NoSQL Databases Explained: Types, Use Cases & Core Characteristics

NoSQL (which stands for "Not Only SQL") represents a new class of data management systems that deviate from the traditional relational approach to information storage. Unlike conventional DBMSs, such as MySQL or PostgreSQL, which store data in tables with fixed structures and strict relationships, NoSQL offers more flexible methods for organizing and storing information. This technology doesn't reject SQL; rather, it expands the ways to handle data.

The origin of the term NoSQL has an interesting backstory that began not with technology but with the name of a tech conference. In 2009, organizers of a database event in San Francisco adopted the term, and it unexpectedly caught on in the industry. Interestingly, a decade earlier, in 1998, developer Carlo Strozzi had already used the term "NoSQL" for his own project, which had no connection to modern non-relational systems.

Modern NoSQL databases fall into several key categories of data storage systems. These include:

• Document-oriented databases (led by MongoDB)
• Key-value stores (e.g., Redis)
• Graph databases (Neo4j is a prominent example)
• Column-family stores (such as ClickHouse)

The unifying feature among these systems is their rejection of the classic SQL language in favor of proprietary data processing methods.

Unlike relational DBMSs, where SQL serves as a standardized language for querying and joining data through operations like JOIN and UNION, NoSQL databases have developed their own query languages. Each NoSQL database offers a unique syntax for manipulating data. Here are some examples:

// MongoDB (uses a JavaScript-like syntax):
db.users.find({ age: { $gt: 21 } })

// Redis (uses command-based syntax):
HGET user:1000 email
SET session:token "abc123"

NoSQL databases are particularly efficient in handling large volumes of unstructured data. A prime example is the architecture of modern social media platforms, where MongoDB enables storage of a user's profile, posts, responses, and activity in a single document, thereby optimizing data retrieval performance.

NoSQL vs SQL: Relational and Non-Relational Databases

The evolution of NoSQL databases has paralleled the growing complexity of technological and business needs. The modern digital world, which generates terabytes of data every second, necessitated new data processing approaches. As a result, two fundamentally different data management philosophies have emerged:

1. Relational approach, focused on data integrity and reliability
2. NoSQL approach, prioritizing adaptability and scalability

Each concept is grounded in its own core principles, which define its practical applications.

Relational systems adhere to ACID principles:

• Atomicity ensures that transactions are all-or-nothing.
• Consistency guarantees that data remains valid throughout.
• Isolation keeps concurrent transactions from interfering.
• Durability ensures that once a transaction is committed, it remains so.

NoSQL systems follow the BASE principles:

• Basically Available – the system prioritizes continuous availability.
• Soft state – the system state may change over time.
• Eventually consistent – consistency is achieved eventually, not instantly.

Key Differences:

These fundamental differences define their optimal use cases:

• Relational systems are irreplaceable where data precision is critical (e.g., financial systems).
• NoSQL solutions excel in processing high-volume data flows (e.g., social media, analytics platforms).

Key Features and Advantages of NoSQL

Most NoSQL systems are open source, allowing developers to explore and modify the core system without relying on expensive proprietary software.

Schema Flexibility

One of the main advantages of NoSQL is its schema-free approach. Unlike relational databases, where altering the schema often requires modifying existing records, NoSQL allows the dynamic addition of attributes without reorganizing the entire database.

// MongoDB: Flexible schema supports different structures in the same collection
db.users.insertMany([
  { name: "Emily", email: "emily@email.com" },
  { name: "Maria", email: "maria@email.com", phone: "+35798765432" },
  { name: "Peter", social: { twitter: "@peter", facebook: "peter.fb" } }
])

Horizontal Scalability

NoSQL databases employ a fundamentally different strategy for boosting performance. While traditional relational databases rely on upgrading a single server, NoSQL architectures use distributed clusters. Performance is improved by adding nodes, with workload automatically balanced across the system.

Sharding and Replication

NoSQL databases support sharding—a method of distributing data across multiple servers. Conceptually similar to RAID 0 (striping), sharding enables:

• Enhanced system performance
• Improved fault tolerance
• Efficient load distribution

High Performance

NoSQL systems offer exceptional performance due to optimized storage mechanisms and avoidance of resource-heavy operations like joins. They perform best in scenarios such as:

• Basic read/write operations
• Large-scale data management
• Concurrent user request handling
• Unstructured data processing

Handling Unstructured Data

NoSQL excels in working with:

• Large volumes of unstructured data
• Heterogeneous data types
• Rapidly evolving data structures

Support for Modern Technologies

NoSQL databases integrate well with:

• Cloud platforms
• Microservice architectures
• Big Data processing systems
• Modern development frameworks

Cost Efficiency

NoSQL solutions can be cost-effective due to:

• Open-source licensing
• Efficient use of commodity hardware
• Scalability using standard servers
• Reduced administrative overhead

Main Types of NoSQL Databases

In modern distributed system development, several core types of NoSQL solutions are distinguished, each with a mature ecosystem and strong community support.

