Code-Memo

Database Design and Management

1. Database Design Principles

a. Normalization:

• Normalization is the process of organizing data to reduce redundancy and improve data integrity. The goal is to divide data into related tables, each with a primary key. The process typically involves three main forms:
  • 1st Normal Form (1NF): Ensure each column holds atomic values and that each record is unique.
  • 2nd Normal Form (2NF): Achieve 1NF and remove partial dependencies by ensuring that non-key attributes depend on the entire primary key.
  • 3rd Normal Form (3NF): Achieve 2NF and remove transitive dependencies, where non-key attributes depend only on the primary key.

b. Denormalization:

• Denormalization involves intentionally introducing redundancy to improve read performance. It’s often used in situations where the database needs to handle heavy read operations, like in reporting systems or data warehouses. Denormalization must be done carefully to avoid compromising data integrity.

c. Data Integrity:

• Data integrity ensures that the data in the database is accurate, consistent, and reliable. This is maintained through:
  • Entity Integrity: Each table has a primary key that uniquely identifies its records.
  • Referential Integrity: Foreign keys maintain relationships between tables, ensuring that relationships between records are preserved.
  • Domain Integrity: Enforces valid entries for a given column by restricting the type, format, or range of possible values.

d. Relationships:

• Relationships between tables are critical in relational database design. The three types of relationships are:
  • One-to-One (1:1): A record in one table corresponds to exactly one record in another table.
  • One-to-Many (1:N): A record in one table can relate to multiple records in another table.
  • Many-to-Many (M:N): Records in one table can relate to multiple records in another, typically managed with a junction table.

e. Indexing:

• Indexes are used to speed up the retrieval of data. They are crucial for query performance, especially in large datasets. However, indexes also consume additional storage and can slow down write operations, so they must be used judiciously.

f. Data Types and Constraints:

• Choosing appropriate data types and constraints is critical for both storage efficiency and data integrity. Constraints like NOT NULL, UNIQUE, CHECK, and DEFAULT ensure that data adheres to specific rules.

2. Database Management

a. Backup and Recovery:

• Regular backups are essential to protect against data loss due to hardware failure, human error, or cyberattacks. Backup strategies include full backups, incremental backups, and differential backups. Recovery plans should be in place to restore data quickly and with minimal downtime.

b. Performance Tuning:

• Database performance can degrade over time as data grows. Key performance tuning strategies include:
  • Query Optimization: Write efficient SQL queries, use EXPLAIN to analyze and improve query performance.
  • Index Optimization: Regularly review and optimize indexes, and avoid unnecessary or redundant indexes.
  • Database Partitioning: Split large tables into smaller, more manageable pieces to improve performance.
  • Caching: Implement caching strategies to reduce the load on the database for frequently accessed data.

c. Scaling:

• As the application grows, the database must scale to handle increased load. Scaling strategies include:
  • Vertical Scaling: Adding more resources (CPU, RAM) to the existing database server.
  • Horizontal Scaling: Distributing data across multiple servers using techniques like sharding or replication.

d. Security:

• Database security involves protecting data from unauthorized access, breaches, and other threats. Key practices include:
  • Encryption: Encrypt sensitive data both at rest and in transit.
  • Access Control: Implement role-based access control (RBAC) to limit who can access or modify data.
  • Auditing and Monitoring: Regularly audit database access and monitor for suspicious activity.
  • Patch Management: Keep the database software up to date with security patches.

e. Transaction Management:

• Transactions ensure that database operations are completed reliably and maintain data integrity. They follow the ACID properties:
  • Atomicity: Ensures that all parts of a transaction are completed, or none are.
  • Consistency: Ensures that a transaction takes the database from one valid state to another.
  • Isolation: Ensures that concurrently executing transactions do not affect each other’s outcomes.
  • Durability: Ensures that once a transaction is committed, it will remain so even in the event of a system failure.

f. Monitoring and Maintenance:

• Continuous monitoring of the database helps identify and resolve performance issues before they affect users. Regular maintenance tasks include:
  • Vacuuming and Analyzing: For databases like PostgreSQL, regular vacuuming and analyzing are essential to reclaim space and update statistics.
  • Index Rebuilding: Rebuild fragmented indexes periodically to maintain performance.
  • Archiving Logs: Regularly archive and compress transaction logs to free up space.