NoSQL Interview Questions and Answers (2025) | JaganInfo

NoSQL refers to non-relational databases designed for flexible schema, scalability, and high performance, differing from traditional relational databases.

NoSQL databases typically have dynamic schemas, support horizontal scaling, and are optimized for specific data models; SQL databases use structured schemas, ACID transactions, and vertical scaling.

Key-value stores, document databases, column-family stores, and graph databases.

A type of NoSQL that stores data as a collection of key-value pairs, optimized for simple retrieval by key.

MongoDB (document), Cassandra (column-family), Redis (key-value), Neo4j (graph).

For applications requiring flexible schema, high write/read throughput, large volumes of unstructured data, or horizontal scaling.

It’s the capability to add more servers to distribute the data and load, enhancing performance and availability.

Many NoSQL databases prioritize availability and partition tolerance over full ACID compliance but some support ACID within limited scopes or single documents.

A consistency model where data updates propagate asynchronously, with replicas becoming consistent over time.

A NoSQL type that stores data as JSON, BSON, or XML documents with nested structures, providing flexible schema.

Sharding is horizontal partitioning of data across multiple nodes to improve scalability and performance.

Replication duplicates data across nodes to enhance availability and fault tolerance.

CAP theorem states a system can only guarantee two of Consistency, Availability, and Partition Tolerance simultaneously; NoSQL databases often choose availability and partition tolerance over strict consistency.

It involves designing schema-less or flexible schemas considering access patterns and variable data structures.

Challenges include lack of mature access controls, injection attacks, misconfigurations, and insufficient encryption.

Indexing improves query performance by maintaining lookup data structures, customized based on NoSQL data types and query patterns.

Secondary indexes allow querying on fields other than the primary key, enhancing flexibility at the cost of additional storage and performance overhead.

Models include eventual consistency, strong consistency, causal consistency, and tunable consistency depending on database and requirements.

MongoDB is a document store supporting rich queries and secondary indexes; Cassandra is a wide-column store known for linear scalability and high availability across multiple data centers.

Graph databases store data as nodes and relationships, ideal for social networks, recommendation engines, and fraud detection.

Write concern defines the level of acknowledgment requested from MongoDB for write operations, impacting durability and performance.

Event sourcing stores changes as events in an append-only log, enabling audit trails and reconstruction of current state.

MongoDB supports multi-document ACID transactions starting from version 4.0, improving consistency for complex operations.

Compaction merges multiple SSTables to reduce space usage and improve read performance by discarding obsolete data.

Design according to query patterns, avoid joins, embed related data, and consider write/read trade-offs.

Time-To-Live (TTL) automatically expires data after a set duration, useful for cache or session management.

Applications must handle temporary inconsistencies gracefully and use conflict resolution strategies.

Through replication, partition tolerance, and automatic failover mechanisms.

Challenges include operational complexity, data consistency, backup/restore, and evolving schemas.

Secondary indexes improve query flexibility but introduce overhead on writes, slowing insert/update operations.

Distributed NoSQL databases use consistent hashing for data distribution, replication protocols for durability, consensus algorithms for coordination, and partitioning for scalability.

Implement authentication, authorization, encryption at rest and in transit, audit logging, and regularly update software.

Synchronous replication waits for confirmation from replicas before completing writes; asynchronous replication returns immediately, with replication occurring in the background.

Tunable consistency allows clients to specify the number of replicas that must acknowledge read or write operations, balancing between consistency and latency.

They use geo-replication, data partitioning, and conflict resolution mechanisms to maintain availability and latency across regions.

WAL is a sequential log of data modifications written before actual data updates to ensure durability and recoverability.

Strategies include last write wins, vector clocks, timestamps, and application-level reconciliation.

Maintaining index consistency, performance degradation, and update propagation delays across shards.

Eventual consistency favors availability and scalability, allowing stale reads; strong consistency guarantees up-to-date reads but may affect availability and latency.

Indexes are rebuilt by scanning stored data or replaying write logs to restore query efficiency post-recovery.

Via replication, automatic failover, data partitioning, and consensus protocols to handle node failures.

A precomputed query result stored for fast reads, updated asynchronously when underlying data changes.

By using indexes, denormalized data, query planners, and limiting costly operations suited to NoSQL’s data model.

Vector clocks track causality among distributed updates to detect and resolve conflicts in eventually consistent systems.

Strategies include dual writes, phased migration, map-reduce jobs, and application-level transformation scripts.

Using snapshotting, incremental backups, or export/import tools ensuring data integrity and minimal downtime.

Use cases include big data analytics, content management systems, IoT, social networks, and real-time data streams.

Handling backward/forward compatibility, data migration, and maintaining query consistency over time.

Prometheus, Grafana, Datadog, and native database monitoring dashboards to track performance, throughput, and failures.

By careful data partitioning, load balancing, caching hot data, and adaptive cluster scaling.