Database Design and Management Standards

This document outlines the standards and best practices for database design, development, and management at Bayat.

Introduction

Database systems are critical components of our applications. This document provides guidelines for designing, implementing, and maintaining database systems that are secure, performant, and maintainable.

Scope

These standards apply to:

Relational databases (PostgreSQL, MySQL, SQL Server, etc.)
NoSQL databases (MongoDB, DynamoDB, Cassandra, etc.)
In-memory databases (Redis, Memcached, etc.)
Time-series databases (InfluxDB, TimescaleDB, etc.)
Graph databases (Neo4j, Amazon Neptune, etc.)

Database Selection

Selection Criteria

Choose the appropriate database type based on:

Data structure: Structured, semi-structured, or unstructured
Query patterns: Read-heavy, write-heavy, or balanced
Scalability requirements: Vertical vs. horizontal scaling
Consistency requirements: Strong vs. eventual consistency
Availability requirements: High availability needs
Transaction requirements: ACID compliance needs

Database Types and Use Cases

Database Type	Best For	Examples	When to Use
Relational	Structured data with relationships	PostgreSQL, MySQL	Complex queries, transactions, data integrity
Document	Semi-structured data	MongoDB, Couchbase	Flexible schema, hierarchical data
Key-Value	Simple lookups	Redis, DynamoDB	Caching, session storage, high throughput
Column-Family	Wide column data	Cassandra, HBase	Time-series, high write throughput
Graph	Highly connected data	Neo4j, Neptune	Relationship-heavy data, path finding
Time-Series	Time-ordered measurements	InfluxDB, TimescaleDB	Metrics, monitoring, IoT data
Search	Full-text search	Elasticsearch, Solr	Text search, log analysis

Preferred Databases

For new projects, prefer these databases unless specific requirements dictate otherwise:

Relational: PostgreSQL
Document: MongoDB
Key-Value: Redis
Search: Elasticsearch
Time-Series: InfluxDB
Graph: Neo4j

Data Modeling

Relational Data Modeling

Normalization Guidelines

Use appropriate normalization level (typically 3NF)
Denormalize strategically for performance when necessary
Document denormalization decisions

Table Design

Each table should represent a single entity or concept
Use appropriate data types for columns
Define primary keys for all tables
Use foreign keys to enforce referential integrity
Consider using UUIDs for primary keys in distributed systems

Relationship Design

Define clear one-to-many, many-to-many, and one-to-one relationships
Use junction tables for many-to-many relationships
Consider using foreign key constraints with appropriate actions:
- ON DELETE: CASCADE, SET NULL, or RESTRICT
- ON UPDATE: CASCADE or RESTRICT

NoSQL Data Modeling

Document Database Design

Design for query patterns, not for normalization
Embed related data that is queried together
Use references for large sub-documents or frequently changing data
Consider document size limits
Design indexes based on query patterns

Key-Value Design

Use consistent key naming conventions
Consider key distribution for sharded systems
Design for efficient key lookups
Use composite keys when appropriate

Column-Family Design

Design column families based on access patterns
Group related columns that are accessed together
Consider time-to-live (TTL) for temporal data
Design row keys for efficient data distribution and access

Graph Database Design

Identify entities as nodes and relationships as edges
Use properties on both nodes and edges
Design for traversal efficiency
Consider bidirectional relationships

Naming Conventions

General Naming Rules

Use clear, descriptive names
Be consistent across the database
Avoid reserved words and special characters
Use singular or plural consistently (prefer singular)
Use lowercase with underscores for multi-word names in SQL databases
Use camelCase for NoSQL databases that support it

SQL Naming Conventions

Object Type	Convention	Example
Table	snake_case, singular	`user`, `order_item`
Column	snake_case	`first_name`, `created_at`
Primary Key	`id` or `{table_name}_id`	`id`, `user_id`
Foreign Key	`{referenced_table}_id`	`user_id`, `product_id`
Index	`idx_{table}_{columns}`	`idx_user_email`, `idx_order_created_at`
Constraint	`{type}_{table}_{column}`	`pk_user_id`, `fk_order_user_id`, `uq_user_email`
View	`vw_{description}`	`vw_active_users`, `vw_monthly_sales`
Stored Procedure	`sp_{action}_{entity}`	`sp_get_user`, `sp_create_order`
Function	`fn_{description}`	`fn_calculate_tax`, `fn_format_phone`
Trigger	`trg_{table}_{event}`	`trg_user_after_insert`, `trg_order_before_update`

NoSQL Naming Conventions

Database Type	Collection/Table	Field/Attribute	Example
MongoDB	PascalCase, singular	camelCase	Collection: `User`, Field: `firstName`
DynamoDB	PascalCase, singular	camelCase	Table: `Product`, Attribute: `productName`
Cassandra	snake_case	snake_case	Table: `user_activity`, Column: `login_time`
Redis	colon:separated	n/a	Key: `user:1000:session`

SQL Standards

SQL Query Style

Use uppercase for SQL keywords
Use table aliases for complex queries
Format queries for readability
Use explicit column names in SELECT statements (avoid SELECT *)
Use parameterized queries to prevent SQL injection

-- Good example
SELECT 
    u.id, 
    u.first_name, 
    u.last_name, 
    o.order_date
FROM 
    user u
JOIN 
    order o ON u.id = o.user_id
WHERE 
    u.status = 'active'
    AND o.order_date > ?
ORDER BY 
    o.order_date DESC
LIMIT 10;

Indexing Guidelines

Index columns used in WHERE, JOIN, and ORDER BY clauses
Consider covering indexes for frequently used queries
Avoid over-indexing (balance read vs. write performance)
Monitor and maintain indexes regularly
Consider partial indexes for filtered queries
Use appropriate index types (B-tree, Hash, GiST, etc.)

