SQL Antipatterns, Volume 1

1. SQL Antipatterns: Common Mistakes to Avoid in Database Design

If you break the relationship between the data and the database, then you take on the burden of managing that data yourself.

Recognizing antipatterns: SQL antipatterns are common but problematic solutions to database design challenges. They often arise from misunderstandings about relational database principles or attempts to simplify complex data relationships.

Common antipatterns include:

• Storing multiple values in a single column (e.g., comma-separated lists)
• Using generic attribute tables instead of proper normalization
• Ignoring the importance of foreign key constraints
• Overusing FLOAT data types for precise numeric values

By recognizing these antipatterns, developers can avoid pitfalls that lead to data inconsistency, poor performance, and difficult-to-maintain code.

2. Logical Database Design: Structuring Data Relationships Effectively

Model both of these in a way that supports the queries you need to make against the hierarchy.

Normalization and relationships: Proper logical database design involves structuring data to minimize redundancy and ensure data integrity. This includes normalizing tables and establishing appropriate relationships between entities.

Key aspects of logical design:

• Identifying entities and their attributes
• Establishing primary and foreign keys
• Determining cardinality of relationships (one-to-one, one-to-many, many-to-many)
• Implementing junction tables for many-to-many relationships

Effective logical design supports efficient querying and data manipulation while maintaining data consistency across the database.

3. Physical Database Design: Optimizing Storage and Performance

If you can read passwords, so can a hacker.

Optimizing for efficiency: Physical database design focuses on how data is actually stored and accessed. This involves choosing appropriate data types, indexing strategies, and storage structures to optimize performance and security.

Important considerations:

• Selecting the right data types (e.g., NUMERIC for precise calculations instead of FLOAT)
• Implementing indexes strategically to improve query performance
• Using appropriate storage engines based on data access patterns
• Securing sensitive data through encryption or hashing

Proper physical design ensures that the database can handle the required workload efficiently and securely.

4. Query Antipatterns: Writing Efficient and Accurate SQL

SQL treats a column as an atomic value. If you need to optimize searching for a substring, then you need to use an extension to SQL or a complementary technology.

Avoiding common pitfalls: Query antipatterns often result from misunderstanding SQL's capabilities or attempting to force relational databases to behave like non-relational systems.

Examples of query antipatterns:

• Using subqueries where joins would be more efficient
• Relying on implicit type conversion instead of explicit casting
• Overusing wildcard (*) in SELECT statements
• Neglecting to use appropriate indexing for frequently queried columns

By understanding these antipatterns, developers can write more efficient and maintainable SQL queries.

5. Application Development: Integrating SQL with Best Practices

Use software development best practices, including documentation, testing, and version control, for your database as you do for your application code.

Treating database code as first-class: Database code should be subject to the same software engineering practices as application code. This includes version control, testing, and documentation.

Best practices for database development:

• Using database migrations for schema changes
• Implementing automated testing for database queries and procedures
• Documenting database schema, relationships, and key queries
• Applying code review processes to database changes

Integrating these practices ensures that database development is aligned with overall software development processes and quality standards.

6. Security and Data Integrity: Protecting Your Database

Let users input values, but never let users input code.

Implementing robust safeguards: Database security and data integrity are critical aspects of database design and management. This involves protecting against unauthorized access, data corruption, and SQL injection attacks.

Key security measures:

• Implementing proper authentication and authorization mechanisms
• Using prepared statements to prevent SQL injection
• Encrypting sensitive data at rest and in transit
• Regularly auditing and monitoring database access and changes

Maintaining data integrity through constraints, triggers, and validation ensures the accuracy and reliability of stored information.

7. Evolving Database Design: Adapting to Changing Requirements

Don't let data spawn metadata.

Flexible yet structured design: Database designs must evolve to meet changing business requirements while maintaining data integrity and performance. This involves strategies for handling schema changes and data migrations.

Approaches to evolving design:

• Using flexible schema designs (e.g., EAV model) judiciously
• Implementing versioning strategies for schema changes
• Planning for data migration and transformation
• Balancing between normalization and denormalization based on evolving needs

A well-designed database should be able to accommodate change without requiring a complete overhaul of the existing structure.

Copyright © 2022, The Pragmatic Bookshelf.

Last updated: October 30, 2024