20 Scalability Interview Questions and Answers

Scalability is the ability of a system to handle a growing amount of work or its potential to be enlarged to accommodate that growth. In an interview, questions about scalability are meant to test a candidate’s ability to think about how a system will perform when faced with an increased load. The interviewer is looking to see if the candidate can identify potential bottlenecks and has the experience to suggest solutions. Reviewing common scalability questions ahead of time can help you prepare your responses and feel confident on the day of your interview.

Scalability Interview Questions and Answers

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

1. What is scalability?

Scalability is the ability of a system to handle a growing amount of work, or its potential to be enlarged to accommodate that growth.

2. Can you explain what horizontal and vertical scaling are in the context of databases?

Horizontal scaling means that you add more machines to your system in order to increase capacity. Vertical scaling means that you add more resources to a single machine in order to increase capacity.

3. How can you make a database more scalable?

There are a few ways to make a database more scalable:

1. Use a database that is designed for scalability from the ground up. For example, Google’s BigTable is designed to be scalable.

2. Partition your data. This means breaking up your data into smaller pieces that can be spread across multiple servers.

3. Use a caching system. This will help reduce the load on your database by storing frequently accessed data in memory.

4. Use a load balancer. This will distribute the load across multiple servers and help prevent any one server from becoming overloaded.

4. Is it possible to horizontally scale an Oracle database? If yes, then how would you go about doing that?

Yes, it is possible to horizontally scale an Oracle database. In order to do so, you would need to create additional database instances on separate servers and then use a load balancer to distribute traffic among them.

5. Why do you think there is so much hype around MySQL when compared to other SQL databases?

I think there are a few reasons. First, MySQL is open source, so it’s free to download and use. Second, it’s relatively easy to learn and use, so it’s a good option for people who are just getting started with SQL databases. Finally, it’s very popular, so there are a lot of resources available for people who want to learn more about it.

6. How can you add fault tolerance to systems?

One way to add fault tolerance to systems is to use a technique called replication. With replication, you create multiple copies of data or components and store them in different locations. If one copy becomes unavailable, the others can take its place. Another way to add fault tolerance is to use redundancy, which involves having extra capacity built into the system so that if one component fails, there is still enough capacity to handle the load.

7. Can you give me some examples of applications that use load balancing at massive scale?

Some examples of applications that use load balancing at massive scale include Google Search, Gmail, and Facebook. All of these applications need to be able to handle large numbers of users simultaneously, and load balancing is a key part of ensuring that they can do so.

8. How does clustering work?

Clustering is a method of organizing servers so that they can work together to provide a service or share a workload. When a server cluster is created, each server in the cluster is configured to be aware of the other servers in the cluster. This way, if one server in the cluster goes down, the other servers can take over its workload. Clustering can be used to improve the performance or availability of a service, or to provide a more cost-effective solution by consolidating servers.

9. Can you explain sharding as a mechanism for adding scalability to web services?

Sharding is a process of splitting up data across multiple servers in order to improve performance and scalability. When a user makes a request to a sharded web service, the request is routed to the server that contains the requested data. This can help to improve performance by reducing the amount of data that needs to be transferred between servers.

10. What is your understanding of caching?

Caching is a technique that is used in order to improve the performance of a system by storing data in a temporary location so that it can be accessed more quickly. There are a variety of different types of caching that can be used in order to achieve this, such as page caching, database caching, and object caching.

11. Can you explain what read-through cache is?

Read-through cache is a type of caching that is used in order to improve the performance of applications. When using read-through cache, the application will first check the cache for the requested data. If the data is not found in the cache, then the application will fetch the data from the database and store it in the cache. This way, the next time the data is requested, it will be retrieved from the cache instead of the database, which will improve performance.

12. Can you explain what write-behind cache is?

Write-behind cache is a type of caching where writes to the cache are not immediately propagated to the underlying data store. This can improve performance, since the data store may be slow or unavailable. However, it can also lead to data inconsistencies if the cache is not properly managed.

13. Can you explain what optimistic locking is? How does it differ from pessimistic locking?

Optimistic locking is a strategy for managing concurrent access to data where each user is allowed to edit the data without first locking it. The system only checks for conflicts when the data is saved. This is in contrast to pessimistic locking, where a lock is placed on the data as soon as it is opened for editing, preventing other users from accessing it.

14. What are some common techniques used to reduce contention between readers and writers when using caches?

Some common techniques used to reduce contention between readers and writers when using caches include using a lock-free data structure, using a read-write lock, or using a concurrent data structure.

15. How can you ensure data consistency across all instances of a distributed system?

There are a few ways to ensure data consistency across all instances of a distributed system. One way is to use a database that supports transactions. This way, if one instance of the system tries to update the data while another instance is also trying to update the data, the database will be able to ensure that both updates are made in a consistent way. Another way to ensure data consistency is to use a message queue. This way, if one instance of the system tries to update the data while another instance is also trying to update the data, the message queue will ensure that both updates are made in a consistent way.

16. What’s your opinion on NoSQL databases like MongoDB or Redis?

I think that NoSQL databases are great for situations where you need to be able to scale quickly and easily. They are also generally easier to work with, since you don’t need to worry about setting up and maintaining a relational database. However, they do have some drawbacks – for example, it can be harder to query data in a NoSQL database, and you may not have as much control over your data.

17. Can you explain what eventual consistency means?

Eventual consistency is a model of data consistency where data is eventually synchronized across all nodes in a system, even if it is not done in real-time. This means that if one node in the system is updated, it may take some time for that change to propagate to all other nodes. Eventual consistency is often used in distributed systems where it is not possible or practical to have all nodes in the system be updated at the same time.

18. What issues might arise as a result of not implementing good caching strategies?

Not implementing good caching strategies can lead to a number of issues, including decreased performance, increased latency, and increased costs.

19. What steps need to be taken to avoid downtime when updating deployed systems?

There are a few key steps that need to be taken in order to avoid downtime when updating deployed systems. First, you need to ensure that you have a solid plan in place for the update. This plan should be thoroughly tested before being put into action. Second, you need to have a way to roll back the changes if something goes wrong. Finally, you need to have a good communication plan in place so that everyone knows what is happening and when.

20. Can you explain what CAP theorem is?

CAP theorem is a theory in computer science that states that it is impossible for a distributed computer 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