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:
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.
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:
Each concept is grounded in its own core principles, which define its practical applications.
Key Differences:
Aspect |
Relational Databases |
NoSQL Databases |
Data Organization |
Structured in predefined tables and schemas |
Flexible format, supports semi-structured/unstructured data |
Scalability |
Vertical (via stronger servers) |
Horizontal (adding more nodes to the cluster) |
Data Integrity |
Maintained at the DBMS core level |
Managed at the application level |
Performance |
Efficient for complex transactions |
High performance in basic I/O operations |
Data Storage |
Distributed across multiple interrelated tables |
Groups related data into unified blocks/documents |
These fundamental differences define their optimal use cases:
Most NoSQL systems are open source, allowing developers to explore and modify the core system without relying on expensive proprietary software.
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" } }
])
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.
NoSQL databases support sharding—a method of distributing data across multiple servers. Conceptually similar to RAID 0 (striping), sharding enables:
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:
NoSQL excels in working with:
NoSQL databases integrate well with:
NoSQL solutions can be cost-effective due to:
In modern distributed system development, several core types of NoSQL solutions are distinguished, each with a mature ecosystem and strong community support.
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.
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"
}
}
// 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" })
Flexible Data Schema
Natural Data Representation
Performance
Working with Hierarchical Data
The architecture is particularly effective in:
Typical Use Scenarios
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.
The architecture of key-value stores is based on three fundamental components for each data record:
# 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
High Performance
Storage Flexibility
Reliability
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 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.
Nodes
Relationships
// 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)
// 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
Natural Representation of Relationships
Graph Traversal Performance
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
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.
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.
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]
Storage Structure
Scalability
-- 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;
Analytical Efficiency
Data Compression
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;
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.
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.
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
}
}
}
}
Efficient Search
Analytical Capabilities
E-commerce Search
Monitoring and Logging
Analytical Dashboards
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.
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.
Large-scale Data Streams
Dynamic Data Structures
Performance Prioritization
Unconventional Data Formats
Guaranteed Integrity
Complex Relationships
Immutable Structure
// 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
Technical Aspects
Business Requirements
Development Team