Database Backup and Recovery

• Database management involves various strategies and techniques to ensure data integrity, availability, and performance.

Backup and Recovery Strategies

• Backup and recovery strategies are critical for protecting data against loss due to hardware failure, software errors, or disasters.
• Backups are copies of data that can be used to restore the database to a previous state.
• Recovery involves restoring the database from backups and applying necessary logs to bring it to a consistent state.
• Full backups copy the entire database.
• Incremental backups copy only the data that has changed since the last backup (full or incremental).
• Differential backups copy all the data that has changed since the last full backup.
• Backup frequency depends on the rate of data change and the acceptable data loss window.
• Recovery Point Objective (RPO) defines the maximum acceptable data loss in case of an outage.
• Recovery Time Objective (RTO) defines the maximum acceptable time to restore the database after an outage.
• Regular testing of backup and recovery procedures is essential to ensure their effectiveness.
• Offsite storage of backups protects against local disasters.
• Backup media include disk, tape, and cloud storage.

Data Normalization

• Data normalization is the process of organizing data to reduce redundancy and improve data integrity.
• Normalization involves dividing databases into tables and defining relationships between the tables.
• The goal is to isolate data so that modifications to a field can be made in only one table.
• First Normal Form (1NF) requires that each column in a table contains only atomic values.
• Second Normal Form (2NF) requires that a table is in 1NF and all non-key attributes are fully functionally dependent on the primary key.
• Third Normal Form (3NF) requires that a table is in 2NF and all non-key attributes are non-transitively dependent on the primary key.
• Boyce-Codd Normal Form (BCNF) is a stricter version of 3NF, addressing certain types of anomalies not handled by 3NF.
• Higher normal forms (4NF, 5NF) address more complex dependencies but are less commonly used in practice.
• Denormalization is the process of adding redundancy to a database to improve performance, often used in data warehousing.

SQL Query Optimization

• SQL query optimization focuses on improving the performance of SQL queries.
• Query optimization involves selecting the most efficient execution plan for a given query.
• The query optimizer analyzes different execution plans and chooses the one with the lowest estimated cost.
• Indexes can significantly speed up queries by allowing the database to quickly locate specific rows.
• Proper indexing requires balancing the need for faster queries with the overhead of index maintenance.
• Query profiling tools can help identify performance bottlenecks in SQL queries.
• Rewriting queries to use more efficient constructs can improve performance.
• Avoiding SELECT * and specifying only the necessary columns reduces the amount of data transferred.
• Using appropriate JOIN types (INNER, LEFT, RIGHT) can improve query performance.
• Subqueries can sometimes be rewritten as JOINs for better performance.
• Using stored procedures can improve performance by precompiling and caching execution plans.
• Partitioning large tables can improve query performance by allowing the database to process only relevant partitions.

Transaction Management

• Transaction management ensures that database operations are performed reliably and consistently.
• A transaction is a logical unit of work that must be executed atomically, consistently, isolated, and durably (ACID properties).
• Atomicity means that a transaction is either entirely completed or entirely rolled back.
• Consistency means that a transaction must maintain the integrity of the database.
• Isolation means that concurrent transactions should not interfere with each other.
• Durability means that once a transaction is committed, its changes are permanent.
• Concurrency control mechanisms, such as locking, are used to ensure isolation between transactions.
• Optimistic concurrency control assumes that conflicts are rare and checks for conflicts at the end of the transaction.
• Pessimistic concurrency control assumes that conflicts are common and prevents conflicts by locking resources.
• Deadlocks occur when two or more transactions are blocked indefinitely, waiting for each other to release locks.
• Deadlock detection and resolution mechanisms are used to break deadlocks.
• Transaction logging is used to record all changes made to the database, enabling recovery from failures.
• Two-phase commit (2PC) is a distributed transaction protocol that ensures atomicity across multiple databases.

Database Design

• Database design involves creating the structure of a database to efficiently store and manage data.
• Requirements analysis involves gathering information about the data to be stored and the operations to be performed on the data.
• Conceptual design involves creating a high-level model of the database, typically using an entity-relationship (ER) diagram.
• Logical design involves translating the conceptual model into a specific database schema, defining tables, columns, and relationships.
• Physical design involves specifying the physical storage structures and access methods for the database.
• Choosing appropriate data types for columns is important for data integrity and performance.
• Primary keys uniquely identify each row in a table.
• Foreign keys establish relationships between tables.
• Indexes can improve query performance but should be used judiciously.
• Constraints enforce data integrity rules.
• Views provide a simplified or customized view of the data in the database.
• Stored procedures and functions encapsulate complex database operations.
• Triggers automatically execute in response to certain database events.
• Data warehousing involves designing databases for analytical purposes, often using a star schema or snowflake schema.
• NoSQL databases offer alternative data models, such as document, key-value, and graph databases, which may be more suitable for certain applications.