Database Interview Questions for Node.js Developers
Database questions come up in Node.js backend interviews. Here are the common ones and how to answer them.
Database Interview Questions for Node.js Developers
Database questions come up in Node.js backend interviews. Here are the common ones and how to answer them.
SQL vs NoSQL and when to use each?
SQL for highly relational data, ACID transactions, and complex joins. NoSQL for flexible schemas, horizontal scaling, and document-like data. Choose based on data shape and query patterns.
How do you design a database schema for a Node.js app?
Start with the queries you will run. For NoSQL, embed small coupled data, reference large or shared data, and use Mongoose for schema management. For SQL, normalize and use migrations.
What is the difference between embed and reference in MongoDB?
Embed stores nested data within a document (fast reads, duplication). Reference stores related data by id (consistent, requires populate). Embed for always-read-together data; reference for shared or large data.
What are indexes and why do they matter?
Indexes make queries fast. Without indexes, queries scan the entire collection. Add indexes on frequently queried fields (email, userIds, foreign keys). Profile slow queries with explain.
How do you handle database connections in Node.js?
Use connection pooling (Mongoose handles this by default). Store the connection string in an environment variable. Handle connection errors gracefully. Use one connection for the whole app, not one per request.
The Takeaway
Know SQL vs NoSQL, schema design for queries, embed vs reference in MongoDB, indexes and their importance, and connection handling in Node.js. These are the core database questions for backend interviews.
SQL is relational with tables, rows, fixed schema, ACID transactions, and complex joins. NoSQL is non-relational with flexible schemas, designed for horizontal scaling and document-like data. Choose based on data shape and query patterns.
Start with the queries you will run. For NoSQL, embed small coupled data, reference large or shared data, use Mongoose for schema management. For SQL, normalize and use migrations. Design for queries, not for storage elegance.
Embed stores nested data within a document for fast reads with duplication. Reference stores related data by id for consistency, requiring populate. Embed for always-read-together data; reference for shared or large data.
Because they make queries fast. Without indexes, queries scan the entire collection, which gets slow as data grows. Add indexes on frequently queried fields like email and userIds. Profile slow queries with explain.
Use connection pooling (Mongoose handles this by default). Store the connection string in an environment variable. Handle connection errors gracefully. Use one connection for the whole app, not one per request, to reuse the pooled connection.
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