SQL vs Non-SQL in Multi-Model Databases

2.1 Definition

SQL (Structured Query Language) databases are relational databases that use a structured format to store data in tables with predefined schemas. Common examples include MySQL, PostgreSQL, and Oracle.

2.2 Key Features

• ACID Compliance: Ensures data integrity and reliability.
• Structured Data: Requires a predefined schema.
• Rich Query Language: Supports complex queries using SQL.

2.3 Example Code

SELECT * FROM users WHERE age > 30;

3.1 Definition

Non-SQL databases, also known as NoSQL databases, are designed to allow for flexible data models and typically do not require a fixed schema. Examples include MongoDB, Cassandra, and Redis.

3.2 Key Features

• Schema-less: Allows for dynamic data structures.
• Scalability: Designed to scale out easily.
• Variety of Data Models: Supports document, key-value, graph, and wide-column stores.

3.3 Example Code

db.users.find({ age: { $gt: 30 } });

4.1 Key Differences

• Data Structure: SQL uses tables; Non-SQL uses various formats (documents, key-value pairs).
• Scalability: SQL databases are typically vertically scalable; Non-SQL databases are horizontally scalable.
• Query Language: SQL databases use SQL; Non-SQL databases may use their own query languages.

4.2 When to Use Each

SQL is best for complex queries and transactions requiring high consistency, while Non-SQL is suited for high-volume data and flexibility in data structure.

5.1 Considerations

• Understand Data Requirements: Choose based on data structure and access patterns.
• Evaluate Scalability Needs: Consider future growth and load.
• Test Performance: Benchmark different database types against your use case.