Cosmos DB Interview Questions and Answers for 7 years experience
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What are the core differences between Cosmos DB's different API types (SQL, MongoDB, Cassandra, Gremlin, Table)?
- Answer: Each API offers a different data model and query language. SQL API uses a relational model with SQL queries; MongoDB API uses a document model with MongoDB query language; Cassandra API uses a wide-column store with CQL; Gremlin API uses a graph model with Gremlin traversal language; and Table API uses a key-value store with a simple API. The choice depends on your application's data structure and querying needs. They share the same underlying infrastructure but offer different programming paradigms and capabilities.
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Explain the concept of Request Units (RUs) in Cosmos DB. How do they impact performance and cost?
- Answer: RUs are a unit of measurement that represents the processing power required to perform operations in Cosmos DB. Higher RU/s provision means faster response times and higher throughput. However, higher RU/s also translate to higher costs. Efficient query design and data modeling are crucial to minimizing RU consumption and optimizing cost.
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Describe different consistency levels offered by Cosmos DB and their trade-offs.
- Answer: Cosmos DB offers various consistency levels, ranging from Strong (highest consistency, potentially lower availability) to eventual consistency (highest availability, potentially stale data). Consistency levels impact data integrity and application behavior. The choice depends on the application's tolerance for data staleness versus the need for strong consistency guarantees. Options include Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual.
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How do you handle scaling in Cosmos DB? Explain both horizontal and vertical scaling.
- Answer: Cosmos DB scales automatically (horizontally) by adding or removing resources as needed, based on RU provision. Vertical scaling involves increasing the provisioned throughput (RU/s) for a container or database. Horizontal scaling handles increasing data volume and requests, while vertical scaling addresses performance needs within existing data.
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Explain the concept of partitions and partition keys in Cosmos DB. Why are they important?
- Answer: Partitioning divides a large dataset into smaller, manageable partitions. A partition key determines which partition a document belongs to. This is crucial for scalability and performance as it allows for parallel reads and writes across multiple partitions, preventing performance bottlenecks.
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Describe different indexing strategies in Cosmos DB and how to choose the right one.
- Answer: Cosmos DB offers automatic indexing (default), manual indexing (specify indexes), and composite indexes (indexes on multiple fields). The choice depends on the query patterns. Over-indexing can consume RU, while under-indexing can lead to slow queries. Analysis of query workloads is vital for optimal index selection.
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How do you optimize queries in Cosmos DB to improve performance and reduce RU consumption?
- Answer: Optimization involves using appropriate indexing, filtering data effectively, using efficient query patterns (avoiding `SELECT *`), understanding query execution plans, and choosing the right partition key strategy. Proper data modeling also plays a crucial role.
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Explain the importance of throughput provisioning in Cosmos DB. How do you determine the right throughput for your application?
- Answer: Throughput (RU/s) determines the capacity of your database. Under-provisioning leads to performance issues, while over-provisioning increases costs. Determining the right throughput involves analyzing application workload, predicting peak loads, and performing load testing. Autoscale can be used for dynamic scaling.
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How do you handle data backup and recovery in Cosmos DB?
- Answer: Cosmos DB offers automatic backups managed by Azure. You can also leverage point-in-time recovery for restoring data to a previous point. For disaster recovery, geo-replication across Azure regions provides high availability and resilience.
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Explain the concept of change feed in Cosmos DB and its use cases.
- Answer: Change feed provides a stream of changes made to a container. This allows for real-time data processing and integration with other systems. Use cases include building change data capture (CDC) pipelines, real-time analytics dashboards, and event sourcing.
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Describe your experience with Cosmos DB's global distribution capabilities.
- Answer: [Your detailed answer about experience with global distribution, including configurations, latency considerations, and data consistency across regions]
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How have you used Cosmos DB with serverless technologies (e.g., Azure Functions)?
- Answer: [Your detailed answer about integrating Cosmos DB with serverless technologies, including code examples if possible]
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Explain your experience with monitoring and troubleshooting performance issues in Cosmos DB.
- Answer: [Your detailed answer about monitoring tools, metrics, and troubleshooting techniques]
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How do you ensure data security and access control in Cosmos DB?
- Answer: [Your detailed answer about security features like Azure Active Directory integration, role-based access control, and encryption]
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Compare and contrast Cosmos DB with other NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB).
- Answer: [Your detailed comparison and contrast, focusing on features, scalability, cost, and use cases]
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Describe your experience with migrating data to Cosmos DB from other databases.
- Answer: [Your detailed answer about migration strategies, tools, and challenges]
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Explain your experience with using Cosmos DB's SDKs (e.g., .NET, Java, Node.js).
- Answer: [Your detailed answer about specific SDKs used and their features]
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How do you handle data consistency and conflict resolution in a multi-writer scenario in Cosmos DB?
- Answer: [Your detailed answer about strategies for handling data consistency and conflict resolution]
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Describe your experience with using Cosmos DB's stored procedures and triggers.
- Answer: [Your detailed answer about using stored procedures and triggers, including examples if possible]
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