Intro to NoSQL database
Find out all about this introduction to NoSQL databases and also explore how it can help you in your programming projects.
- What is a NoSQL database?
- Differences between SQL and NoSQL
- When to use SQL databases and when to use NoSQL
- Types of NoSQL
- NoSQL performance and scalability
- NoSQL consistency and availability
- NoSQL Schema and Queries
- NoSQL transactions
What is a NoSQL database?
NoSQL databases are non-relational databases that store and retrieve unstructured or semi-structured data.
The four main types of NoSQL databases are document, key-value, column and graph databases.
They are more scalable and faster than traditional relational databases.
These databases were born to address the limitations of relational databases (SQL) and meet the needs of modern applications that require flexibility, scalability, performance and handling of varied and unstructured data.
Differences between SQL and NoSQL
The main differences include the model, data, schema, transactions, consistency and availability, performance and scalability.
List of differences between NoSQL and SQL:
|✔ Non Relational
✔ Storing data in JSON documents, key-value pairs, column-oriented warehouses or graphs
✔ Storing data in tables
|✔ Flexibility due to the fact that all
records do not have to store the same characteristics
✔ Quickly add new features
✔ Links are graded by denormalizing data and presenting all data for one entity in a single record
✔ Suitable for semi-structured, complex or nested data
|✔ Excellent for solutions where each
record has the same characteristics
✔ Adding a new feature requires modifying the scheme
✔ Links are built in normalized models by joining tables
✔ Suitable for structured data
|✔ Dynamic or flexible schemes
Database does not accept the scheme, and it is specified by the application. This results in agility and highly interactive development
|✔ Clearly defined schemes
✔ Scheme must be maintained and be synchronized between applications and databases
|✔ ACID transaction support varies depending on the solutions
|✔ Supports ACID transactions
|✔ Supports eventual consistency to strong consistency, depending on the solutions
✔ Consistency, availability, and performances may vary depending on the application requirements (CAP theorem)
|✔ Strong consistency
✔ Consistency has priority over availability and performance
|✔ Performances can be maximised on account of consistency (if necessary)
|✔ Insertion and update performances depend on the speed of record creation, with strong consistency Performances can be maximized by scaling available resources
|✔ Support distributed structures
✔ Horizontal scalability
|✔ Vertical scalability
|✔ Oracle, MySQL, Microsoft SQL Server, PostgreSQL, etc.
|✔ All types of data: Cosmos DB
✔ Documents: MongoDB
✔ Key-value: Redis
✔ Wide-column: Cassandra
✔ Graph: Neo4j
When to use SQL databases and when to use NoSQL
Choosing between a SQL database and a NoSQL database depends on several factors and specific requirements of your application. Here are some general guidelines to help you decide when to use SQL and when to use NoSQL:
When to use a SQL database:
- Structured data: If your data is highly structured and follows a fixed schema.
- Data integrity and complex relationships: When you need to maintain data integrity and manage complex relationships between tables, SQL is the right choice. SQL databases are ideal for applications that require ACID transactions and clear relationships between data.
- Strong consistency: If your application requires strong consistency, where data integrity is critical, SQL is the best option. This is especially important in financial applications and critical systems.
- Vertical Scalability: SQL databases are suitable when you can scale vertically, which means adding more resources (CPU, RAM, etc.) to a single server to increase performance. This is common in enterprise applications.
When to use a NoSQL database:
- Unstructured or semi-structured data: If your data is flexible and does not follow a fixed schema, NoSQL is a good choice. Examples include modern web applications that handle various data, such as social networks and IoT applications.
- Horizontal Scalability: When you need easy and fast scalability, NoSQL is the preferred option. NoSQL databases are typically distributed and scale horizontally by adding more servers as needed.
- High read/write speed and performance: If read and write speed is critical for your application, NoSQL can offer better performance, especially in high-traffic environments.
- Specific data models: NoSQL is ideal when you need specific data models, such as document databases, key-value stores, graph-oriented databases, or column-oriented stores.
These points summarize when it is appropriate to consider an SQL or NoSQL database, depending on your specific needs and the characteristics of your application.
Types of NoSQL
There are different ways to classify NoSQL databases based on their data model, such as key-value stores, document stores, graph databases, etc. Each type has its advantages and disadvantages depending on the problem to be solved.
The simplest of NoSQL databases, data is represented as a collection of key-value pairs, where each value is associated with a unique key. These databases are ideal for applications that require high read/write speed and caching. Performance focuses on fast data recovery by key. Example: Redis.
wide column warehouse
Related data is stored as a set of key-value pairs nested within a single column. They are ideal for applications that require efficient reading of large data sets. Example: Cassandra.
Data is stored in a graph structure as node, edge, and data properties. They are designed to manage data with complex relationships, such as social networks or web browsing. They use graph structures to represent and query data. Example: Neo4j.
They store data primarily in RAM for ultra-fast access. Example: Memcached, Redis.