Document-Oriented Databases

Document-based systems are the most mature and widely adopted type of NoSQL databases. MongoDB, the leading technology in this segment, is the benchmark example of document-oriented data storage architecture.

Data Storage Principle

In document-oriented databases, information is stored as documents grouped into collections. Unlike relational databases, where data is distributed across multiple tables, here, all related information about an object is contained within a single document.

Example of a user document with orders:

{
  "_id": ObjectId("507f1f77bcf86cd799439011"),
  "user": {
    "username": "stephanie",
    "email": "steph@example.com",
    "registered": "2024-02-01"
  },
  "orders": [
    {
      "orderId": "ORD-001",
      "date": "2024-02-02",
      "items": [
        {
          "name": "Phone",
          "price": 799.99,
          "quantity": 1
        }
      ],
      "status": "delivered"
    }
  ],
  "preferences": {
    "notifications": true,
    "language": "en"
  }
}

Basic Operations with MongoDB

// Insert a document
db.users.insertOne({
  username: "stephanie",
  email: "steph@example.com"
})

// Find documents
db.users.find({ "preferences.language": "en" })

// Update data
db.users.updateOne(
  { username: "stephanie" },
  { $set: { "preferences.notifications": false }}
)

// Delete a document
db.users.deleteOne({ username: "stephanie" })

Advantages of the Document-Oriented Approach

Flexible Data Schema

• Each document can have its own structure
• Easy to add new fields
• No need to modify the overall database schema

Natural Data Representation

• Documents resemble programming objects
• Intuitive structure
• Developer-friendly

Performance

• Fast retrieval of complete object data
• Efficient handling of nested structures
• Horizontal scalability

Working with Hierarchical Data

• Naturally stores tree-like structures
• Convenient nested object representation
• Effective processing of complex structures

Use Cases

The architecture is particularly effective in:

• Developing systems with dynamically evolving data structures
• Processing large volumes of unstandardized data
• Building high-load distributed platforms

Typical Use Scenarios

• Digital content management platforms
• Distributed social media platforms
• Enterprise content organization systems
• Event aggregation and analytics services
• Complex analytical platforms

Key-Value Stores

Among key-value stores, Redis (short for Remote Dictionary Server) holds a leading position in the NoSQL market. A core architectural feature of this technology is that the entire data set is stored in memory, ensuring exceptional performance.

Working Principle

The architecture of key-value stores is based on three fundamental components for each data record:

• Unique key (record identifier)
• Associated data (value)
• Optional TTL (Time To Live) parameter

Data Types in Redis

# Strings
SET user:name "Stephanie"
GET user:name

# Lists
LPUSH notifications "New message"
RPUSH notifications "Payment received"

# Sets
SADD user:roles "admin" "editor"
SMEMBERS user:roles

# Hashes
HSET user:1000 name "Steph" email "steph@example.com"
HGET user:1000 email

# Sorted Sets
ZADD leaderboard 100 "player1" 85 "player2"
ZRANGE leaderboard 0 -1

Key Advantages

High Performance

• In-memory operations
• Simple data structure
• Minimal overhead

Storage Flexibility

• Support for multiple data types
• Ability to set data expiration
• Atomic operations

Reliability

• Data persistence options
• Master-slave replication
• Clustering support

Typical Use Scenarios

Caching

# Cache query results
SET "query:users:active" "{json_result}"
EXPIRE "query:users:active" 3600  # Expires in one hour

Counters and Rankings

# Increase view counter
INCR "views:article:1234"

# Update ranking
ZADD "top_articles" 156 "article:1234"

Message Queues

# Add task to queue
LPUSH "task_queue" "process_order:1234"

# Get task from queue
RPOP "task_queue"

Redis achieves peak efficiency when deployed in systems with intensive operational throughput, where rapid data access and instant processing are critical. A common architectural solution is to integrate Redis as a high-performance caching layer alongside the primary data store, significantly boosting the overall application performance.

Graph Databases

Graph DBMS (Graph Databases) stand out among NoSQL solutions due to their specialization in managing relationships between data entities. In this segment, Neo4j has established a leading position thanks to its efficiency in handling complex network data structures where relationships between objects are of fundamental importance.

