Sign in to unlock

Get unlimited access to all questions and answers

checked

Get lifetime access

checked

Track progress

checked

Save time

13 CAP Theorem interview questions and answers for software engineers

In theoretical computer science, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: Consistency: Every read receives the most recent write or an error. Here is a list of coding interview questions on CAP Theorem to help you get ready for your next data structures interview in 2021.

  • 1.

    What Is CAP Theorem?

    Answer:

    CAP theorem is a fundamental principle in the realm of distributed systems, laying the groundwork for understanding how these systems function and the trade-offs they entail. CAP is an acronym for three crucial properties that a distributed system might aim to fulfill:

    • Consistency: Every read operation will return the most recent write or an error.
    • Availability: Every request will receive a response, without any error, even when certain parts of the system are failing.
    • Partition Tolerance: The system will continue to function despite communication issues (partition) between different parts of the system.

    According to the CAP theorem, it's not possible for a distributed system to achieve all three of these properties. The system must consequently make a trade-off between Consistency and Availability when faced with a network Partition.

    Let's explore these three concepts in more detail:

    CAP Theorem Components

    Consistency

    Consistency ensures that each node in a distributed system has the same view of the data, even during concurrent updates. In simple terms, if Node A writes a value to a database, and then Node B reads that value, it should see the updated value.

    For example, in a banking system, if you transfer $100 from Account A to Account B, both Account A and B should reflect this change and always show a consistent balance. In a strongly consistent system, a read after a write operation will always return the updated value.

    Availability

    Availability guarantees that every request made to a non-failing node in the system will receive a response, without errors. High availability is typically a crucial requirement for systems that cannot tolerate downtime.

    For example, in a Twitter-like application, if you post a tweet, you should immediately see your tweet. The system should not be unavailable due to a network partition or any other temporary issues.

    Partition Tolerance

    Partition tolerance denotes a system's ability to continue functioning even if communication between nodes fails, creating network partitions. This is particularly relevant in distributed systems which are, by design, prone to network failures.

    For example, if two data centers are not able to communicate with each other, the system should be able to operate with full functionality in both data centers.

    CAP Theorem: Two Out of Three

    The CAP theorem states that it is impossible for a distributed system to simultaneously provide all three guarantees of Consistency, Availability, and Partition Tolerance. A system can, however, strive to optimize for a combination of two these properties.

    Visual Representation

    Here's a visual depiction of the CAP theorem:

    CAP Theorem

    Practical Implications

    • Key-Value Stores: Systems like Amazon Dynamo prioritize Availability over Consistency.

    • Relational Databases: ACID-compliant RDBMS are generally Consistency-focused, whereas NoSQL databases lean towards Availability.

    • Multi-Datacenter Replication: When data is replicated across data centers, the system might choose to be either Consistency- or Availability-oriented.

    • Business Requirements: The choice of CAP characteristics is often driven by business requirements, such as the type of data, read/write patterns, and SLA.

    CAP Theorem in Code

    Let's take a simple example to demonstrate the CAP theorem trade-off in a distributed database.

    # Pseudo code for Consistency and Availability trade-off
    from threading import Thread
    
    def read_data():
        # Read from a strongly consistent system
        value = read_consistent_db()
        print(f"Read value: {value}")
    
    def write_data():
        # Write to a highly available system
        write_available_db("New Value")
        print("Successfully written to the database")
    
    thread1 = Thread(target=read_data)
    thread2 = Thread(target=write_data)
    
    thread1.start()
    thread2.start()
    
    thread1.join()
    thread2.join()
    

    In this code, thread1 tries to read from a strongly consistent database, while thread2 writes to a highly available database. Due to the CAP theorem, these two operations may not see the same data, as consistency is being sacrificed for availability.

One tip that got me hired by Google, Microsoft, and Stripe

I've worked for Microsoft, Google, Stripe, and received offers from many other companies. One thing I learned when I was interviewing myself is that standard interview tips are woefully inadequate.

Reverse Tech Interview: Questions to Stump an Interviewer

Few people want to get into an uncomfortable environment or an unprofitable company with no prospects. If you’re wondering how to get a real feel for a company during an interview, you’re welcome. I’ll give a list of questions that interviewers don’t usually expect...

How to become a programmer, move to the US, and get a dream job

Are you curious about what you need to do to earn more than $15.000 a month, drive a Tesla, live in sunny California, and work at Google, Apple, Facebook, or Amazon?

11 Reactive Systems interview questions and answers for software engineers

Reactive systems are an architectural style that allows multiple individual applications to blend into one unit, reacting to their environment, while staying aware of each other. Here is a list of coding interview questions on Reactive Systems to help you get ready for your next data struc...

. Reactive Systems
34 Microservices interview questions and answers for software engineers

Microservice architecture – a variant of the service-oriented architecture structural style – arranges an application as a collection of loosely coupled services. In a microservices architecture, services are fine-grained and the protocols are lightweight. Here is a list of coding intervie...

. Microservices
7 Layering & Middleware interview questions for developers

Middleware in the context of distributed applications is software that provides services beyond those provided by the operating system to enable the various components of a distributed system to communicate and manage data. Middleware supports and simplifies complex distributed application...

. Layering & Middleware
55 Docker interview questions and answers for software engineers

Docker is a set of platform as a service products that use OS-level virtualization to deliver software in packages called containers. Here is a list of coding interview questions on Docker to help you get ready for your next data structures interview in 2021.

. Docker
23 Databases interview questions and answers for software engineers

A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques. Here is a list of coding interview questions on Databases to help you g...

. Databases
21 Concurrency interview questions and answers for software engineers

In computer science, concurrency is the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the final outcome. Here is a list of coding interview questions on Concurrency to help you get ready for your n...

. Concurrency
13 CAP Theorem interview questions and answers for software engineers

In theoretical computer science, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: Consistency: Every read receives the ...

. CAP Theorem
Load more posts

Features

folder icon

Access all answers

Get the inside track on what to expect in your next tech interview. We offer you a collection of high quality technical interview questions to help you prepare for your next coding interview.

graph icon

Track progress

A smart interface helps you track your progress and focus on the topics you need. You can search through questions or sort them by difficulty or type.

clock icon

Save time

Save tens of hours searching for information on hundreds of low-quality sites designed to drive traffic and make money from advertising.

Land a six-figure job at one of the top companies.

amazon logo facebook logo google logo microsoft logo uber logo
Prepare for a technical interview

Get hired with our
tech interview questions & answers