Data fragmentation strategies in DDBMS
Data fragmentation is a method used in distributed database management systems (DDBMS) to divide a database into smaller units called fragments, which are then distributed across multiple nodes in a distributed system. There are various strategies for data fragmentation in DDBMS, including:
1. Horizontal fragmentation: This strategy divides a relation into smaller fragments based on rows. Each fragment contains a subset of rows from the original relation. For example, if a relation has 100 rows, it can be horizontally fragmented into two fragments, with the first fragment containing 50 rows and the second fragment containing the remaining 50 rows.
2. Vertical fragmentation: This strategy divides a relation into smaller fragments based on columns. Each fragment contains a subset of columns from the original relation. For example, if a relation has 10 columns, it can be vertically fragmented into two fragments, with the first fragment containing columns 1-5 and the second fragment containing columns 6-10.
3. Hybrid fragmentation: This strategy combines horizontal and vertical fragmentation. It divides a relation into smaller fragments based on both rows and columns. Each fragment contains a subset of rows and columns from the original relation. For example, a relation with 100 rows and 10 columns can be fragmented horizontally into two fragments, with the first fragment containing 50 rows and vertically into two fragments, with the first fragment containing columns 1-5.
4. Hash fragmentation: This strategy uses a hash function to distribute the data across multiple nodes in a distributed system. The hash function generates a hash value for each tuple, and based on this value, the tuple is assigned to a specific node. This ensures a uniform distribution of data across nodes.
5. Range fragmentation: This strategy divides the data based on a specific range of values in a column. For example, if a column contains values from 1 to 100, it can be range fragmented into four fragments, with the first fragment containing values 1-25, the second fragment containing values 26-50, and so on.
6. Round-robin fragmentation: This strategy distributes the data in a round-robin manner across multiple nodes. Each tuple is assigned to a different node in a cyclic manner. This strategy ensures an even distribution of data across nodes but may result in increased network traffic due to the need for inter-node communication during query processing.
Each of these fragmentation strategies has its advantages and disadvantages, and the choice of strategy depends on factors such as the size and structure of the database, the distribution requirements, and the performance goals of the DDBMS.