Core Components

Nodes

• Represent entities
• Contain properties
• Have labels

Relationships

• Connect nodes
• Are directional
• Can contain properties
• Define the type of connection

Example of a Graph Model in Neo4j

// Create nodes
CREATE (anna:Person { name: 'Anna', age: 30 })
CREATE (mary:Person { name: 'Mary', age: 28 })
CREATE (post:Post { title: 'Graph Databases', date: '2024-02-04' })

// Create relationships
CREATE (anna)-[:FRIENDS_WITH]->(mary)
CREATE (anna)-[:AUTHORED]->(post)
CREATE (mary)-[:LIKED]->(post)

Typical Queries

// Find friends of friends
MATCH (person:Person {name: 'Anna'})-[:FRIENDS_WITH]->(friend)-[:FRIENDS_WITH]->(friendOfFriend)
RETURN friendOfFriend.name

// Find most popular posts
MATCH (post:Post)<-[:LIKED]-(person:Person)
RETURN post.title, count(person) as likes
ORDER BY likes DESC
LIMIT 5

Key Advantages

Natural Representation of Relationships

• Intuitive data model
• Efficient relationship storage
• Easy to understand and work with

Graph Traversal Performance

• Fast retrieval of connected data
• Efficient handling of complex queries
• Optimized for recursive queries

Practical Applications

Social Networks

// Friend recommendations
MATCH (user:Person)-[:FRIENDS_WITH]->(friend)-[:FRIENDS_WITH]->(potentialFriend)
WHERE user.name = 'Anna' AND NOT (user)-[:FRIENDS_WITH]->(potentialFriend)
RETURN potentialFriend.name

Recommendation Systems

// Recommendations based on interests
MATCH (user:Person)-[:LIKES]->(product:Product)<-[:LIKES]-(otherUser)-[:LIKES]->(recommendation:Product)
WHERE user.name = 'Anna' AND NOT (user)-[:LIKES]->(recommendation)
RETURN recommendation.name, count(otherUser) as frequency

Routing

// Find shortest path
MATCH path = shortestPath(
  (start:Location {name: 'A'})-[:CONNECTS_TO*]->(end:Location {name: 'B'})
)
RETURN path

Usage Highlights

• Essential when working with complex, interrelated data structures
• Maximum performance in processing cyclic and nested queries
• Enables flexible design and management of multi-level relationships

Neo4j and similar platforms for graph database management show exceptional efficiency in systems where relationship processing and deep link analysis are critical. These tools offer advanced capabilities for managing complex network architectures and detecting patterns in structured sets of connected data.

Columnar Databases

The architecture of these systems is based on column-oriented storage of data, as opposed to the traditional row-based approach. This enables significant performance gains for specialized queries. Leading solutions in this area include ClickHouse and HBase, both recognized as reliable enterprise-grade technologies.

How It Works

Traditional (row-based) storage:

Row1: [id1, name1, email1, age1]  
Row2: [id2, name2, email2, age2]

Column-based storage:

Column1: [id1, id2]  
Column2: [name1, name2]  
Column3: [email1, email2]  
Column4: [age1, age2]

Key Characteristics

Storage Structure

• Data is grouped by columns
• Efficient compression of homogeneous data
• Fast reading of specific fields

Scalability

• Horizontal scalability
• Distributed storage
• High availability

Example Usage with ClickHouse

-- Create table
CREATE TABLE users (
    user_id UUID,
    name String,
    email String,
    registration_date DateTime
) ENGINE = MergeTree()
ORDER BY (registration_date, user_id);

-- Insert data
INSERT INTO users (user_id, name, email, registration_date)
VALUES (generateUUIDv4(), 'Anna Smith', 'anna@example.com', now());

-- Analytical query
SELECT 
    toDate(registration_date) as date,
    count(*) as users_count
FROM users 
GROUP BY date
ORDER BY date;

Key Advantages

Analytical Efficiency

• Fast reading of selected columns
• Optimized aggregation queries
• Effective with large datasets

Data Compression

• Superior compression of uniform data
• Reduced disk space usage
• I/O optimization

Typical Use Cases

Big Data

-- Log analysis with efficient aggregation
SELECT 
    event_type,
    count() as events_count,
    uniqExact(user_id) as unique_users
FROM system_logs 
WHERE toDate(timestamp) >= '2024-01-01'
GROUP BY event_type
ORDER BY events_count DESC;

