Database/Schema Design Principles

Tacnode is fully compatible with the PostgreSQL ecosystem, organizing data management into three hierarchical levels: database, schema, and table. This three-tier architecture is fundamental for ensuring system maintainability, scalability, and optimal performance.

A well-designed database and schema structure provides multiple benefits:

• Reduces system complexity and management overhead
• Improves query performance and data access efficiency
• Ensures data integrity and security compliance
• Facilitates easier maintenance and future scaling

This guide outlines key principles for effective database and schema design in Tacnode.

Database Design Principles

Clearly Define Database Responsibilities

Each database should serve a specific, well-defined purpose to maintain clear boundaries and reduce complexity.

Best Practices:

• Dedicated Purpose: Assign each database a distinct role (e.g., transactional data, analytical tasks, user management)
• Separation of Concerns: Avoid mixing different types of data and application logic within a single database
• Simplified Management: Clear database boundaries facilitate easier backup, recovery, and maintenance processes

Example:

-- Separate databases for different purposes
CREATE DATABASE ecommerce_transactions;  -- For order processing
CREATE DATABASE analytics_warehouse;     -- For reporting and analytics
CREATE DATABASE user_management;         -- For authentication and profiles

Implement Reasonable Database Separation

Organize data across multiple databases based on application domains and access patterns.

Guidelines:

• Application-Based Separation: Store data for different applications or modules in dedicated databases
• Avoid Shared Dependencies: Prevent data access conflicts and complex transaction management issues
• Independent Scaling: Enable each database to scale according to its specific requirements

Benefits:

• Simplified database management and monitoring
• Improved security through isolation
• Better performance optimization for specific workloads

Minimize Data Redundancy

Apply normalization principles to reduce duplicate data storage and ensure consistency.

Strategies:

• Normalization: Use appropriate normal forms to eliminate redundant data
• Reference Integrity: Implement foreign key constraints to maintain data relationships
• Single Source of Truth: Ensure each piece of information is stored in only one place

Impact:

• Reduced storage requirements
• Improved data consistency
• Simplified data maintenance and updates

Choose Appropriate Data Types

Select data types that align with your business requirements and optimize storage efficiency.

Considerations:

• Type Accuracy: Use data types that accurately represent your data (avoid storing integers as strings)
• Storage Optimization: Choose types that minimize storage space while maintaining precision
• Query Performance: Select types that enhance query execution speed and index efficiency

Examples:

-- Good: Appropriate data types
CREATE TABLE products (
    id INTEGER PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    price DECIMAL(10,2),
    created_at TIMESTAMP DEFAULT NOW(),
    is_active BOOLEAN DEFAULT TRUE
);
 
-- Avoid: Inefficient data type choices
CREATE TABLE products_bad (
    id TEXT,           -- Should be INTEGER
    price TEXT,        -- Should be DECIMAL
    created_at TEXT,   -- Should be TIMESTAMP
    is_active TEXT     -- Should be BOOLEAN
);

Design for Scalability and Performance

Anticipate growth and performance requirements during the initial design phase.

Key Areas:

• Query Patterns: Analyze expected query patterns and optimize accordingly
• Indexing Strategy: Plan indexes based on common access patterns
• Partitioning: Consider table partitioning for large datasets
• Resource Planning: Ensure the design can handle increasing data volumes and concurrent users

Performance Optimization Techniques:

-- Example: Partitioned table for time-series data
CREATE TABLE sales_data (
    id SERIAL,
    sale_date DATE NOT NULL,
    amount DECIMAL(10,2),
    customer_id INTEGER
) PARTITION BY RANGE (sale_date);
 
-- Create monthly partitions
CREATE TABLE sales_2024_01 PARTITION OF sales_data
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

Implement Database Security

Establish robust security measures to protect sensitive data and control access.

Security Principles:

• Principle of Least Privilege: Grant minimal necessary permissions to users and applications
• Role-Based Access Control: Implement appropriate user roles and permission management
• Database Isolation: In Tacnode, different databases are independent and cannot access each other by default

Security Implementation:

-- Create roles with specific permissions
CREATE ROLE read_only_user;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO read_only_user;
 
-- Create application-specific roles
CREATE ROLE app_user;
GRANT SELECT, INSERT, UPDATE ON specific_table TO app_user;

Schema Design Principles

Organize Database Objects Logically

Schemas serve as logical containers for organizing related database objects such as tables, views, indexes, and functions.

