FaunaDB Interview Questions and Answers for 5 years experience

1. What is FaunaDB and what are its key features?

   • Answer: FaunaDB is a serverless, globally distributed database built for modern applications. Key features include its serverless architecture (no server management), ACID transactions, built-in security, GraphQL API, and its ability to handle complex data models with ease. It offers a unique blend of ease of use and powerful capabilities, ideal for applications needing scalability and reliability.

2. Explain the difference between FaunaDB and traditional relational databases like MySQL or PostgreSQL.

   • Answer: FaunaDB is a NoSQL database, specifically a document database, unlike relational databases which use tables and schemas. FaunaDB prioritizes ease of development and scalability with its serverless nature and flexible schema. Relational databases offer strong ACID properties and mature tooling, but often require more complex management and scaling strategies. FaunaDB handles schema evolution more smoothly than relational databases and integrates well with modern development practices.

3. How does FaunaDB's serverless architecture work? What are its advantages?

   • Answer: FaunaDB's serverless architecture means you don't manage servers. FaunaDB handles all the infrastructure, scaling, and maintenance. Advantages include reduced operational overhead, improved scalability (automatically handles traffic spikes), cost efficiency (pay-as-you-go pricing), and faster development cycles.

4. Describe FaunaDB's data modeling capabilities. How do you define collections and indexes?

   • Answer: FaunaDB uses a schema-less approach, allowing flexible data structures. Data is organized into collections (analogous to tables). Indexes are created to efficiently query data based on specific attributes. Indexes define how data is accessed and optimized for specific queries. They can be defined across multiple collections, improving query performance significantly.

5. Explain the concept of ACID transactions in FaunaDB. Why are they important?

   • Answer: FaunaDB guarantees ACID (Atomicity, Consistency, Isolation, Durability) properties for all transactions. This ensures data integrity, even under concurrent operations. Atomicity ensures all changes within a transaction succeed or fail as a unit. Consistency ensures data remains in a valid state. Isolation ensures concurrent transactions don't interfere with each other. Durability guarantees that committed transactions survive failures.

6. How do you handle schema changes in FaunaDB?

   • Answer: FaunaDB's flexible schema allows for schema evolution without downtime. Adding new fields to documents is typically seamless. Removing fields might require updating existing documents, but FaunaDB's query language allows for efficient updates. The absence of a rigid schema makes adapting to changing requirements much easier compared to relational databases.

7. Explain the role of UDFs (User Defined Functions) in FaunaDB. Give an example.

   • Answer: UDFs are functions written in FaunaDB's query language (FQL) that encapsulate reusable logic. They can perform complex operations on data, enhancing the database's functionality. For example, a UDF could calculate the average price of products in a collection or perform data validation before insertion.

8. How does FaunaDB handle data replication and global distribution?

   • Answer: FaunaDB automatically replicates data across multiple data centers globally, ensuring high availability and low latency. This is managed by FaunaDB, relieving the developer of infrastructure concerns. Data consistency is maintained through its distributed consensus mechanism.

9. Describe FaunaDB's security features.

   • Answer: FaunaDB offers robust security features including role-based access control (RBAC), secure authentication mechanisms, encryption at rest and in transit, and audit logging. These features help protect sensitive data and ensure compliance with security standards.

10. How would you optimize a slow FaunaDB query?

    • Answer: I would start by analyzing the query plan using FaunaDB's query profiling tools. This helps identify bottlenecks. Common optimizations include creating appropriate indexes, refactoring the query to use more efficient functions, and reducing the amount of data retrieved. Reviewing the data model to ensure it's well-suited for the queries is also crucial.

11. What are some common challenges you've faced while working with FaunaDB, and how did you overcome them?

    • Answer: [This answer should be personalized based on actual experiences. Example: "One challenge was optimizing queries for large datasets. I overcame this by carefully analyzing query plans and creating composite indexes targeted at specific query patterns. Another challenge was understanding the nuances of FQL. This was resolved through extensive practice and studying the official documentation."]

12. Explain your experience with FaunaDB's GraphQL API.

    • Answer: [This should describe experience with GraphQL schema definition, resolvers, integrating with frontend applications, and understanding GraphQL's strengths and weaknesses within the FaunaDB context.]

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