Time Series

-- Aggregating metrics by time intervals
SELECT 
    toStartOfInterval(timestamp, INTERVAL 5 MINUTE) as time_bucket,
    avg(cpu_usage) as avg_cpu,
    max(cpu_usage) as max_cpu,
    quantile(0.95)(cpu_usage) as cpu_95th
FROM server_metrics
WHERE server_id = 'srv-001'
    AND timestamp >= now() - INTERVAL 1 DAY
GROUP BY time_bucket
ORDER BY time_bucket;

Analytics Systems

-- Advanced user statistics
SELECT 
    country,
    count() as users_count,
    round(avg(age), 1) as avg_age,
    uniqExact(city) as unique_cities,
    sumIf(purchase_amount, purchase_amount > 0) as total_revenue,
    round(avg(purchase_amount), 2) as avg_purchase
FROM user_statistics
GROUP BY country
HAVING users_count >= 100
ORDER BY total_revenue DESC
LIMIT 10;

Usage Highlights

• Maximum performance in systems with read-heavy workloads
• Proven scalability for large-scale data processing
• Excellent integration in distributed computing environments

Columnar database management systems show exceptional efficiency in projects requiring deep analytical processing of large datasets. This is particularly evident in areas such as enterprise analytics, real-time performance monitoring systems, and platforms for processing timestamped streaming data.

Full-Text Databases (OpenSearch)

The OpenSearch platform, built on the architectural principles of Elasticsearch, is a comprehensive ecosystem for high-performance full-text search and multidimensional data analysis. This solution, designed according to distributed systems principles, stands out for its capabilities in data processing, intelligent search, and the creation of interactive visualizations for large-scale datasets.

Key Features

Full-Text Search

// Search with multilingual support
GET /products/_search
{
  "query": {
    "multi_match": {
      "query": "wireless headphones",
      "fields": ["title", "description"],
      "type": "most_fields"
    }
  }
}

Data Analytics

// Aggregation by categories
GET /products/_search
{
  "size": 0,
  "aggs": {
    "popular_categories": {
      "terms": {
        "field": "category",
        "size": 10
      }
    }
  }
}

Key Advantages

Efficient Search

• Fuzzy search support
• Result ranking
• Match highlighting
• Autocomplete functionality

Analytical Capabilities

• Complex aggregations
• Statistical analysis
• Data visualization
• Real-time monitoring

Common Use Cases

E-commerce Search

• Product search
• Faceted navigation
• Product recommendations
• User behavior analysis

Monitoring and Logging

• Metrics collection
• Performance analysis
• Anomaly detection
• Error tracking

Analytical Dashboards

• Data visualization
• Business metrics
• Reporting
• Real-time analytics

OpenSearch is particularly effective in projects that require advanced search and data analytics. At Hostman, OpenSearch is available as a managed service, simplifying integration and maintenance.

When to Choose NoSQL?

The architecture of various database management systems has been developed with specific use cases in mind, so choosing the right tech stack should be based on a detailed analysis of your application's requirements.In modern software development, a hybrid approach is becoming increasingly common, where multiple types of data storage are integrated into a single project to achieve maximum efficiency and extended functionality.

NoSQL systems do not provide a one-size-fits-all solution. When designing your data storage architecture, consider the specific nature of the project and its long-term development strategy.

Choose NoSQL databases when the following matter:

Large-scale Data Streams

• Efficient handling of petabyte-scale storage
• High-throughput read and write operations
• Need for horizontal scalability

Dynamic Data Structures

• Evolving data requirements
• Flexibility under uncertainty

Performance Prioritization

• High-load systems
• Real-time applications
• Services requiring high availability

Unconventional Data Formats

• Networked relationship structures
• Time-stamped sequences
• Spatial positioning

Stick with Relational Databases when you need:

Guaranteed Integrity

• Banking transactions
• Electronic health records
• Mission-critical systems

Complex Relationships

• Multi-level data joins
• Complex transactional operations
• Strict ACID compliance

Immutable Structure

• Fixed requirement specifications
• Standardized business processes
• Formalized reporting systems

Practical Recommendations

Hybrid Approach

// Using Redis for caching
// alongside PostgreSQL for primary data
const cached = await redis.get(`user:${id}`);
if (!cached) {
    const user = await pg.query('SELECT * FROM users WHERE id = $1', [id]);
    await redis.set(`user:${id}`, JSON.stringify(user));
    return user;
}
return JSON.parse(cached);

Gradual Transition

• Start with a pilot project
• Test performance
• Evaluate support costs

Decision-Making Factors

Technical Aspects

• Data volume
• Query types
• Scalability requirements
• Consistency model

Business Requirements

• Project budget
• Development timeline
• Reliability expectations
• Growth plans

Development Team

• Technology expertise
• Availability of specialists
• Maintenance complexity