Database

Question 1

What is the difference between SQL and NoSQL databases?

Answer 1

SQL databases are relational with structured schemas and ACID properties. NoSQL databases are non-relational, schema-flexible, and designed for horizontal scaling. SQL is good for complex queries; NoSQL for high-volume, flexible data.

Question 2

What is database sharding?

Answer 2

Sharding is partitioning data across multiple database instances. Each shard contains a subset of data. It enables horizontal scaling by distributing load across multiple servers.

Question 3

What is database replication?

Answer 3

Replication is copying data from one database server to others. Master-slave replication has one write node and multiple read nodes. Master-master allows writes on multiple nodes.

Question 4

What is a database index?

Answer 4

An index is a data structure that improves query speed by creating pointers to data locations. It trades write performance and storage space for faster reads on indexed columns.

Question 5

What is denormalization?

Answer 5

Denormalization is intentionally introducing redundancy in a database to improve read performance. It trades storage and update complexity for faster queries by reducing joins.

Question 6

What are ACID properties?

Answer 6

ACID stands for Atomicity (all-or-nothing transactions), Consistency (valid state transitions), Isolation (concurrent transactions don’t interfere), and Durability (committed data persists).

Question 7

What is eventual consistency?

Answer 7

Eventual consistency means that given enough time without new updates, all replicas will converge to the same value. It allows temporary inconsistencies for better availability and partition tolerance.

Question 8

What is a connection pool?

Answer 8

A connection pool maintains a cache of database connections that can be reused. It reduces the overhead of creating new connections for each request and limits concurrent connections.

Question 9

When would you choose NoSQL over SQL?

Answer 9

Choose NoSQL for: massive scale, flexible schemas, high write throughput, hierarchical data, or when eventual consistency is acceptable. Examples: user profiles, time-series data, real-time analytics.

Question 10

What is database partitioning?

Answer 10

Partitioning divides a large table into smaller pieces based on criteria like range, hash, or list. Horizontal partitioning splits rows; vertical partitioning splits columns.