Distributed Database

Distributed Database is a collection of databases distributed across multiple physical locations that function as a single logical database. Each site can operate independently while participating in a unified database system through communication over a network.

Key Concepts

• Data Distribution: Data is distributed across multiple sites based on factors like performance, reliability, and locality.
• Transparency: Users interact with the distributed database as if it were a single database, regardless of the underlying distribution.
• Replication: Data is duplicated across multiple sites to improve fault tolerance and availability.
• Partitioning: Data is divided into subsets, each stored at a specific location.

Characteristics

Distributed databases are defined by the following characteristics:

• Distributed Data Storage: Data is stored on multiple nodes or sites.
• Autonomy: Each node can function independently and manage its local database.
• Transparency:
  • Location Transparency: Users do not need to know where data is physically stored.
  • Replication Transparency: Users are unaware of data being replicated across sites.
  • Fragmentation Transparency: Users do not need to know how data is partitioned.
• Scalability: The system can grow by adding more nodes.
• Fault Tolerance: Replication and redundancy provide resilience to failures.

Types of Distributed Databases

Distributed databases can be classified based on their architecture:

1. Homogeneous Distributed Database:
   • All nodes use the same database management system (DBMS).
   • Example: A PostgreSQL cluster.
2. Heterogeneous Distributed Database:
   • Nodes may use different DBMSs but are integrated into a single system.
   • Example: A system integrating MySQL and Oracle databases.
3. Federated Database:
   • Autonomous databases are integrated through a middleware layer.
   • Example: A research database integrating multiple institutional datasets.

Advantages

• Improved Performance: Data is stored closer to where it is needed, reducing access time.
• Fault Tolerance: Data replication ensures system availability during node failures.
• Scalability: The system can handle growing amounts of data by adding more nodes.
• Resource Sharing: Enables sharing of hardware, software, and data resources.

Limitations

• Complexity: Managing a distributed database is more complex than a centralized one.
• Consistency: Maintaining consistency across nodes in a distributed system can be challenging.
• Communication Overhead: Data synchronization and query execution across nodes incur network overhead.
• Latency: Network delays can affect query response times.

Example: Distributed Query in a Distributed Database

Consider a distributed database with two nodes:

• Node 1 stores employee data.
• Node 2 stores department data.

Query: Retrieve the names of employees in the "Sales" department.

Steps

Step	Action	Performed On
1	Parse query: SELECT employees.name FROM employees JOIN departments ON employees.dept_id = departments.dept_id WHERE departments.name = 'Sales'.	Query Coordinator
2	Decompose query into sub-queries: Query 1: Retrieve department IDs for "Sales" from Node 2. Query 2: Retrieve employee names for the matching department IDs from Node 1. || Query Coordinator
3	Execute sub-queries on respective nodes: Node 2 returns department IDs for "Sales." Node 1 returns employee names for matching department IDs. || Node 1, Node 2
4	Combine results and return final output.	Query Coordinator

Data Distribution Techniques

Distributed databases use the following techniques to distribute data:

• Replication:
  • Duplicates data across multiple sites.
  • Improves fault tolerance and read performance but requires synchronization.
• Fragmentation:
  • Divides data into fragments, stored at different sites.
  • Types:
    • Horizontal Fragmentation: Divides a table into rows.
    • Vertical Fragmentation: Divides a table into columns.
    • Hybrid Fragmentation: Combines horizontal and vertical fragmentation.
• Hybrid Distribution:
  • Combines replication and fragmentation to optimize performance and fault tolerance.

Applications

Distributed databases are widely used in:

• Global Enterprises: Managing geographically dispersed data.
• Cloud Databases: Supporting distributed cloud-based platforms like Google Spanner and Amazon Aurora.
• IoT Systems: Managing data from distributed devices.
• Big Data Analytics: Processing large-scale distributed datasets.

Challenges

Distributed databases face several challenges:

• Data Consistency: Ensuring consistency across replicas while maintaining performance.
• Network Partitioning: Handling situations where communication between nodes is disrupted.
• Query Optimization: Efficiently executing queries across distributed nodes.
• Security: Securing data transmission and storage across multiple locations.

See Also

• Distributed Systems
• Query Optimization
• Database Replication
• Sharding
• CAP Theorem
• Distributed Query Processing
• Cloud Databases