Database Management

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Databases are organized collections of data that are stored and accessed electronically. They are fundamental to modern computing and are used in a variety of applications, from small personal projects to large enterprise systems. Databases are managed by Database Management Systems (DBMS) that provide tools for data storage, retrieval, and manipulation.

Types of Databases

• Relational Databases :
  • Store data in tables with rows and columns.
  • Use SQL (Structured Query Language) for data manipulation.
  • Examples: MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server.
• NoSQL Databases :
  • Designed for unstructured or semi-structured data.
  • Include document databases, key-value stores, column-family stores, and graph databases.
  • Examples: MongoDB (document), Redis (key-value), Cassandra (column-family), Neo4j (graph).
• Object-Oriented Databases :
  • Store data as objects, similar to object-oriented programming.
  • Examples: db4o, ObjectDB.
• NewSQL Databases :
  • Combine the scalability of NoSQL databases with the ACID (Atomicity, Consistency, Isolation, Durability) properties of relational databases.
  • Examples: Google Spanner, VoltDB.
• In-Memory Databases :
  • Store data in the main memory for faster access.
  • Examples: Redis, SAP HANA.

Database Management Systems (DBMS)

• Functions :
  • Data storage, retrieval, and updating.
  • Data security and integrity.
  • Backup and recovery.
  • Concurrency control.
• Components :
  • Database engine for processing queries and transactions.
  • Database schema defining the structure of the data.
  • Query processor for interpreting and executing SQL commands.
  • Transaction manager for ensuring ACID properties.

Database Design

• Data Modeling :
  • Process of defining the structure, relationships, and constraints of the data to be stored in the database.
  • Entity-Relationship (ER) modeling is a common technique.
• Normalization :
  • Process of organizing data to reduce redundancy and improve data integrity.
  • Involves dividing large tables into smaller tables and defining relationships between them.
  • Forms: First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and Boyce-Codd Normal Form (BCNF).
• Denormalization :
  • Process of combining normalized tables into larger tables to improve read performance.
  • Used in specific scenarios where read speed is more critical than write speed.

SQL (Structured Query Language)

• Data Definition Language (DDL) :
  • Defines the structure of the database, including tables, columns, and constraints.
  • Commands: CREATE, ALTER, DROP.

  CREATE TABLE employees (
   id INT PRIMARY KEY,
   first_name VARCHAR(50),
   last_name VARCHAR(50),
   department VARCHAR(50)
  );
  ALTER TABLE employees ADD COLUMN salary DECIMAL(10, 2);
  DROP TABLE employees;

• Data Manipulation Language (DML) :
  • Handles data manipulation within the database.
  • Commands: SELECT, INSERT, UPDATE, DELETE.

  SELECT * FROM employees;
  INSERT INTO employees (id, first_name, last_name, department)
  VALUES (1, 'John', 'Doe', 'Sales');
  UPDATE employees SET department = 'Marketing' WHERE id = 1;
  DELETE FROM employees WHERE id = 1;

• Data Control Language (DCL) :
  • Manages access to the data within the database.
  • Commands: GRANT, REVOKE.

  GRANT SELECT ON employees TO user_name;
  REVOKE SELECT ON employees FROM user_name;

• Transaction Control Language (TCL) :
  • Manages transactions within the database.
  • Commands: COMMIT, ROLLBACK, SAVEPOINT.

  START TRANSACTION;
  UPDATE employees SET salary = salary * 1.1 WHERE department = 'Sales';
  COMMIT;
  ROLLBACK;

Indexes and Query Optimization

• Indexes :
  • Data structures that improve the speed of data retrieval.
  • Can be created on one or more columns of a table.
  • Example:

    CREATE INDEX idx_last_name ON employees (last_name);

• Query Optimization :
  • Techniques used to improve query performance.
  • Includes using indexes, query rewriting, and analyzing query execution plans.
  • Tools: EXPLAIN statement to analyze how queries are executed.

  EXPLAIN SELECT * FROM employees WHERE last_name = 'Doe';

Backup and Recovery

• Backup :
  • Process of creating copies of the database to prevent data loss.
  • Types: Full, Incremental, Differential.
  • Example:

    mysqldump -u username -p database_name > backup.sql

• Recovery :
  • Process of restoring the database from backups.
  • Essential for disaster recovery and data integrity.
  • Example:

    mysql -u username -p database_name < backup.sql

Summary

Databases are integral to computer science, enabling the efficient storage, retrieval, and manipulation of data. Understanding the types of databases, their management through DBMS, database design principles, SQL for data handling, indexing, query optimization, and backup and recovery processes are essential for building and maintaining robust and efficient database systems.