20 Cosmos DB Interview Questions and Answers

As a Microsoft Azure cloud database, Cosmos DB is a popular choice for developers who need a scalable, high-performance solution. If you’re interviewing for a position that involves Cosmos DB, you can expect questions about your experience and knowledge of the platform. In this article, we’ll review some of the most common Cosmos DB interview questions and provide guidance on how to answer them. By preparing in advance, you can increase your chances of impressing the interviewer and landing the job.

Cosmos DB Interview Questions and Answers

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

1. What is Cosmos DB?

Cosmos DB is a Microsoft Azure cloud database service. It offers global distribution, elastic scaling, and supports multiple data models including document, graph, and key-value.

2. Why do you think it’s a good idea to use Cosmos DB as the backend for your application?

There are several reasons why I think Cosmos DB would be a good idea for the backend of my application. First, Cosmos DB is a globally distributed database, which means that my application will be able to scale to users all over the world. Second, Cosmos DB offers multiple consistency levels, so I can choose the level of consistency that is right for my application. Finally, Cosmos DB offers a variety of APIs, so I can choose the one that is best suited for my application.

3. Can you explain what Azure Table Storage is and how does it relate to Cosmos DB?

Azure Table Storage is a cloud-based NoSQL data storage service that is designed to provide highly scalable, reliable, and affordable storage for applications that require large amounts of data. Cosmos DB is a cloud-based NoSQL database service that is built on top of Azure Table Storage. It offers additional features and capabilities that are not available in Azure Table Storage, such as the ability to index data, perform transactions, and scale horizontally.

4. What are some of the advantages of using Cosmos DB instead of SQL Server or MongoDB?

Cosmos DB is a highly scalable database that can be easily scaled up or down to meet the needs of your application. It is also highly available, with built-in replication and failover capabilities. Additionally, Cosmos DB supports a wide range of data models and query languages, making it a very versatile database.

5. How would you describe the performance characteristics of Cosmos DB?

Cosmos DB is a high performance, globally distributed database service that offers low latency and high throughput. It is designed to scale horizontally and vertically, and can be used to power mission-critical applications.

6. Is there any difference between running queries on a local database vs via Cosmos DB REST API? If yes, then what is it?

There is a slight difference between running queries on a local database vs via Cosmos DB REST API. When you query a local database, the database engine will optimize the query based on the available resources and the data that is stored locally. When you query via the Cosmos DB REST API, the query is first sent to the Cosmos DB server, which then optimizes the query based on the resources and data that are available to it. This can sometimes result in a slightly different query execution plan, which can impact performance.

7. What are some ways to make Cosmos DB more cost-effective?

One way to make Cosmos DB more cost-effective is to use the Azure Cosmos DB’s free tier. The free tier offers a shared throughput of 400 RU/s and 5 GB of storage. Additionally, you can use the Azure Cosmos DB’s reserved capacity to make further cost savings. The reserved capacity offers a significant discount (up to 70%) on the price of Cosmos DB when you commit to a certain amount of throughput and storage for a period of time.

8. What are the main components that make up Cosmos DB?

The main components of Cosmos DB are the account, database, container, and items. The account is the top-level resource and contains all the databases. Each database can contain multiple containers, and each container can store multiple items.

9. How is data stored in Cosmos DB?

Cosmos DB stores data in a columnar format. This means that data is stored in columns rather than rows. This makes it easier to query and update data in Cosmos DB.

10. What are the different consistency levels available with Cosmos DB?

The different consistency levels available with Cosmos DB are:

– Strong: All reads and writes will go to the same physical location.
– Bounded Staleness: Reads may go to a different physical location than writes, but all reads will return data that is no more than N versions old, where N is configurable.
– Session: All reads and writes within the same session will go to the same physical location.
– Eventual: Reads and writes may go to different physical locations, but all reads will eventually return the most recent data.

11. Do all databases within an account share the same throughput provisioned within Cosmos DB?

No, each database can have its own unique throughput provisioning. This allows for more granular control over how resources are utilized within an account.

12. Are all collections in a database guaranteed to have equal distribution of data across various partitions?

No, all collections in a database are not guaranteed to have equal distribution of data across various partitions. However, you can use the partition key to control the distribution of data across partitions.

13. What is the minimum number of replicas required for each partition in order to guarantee availability during write operations?

The minimum number of replicas required for each partition in order to guarantee availability during write operations is three. This is because Cosmos DB uses a quorum-based commit protocol, which means that a write operation will only be considered successful if it is acknowledged by a majority of replicas. Therefore, if there are only two replicas per partition, then it is possible for one replica to go down and for the write operation to fail.

14. Can you explain what indexing policies are in the context of Cosmos DB?

Indexing policies in Cosmos DB are used to determine which attributes of your data should be indexed in order to support fast and efficient querying. You can specify different indexing policies for different collections of data, and you can also specify how often the index should be updated.

15. Can you give me some examples of real-world applications built on Cosmos DB?

Some examples of real-world applications built on Cosmos DB include:

– A social media application that needs to store and query large amounts of data in real-time
– An e-commerce application that needs to handle a large number of concurrent users and transactions
– A gaming application that needs to store and query player data and game state data in real-time

16. What is the best way to ensure high availability when reading or writing data from Cosmos DB?

The best way to ensure high availability when reading or writing data from Cosmos DB is to use the built-in replication feature. This will ensure that your data is always available, even if one of the servers goes down.

17. Can you run bulk updates on Cosmos DB data? If yes, then what are some ways to achieve this?

Yes, you can run bulk updates on Cosmos DB data. There are a few ways to achieve this, but one common way is to use the Azure Cosmos DB Data Migration tool. This tool can help you migrate data from a variety of sources into Azure Cosmos DB.

18. What happens if throughput requirements suddenly increase beyond the current limit set for a container? Does Cosmos DB automatically handle this scenario?

Yes, Cosmos DB automatically scales up to meet increased throughput requirements. However, it is important to note that this will result in increased costs.

19. What are the key metrics for measuring throughput usage?

The key metrics for measuring throughput usage in Cosmos DB are request units (RU) and throughput. RUs are a measure of the amount of work that is required to perform a given operation, such as reading or writing a document. Throughput is a measure of the number of requests that can be processed per second.

20. What are some common reasons why read latency increases suddenly without any obvious reason?

There are a few reasons why read latency might increase suddenly without any obvious reason. One possibility is that the data you are trying to read is spread out across multiple partitions, and thus it takes longer to retrieve all of the data. Another possibility is that the data you are trying to read is located on a slow disk, which is causing the increased latency.