Transaction Management

Keep transactions as short as possible
Use appropriate isolation levels
Handle deadlocks gracefully
Consider using optimistic concurrency control
Document transaction boundaries in code

NoSQL Standards

Document Database Standards

Design documents for query efficiency
Use consistent document structure
Implement schema validation where supported
Use appropriate indexing strategies
Consider document versioning for schema evolution

Key-Value Standards

Use consistent key naming patterns
Consider key expiration for temporal data
Implement key namespacing to avoid collisions
Document key structure and patterns

Time-Series Standards

Define appropriate retention policies
Use tags/labels for efficient filtering
Consider downsampling for historical data
Optimize for time-range queries

Schema Management

Schema Versioning

Use incremental version numbers for schema changes
Maintain a schema version history
Document breaking vs. non-breaking changes

Migration Strategy

Use database migration tools:
- SQL: Flyway, Liquibase, or framework-specific tools
- NoSQL: Custom migration scripts or framework tools
Test migrations thoroughly before production
Include rollback procedures for each migration
Automate migration execution in CI/CD pipeline

Schema Change Guidelines

Prefer additive, non-breaking changes
Implement multi-phase migrations for breaking changes:
1. Add new structure
2. Migrate data
3. Update application code
4. Remove old structure
Coordinate schema changes with application deployments
Consider database refactoring patterns

Performance Optimization

Query Optimization

Write efficient queries
Use EXPLAIN/EXPLAIN ANALYZE to understand query plans
Optimize JOIN operations
Use appropriate filtering
Implement pagination for large result sets
Consider query caching where appropriate

Indexing Strategy

Index based on query patterns
Monitor index usage and performance
Rebuild/reorganize indexes regularly
Consider specialized indexes (spatial, full-text, etc.)

Connection Management

Use connection pooling
Monitor connection usage
Set appropriate connection timeouts
Handle connection errors gracefully

Caching Strategy

Implement appropriate caching layers:
- Application-level cache
- Database query cache
- Result cache
Define cache invalidation strategy
Monitor cache hit rates

Security

Authentication and Authorization

Use strong authentication mechanisms
Implement principle of least privilege
Create application-specific database users
Avoid using root/admin accounts in applications
Implement row-level security where appropriate

Data Protection

Encrypt sensitive data at rest
Use TLS/SSL for data in transit
Implement column-level encryption for sensitive fields
Consider using data masking for non-production environments

Access Control

Document database access requirements
Regularly audit database permissions
Implement role-based access control
Use database proxies or connection poolers with authentication

Auditing

Enable database audit logging
Track schema changes
Monitor for suspicious activities
Implement compliance-required auditing

Data Security

Use appropriate data masking and anonymization for non-production data
Encrypt sensitive data at rest and in transit
Follow the Data Protection Standards

Backup and Recovery

Backup Strategy

Implement regular automated backups
Use point-in-time recovery capabilities
Test backup restoration regularly
Store backups securely and redundantly
Document backup procedures

Recovery Procedures

Define recovery time objectives (RTO)
Define recovery point objectives (RPO)
Document step-by-step recovery procedures
Test disaster recovery scenarios regularly
Implement automated recovery where possible

Monitoring

Performance Monitoring

Monitor key database metrics:
- Query performance
- Resource utilization (CPU, memory, disk, network)
- Connection counts
- Lock contention
- Cache hit rates
Set up alerting for performance thresholds
Implement query performance logging

Health Monitoring

Monitor database availability
Check replication status
Verify backup success
Monitor disk space usage
Set up proactive alerts

Tools and Dashboards

Use database-specific monitoring tools
Implement centralized monitoring dashboards
Retain historical performance data
Document common performance patterns

Data Access Patterns

Object-Relational Mapping (ORM)

Use consistent ORM frameworks
Understand ORM-generated SQL
Avoid N+1 query problems
Use eager loading appropriately
Consider performance implications of ORM abstractions

Microservice Data Access

Define clear data ownership boundaries
Consider data duplication vs. shared database tradeoffs
Implement appropriate consistency patterns
Document cross-service data dependencies

API-Based Access

Implement data access through APIs when appropriate
Define clear data access contracts
Consider GraphQL for flexible data querying
Implement proper API versioning for schema changes

Polyglot Persistence

Document database technology choices
Define clear boundaries between different database systems
Implement appropriate synchronization mechanisms
Consider eventual consistency implications

Version History

Version	Date	Description
1.0	2025-03-20	Initial version

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