Distributed Integrity Constraints are rules that ensure data consistency, accuracy, and reliability across multiple sites in a Distributed Database Management System (DDBMS). These constraints help maintain data integrity even when data is stored at different locations and accessed by multiple users simultaneously.
The types of distributed integrity constraints are as follows:
i. Primary Key Constraint: Ensures that each record in a table has a unique identifier, even when data is distributed across multiple sites.
ii. Foreign Key Constraint: Maintains referential integrity between related tables stored at different locations.
iii. Unique Constraint: Ensures that specific attribute values remain unique across distributed sites.
iv. Check Constraint: Ensures data values meet specific conditions before being inserted or updated, even when data is fragmented.
v. Replication Consistency: Ensures that copies of data stored at different locations remain synchronized.
Enforcing data integrity in a distributed database system (DBMS) is more complex than in a centralized DBMS because of several challenges:
i. Data Fragmentation and Distribution: In a centralized system, all data is stored in one place, making it easier to enforce rules. But in a distributed system, data is spread across multiple locations. For example, if a university has student records at one site and course enrollments at another, deleting a student record requires checking and updating course enrollments at another site. This requires extra coordination between sites.
ii. Concurrency Control Issues: In a distributed system, many users can access and modify data at the same time. This makes it hard to maintain rules that prevent mistakes, like ensuring that a bank account doesn’t go below a minimum balance. It requires synchronizing the actions of multiple users, which can slow down the system.
iii. Foreign Key Enforcement Across Sites: Checking if a record is valid (like ensuring an order has a valid customer) is easy in a centralized system. But in a distributed system, where customer data and order data are stored on different servers, the system needs to check multiple locations, which adds delays.
iv. Replication and Consistency Issues: In a distributed system, data is often copied across different locations to improve reliability. But this can cause problems when enforcing rules like “total stock should not exceed 1000 items” in an e-commerce system with multiple warehouses. Synchronizing these copies and enforcing rules can lead to conflicts and delays.
v. Global Transaction Management Overhead: Distributed systems use protocols like Two-Phase Commit to manage transactions across multiple sites. While this helps maintain consistency, it requires extra communication, locking, and synchronization between sites, which can slow down performance.
Hence, enforcing data integrity in a distributed database is more complex due to the need for coordination between different sites, handling multiple users, and managing replicated data. While there are techniques to help, like distributed locking and transaction protocols, they add extra complexity and can slow down the system.