Partitioning Techniques - I/O Parallelism(Data Partitioning)

Partitioning Techniques

I/O Parallelism(Data Partitioning) 

Input/Output (I/O) parallelism is the simplest form of parallelism in which the relations (tables) are partitioned on multiple disks to reduce the retrieval time of relations from disk. In .I/O parallelism, the input data is partitioned and then each partition is processed in parallel. The results are combined after the processing of all partitioned data. 

I/O parallelism is also called data partitioning. 

Partitioning Techniques 

The following four types of partitioning techniques can be used: 

Type of Partitioning Techniques 
  • Hash Partitioning Techniques 
  • Round-Robin Partitioning Techniques 
  • Range Partitioning Techniques 
  • Schema Partitioning Techniques 
Hash Partitioning Techniques: With hash partitioning, Adaptive Server uses a hash function to specify the partition assignment for each row. You select the partitioning key columns, but Adaptive Server chooses the hash function that controls the partition assignment. 

Range Partitioning: Rows in a range-partitioned table or index are distributed among partitions according to values in the partitioning key columns. Range partitioning is particularly useful for high-performance applications in both OLTP (Online Transaction Processing) and decision-support environments.

Round-Robin Partitioning: In round-robin partitioning, Adaptive Server does not use partitioning criteria. Roundrobinpartitioned tables have no partition key.

Round-Robin Partitioning

Schema Partitioning: In schema partitioning technique, different relations (tables) within a. database are placed n different disks.

Schema Partitioning