FaunaDB Interview Questions and Answers for 10 years experience

1. What are the core advantages of FaunaDB compared to traditional relational databases?

   • Answer: FaunaDB offers several key advantages over traditional relational databases, including its serverless architecture (reducing operational overhead), built-in security features (with fine-grained access control), its globally distributed nature (for high availability and low latency), and its ease of use for developers, especially with its intuitive query language FQL. It also excels in handling complex data relationships and schema flexibility, making it well-suited for modern applications.

2. Explain the concept of a "collection" in FaunaDB.

   • Answer: In FaunaDB, a collection is analogous to a table in a relational database. It's a container for documents (similar to rows), where each document has its own unique ID and can contain various fields (similar to columns). Collections are the foundational building blocks for organizing data within a FaunaDB database.

3. Describe the difference between indexes and collections in FaunaDB.

   • Answer: Collections store data, while indexes provide efficient ways to query that data. Collections are unordered sets of documents, while indexes are ordered structures that allow you to retrieve documents based on specific criteria. You create indexes to optimize query performance by defining search paths and data arrangements that accelerate data retrieval.

4. How does FaunaDB handle transactions?

   • Answer: FaunaDB provides robust transaction support using its built-in ACID properties. This ensures data consistency and integrity even under concurrent operations. Transactions in FaunaDB are atomic, consistent, isolated, and durable, guaranteeing that either all operations within a transaction succeed, or none do.

5. Explain the role of UDFs (User Defined Functions) in FaunaDB.

   • Answer: UDFs allow developers to extend FaunaDB's functionality by writing custom functions in JavaScript. These functions can perform complex logic, data transformations, and calculations within the database itself, enhancing data processing capabilities and reducing the need for external logic.

6. What are the different types of indexes available in FaunaDB?

   • Answer: FaunaDB offers several index types, including unique indexes (ensuring uniqueness of data values), term indexes (for efficient text searches), and composite indexes (allowing queries across multiple fields). The choice of index type depends on the specific querying needs of your application.

7. How does FaunaDB handle schema changes?

   • Answer: FaunaDB uses a schema-less approach, offering flexibility in handling schema changes. You can add, modify, or remove fields within documents without requiring downtime or major schema migrations. This flexibility simplifies development and adaptation to evolving data needs.

8. Describe the concept of roles and access control in FaunaDB.

   • Answer: FaunaDB employs a robust role-based access control system (RBAC). You can define roles with specific permissions, granting users access to specific collections, documents, or operations. This fine-grained control enhances security by restricting access to sensitive data based on user roles.

9. How can you perform pagination in FaunaDB queries?

   • Answer: Pagination in FaunaDB is typically achieved using the `after` and `before` options in queries. These options allow you to specify the starting and ending points for a result set, retrieving only a subset of data at a time. This is crucial for handling large datasets efficiently.

10. Explain how to implement data validation in FaunaDB.

    • Answer: Data validation can be implemented in FaunaDB using various techniques. You can use UDFs to perform custom validation logic before writing data, leveraging JavaScript to enforce rules. FaunaDB's validation capabilities can also be combined with client-side validation for a comprehensive approach to data integrity.

11. How do you handle relationships between documents in FaunaDB?

    • Answer: FaunaDB handles relationships primarily through references. Instead of joins like in relational databases, you embed the ID of a related document within another document. This allows for flexible relationships and querying based on these references using FQL.

12. What are the best practices for designing FaunaDB schemas?

    • Answer: Best practices include considering data relationships, anticipating query patterns, optimizing index designs for performance, and balancing data normalization with application requirements. It's also crucial to plan for scalability and future data growth.

13. How can you optimize query performance in FaunaDB?

    • Answer: Query optimization involves proper index design, choosing appropriate query methods (e.g., using indexes strategically), avoiding unnecessary computations within queries, and using pagination for large result sets. Profiling queries can reveal performance bottlenecks.

14. Explain the concept of "ref" in FaunaDB.

    • Answer: A "ref" in FaunaDB is a unique identifier for a document within a collection. It's not simply the ID, but rather an object that contains the collection name and document ID. Refs are essential for referencing other documents within the database and establishing relationships.

15. How does FaunaDB handle data replication and high availability?

    • Answer: FaunaDB is inherently globally distributed, employing a multi-region architecture for data replication and high availability. Data is automatically replicated across multiple data centers to ensure resilience and low latency access for users worldwide.

16. Describe different ways to manage schema evolution in FaunaDB.

    • Answer: Because FaunaDB is schemaless, schema evolution is generally handled by adding or removing fields from documents as needed. Careful planning and versioning strategies help ensure data consistency and smooth transitions during schema changes. UDFs can aid in migration strategies.

17. How can you implement full-text search in FaunaDB?

    • Answer: FaunaDB supports full-text search using term indexes. These indexes allow searching for documents based on keywords within text fields. The search functionality offers features like stemming and case-insensitive matching.

18. How would you approach debugging FaunaDB queries?

    • Answer: Debugging FaunaDB queries involves using the FaunaDB dashboard for query inspection, checking logs for errors, carefully reviewing the query syntax, and using the `print` function within UDFs for step-by-step debugging.

19. What are some common performance pitfalls to avoid in FaunaDB?

    • Answer: Common pitfalls include inefficient query design (lack of indexes or poorly designed indexes), performing complex calculations in queries instead of UDFs, and neglecting pagination for large datasets. Overly complex data structures can also impact performance.

20. Explain the use of FaunaDB's `Let` statement.

    • Answer: The `Let` statement in FQL allows you to define variables and reuse them within a query, improving readability and reducing redundancy. This is particularly helpful for complex queries that involve repeated calculations or subqueries.

21. How would you secure your FaunaDB instance?

    • Answer: Securing a FaunaDB instance involves using strong passwords, enabling multi-factor authentication, regularly rotating API keys, employing RBAC to control access to data, and configuring network security measures like VPCs or private endpoints.

22. Describe your experience with FaunaDB's different pricing tiers.

    • Answer: [This answer will depend on the individual's experience, discussing their knowledge of FaunaDB's pricing models, including free tiers, pay-as-you-go options, and potentially enterprise contracts. They should mention factors like reads, writes, storage, and compute costs.]

23. How have you integrated FaunaDB with other services or technologies?

    • Answer: [This answer should detail specific integrations, such as connecting FaunaDB with serverless functions (AWS Lambda, Google Cloud Functions, Azure Functions), frontend frameworks (React, Angular, Vue), or other databases. It should emphasize the approaches used to handle data consistency and efficiency in these integrations.]

24. How would you monitor and troubleshoot performance issues in a FaunaDB application?

    • Answer: [This requires a detailed answer encompassing using FaunaDB's monitoring tools, setting up alerts, analyzing query execution times, identifying bottlenecks, and using logging and tracing mechanisms to track down performance issues. Knowledge of relevant metrics is crucial.]

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