NoSQL Cheat Sheet

Updated 2026-04-21

Next Topic: NoSQL Data Modeling Patterns Cheat Sheet

NoSQL databases are non-relational data stores designed for massive volumes of unstructured, semi-structured, and structured data with horizontal scalability and flexible schemas. Born from the needs of web-scale companies like Google, Amazon, and Facebook, NoSQL systems embrace schema flexibility, eventual consistency, and diverse data models — document stores, key-value pairs, wide-column families, graph structures, and specialized types like time-series and vector databases. As of 2026, the NoSQL ecosystem continues to evolve with Redis 8.0 unifying its data platform, Cassandra 5.0 introducing Storage Attached Indexes and vector search, MongoDB 8.0 enhancing queryable encryption, and the GQL ISO standard (ISO/IEC 39075) establishing a universal graph query language. Understanding when to use NoSQL over SQL, how different consistency levels impact performance, and which database type fits your access patterns is crucial for building modern, scalable applications.

What This Cheat Sheet Covers

This topic spans 17 focused tables and 151 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: NoSQL Database TypesTable 2: MongoDB Document Database OperationsTable 3: Redis Key-Value OperationsTable 4: Cassandra Wide-Column OperationsTable 5: Neo4j Graph Database QueriesTable 6: DynamoDB OperationsTable 7: Elasticsearch Search OperationsTable 8: Data Modeling PatternsTable 9: Consistency LevelsTable 10: CAP Theorem and PACELCTable 11: Replication and ShardingTable 12: Indexing StrategiesTable 13: NoSQL vs SQL When to UseTable 14: Security FeaturesTable 15: Performance OptimizationTable 16: Backup and RecoveryTable 17: Advanced NoSQL Features

Table 1: NoSQL Database Types

Type	Example	Description
Document Store	`MongoDB: db.users.find({age: {$gt: 30}})`	• Stores data as JSON/BSON documents with nested structures • ideal for content management, catalogs, and user profiles where schema varies per record.
Key-Value Store	`Redis: SET user:1001 "John"` `GET user:1001`	• Maps unique keys to values (strings, hashes, lists, sets) • fastest NoSQL model for caching, session storage, and real-time counters with sub-millisecond latency.
Wide-Column Store	`Cassandra: SELECT * FROM users` `WHERE user_id = '123'`	• Organizes data into column families with flexible columns per row • excels at time-series data, event logging, and IoT workloads requiring high write throughput.
Graph Database	`Neo4j: MATCH (a)-[:KNOWS]->(b)` `RETURN a, b`	• Stores nodes and relationships as first-class citizens • optimized for traversing connections in social networks, fraud detection, and recommendation engines.

Table 1: NoSQL Database Types

Type	Example	Description
Document Store	`MongoDB: db.users.find({age: {$gt: 30}})`	• Stores data as JSON/BSON documents with nested structures • ideal for content management, catalogs, and user profiles where schema varies per record.
Key-Value Store	`Redis: SET user:1001 "John"` `GET user:1001`	• Maps unique keys to values (strings, hashes, lists, sets) • fastest NoSQL model for caching, session storage, and real-time counters with sub-millisecond latency.
Wide-Column Store	`Cassandra: SELECT * FROM users` `WHERE user_id = '123'`	• Organizes data into column families with flexible columns per row • excels at time-series data, event logging, and IoT workloads requiring high write throughput.
Graph Database	`Neo4j: MATCH (a)-[:KNOWS]->(b)` `RETURN a, b`	• Stores nodes and relationships as first-class citizens • optimized for traversing connections in social networks, fraud detection, and recommendation engines.