FaunaDB Interview Questions and Answers for freshers

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100 FaunaDB Interview Questions and Answers for Freshers

  1. What is FaunaDB?

    • Answer: FaunaDB is a serverless, distributed NoSQL database that uses a schema-based approach and offers ACID transactions. It's known for its ease of use, scalability, and integration with GraphQL.

  2. What are the key differences between FaunaDB and traditional relational databases like MySQL or PostgreSQL?

    • Answer: FaunaDB is a NoSQL database, meaning it doesn't rely on tables and rows like relational databases. It uses a document-based model and offers built-in support for ACID transactions, unlike many other NoSQL databases. It also emphasizes serverless architecture and integrates directly with GraphQL.

  3. Explain FaunaDB's data modeling approach.

    • Answer: FaunaDB uses a schema-based approach where you define collections and indexes to structure your data. Collections are analogous to tables, and indexes are used to efficiently query the data. It uses FQL (Fauna Query Language) for data manipulation.

  4. What is FQL (Fauna Query Language)?

    • Answer: FQL is FaunaDB's query language. It's a strongly-typed, declarative language designed for querying and manipulating data within the FaunaDB database. It's different from SQL and has its own syntax and functions.

  5. How does FaunaDB handle transactions?

    • Answer: FaunaDB guarantees ACID properties (Atomicity, Consistency, Isolation, Durability) for all transactions. This ensures data integrity even in concurrent operations.

  6. What are collections in FaunaDB?

    • Answer: Collections are the basic building blocks for organizing data in FaunaDB. They are analogous to tables in relational databases, holding sets of documents.

  7. What are indexes in FaunaDB and why are they important?

    • Answer: Indexes in FaunaDB are used to optimize query performance. They allow you to efficiently retrieve data based on specific attributes within your collections. Without properly designed indexes, queries can become very slow, especially on large datasets.

  8. Explain the concept of UIDs in FaunaDB.

    • Answer: UIDs (Universally Unique Identifiers) are unique identifiers automatically generated by FaunaDB for each document. They are used to reference specific documents within collections.

  9. How do you create a new collection in FaunaDB using FQL?

    • Answer: You would use the `CreateCollection` function in FQL. The exact syntax depends on the desired collection options, but a basic example would be: `q.CreateCollection({ name: "myCollection" })`

  10. How do you create a new index in FaunaDB using FQL?

    • Answer: Similar to collections, you use the `CreateIndex` function in FQL. This requires specifying the collection, terms (fields for indexing), and unique constraints (if any). A basic example might look like: `q.CreateIndex({ name: "myIndex", source: q.Collection("myCollection"), terms: [{field: ["data", "name"]}] })`

  11. What is the role of `q` in FQL?

    • Answer: `q` is the FaunaDB query client object. All FQL expressions are invoked through `q`.

  12. Explain the difference between `Get` and `Ref` in FQL.

    • Answer: `Get` retrieves the actual document data, while `Ref` returns a reference to a document, which can be used later to fetch the data or perform other operations.

  13. How do you perform a simple query to retrieve all documents in a collection?

    • Answer: `q.Map(q.Paginate(q.Documents(q.Collection("myCollection"))), q.Lambda("X", q.Get(q.Var("X"))))`

  14. How do you filter documents based on a specific attribute?

    • Answer: Use the `Filter` function along with an index that supports filtering on the desired attribute.

  15. Explain the concept of pagination in FaunaDB.

    • Answer: Pagination is used to retrieve large datasets in smaller, manageable chunks. It avoids loading the entire dataset into memory.

  16. How do you handle errors in FQL?

    • Answer: Use try/catch blocks to handle potential errors during FQL operations.

  17. What are the different data types supported by FaunaDB?

    • Answer: FaunaDB supports various data types, including strings, numbers, booleans, arrays, objects, dates, references, and more.

  18. What are the security features of FaunaDB?

    • Answer: FaunaDB offers robust security features including role-based access control, encryption at rest and in transit, and network security measures.

  19. How does FaunaDB handle data replication and availability?

    • Answer: FaunaDB is a distributed database and automatically handles data replication across multiple data centers for high availability and fault tolerance.

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