Organizational Strategies:

• Business Module Separation: Group objects by business functionality
• Application-Based Organization: Separate objects by application or service
• Functional Grouping: Organize by data processing purpose or access patterns

Example Structure:

-- Business module-based schemas
CREATE SCHEMA finance;      -- Financial data and operations
CREATE SCHEMA users;        -- User management and profiles  
CREATE SCHEMA inventory;    -- Product and stock management
CREATE SCHEMA reporting;    -- Analytics and reporting objects
 
-- Create tables within appropriate schemas
CREATE TABLE finance.transactions (...);
CREATE TABLE users.profiles (...);
CREATE TABLE inventory.products (...);

Avoid Over-Segmentation of Schemas

Balance organization with complexity to prevent management overhead.

Guidelines:

• Practical Segmentation: For small applications, fewer schemas or using the public schema may be sufficient
• Growth Consideration: Start simple and add schemas as complexity increases
• Maintenance Balance: Weigh organizational benefits against management complexity

When to Use Multiple Schemas:

• Large applications with distinct modules
• Multi-tenant applications requiring data isolation
• Complex systems with different access patterns
• Applications with varying security requirements

Implement Consistent Schema Naming

Establish clear, descriptive naming conventions for better maintainability.

Naming Best Practices:

• Descriptive Names: Use names that clearly indicate purpose (e.g., accounting, inventory, user_management)
• Consistent Conventions: Apply uniform naming patterns across all schemas
• Domain-Based Naming: Align schema names with business domains or functional areas

Examples:

-- Good: Clear, descriptive names
CREATE SCHEMA customer_management;
CREATE SCHEMA order_processing;
CREATE SCHEMA financial_reporting;
 
-- Avoid: Ambiguous or unclear names
CREATE SCHEMA data1;
CREATE SCHEMA temp_stuff;
CREATE SCHEMA misc;

Control Access Permissions to Schemas

Implement granular access control to ensure appropriate data security and isolation.

Access Control Features:

• Schema-Level Permissions: Assign different access levels to different schemas
• User Isolation: Allow specific users to access only designated schemas
• Application Security: Enable permission isolation between different applications or services

Permission Management Examples:

-- Grant schema-specific permissions
GRANT USAGE ON SCHEMA finance TO finance_team;
GRANT ALL ON ALL TABLES IN SCHEMA finance TO finance_admin;
 
-- Restrict access to sensitive schemas
REVOKE ALL ON SCHEMA payroll FROM public;
GRANT USAGE ON SCHEMA payroll TO hr_manager;

Manage Table-Schema Coupling

Design schemas with appropriate relationships and dependencies between tables.

Key Principles:

Cross-Schema Accessibility:

• Tables in different schemas within the same database can reference and join with each other
• Data sharing is possible across schemas when appropriate permissions are granted

Naming Independence:

• Tables in different schemas can have identical names since they exist in separate namespaces
• Schema qualification resolves naming conflicts: schema1.users vs schema2.users

High Cohesion Within Schemas:

• Tables within the same schema should be closely related and frequently accessed together
• Design for common query patterns and business operations

Example of Proper Coupling:

-- High cohesion: Related tables in same schema
CREATE SCHEMA ecommerce;
CREATE TABLE ecommerce.customers (...);
CREATE TABLE ecommerce.orders (...);
CREATE TABLE ecommerce.order_items (...);
 
-- Cross-schema reference when appropriate
CREATE TABLE inventory.products (...);
-- order_items can reference inventory.products with proper permissions

Minimize Cross-Schema Dependencies

Reduce complexity by limiting unnecessary dependencies between schemas.

Best Practices:

• Self-Contained Schemas: Design schemas to be as independent as possible
• Clear Interfaces: When cross-schema access is necessary, establish clear, documented interfaces
• Simplified Maintenance: Minimize dependencies to reduce complexity during maintenance and migration

Benefits of Reduced Dependencies:

• Easier code and query maintenance
• Simplified database migration processes
• Reduced risk of cascading changes
• Better performance through optimized query paths

Best Practices

Use the public Schema Strategically

The public schema is created by default in Tacnode, but its usage should be carefully considered.