NoSQL performance and scalability
Performance in a NoSQL database refers to the database’s ability to efficiently handle inserting, updating, querying, and deleting data under these circumstances.
Unlike traditional relational databases, NoSQL databases are designed to handle large volumes of distributed data in scalable environments.
The performance of NoSQL databases is typically evaluated using the performance metric, which is measured as operations/second.
Scalability refers to the ability to expand the system to handle more data and users. NoSQL databases are typically highly horizontally scalable, meaning they can handle large workloads by distributing data and operations across multiple servers or nodes. This allows more servers to be added as needed to handle an increase in workload.
NoSQL consistency and availability
Consistency refers to ensuring that all nodes in a distributed system see the same data at the same time. And availability refers to the ability of a distributed system to respond to read and write requests, even in the presence of network failures or interruptions. Partition tolerance implies that the system remains functional even if some nodes cannot communicate with others due to problems.
Consistency and availability are two of the three key aspects of the CAP (Consistency, Availability, Partition Tolerance) theorem that apply to distributed systems, including NoSQL databases.
The CAP theorem is a principle that states that a distributed system can only guarantee two of these three attributes: consistency, availability and partitioning tolerance. These attributes affect the performance and scalability of databases.
Relational databases: These are systems that prioritize the consistency and availability of information, but have limited tolerance for partitioning. This means they cannot scale horizontally to many nodes and have difficulty handling unstructured data.
NoSQL databases: These are systems that prioritize tolerance to partitioning , but give up consistency or availability. This means they can scale out to thousands of nodes and handle unstructured data, but may have inconsistencies or temporary unavailability.
Many NoSQL databases sacrifice consistency for availability, partition tolerance, and speed. This means that data changes may not immediately propagate to all nodes or some data may be lost. Some NoSQL databases offer concepts such as early logging or ACID transactions to prevent data loss.
Examples of databases according to CAP theorem:
- CA (Consistency/Availability): MySQL, SQL Server, y Oracle Database, PostgreSQL, etc.
- AP (Availability/Partition Tolerance): Cassandra, etc.
- CP (Consistency/Partition Tolerance): MongoDB, Redis, etc.
NoSQL Schema and Queries
NoSQL databases do not require a fixed schema for data, allowing for greater flexibility and adaptability. However, this also means that queries are typically lower than standard SQL and that joins between tables cannot be performed. There are different ways to handle relational data in a NoSQL database, such as making multiple queries, storing foreign values, or grouping documents.
Within a NoSQL database, data is stored in a way that both writing and reading are fast, even under heavy load.
Unlike relational databases, which follow the ACID (Atomicity, Consistency, Isolation, Durability) model to ensure data integrity, NoSQL databases often adopt an eventual consistency model, allowing them to offer high availability and fault tolerance in distributed systems. This choice is linked to the CAP Theorem (Consistency, Availability and Partition Tolerance), which states that in a distributed system, it is impossible to simultaneously guarantee the three aspects: consistency, availability and partition tolerance.
When it is said that “ACID transaction support varies across solutions” in the context of NoSQL databases, it means that different NoSQL databases may provide different degrees of support for these ACID properties. Some NoSQL databases may offer full ACID support, similar to relational databases, while others may prioritize scalability and availability at the expense of strict consistency, resulting in more limited ACID support.
“Eventual consistency” is a key concept in NoSQL databases. It refers to a level of consistency where, after a write operation, systems guarantee that, at some point in the future, all data nodes or replicas will have the same version of the data. However, this consistency is not guaranteed to be immediate. In other words, there may be a short period of time during which nodes do not have the same data after a write.
This approach is used in many NoSQL databases to achieve high availability and scalability. By allowing local nodes to perform writes without requiring immediate coordination with other nodes, potential bottlenecks and delays in the system are avoided. This is especially useful in large-scale distributed systems, where immediate consistency can be costly in terms of performance and latency.
It is important to note that eventual consistency is not suitable for all use cases. If your application requires strict consistency at all times, then a NoSQL database that prioritizes eventual consistency may not be the right choice. You should evaluate the needs of your application and the consistency and availability trade-offs you are willing to make when selecting a NoSQL database.
In conclusion, NoSQL databases have revolutionized the world of data management by offering flexible and highly scalable solutions for a wide range of applications and use cases. These databases have proven to be ideal for handling unstructured or semi-structured data, and their ability to manage large volumes of information in distributed systems has driven their adoption in companies of all sizes.
However, it is crucial to recognize that the choice of a NoSQL database should be based on the specific needs of each project. Each type of NoSQL database has its own strengths and limitations, and the right choice will depend on considerations such as data consistency, performance, scalability, ease of development, and data model complexity.
Finally, NoSQL databases represent a valuable addition to the arsenal of data management tools, and their proper use can lead to improved performance and scalability in a variety of applications. Constant evolution in this field ensures that NoSQL databases will continue to play a central role in the world of technology and data management in the future.