Types of Database Management Systems

What is DBMS?

A DBMS (Database Management System) is a software or technology used to manage data from a database. It allows users to store, modify and retrieve data in an organized way. It also provides security to the database.

Types of Database Management Systems

1. Hierarchical DBMS

• Hierarchical DBMS organizes data in a tree-like structure with parent-child relationships.
• Each parent can have multiple children, but a child can have only one parent.
• Data navigation is done through paths.

Syntax:

DBD NAME(database_name)
SEGM NAME(segment_name)
FIELD NAME(field_name), BYTES(bytes), START(start_byte)

Example:

GET /employees/123/projects/456

This retrieves project 456 for employee 123.

2. Network DBMS

• Network DBMS allows more flexible relationships, where a child can have multiple parents.
• Uses a network model with sets and records.
• Data navigation is done through sets.

Syntax: (Define Record Type)

RECORD NAME IS record_name
CONTAINS field_specifications

Syntax: (Define Set)

SET NAME IS set_name
OWNER IS owner_record
MEMBER IS member_record

Example

FIND owner OF project 456

This finds the owner(s) of project 456.

3. Relational DBMS (RDBMS)

• Relational DBMS stores data in tables with rows and columns.
• Relationships are established using keys.
• Data access is done using SQL (Structured Query Language).

Syntax: (Create Table)

CREATE TABLE table_name (
    column1 datatype PRIMARY KEY,
    column2 datatype,
    ...
);

Syntax: (Insert Data)

INSERT INTO table_name (column1, column2, ...)
VALUES (value1, value2, ...);

Syntax: (Select Data)

SELECT column1, column2, ...
FROM table_name
WHERE condition;

Example:

SELECT * FROM employees WHERE salary > 50000

This retrieves all employees with a salary greater than 50000.

4. NoSQL DBMS

• NoSQL DBMS is designed for handling large volumes of unstructured or semi-structured data.
• Includes document databases, key-value stores, column-family stores, and graph databases.
• Data access varies depending on the specific NoSQL type.

Syntax: (Insert Document)

db.collection.insertOne(
    { key1: "value1", key2: "value2", ... }
);

Syntax: (Find Document)

db.collection.find(
    { key: "value" }
);

Example:

db.inventory.find( { status: "A" } )

This retrieves inventory items with status "A" in MongoDB.

5. Object Oriented DBMS (ODBMS)

• Object Oriented DBMS stores data as objects, which can have attributes and methods.
• Supports inheritance, encapsulation, and polymorphism.
• Data access is done using object query language (OQL).

Syntax: (Store Object)

ObjectContainer db = Db4o.openFile("database.db4o");
db.store(new Person("John", 30));
db.close();

Syntax: (Retrieve Object)

ObjectContainer db = Db4o.openFile("database.db4o");
ObjectSet result = db.queryByExample(new Person(null, 0));
while(result.hasNext()) {
    Person p = (Person)result.next();
    System.out.println(p);
}
db.close();

Example:

SELECT e.name FROM employees e WHERE e.age > 30

This retrieves the names of employees older than 30.

6. Graph-based DBMS

• Graph-based DBMS stores data in graph structures with nodes (entities) and edges (relationships).
• Uses graph query languages like Gremlin or SPARQL.

Syntax: (Create Node)

CREATE (n:Person {name: 'Alice', age: 30});

Syntax: (Create Relationship)

MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
CREATE (a)-[:FRIEND]->(b);

Syntax: (Query Nodes and Relationships)

MATCH (a:Person)-[:FRIEND]->(b:Person)
RETURN a, b;

Example:

g.V().has('name', 'Alice').out('knows').values('name')

This retrieves the names of people Alice knows.

7. Document Database

• Document Database stores data in flexible, semi-structured documents (e.g., JSON, XML, BSON).
• No predefined schema, allowing for dynamic data structures.
• Data access is done using query languages specific to the document database (e.g., MongoDB Query Language).

Syntax: (Insert Document)

db.collection.insertOne(
    { name: "John", age: 30, department: "HR" }
);

Syntax: (Query Document)

db.collection.find(
    { name: "John" }
);

Example:

db.collection.find({field: "value"})

This retrieves documents from a collection where a specific field matches the given value.

8. Centralized Database

• Centralized Database is used to stored data in a single, central location.
• Easier to manage and maintain data consistency.
• Can become a single point of failure if the central system fails.

Distributed Database

• Distribute Database is used to store data which is distributed across multiple physical locations.
• Improved performance, scalability, and fault tolerance.

Examples with Outputs

Example 1: Relational DBMS (MySQL)

Create Table and Insert Data:

CREATE TABLE Employees (
    ID INT PRIMARY KEY,
    Name VARCHAR(50),
    Department VARCHAR(50)
);

INSERT INTO Employees (ID, Name, Department)
VALUES (1, 'John Doe', 'HR'), (2, 'Jane Smith', 'IT');

Select Data:

SELECT * FROM Employees;

Output:

Example 2: NoSQL DBMS (MongoDB)

Insert Document and Find Document:

db.users.insertOne(
    { _id: 1, name: "Alice", age: 30 }
);

db.users.find(
    { name: "Alice" }
);

Output:

{ "_id": 1, "name": "Alice", "age": 30 }

Example 3: Hierarchical DBMS (IBM IMS)

Define Segment and Field

DBD NAME(EmployeeDB)
SEGM NAME(Employee)
FIELD NAME(EmpID), BYTES(4), START(1)
FIELD NAME(EmpName), BYTES(30), START(5)
FIELD NAME(Dept), BYTES(20), START(35)

Output: This defines a database called EmployeeDB with a segment Employee containing three fields: EmpID, EmpName, and Dept.