Recommendations:

• Small Applications: The public schema may be sufficient for simple applications with minimal complexity
• Growing Systems: Create custom schemas as your system evolves and becomes more complex
• Organization Benefits: Custom schemas provide better organization and security isolation
• Migration Path: Start with public if needed, but plan migration to custom schemas for long-term maintainability

Implement Strategic Partitioning

Use Tacnode's partitioning features to improve performance and management for large datasets.

Partitioning Strategies:

• Time-Based Partitioning: Partition by date for time-series data and efficient data archival
• Geographic Partitioning: Partition by location or region for distributed applications
• Hash Partitioning: Distribute data evenly across partitions for load balancing

Partitioning Examples:

-- Time-based partitioning for log data
CREATE TABLE access_logs (
    id SERIAL,
    timestamp TIMESTAMP NOT NULL,
    user_id INTEGER,
    action TEXT
) PARTITION BY RANGE (timestamp);
 
-- Geographic partitioning for global applications
CREATE TABLE customer_data (
    id SERIAL,
    region TEXT NOT NULL,
    customer_name TEXT
) PARTITION BY LIST (region);
 
CREATE TABLE customer_data_us PARTITION OF customer_data FOR VALUES IN ('US');
CREATE TABLE customer_data_eu PARTITION OF customer_data FOR VALUES IN ('EU');

Design Indexes Strategically

Plan your indexing strategy based on query patterns and performance requirements.

Index Planning:

• Query Analysis: Identify common query patterns and frequently accessed columns
• Performance Balance: Balance query performance improvements against storage overhead and write performance impact
• Index Types: Choose appropriate index types (B-tree, Hash, GiST, GIN) based on data types and query patterns

Index Examples:

-- Indexes for common query patterns
CREATE INDEX idx_orders_customer_date ON orders(customer_id, order_date);
CREATE INDEX idx_products_category ON products(category_id) WHERE active = true;
 
-- Composite indexes for complex queries
CREATE INDEX idx_sales_reporting ON sales(region, product_type, sale_date);

Plan for Backup and Recovery

Design your database structure with disaster recovery and business continuity in mind.

Recovery Strategies:

• Schema-Based Recovery: Organize schemas to enable selective backup and recovery of specific business modules
• Granular Backups: Structure databases to allow targeted recovery rather than full database restoration
• Testing Procedures: Regularly test backup and recovery procedures to ensure they work as expected

Implement Regular Maintenance Procedures

Establish ongoing maintenance practices to keep your database performing optimally.

Maintenance Areas:

• Data Lifecycle Management: Implement automated archiving and deletion policies for aging data
• Performance Monitoring: Regularly review query performance and adjust indexes as needed
• Storage Management: Monitor disk usage and implement cleanup procedures
• Statistics Updates: Ensure database statistics are current for optimal query planning

Summary

Effective database and schema design is fundamental to building scalable, maintainable, and high-performance data systems. The principles and best practices outlined in this guide provide a foundation for making informed design decisions.

Key Takeaways

Database-Level Design:

• Define clear responsibilities and purposes for each database
• Implement logical separation based on application domains
• Choose appropriate data types and plan for scalability
• Establish robust security measures and access controls

Schema-Level Organization:

• Organize objects logically within schemas based on business functions
• Use consistent naming conventions for better maintainability
• Balance organization benefits with management complexity
• Design for appropriate coupling and minimal cross-schema dependencies

Operational Excellence:

• Strategic use of partitioning for large datasets
• Thoughtful index design based on query patterns
• Comprehensive backup and recovery planning
• Regular maintenance and data lifecycle management

Design Philosophy

The optimal database and schema design balances multiple competing factors:

• Flexibility vs. Structure: Provide enough flexibility for future growth while maintaining clear organizational structure
• Performance vs. Complexity: Optimize for performance without creating unnecessarily complex architectures
• Security vs. Accessibility: Implement appropriate security measures while enabling necessary data access
• Normalization vs. Practicality: Apply normalization principles while considering real-world usage patterns

Getting Started

When implementing these principles:

1. Start Simple: Begin with a straightforward design and evolve as requirements become clearer
2. Document Decisions: Maintain clear documentation of design choices and their rationale
3. Monitor and Adapt: Regularly review performance and adjust the design as needed
4. Plan for Growth: Consider future scalability requirements in your initial design

By following these guidelines and continuously evaluating your design decisions against changing requirements, you can build robust database architectures that serve your organization's needs both today and in the future.