

This means that in order for a NoSQL database to provide high availability, it must sacrifice either consistency or partition tolerance. This can be a problem in situations where data consistency is critical, such as in financial applications.

19. How does DynamoDB implement the CAP theorem?

DynamoDB is a fully managed, NoSQL database service that supports both document and key-value store models. It is designed to be highly scalable and to offer low-latency performance. DynamoDB is a managed service, so it automatically replicates data across multiple Availability Zones in a Region to provide fault tolerance.

20. What is linearizability?

Linearizability is a consistency model for shared data that requires all reads and writes to be atomic, consistent, and isolated. In other words, when multiple clients are accessing and modifying a shared data store, each client must see a consistent view of the data, and no two clients can see conflicting versions of the data.